The Influence of State-Specific Programmatic Characteristics in the Modeling 

of Medicaid Undercount; 

A Record-Check Study of the 2001 Current Population Survey Annual Social and 

Economic Supplement (CPS ASEC)

Matthew Stevens

US Census Bureau 

4600 Silver Hill Rd. Washington, DC 20233 / matthew.j.stevens@census.gov

Any views expressed on statistical, methodological, and technical issues are those of the author and not necessarily those of 

the U.S. Census Bureau

Abstract 

Much research has been conducted regarding underreporting of benefit receipt on population surveys. Specifically, much work has been devoted to 

measuring Medicaid undercount on the Current Population Survey Annual Social and Economic Supplement (CPS ASEC). Previous studies have revealed 

estimates of false-negative reporting rates varying from 10-36%. As one of the primary indicators of Medicaid benefit receipt, measurement error within this 

estimate will not only bias survey results, but subsequently bias the program evaluation, policy development, and funding decisions informed by the CPS 

ASEC. Upon gaining estimates of the size of underreporting, the next round of research focused on seeking to explain why Medicaid recipients provide 

erroneous reports. Record check studies conducted by the Census Bureau revealed significant associations between respondent-level characteristics (such as 

recency and intensity of coverage, as well as household income and relationship to person being reported for) and underreporting. 

Using the same administrative records database utilized in previous studies, this study searches for the root of state-level differences in underreporting that 

still exist after controlling for respondent-level characteristics. Specifically, how effectively can the characteristics of state-run Medicaid programs (such as 

eligibility rules, policy on premiums, and portrayal to the public) explain the likelihood of recipients within in said state to fail-to-report benefits? 

Examination of Medicaid programmatic literature enabled creation of a series of indicator variables to represent these state programmatic differences within 

logistic regression models. Results suggest that while many characteristics of the respondent effect Medicaid reporting, characteristics of the state Medicaid 

program itself may equally impact reporting propensity, and that such sources of measurement error may be easier to remedy on the CPS ASEC than 

previously identified, respondent specific sources.

Introduction

Policymakers and foundations rely on survey estimates to provide a glimpse of the uninsured and benefit recipients. While 

these estimates fall short in their attempts to capture the entire population of recipients, they are a necessity when discussing 

programs lacking a centralized database of participants. Prior to 1999, Medicaid, a state-run program providing health 

coverage and long-term care support services, lacked a centralized database. The Balanced Budget Act (BBA) of 1997 

changed this by requiring all states to submit participant-level data quarterly to the Centers for Medicare and Medicaid 

Services (CMS), thus creating a centralized administrative records database: the Medicaid Statistical Information System 

(MSIS). 

With the creation of such a database, the quality of survey estimates regarding public participation rates could finally be 

examined at a national level. Consistent with the notion of imperfect survey estimates, prior state-level studies had revealed a 

consistent undercount of Medicaid reporting (estimates varying from 10-30%) (Card et al, 2001; Czajka & Lewis, 1999; 

Blumberg and Cynamon, 1999; Lewis et al, 1998; Klerman, Ringel and Roth, 2005). These studies not only revealed the 

presence of measurement error (in the form of undercount) in prior surveys of Medicaid participation, but patterns of nonrandomness within this error. Respondents with certain demographic and Medicaid-related characteristics were more or less 

likely to provide false-negative reporting (claim to not be receiving Medicaid coverage when the administrative records 

database proved that they were covered) than respondents with other characteristics. With aspirations of both establishing the

true Medicaid undercount as well as examining the relationship between false-negative reporting and respondent attributes, 

the University of Minnesota’s State Health Access Data Assistance Center, the Centers for Medicare and Medicaid Services, 

the Department of Health and Human Services Office of the Assistant Secretary for Planning and Evaluation, and the US 

Census Bureau (this collaborative deemed SNACC based on the first letters of each participating organization) worked in 

collaboration on the four-phase “Research Project to Understand the Medicaid Undercount.” 

The task central to this project involved the linkage of Current Population Survey’s Annual Social and Economic Supplement 

(CPS ASEC) responses (from years 2000 and 2001) to Medicaid administrative records data in the MSIS. After steps are 

taken to remove the known difference between the data sets and comparability is ensured, this research was able to attribute 
almost all of the 36.2% Medicaid undercount to measurement error (SNACC Phases I & II). Consistent with the results of 

previous smaller, state-specific analyses, this measurement error was found to be highly non-random. Specifically, levels of 

recency and intensity of Medicaid coverage (Pascalle et al 2008) as well as the income level and relationship to the person 

being reported for (Lynch 2008) were all determined to be significant predictors of false-negative Medicaid reporting. The 

CPS ASEC serves as one of the primary indicators of Medicaid benefit receipt, and thus it is important that the estimates are 

not only accurate, but that they are free from measurement error to ensure accurate representation of participants within the 

results. Measurement error with regards to program participation not only biases survey results, but also subsequently biases 

program evaluation, policy development, and funding decisions informed by the CPS ASEC. Therefore, it is essential to not 

only reduce measurement error, but to explore and understand the patterns of error that exist. 

Construct validity is a difficultly detected but prominently present source of measurement error in surveys. The underlying 

motivation for survey administration is to extract information (through questions) in an attempt to map the collective of 

individual, personal experiences to the sum of these experiences, thereby capturing the pulse of society as a whole. The 

ability to attain this underlying goal is limited to the extent to which the information being provided by the respondent 

matches that which the question intends. Traditionally, discussion of construct validity has been limited to questions 

regarding abstract concepts (Peter 1981; Andrews 1984), but cross-cultural survey theory suggests that socio-geographic 

context can alter the construct validity of even the most straight-forward, concrete concepts. For instance, Verba (1971) 

suggests that a concept as simple as that of fruit can elicit very different imagery depending what fruits are traditionally 

available in the vicinity of a respondent. With this in mind, we would expect the respondent’s perception of the construct of 

Medicaid to vary based on the characteristics of the program in his/her state. 

The aforementioned studies of CPS Medicaid underreporting have revealed significant patterns in measurement error. 

Characteristics of the respondent and his/her Medicaid history create a unique reality from which the reports of coverage are 

based. Variation in these realities should be expected to produce variation in propensity to recognize, much less report, 

benefit receipt. As a state-run, federally funded program, the experience of Medicaid receipt will be unique for respondents in

different states. Additionally, we would expect these environmental differences to impact the manner in which respondent 

specific characteristics lead to measurement error. This paper will decompose the individual-level characteristics from the 

state-level characteristics related to a respondent’s Medicaid experience to answer the following question: can these statespecific factors further explain the underreporting amongst the population of CPS respondents receiving benefits? First, 

literature on Medicaid policy will be examined to identify programmatic characteristics known to vary between states. Next, 

upon controlling for the recipient-level factors identified in the Lynch (2008) paper, we will use a series of logistic regression 

models to capture the remaining effect of state-level programmatic factors. Finally, we will interpret the role of state-level 

factors within these models to make recommendations for reducing underreporting on the CPS ASEC. 

Methods 

Highlighting Medicaid underreporting at this theoretical level, we create a format that will be used to describe measurement 

error for the remainder of the paper. Figure One below describes the propensity for underreporting as the synthesis of both 

individual-specific and state-specific factors. ‘Y’ is set to either ‘0’ (report receipt) or ‘1’ (fail to report receipt, i.e. underreport/false-negative response). Modeling Y through logistic regression, under-reporting propensity can be decomposed into 

two main aspects: (1) those factors specific to the individual regarding his/her demographic characteristics as well as his/her

experience with Medicaid, and (2) factors specific to the state Medicaid program which the respondent is enrolled in. 

Figure One: Model to Decompose the Roots of False-Negative Reporting 

Υˆ = β0 + β1,i

xi + β 2, jxj

 where... 

Υˆ: propensity to underreport Medicaid coverage 

β0: intercept of logistic regression equation 

β : effect of factor “i”, a factor regarding the respondent and his/her Medicaid experience 1,i

x : the level for variable “i” for a given respondent i

β : the effect of factor “j”, a factor regarding the state Medicaid program in which the respondent is enrolled 2, j

x : the level for variable “j” for a respondent j
Prior research (Lynch 2008) took a similar approach to modeling the underreporting of Medicaid receipt. Using individuallevel variables such as age, minority status, income, as well as a series of variables to represent recent medical and medical 

benefit history, her study was able to identify the factors that significantly represent the ß1 aspect of our model. Her models 

also included indicator variables for 50 states and the District of Columbia that would represent the ß2 portion of the model 

above. Finding state-differences but no discernible patterns with strong enough significance to use ‘state’ as a predictor 

(within her analysis) justifies the need to look deeper at the inherent state-level programmatic differences behind these 

effects. 

Figure One outlines the conceptual basis for the models used in this paper. Using logistic regression, we will predict 

propensity to underreport as a combination of these respondent specific (ß1,i’s) and state-specific (ß2,j’s) variables. Within 

this notation ß1,i* xi represents the summation of the products of all respondent-specific variable values multiplied with the 

appropriate regression coefficients. The ß1,j* xj

, on the other hand, represents the summation of the products of all statespecific variables values multiplied with the appropriate regression coefficients. While more thorough notation would 

include all 29 ß1,i’s and 6 ß2,j’s, such a summary would place too much emphasis on the predictors themselves and not 

enough to the overlying concepts they represent. Though more detail will be given to these specific predictors later in the 

paper, Figure One exists merely to provide a framework in which to group (and more importantly, think about) our 

predictors. 

As state-funded and implemented entities receiving additional federal funding, the characteristics of specific Medicaid 

programs change from year to year with budgetary constraints. As the faces of these programs change, so do the experiences 

of the program participants. Therefore, even though the CPS ASEC asks the same Medicaid question to respondents from all 

states, the programs that respondents are enrolled in may be quite different from one another, and thus the process of 

providing an accurate response may be quite different. Specifically, there are three main attributes of state Medicaid 

programs in which variation occurs: program eligibility rules, policy on charging premiums, and program portrayal to the 

public (Ross and Marks 2009). 

With regards to eligibility rules, some states include presumptive eligibility for children, providing immediate but temporary 

access to Medicaid benefits. In order to maintain benefits, completion of the full application process is required within a 

month of initial receipt. Respondents covered under presumptive eligibility have therefore experienced both a medical 

incident on a previously non-covered child, as well as completed a full-application process within the immediate period. One 

would expect such experiences to be more salient for respondents than those receiving standard Medicaid coverage. 

Enrollment procedures vary with eligibility requirements from state to state. Specifically, many states offer 12-month 

continuous eligibility: guaranteeing uninterrupted eligibility for families with children for 12 months regardless of changes in 

income or other circumstances that may otherwise compromise eligibility (Broaddus et al., 2003). In addition to being 

guaranteed eligibility, families are not required to re-apply for coverage until the 12-month period has elapsed. Recipients in

such states therefore experience less fluctuation in coverage and fewer experiences with the administrative aspects of 

Medicaid, which often serve a construct for an otherwise face-less entity. Our theory would therefore suggest less salience for

recipients residing in states with these characteristics. 

The last aspect of program eligibility that will be examined concerns the maximum income in which a family can still receive 

Medicaid benefits. Within our data set, this variable is expressed as maximum income with respect to the federal poverty line. 

All states provide coverage to low-income families. States with higher cutoff levels will simply be providing coverage to a 

more heterogeneous pool of recipients with a wider range of income levels. Therefore, any social stigma that may be related 

to receiving public benefits would expect to be reduced. With fewer stigmas, we would expect to see lower level of 

underreporting in these states. 

Policy on charging premiums for coverage varies from state to state and is the second attribute in our explanation of statelevel trends in underreporting. Beginning in the 1990s, states were allowed to apply for section 1115 waivers. Introduced to 

encourage states to implement premium assistance within their Medicaid programs, these waivers relax benefit, cost-sharing, 

and cost effectiveness requirements that were previously mandatory for states wishing to receive federal funding (Alker 

2005). Medicaid recipients in states with such waivers are less likely to pay premiums for coverage. Our theory of salience of 

participation suggests that the cost of paying for coverage would increase the salience, especially amongst lower-income 

respondents. 
Public portrayal is the third category of variation in state-level Medicaid underreporting. Literature suggests that confusion 

surrounding the name of Medicaid programs is one the sources contributing to the Medicaid undercount (Kincheloe et al., 

2006). As some states have state-specific names for their programs other than “Medicaid” it will be interesting to examine 

under-reporting in these states compared to the remainder of states. Intensity of media effort in Medicaid promotion will 

serve as the other indicator of program portrayal to the public. We would expect to see lower under-reporting rates in states 

that have more intensive media programs (those utilizing more than one media format), as Medicaid would be more salient to 

CPS respondents within such states. 

Building on the research of SNACC to identify both the presence and the roots of the Medicaid undercount, this paper 

explores the programmatic differences at the state-level as well as their contribution to the differences in state-level 

undercount. First, the state-level trends in underreporting will be examined in order to gain a better understanding of the 

scope of variation. Second, indicator variables will be coded to represent the state-level, programmatic contribution to 

underreporting propensity. Next, a model developed through prior research from the Census Bureau will be run to re-identify 

the respondent-specific attributes related to underreporting. Controlling for these individual-level predictors, the state-level 

programmatic indicators will be used to model the remaining variation in underreporting. Finally, examination of the

coefficients on these indicator variables will provide insight on the sources of measurement error in the CPS ASEC Medicaid 

question, and recommendations will be made on how to create a less error-prone measurement of Medicaid receipt. 

Data Resources 

This paper builds on the findings of the Phase II SNACC project, and therefore the analysis file is constructed around the preexisting file. Phase I of the SNACC project focused on the creation of a national database of Medicaid recipients by first 

evaluating the quality of records contained within the MSIS administrative records file. These records were run through the 

Person Verification System (PVS), which in a two iteration process first validates the Social Security Number (SSN) and 

then in a separate process replaces each SSN with a Protected Identification Key (PIK). This PIK will eventually be utilized 

to match these records to survey data while maintaining the anonymity of the individual respondents (SNACC I 

Documentation). 

For the next step, efforts were made to remove respondents from the MSIS who may have been living in institutionalized 

group quarters and thus outside of the 2001 CPS-eligible universe. For the remaining MSIS records, “months enrolled in

Medicaid” were determined and output to the MSIS Summarized Enrollment History File (MSEHF). The final MSEHF 

consisted of 39,911,501 person-records: one record for each unique PIK, each record containing information highlighting the 

entirety of Medicaid receipt. 

CPS ASEC records underwent a similar process, preparing them for accurate linkage. First, the 218,269 sampled person 

records were run through the PVS to attain anonymity and assign PIK. In about 20% of the persons, SSN were either 

unknown or unverifiable. Such cases were deemed unusable and dropped from the analysis file. The weights of dropped CPS 

cases were reassigned proportionally to retained records within the same re-weighting strata. The authors of the SNACC 

Phase II report admit that this re-weighting is likely to introduce bias into the data, since valid SSN’s are more-than-likely not 

missing at random, but nonetheless, they deem it a necessary move in order to make the results of their study represent the 

entire CPS frame. Finally, CPS (173,967 records) and MSEHF (the 39,911,591 processed MSIS records) files are linked via 

PIK. This final analysis data set contains one record for every person in CPS. Since we are interested in false-negative 

reporting versus true-positive reporting, we will be looking exclusively at the 23,643 person records in which we have MSIS 

information. 

Having established how the analysis file is constructed, some mention should be made regarding the CPS ASEC, as well as 

what Medicaid information it contains. While the Current Population Survey (CPS) is a monthly face-to-face survey that 

collects data regarding labor force participation amongst non-institutionalized civilians living in the United States, the Annual 

Social and Economic Supplement (ASEC) is conducted once a year (usually in March) to collect information specific to 

work, income, public assistance, and health insurance in the previous year. Integral to this analysis are seven questions 

relating to health insurance. Following three questions on private coverage (employer-sponsored, directly-purchased, and 

coverage from someone outside the household) are questions on four government-related plans (Medicare, Medicaid, SCHIP, 

and military plans). For this analysis, a false-negative response is any person for whom a MSIS record exists but who did not 

have a “yes” response to the CPS ASEC Medicaid question. Therefore, the dataset used for analysis will consist of the 23,643 

Medicaid recipients, their Medicaid information from the MSIS, socioeconomic and demographic information from the CPS, 

as well as a flag to represent whether or not they reported receipt of benefits. 
Results 

Before understanding the roots of state-level differences in Medicaid underreporting, we must first examine underreporting 

levels on a state-by-state basis. Figure 2 (in the Appendix) provides the details of the underreporting rate broken out by state 

for the 23,643 respondents included in the analysis. As described in the methods section, these respondents represent CPS 

ASEC respondents who were successfully matched to the MSIS Medicaid database; therefore all are true Medicaid 

recipients. The rate of underreporting is expressed as the ratio of persons in our data set whom responded “no” to the 

Medicaid question over the total number of persons from that state. It should be noted that these estimates are unweighted, as 

we hope to draw conclusions on the individual sample respondents rather than make inferences about the entire Medicaid 

population within the CPS. With this in mind, when discussing state-levels of underreporting rate, we are simply referring to 

the underreporting rates in a given state amongst CPS respondents who were successfully matched to persons known to 

receive Medicaid benefits (those on the MSIS). In so much as the intention of this paper is to understand patterns of 

underreporting within the universe of matchable CPS respondents, this should be viewed less as limitation and more as an 

assumption for interpretation. 

Prior research from the Census Bureau (Lynch 2008) used the same MSIS-matched CPS respondent data set to examine the 

factors that were related to Medicaid underreporting. This research found significant relationships between respondent 

characteristics and underreporting. These findings reiterated the nonrandom nature of underreporting amongst CPS and 

suggested that after controlling for several demographic and Medicaid programmatic variables, significant differences still 

existed between states. Figure 2 (Appendix) supports these findings, as much variation in underreporting rates exists between 

states. While CPS states have a mean rate of 43.63% false-negative reporting (std. error of 6.94%) for the people examined, 

state rates vary from 23.54% (Rhode Island) on the low-end to 63.45% (Hawaii) on the high-end. 

Figure 3 is a geospatial display of this distribution: the darkest green areas represent the states with the lowest underreporting 

rates and the darkest red areas represent the states with the highest underreporting rates. There appears to be a slight 

correlation between geographic proximity and underreporting amongst CPS respondents, as states in similar ranges are 

clustered or adjacent to one another. Based on our aforementioned theories, we would expect proximal states to experience 

similar levels of underreporting to the degree that proximal states have socio-political similarities, which in turn, would 

create similar Medicaid programs. 

Figure Three: Geospatial Display of Medicaid Underrporting Rate 
Figure 4 (Appendix) shows how states were coded within the research indicator variables. These maps provide the reader 

with a bit more insight about the distribution of these indicator variables. Some of the geospatial clusters that can be seen in 

figure three reappear within these maps, further illuminating the relationships between these programmatic indicators and 

underreporting. Bivariate relationships provide a first glimpse at the relationships between these concepts and underreporting.

For states with 12-month continuous eligibility, higher levels of underreporting can be seen (43.18% compared to 41.90%, 

which falls just a tad short from being significantly different at the 10% level). Looking at the final column of figure five, we 

can see that our other four binary indicator variables all present differences that are statistically significant at the 5% level. 

States with 1115 waivers, alternative program names, and that offer presumptive eligibility for children all appear to have 

decreased underreporting rates: illustrated by lower values in final column amongst the ‘yes’ row than the ‘no’. 1115 waivers 

are often characterized by the decreased likelihood of having some premium. This finding is not consistent with the theory 

that the process of paying would increase the salience of Medicaid program participation. As the Figure 5 (Appendix) 

indicates, states with 1115 waivers have an underreporting rate of 38.10% compared to a rate of 44.05% in all other states. As 

described previously, presumptive eligibility for children is a program characteristic that can grant children immediate, 

temporary access to Medicaid benefits. In order to maintain benefits, the child is required to complete the full application 

within a month. This negative relationship (38.52% for states with presumptive eligibility compared to 43.35% in other 

states) is consistent with our theory that such cases would lead to more salient experiences for the participants. Alternative 

program name, on the other hand, has results that are not consistent with the expectations of this study. While it was 

anticipated that underreporting would be higher in states whose Medicaid programs have alternative names, the opposite 

effect can be seen in Figure 5 (Appendix) (42.34% in states with alternative names; 45.38% in all other states). Finally, the 

variable for multiple media formats appears to be positively correlated with Medicaid underreporting. States who utilized 

more than one format (television, radio, and/or print) to promote their Medicaid program saw higher rates (43.97%) than 

those states that devoted their resources to single format (38.04%). Having found significant differences in underreporting 

rate based on levels of our binary indicator variables, as well as having gained a better understanding of the distribution of 

both our dependent and independent variables across states, we utilize multivariate logistic regression models to explore the 

role of state-specific factors within a respondents propensity to provide false-negative reports. 

We first replicate the Lynch (2008) Model to capture the respondent-specific, individual-level factors that impact propensity 

to provide a false-negative response. Figure 6 (Appendix) summarizes the regression coefficients, standard errors, and 

significance levels for this model in the column marked “Model One/Control Model.” This control model verifies many of 

the findings of the initial study: significant negative relationships for younger reference persons, children of respondents, 

those respondents receiving both Medicaid and Medicare, those receiving Social Security, and those who have paid for 

prescription and/or non prescription services in the past 90 days (with stronger effects for those purchases more recently 

within the 90-day period); significant positive relationships for Hispanic or minority respondents, and other (not spouse, 

child, self, or parent) relationship to reference person (the respondent). The first column in figure six illustrates how this 

model utilizes demographic and recipient-specific programmatic variables to provide an explanation of underreporting trends. 

As the distribution of these variables tends to vary for each state, the fifty-one binary variables representing state had various 

significant and insignificant values within this model (Note: these values have been omitted from Figure 6 for the sake of 

space, as their specific values are not as important to our analysis). Therefore these controls should serve to remove the 

individual-level effects (ß1) from our later models, thereby isolating the contribution of state-level characteristics (ß2) to 

underreporting propensity. These state-level characteristics will later be modeled by our programmatic indicator variables. 

Next, the model is simplified and is re-run using only the indicator variables created to capture the characteristics of the state 

Medicaid programs. Having already described the relationships between each indicator and underreporting, this logistic 

regression model reveals how all the indicators work together to explain the dependent variable. Column two of Figure 6 

(Appendix) (titled “Model Two/Indicator Model”) provides the coefficients for the indicator model. Comparing these values 

to those in Figure 5 (Appendix), we see that the directions of these indicators are consistent between bivariate and 

multivariate models. While this serves to further solidify the direction of the effects of our state-characteristics on 

underreporting, it should be noted that only two of the six variables provide statistically significant coefficients: Multiple 

Media Formats (0.1184 w/ standard error .0404) and Maximum Income-to-Poverty Ratio for Eligibility (-0.2232 with 

standard error .0604). Looking towards the bottom of the Model Three column of figure five, we see 12-month continuous 

eligibility has a coefficient of .0489, but neither this value nor the coefficients for 1115 waivers or Alternative Program 

names are significantly different from zero. At this point in the modeling process, this lack of significance is not troubling, as 

we have seen clearly defined patterns within the data. Given the large number of control variables in later equations, it is 

likely that significance will be hard to attain. Therefore, our conclusions are likely to be constructed from a consolidation of 

results rather than a single definitive model. With this in mind, we will use the findings of model four to reiterate the 

conclusions discussed in the following section. 
Discussion 

The first goal of our research was to replicate the results of the Lynch paper by modeling underreporting propensity with 

respect to the influential participant-level variables (the ß1 component within our model). Consistent with these results, we 

see that answering the Medicaid question for any of the following persons significantly reduces underreporting: younger 

reference persons, reference persons who are the children of the respondent, those respondents receiving multiple public 

benefits such as Medicare and Social Security, and those who have paid for some medical care in the past 90 days (the more 

recent the payment, the less likely underreporting of Medicaid benefits). Younger reference persons, particularly a 

respondent’s children, are more likely to be in the direct care of the respondent. Consistent with the findings of Lee et al. 

1999, information regarding Medicaid benefits for such reference persons is more likely to have been encoded than for some 

other related or non-related household member. Additionally, it could be argued that events affecting closer reference persons 

will be more salient in the mind of the respondent. Significant positive relationship between “reporting for other respondent” 

(other than spouse, child, self, parent) and underreporting further supports this notion. This increased salience is likely 

motivating the decreased underreporting for those respondents with recent medical expenses. These findings are all consistent 

with Census studies performed on this CPS/MSIS data set (Lynch 2008) (Pascale et al). 

Before delving into the interpretation of our findings, it is important to reassess our design and address any potential 

limitations to our results. One limitation that may potentially hinder our ability to detect significant effects is that while a 

state program has a specific characteristic, there is no guarantee that the chosen CPS respondents have experienced this 

characteristic. From this perspective, this modeling is more exploratory than explanatory, providing us with a first glimpse as

to how our variables impact underreporting when taken simultaneously. Our research was based on the logic that if 

respondents had the opportunity to be exposed to a characteristic (i.e.. lived in a state with said characteristic) then the impact 

of that characteristic would be larger (even if just slightly so) in such states. To determine true causality of state-level 

characteristics, future research would need to exclusively study respondents exposed to state-level predictors (for example, a 

sample of respondents who benefited from presumptive eligibility for children). Another limitation involves the dataset itself.

While record check studies are agreed upon as the most effective way to detect measurement error, the samples available for 

such studies are limited to those survey respondents who can successfully be matched to records in the administrative records 

database. The SNACC project utilized Social Security Numbers (SSNs) in its person-matching procedure. Therefore, CPS 

respondents without a reported SSN will not be matched and subsequently excluded from our analysis. This is an 

acknowledged limitation of both this research and all research conducted as a part of the SNACC project. Future research is 

needed to examine the implications of this less-than-ideal data situation. 

While comprehension of the participant-level variables is essential to understanding the full scope of underreporting trends, 

our research focuses on how the respondent behaves within the context of his or her state-specific Medicaid environment (ß2

component within our model). Exclusive analysis of state-specific indicators (Model Two) revealed that the indicators related 

to eligibility (12-month continuous eligibility, maximum family income, and presumptive eligibility) were all related to the 

dependent variable in the same direction as expected, while premiums (1115 waivers) and portrayal to the public (alternative 

program names and multiple media formats) are related in the opposite direction than anticipated. 

Figure 7: Summary of Model Two (Indicator Model) 

Υˆ = β0 + β2, j

xj

or 

Υˆ = β0 + β 2,a, j

xaj + β 2,b, jxaj + +β 2,c, jxaj

 where... 

Υˆ: propensity to underreport Medicaid coverage 

β : intercept of logistic regression equation 0

β : the effect of factor “j”, a factor regarding the state Medicaid program in which the respondent is enrolled 2, j

x : the level for variable “a, j” for a respondent j

β : the effect of factor “a, j”, a factor regarding eligibility rules within the state Medicaid program in which the respondent is enrolled 2,a, j

x : the level for variable “b, j” for a respondent a, j
β : the effect of factor “b, j”, a factor regarding premiums within the state Medicaid program in which the respondent is enrolled 2,b, j

x : the level for variable “j” for a respondent b, j

β : the effect of factor “c, j”, a factor regarding public portrayal within the state Medicaid program in which the respondent is enrolled 2,c, j

x : the level for variable “c, j” for a respondent a, j

While this reduced model provides us with a sense of the general direction of these effects, it possesses no practical 

interpretable implications on its own. Our theory suggests that while state-specific factors may impact how a respondent 

interprets and answers the Medicaid question, we fully expect these factors to be secondary to the participant-level factors. 

That is, those factors at the state-level are merely explaining the differences that exist after controlling for the factors relating 

to a specific respondent within that state. The full model (Model Four as seen in Figure Five) takes the findings of the 

previous exploratory model and applies them to our theory. Three out of six of our indicator variables (representing two out 

of three of our conceptual sources) prove to be significant predictors of Medicaid underreporting. ‘Presumptive eligibility for

children’ and ‘maximum family income’ are both indicators designed to represent the concept of programmatic/eligibility 

rules and both are found to have negative relationships with the dependent variable. That is, states with presumptive 

eligibility and higher maximum incomes for eligibility have lower rates of underreporting and therefore more accurate 

reporting of Medicaid rates. From a measurement error perspective, these findings are consistent with literature regarding 

both encoding and reporting of sensitive topics in face-to-face interviews. More interestingly, it allows us to break down the 

ß2a component into two sub-components: those that elicit purposeful deception and those that spur inadvertent 

underreporting. 

The indicator representing states offering presumptive eligibility for children is consistently found to have a significant 

negative coefficient within our models. As a reminder, this program provides children with immediate but temporary access 

to Medicaid benefits. Essentially, if an uninsured child shows up for emergency medical care in one of these states, he/she 

will be treated and receive support for up to thirty days without having to undergo the full Medicaid application process. 

While literature (Lee et al. 1999) suggests that parents will be less likely to encode specifics regarding medical procedures 

received by a child, it is likely that they will encode the experience of receiving unexpectedly affordable health care in an 

emergency situation. In the case that they then applied for Medicaid for their child, the application process increases saliency, 

again increasing the probability that they will have encoded this receipt of benefits. To the degree that failure to encode 

information can account for underreporting of Medicaid receipt, states with presumptive eligibility should expect more 

accurate results of the CPS question. 

The other significant component of the ß2,a component is represented in our model by maximum family income to still be 

eligible for benefits. This is the aspect of the model influenced by states in which it is socially desirable for some respondents 

to purposefully fail to report Medicaid receipt. The negative relationship between this variable and underreporting suggests 

that states in which the maximum family income for eligibility have lower underreporting rates. This relationship is 

consistent with a multitude of literature regarding the tendency for respondents to provide inaccurate socially desirable 

answers in such instances. To fully understand this relationship it may be advantageous to examine the other side: states with

lower maximum incomes experience higher levels of underreporting. Such states thus experience more economic 

homogeneity amongst Medicaid recipients and these recipients have across-the-board lower incomes. Therefore, within the 

social structure of such states, Medicaid is more likely to be viewed as a lower-class benefit, and thus viewed with social 

stigma. Of course, the CPS instrument cannot hope to affect the level of social stigma, but the designers could take steps to 

decrease the bias associated with it. In states with lower maximum incomes (or even in all states), the Medicaid question 

could be self-administered. To the extent that social desirability is responsible, we would expect to see a reduction in 

underreporting (an increase in reporting accuracy). 

The ß2,b portion of our model ends up being wholly insignificant at predicting underreporting. 1115 waivers were designed to 

represent whether or not participants in the state programs were required to pay premiums for their healthcare. 1115 waivers 

tend to lower the costs of premiums, and in some cases, eliminate them altogether. Therefore, a negative relationship was 

expected: the payment of higher premiums would be more salient than the act of not paying at all. Payment would essentially 

provide an additional opportunity for the respondent to encode their enrollment and use of Medicaid. From the cognitive 

response perspective, perhaps payment and eligibility are deeply intertwined. In that case, the significant impact on saliency 

of coverage inspired by presumptive eligibility may completely account for this effect. 
The final aspect of our model (ß2,c) represents those aspects of Medicaid programs that are visible to the public and the 

respondent but do not have anything to do with the substance of the programs. The indicator to represent those states in 

which Medicaid is called something different was not found to be a significant predictor of underreporting. In fact, this 

variable was the only one to switch positive/negative signs between different models, further suggesting a regression 

coefficient not significantly different from zero. These findings do not support our hypothesis that Medicaid programs 

referred to as such (“Medicaid”) would experience less underreporting. The other indicator in this section represents those 

states that utilized more than one form of media (amongst print, television, and radio) to make the public aware of their 

services. Originally, it was theorized that states utilizing multiple media formats would better reach the public, increasing 

public awareness of Medicaid and thus reducing underreporting. The results of our analysis contradict this theory. Perhaps 

media exposure goes beyond making the public aware of a service. It could be argued that in areas where Medicaid is being 

promoted from multiple sources of media, that this community-shared awareness eventually leads to a shared public 

perception of benefits. Such a situation could very easily lead to perception that some stigma is associated with Medicaid, 

thus increasing the possibility that respondents in these states are purposefully providing inaccurate answers. Similar to 

reducing the error associated with “maximum income for eligibility” providing the respondent a chance to privately selfreport Medicaid receipt could prove to be advantageous. 

Conclusion 

First, we set out to create a framework with which to think about false-positive Medicaid reporting at the respondent level. 

By isolating the respondent specific characteristics impacting this propensity, we theorize that the remaining contributors 

vary along state-lines in so much as the Medicaid programs vary from state-to-state. Utilizing literature on Medicaid policy, 

indicator variables were developed to represent these programmatic state differences. Logistic regression analysis allowed us 

to add these programmatic variables to preexisting models that relied exclusively on respondent characteristics to predict 

propensity of false-negative reporting. Amongst the eligibility-related variables, we conclude that the undercount is caused 

both by purposeful deception (related to the stigma a respondent associates with receiving benefits in the socioeconomic 

climate of his/her state) as well as inadvertent underreporting (based on some states having programmatic details that create 

less salient Medicaid experiences for the respondent). Premiums are found to be a wholly insignificant predictor of 

underreporting. Three interpretations can be made from this finding: 1) the relationship between the increased saliency of 

paying premiums and reporting receipt of benefits is controlled for by some other variables in our model, 2) a relationship 

exists between premiums and reporting, but presence of 1115 waivers within a state are not adequate indicators of premium 

payment, 3) there is in fact no significant relationship between the payment of premiums and reporting. Finally, contradicting 

our initial expectations, we find that factors thought to raise a respondent’ awareness of Medicaid actually increased falsenegative reporting. In conjunction with our other findings, this result suggests that as much as the programmatic details can 

impact a respondent’ perception of his/her benefits, underreporting is largely related to the level of social stigma for benefits 

within a state. 

The next steps for research in this field should further decompose the sources of error in the Medicaid undercount. Where this 

study divided the error sources into two categories (those related to the respondent and those related to the Medicaid program

within a state), future research should decompose the state-level analysis into those factors related to salience vs. those 

factors related to stigma. Salience-based errors can be resolved by tailoring the CPS ASEC survey instrument to better 

represent the reality of the Medicaid program within that state, while allowing the question to be self-administered can reduce

stigma-based errors. Whatever decisions are made in the future regarding the CPS Medicaid question, thorough testing of the 

instrument could ultimately reduce these errors. With the creation of a continually updated MSIS administrative records 

database, the results of any testing could be matched to both determine the undercount rate as well as continue to study to 

underlying patterns and trends that impact false-negative reporting propensity. 

Acknowledgements 

Special thanks to Amy Ohara for her efforts in granting me access to the data set and to Victoria Lynch for her help providing Medicaid background 

information as well as invaluable data resources. Thanks to Toni Warner and Leah Marshall for their efforts in reviewing the final version of this paper. 

Finally, many thanks to Melissa Rodgers for her graphical expertise, as well serving as a constant source of encouragement. 
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Appendix 

Figure Two: CPS Medicaid False-Negative Reporting Rates by State 

Number of 

CPS 

Respondents 

Failing to 

Report 

Medicaid 

Receipt 

Number of 

CPS 

Respondents 

Found on the 

MSIS to have 

Received 

Medicaid 

Benefits 

Percentage of 

FalseNegative 

Reports of 

Medicaid 

Benefit 

Receipt 

 Number of 

CPS 

Respondents 

Failing to 

Report 

Medicaid 

Receipt 

Number of 

CPS 

Respondents 

Found on the 

MSIS to have 

Received 

Medicaid 

Benefits 

Percentage 

of FalseNegative 

Reports of 

Medicaid 

Benefit 

Receipt 

Alabama 158 412 38.35% Montana 71 185 38.38% 

Alaska 189 380 49.74% Nebraska 127 352 36.08% 

Arizona 169 358 47.21% Nevada 132 260 50.77% 

Arkansas 142 323 43.96% New 

Hampshire 

73 273 26.74% 

California 806 2111 38.18% New Jersey 213 437 48.74% 

Colorado 136 250 54.40% New Mexico 228 505 45.15% 

Connecticut 243 393 61.83% New York 447 1304 34.28% 

Delaware 141 301 46.84% North Carolina 251 547 45.89% 

District of 

Columbia 

137 360 38.06% North Dakota 107 252 42.46% 

Florida 495 958 51.67% Ohio 238 661 36.01% 

Georgia 190 364 52.20% Oklahoma 175 365 47.95% 

Hawaii 158 249 63.45% Oregon 113 322 35.09% 

Idaho 112 282 39.72% Pennsylvania 420 763 55.05% 

Illinois 451 841 53.63% Rhode Island 117 497 23.54% 

Indiana 191 341 56.01% South Carolina 157 364 43.13% 

Iowa 123 276 44.57% South Dakota 151 332 45.48% 

Kansas 105 236 44.49% Tennessee 177 525 33.71% 

Kentucky 145 311 46.62% Texas 519 1029 50.44% 

Louisiana 164 323 50.77% Utah 135 283 47.70% 

Maine 160 457 35.01% Vermont 216 692 31.21% 

Maryland 147 237 62.03% Virginia 99 247 40.08% 

Massachusetts 188 585 32.14% Washington 267 505 52.87% 

Michigan 174 533 32.65% West Virginia 161 399 40.35% 

Minnesota 135 323 41.80% Wisconsin 114 349 32.66% 

Mississippi 107 348 30.75% Wyoming 114 252 45.24% 

Missouri 157 391 40.15% Total 10,145 23,643 42.91% 
Figure Four: Geospatial Display of Indicator Variables Across States 
Figure Five: False-Negative Reporting Rate for Respondents Residing in States with Certain Medicaid Characteristics 

[* - significant at the .10 level, ** - significant at the .05 level]

 Number of CPS Number of CPS Percentage of 

Respondents Respondents False-Negative 

Failing to Report Found on the Reports of 

Medicaid Receipt MSIS to have Medicaid 

Received Benefit 

Medicaid Receipt 

Benefits 

12-Month Continuous No 2130 5083 41.90% 

Eligibility Yes 8015 18560 43.18% 

1115 Waiver** No 8421 19118 44.05% 

Yes 1724 4525 38.10% 

Multiple Media 

Formats** 

No 1486 3906 38.04% 

Yes 8271 18809 43.97% 

Alternative Program 

Name** 

No 2090 4606 45.38% 

Yes 7667 18109 42.34% 

Presumptive 

Eligibility** 

No 9316 21491 43.35% 

Yes 829 2152 38.52% 
Figure Six: Summary of Logistic Regression Models to Predict Medicaid False-Negative Reporting [* - significant at the .10 level, ** - significant at the .05 level]

 Model One 

Control Model

Coefficient 

(std. error) 

Model Two 

Indicator Model

Coefficient 

(std. error) 

Model Three 

Indicator Model w/ 

Controls

Coefficient 

(std. error) 

Model Four 

Significant Indicator 

Model w/ Control

Coefficients 

(std. error) 

Intercept 1.5291** (.0736) -0.2065** (.0833) 1.4531** (.1210) 1.5089** (.1158) 

Age of 

Person 

Reference 0-5 -0.3314** (.0618) -0.3154** (.0617) -0.3143** (.0617) 

6-14 -0.1734** (.0550) -0.1751** (.0550) -0.1745** (.0551) 

15-17 -0.1176* (.0671) -0.1088 (.0701) -0.1098 (.0701) 

18-44 0.1088* (.0465) 0.0959** (.0471) 0.0947** (.0471) 

45-64 -0.433 (.0739) -0.0485 (.0724) -0.0499 (.0719) 

Hispanic or Minority Yes 0.2279** (.0594) 0.2308** (.0584) 0.2349** (.0585) 

Sex Male -0.0332 (.0417) -0.0283 (.0410) -0.0286 (.0410) 

Relationship to 

Reference Person 

Parent -0.1123 (.1406) -0.1039 (.1371) -0.1018 (.1365) 

Spouse 0.0660 (.0767) 0.0627 (.0754) 0.0620 (.0750) 

Own Child -0.1968** (.0519) -0.1946** (.0522) -0.1949** (.0523) 

Other 0.2314** (.0647) 0.2156** (.0642) 0.2149** (.0641) 

Income-to-Poverty 

Ratio 

< 50% -0.5250** (.0622) -0.5072** (.0608) -0.5110** (.0607) 

50-74% -0.4842** (.0769) -0.4791** (.0778) -0.4783** (.0778) 

75-99% -0.1975** (.0588) -0.1957** (.0574) -0.1983** (.0573) 

100-124% -0.1122 (.0718) -0.0981 (.0733) -0.0957 (.0731) 

125-149% 0.1157* (.0694) 0.0801 (.0713) 0.0819 (.0718) 

150-174% 0.1308 (.0915) 0.1315 (.0940) 0.1328 (.0943) 

175-199% 0.3727** (.0891) 0.3700** (.0894) 0.3693** (.0898) 

Medicaid Supplements 

Medicaid 

Yes -0.5512** (.1106) -0.5531** (.1131) -0.5525** (.1131) 

Medicaid with Private 

Insurance 

Yes -0.00691 (.0966) -0.00982 (.0979) -0.00049 (.0980) 

Social Security 

Insurance 

Yes -0.7076** (.0696) -0.7230** (.0680) -0.7170** (.0679) 

Payments for Medical 

Service in 2000 

Yes -0.3757** (.0570) -0.3611** (.0564) -0.3670** (.0574) 

Payment for 

Prescriptions (past 30 

days) 

Yes -0.3482** (.0494) -0.3624** (.0512) -0.3624** (.0513) 

Payment for 

Prescriptions (past 31-

60 days) 

Yes -0.2896** (.0797) -0.3025** (.0807) -0.3030** (.0806) 

Payment for 

Prescriptions (past 61-

90 days) 

Yes -0.2337** (.0903) -0.2607** (.0908) -0.2648** (.0915) 

Payment for NonPrescription Service 

(past 30 days) 

Yes -0.7228** (.0593) -0.7567** (.0595) -0.7512** (.0596) 

Payment for NonPrescription Service 

(past 31-60 days) 

Yes -0.6407** (.1030) -0.6309** (.1062) -0.6254** (.1056) 

Payment for NonPrescription Service 

(past 61-90 days) 

Yes -0.3648** (.1261) -0.3587** (.1267) -0.3552** (.1267) 

Temporary Assistance 

for Needy Families 

(TANF) 

Yes -0.3637** (.0741) -0.3861** (.0762) -0.3740** (.0761) 

51 State-Level Binary 

Variables 

 Various Values 

12-Month Continuous 

Eligibility 

Yes 0.0299 (.0357) 0.0489 (.0351) 

1115 Waiver Yes -0.0348 (.0378) -0.0663 (.0427) 

Multiple Media 

Formats 

Yes 0.1184** (.0404) 0.1037** (.0446) 0.1395** (.0399) 

Alternative Program 

Name 

Yes -0.0447 (.0323) 0.00463 (.0357) 

Presumptive Eligibility Yes -0.0736 (.0534) -0.1337** (.0592) -0.1440** (.0570) 

Max Income-toPoverty Ratio for Elig. 

 -0.2232** (.0604) -0.2135** (.0633) -0.2391** (.0582)