The U.S. PIAAC Skills Map provides data on the literacy and numeracy proficiency of adults ages 16–74 in all 50 states, all 3,141 counties, and the District of Columbia. The Skills Map also includes state- and county-level model-based estimates for six age groups and four education groups. Read more
The U.S. PIAAC Skills Map provides state- and county-level data on the literacy and numeracy proficiency of adults ages 16–74. The literacy and numeracy proficiency are reported as the percentage of adults with low, medium and high levels of proficiency (for detailed definitions see Skills Levels) and average score of adults in the area.
The Skills Map also includes state- and county-level estimates for six age groups (16–24, 25–34, 35–44, 45–54, 55–64, and 65–74) and four education groups (less than high school, high school diploma or GED, some college (no degree or attained associate’s degree), and bachelor’s degree or higher).
On the county tab, county-to-county and to-state comparisons are available across all counties, except for the age and education groups, where comparison counties could only be within the same state. On the state tab, state-to-state and to-nation comparisons are available across all states and sub-groups.
Contextual variables are provided through “Add variables” button in the Summary card. The demographic and socioeconomic variables reported from the American Community Survey (ACS) for each state and county may help users to better understand the results for each state or county.
The state and county estimates are based on the combined PIAAC data collected in 2012, 2014, and 2017 and the data from the American Community Survey (ACS). The estimates are modelled using an advanced statistical method generally referred to as small area estimation (SAE). For more information, please refer to the Skills Map Methodology reports . National aggregates derived from the PIAAC state and county model-based estimates are not directly comparable to the PIAAC national estimates. For detailed description on the differences, please refer to the What are the differences… FAQ.
Small area estimation methodology was used to produce the U.S. PIAAC model-based estimates for literacy and numeracy at the state and county levels, as well as for age and education groups within states. These estimates were then used to produce the U.S. PIAAC model-based estimates for age and education groups for counties, using an allocation method.
The U.S. PIAAC state and county estimates are based on area-level hierarchical Bayes linear three-fold models. A bivariate model was used to estimate the percentages at or below Level 1 and at or above Level 3, which were then used to derive the percentage at Level 2. A univariate model was used to estimate the average. Separate models were produced for literacy and numeracy. All models included three levels of random effects: county, state, and census division. Each model used the following seven county-level covariates:
The source for the model covariates was the Census Bureau's 2013–2017 American Community Survey data. Further details are provided in the PIAAC State and County Estimation Methodology Report for the state and county estimates.
Area-level hierarchical Bayes linear univariate models with state-level random effects were used to produce the U.S. PIAAC state estimates for age and education groups. Separate models were produced for each of the six age groups (16-24, 25-34, 35-44, 45-54, 55-64 and 65-74) and four education groups (less than high school, high school diploma or GED, some college, and bachelor’s degree or higher), for the average scores and percentages at or below Level 1 and at or above Level 3, for literacy and numeracy. A total of 60 separate models were produced. A benchmarking adjustment was applied to align the aggregated, model-based estimates for groups to the state model-based estimates. The results were then combined to estimate the percentages at Level 2.
In the age group models, the model covariates included:
In the education group models, the model covariates included:
The source for the model covariates was the Census Bureau's 2013–2017 American Community Survey data. Further details are provided in the PIAAC State-Level Estimation for Age and Education Methodology Report for the state estimates for age and education groups.
The U.S. PIAAC county estimates for age and education groups are based on an allocation method. This method consists of using the county, state, and state by group estimates and allocating them to the county by group level via a deterministic model. The allocation method was applied separately to each of the six age groups (16-24, 25-34, 35-44, 45-54, 55-64 and 65-74) and four education groups (less than high school, high school diploma or GED, some college, and bachelor’s degree or higher), to the average scores and percentages at or below Level 1 and at or above Level 3, to literacy and numeracy. The results were then combined to estimate the percentages at Level 2. Further details are provided in the PIAAC County-Level Estimation for Age and Education Methodology Report.
The seven covariates used in the area-level hierarchical Bayes linear three-fold models to produce county- and state-level model estimates were selected from a large pool of county- and state-level variables that were collected or derived from large surveys and official statistics. The process of selecting covariates was conducted in two phases. In the first phase, the list of all the county- and state-level variables was considered as fixed effect and reduced through various methods (1) correlation matrix between the predictor variables and the outcome variables; (2) tree search algorithm for each outcome variable; and (3) least absolute shrinkage and selection operator (LASSO (Tibshirani 1996)) method for each outcome model. Once the list of covariates were reduced, the second phase evaluated the selected covariates using a cross-validation process adding the random effect estimation to arrive at the final list of covariates.
To produce the state-level estimates for age and education groups, the general strategy was to build on the previous research to the extent feasible, but also to modify the details where necessary. For example, the covariates used for the state and county estimates were modified to reflect age-specific or education-specific rates, rather than the original overall rate. For the educational groups, additional covariates include the percentage of population in the six age groups within the educational group. To avoid fitting models with a high number of parameters to a limited number of areas, several fixed-effect regression models weighted by the state sample size were suggested and fitted to determine the final set of model covariates through comparisons based on the Akaike information criterion (AIC) (Akaike 1974).
The U.S. PIAAC estimates are based on models which are in turn based on assumptions. During the model development process, the model assumptions and fitting algorithms were tested iteratively with the model specification. The predictive power of the nearly final model candidates was evaluated using cross-validation techniques. Upon finalization of the models structures, the resulting model-based estimates were compared against the survey direct estimates, when available, and they were in close agreement when the survey direct estimates were based on large sample sizes. In addition, model-based estimates were also compared across levels of aggregation and estimates at higher levels of aggregation were more precise than estimates at lower levels of aggregation, e.g., state versus county, or state versus state by group levels. When aggregated within state, the state by group model-based estimates matched exactly the state estimates, since they were explicitly benchmarked during the modeling process. Even if the county by group estimates could not be benchmarked to exact county estimates (due in part to challenges related to availability of consistent aggregation weights), they aggregated closely upon aggregation. Finally, county by group estimates were also compared against auxiliary data and similar relationships between them were observed for both county by group domains with sample data and county by group domains without sample data.
A number of federal statistical agencies have developed small area estimation small area estimation (SAE) methods and have published SAE estimates. For example, the Census Bureau's Small Area Income and Poverty Estimates (SAIPE) provides annual estimates of income and poverty for states, counties, and school districts. Indirect estimates are also produced for the National Survey on Drug Use and Health. Other examples can be found in Czajka, Sukasih, and Maccarone (2014). For more information on the PIAAC state and county estimates, please see the PIAAC State and County Estimation Methodology Report.
The previously published PIAAC estimates were computed directly using PIAAC survey data. Those estimates are representative of the national household population or large subgroups of the national household population. Unlike the previously published PIAAC estimates, U.S. PIAAC state and county estimates are model based, using a statistical technique called small area estimation (SAE) to provide valid estimates for U.S. states and counties. SAE refers to a variety of methods to estimate information for subpopulations or smaller areas of interest. SAE uses survey data, in combination with correlated data at the small-area level from other sources, to model the estimates of interest. In this case, the small areas are states and counties in the United States and the estimates of interest are skills proficiencies of the population.
The model-dependent approach was used to produce estimates for states and counties, for which PIAAC sample sizes were too small for direct estimation. The models used the combined 2012/2014/2017 U.S. PIAAC data in conjunction with data from the U.S. Census Bureau's 2013–2017 American Community Survey (ACS) to produce reliable estimates. The estimates are thus predictions of how the adults in the whole state or county would have performed had they been administered the PIAAC assessment.
The U.S. PIAAC state and county estimates are not directly comparable to the previously published PIAAC estimates for the United States because of the following differences:
The PIAAC state and county estimates are likewise not directly comparable to previous PIAAC estimates in international reports published by the Organization for Economic Cooperation and Development (OECD) for the following reasons:
The U.S. PIAAC state and county estimates are not measuring the same population as the previous estimates for PIAAC countries that are reported by the OECD. Specifically, the U.S. PIAAC state and county estimates (1) represent adults ages 16 to 74, whereas the OECD's estimates for participating countries represent adults ages 16 to 65; and (2) include “literacy-related non-respondents” (i.e., adults whose English language skills were too low to participate in the study), whereas the OECD's estimates for countries exclude this group.
The proficiency assessment instruments and scales used in the 2003 National Assessment of Adult Literacy (NAAL) and the 1992 National Adult Literacy Survey (NALS) were different from those used in PIAAC, and thus the estimates for counties and states from NAAL and NALS small area estimation (SAE) are not comparable to the corresponding estimates from PIAAC. The table below summarizes other differences between NAAL and PIAAC state and county estimates.
|NAAL and NALS State and County Estimates||U.S. PIAAC State and County Estimates|
|Age range||16 years and older||16- to 74-year-olds|
|Estimates||Percentage of adults lacking basic prose literacy skills||For literacy and numeracy separately:
|Covariates used in SAE model||2003 NAAL model:
The U.S. PIAAC state- and county-level estimates are reported for both literacy and numeracy for the percentage of the population at or below Level 1 (low proficiency), at Level 2 (medium proficiency), and at or above Level 3 (high proficiency), as well as the average scale score for the population in a county or a state. The table below provides definitions for the proficiency levels.
|U.S. PIAAC Proficiency Measures||Literacy||Numeracy|
At or below Level 1
|Adults at this level can be considered at risk for difficulties using or comprehending print material. Adults at the upper end of this level can read short texts, in print or online, and understand the meaning well enough to perform simple tasks, such as filling out a short form, but drawing inferences or combining multiple sources of text may be too difficult. Adults who are below Level 1 may only be able to understand very basic vocabulary or find very specific information on a familiar topic. Some adults below Level 1 may struggle even to do this and may be functionally illiterate.||Adults at this level can be considered at risk for difficulties with numeracy. Adults at the upper end of this level can understand how to add, subtract, multiply, and divide and can perform basic one-step mathematical operations with given values or common spatial representations (e.g., calculate how many bottles of soda are in a full box with two levels when only the top level can be seen). Adults who are below Level 1 may only be able to count, sort, and do basic arithmetic operations with simple whole numbers and may be functionally innumerate.|
|Adults at this level can be considered nearing proficiency but still struggling to perform tasks with text-based information. Such adults may be able to read print and digital texts, relate multiple pieces of information within or across a couple of documents, compare and contrast, and draw simple inferences. They can navigate in a digital environment to access key information, such as finding two main benefits of one product over another. However, more complex inferencing and evaluation may be too difficult.||Adults at this level can be considered nearing proficiency but still struggling to perform numeracy tasks. Such adults can successfully perform tasks requiring two or three steps involving calculations with whole numbers and common decimals, percentages, and fractions. They can conduct simple measurement and interpret relatively simple data and statistics in texts, tables, and graphs. However, more complicated problem solving (where the information is not explicit or is in an unfamiliar context) may be too difficult.|
At or above Level 3
276 points or more
|Adults at this level can be considered proficient at working with information and ideas in texts. Their higher literacy skills range from the ability to understand, interpret, and synthesize information across multiple, complex texts to the ability to evaluate the reliability of sources and infer sophisticated meanings and complex ideas from written sources.||Adults at this level can be considered proficient at working with mathematical information and ideas. Their higher numeracy skills range from the ability to recognize mathematical relationships and apply proportions to the ability to understand abstract representations of mathematical concepts and engage in complex reasoning about quantities and data.|
Although not always, in general, estimates at higher levels of aggregation are more precise than estimates at lower levels of aggregation. That is, state estimates are more precise than either county, county by group, or state by group estimates; state by group estimates are more precise than county by group estimates; and county estimates are more precise than county by group estimates. Also, estimates for areas with persons in the PIAAC 2012/2014/2017 household sample tend to be more precise than the estimates for areas without persons in the PIAAC sample. Furthermore, the estimates of average scores are much more precise than the percentage estimates in general.
Reporting on PS-TRE skills is problematic at the county level because the percentage of people who completed PIAAC on a computer is likely to vary widely across counties. It could be misleading to compare average PS-TRE scores or the percentage of the population at a certain PS-TRE proficiency level, because they may represent different percentages of the population as well as different populations (e.g., just the most computer savvy in one county versus a cross-section of computer users in another). Without knowing whether 90 percent or 15 percent of a county's population completed the assessment on a computer, a score of, say, 500 is both not meaningful and misleading. While respondents were encouraged to use the computer for the assessment if they were able, the percentage using the computer could be influenced by personal preference as well. Therefore, the meaning of the percentage at each proficiency level is also influenced by the percentage of respondents who used the computer to complete the assessment. In addition, complete covariance information is not available for respondents who did not use the computer for the assessment. The estimates of PS-TRE performance also have large standard errors that depend on estimates of computer use per county, which also have large standard errors that need to be factored in. For these reasons, it was determined that PS-TRE was not a solid domain for reporting state- and county-level estimates.
Yes. The estimates are available for download in the U.S. PIAAC Skills Map. See the last link on the left-hand side of the Skills Map (click the button “Download Data” under the bigger button “Compare Counties”). To access the Skills Map, please visit https://nces.ed.gov/surveys/piaac/skillsmap. Access the Guidance for Using State and County Estimates of Adult Skills for how to use the data from the PIAAC Skills Map.
The PIAAC state and county estimates can be used to estimate the total population at each proficiency level, and to estimate percentages or averages for groups of counties or states. Beyond reporting such estimates, the PIAAC state and county estimates allow interested researchers to conduct various statistical analyses that combine the model-based skills estimates with data from other sources at the state or county level. However, it is important to consider the uncertainty associated with the PIAAC state and county estimates in analyses. If not accounted for, then analysis results may be misleading due to overly precise results, and one may arrive at statistical significance when there is none. PIAAC SAE: Guidance on Use of State and County Estimates provide examples and discussions to guide data users through these analyses. The released model estimates for age and education groups cannot be crossed to arrive at estimates for combinations of age groups and education groups. To do so one would require a methodology that relies on strong assumptions, and the model estimates would rely much less on PIAAC data for those groups due to a small number PIAAC sample cases (if any), and much more on models.