In recent years, there has been significant congressional interest in compensation of the federal workforce. The increased interest has been driven at least in part by the federal fiscal situation and in part by the state of the economy since the recession began in 2007. Issues related to the compensation of federal employees often center on the pay differential between federal workers and their private sector counterparts. For years, the annual President's Pay Agent (PPA) study, which is covered in greater detail later in this report, has shown a large wage penalty for federal workers compared to private sector workers in similar occupations. A spate of recent studies, which use a different analytical approach and data sources, has partially contradicted the findings of the PPA study by concluding that at least some federal workers enjoy a wage premium over comparable private sector workers. These studies and accompanying reporting on the comparison of compensation of federal workers to private sector workers provide an indication of the disparate findings, which makes it difficult to determine how compensation of federal employees actually compares to that of workers in the private sector. This report attempts to clarify why the recent studies have arrived at different conclusions and examines limitations of the approaches employed in the different studies. Based on the review of studies considered in this report, it appears there is no single study that addresses the question of compensation comparability using a widely agreed-upon methodology. That is not to say individual studies are not methodologically sound (some are), but rather that the questions asked and the assumptions made are not necessarily the same across studies, which at times arrive at vastly different conclusions. Any modeling of the relationship between the compensation of federal and private sector workers involves making assumptions, which in turn influence the results. Some assumptions are conceptual (e.g., should "job security" count as a "benefit? "), while others are empirical (e.g., what is the most appropriate model specification for the underlying data structure?). Finally, there are data limitations that prevent, or at least seriously curtail, researchers from specifying models as fully as possible. For example, there is not a dataset available at this time that allows an analyst to combine detailed characteristics of individual workers and detailed characteristics of jobs performed in the federal and private sectors. There have been several attempts in recent years to address the issue of compensation between the federal workforce and the private-sector workforce. In evaluating claims about federal pay, there appear to be two basic approaches to comparing compensation in the federal and private-sector workforces—the human capital approach and the jobs analysis approach. These two approaches are not mutually exclusive but may be difficult to combine given data limitations. In addition, the two approaches could even produce opposite results. Each approach is outlined below, followed by an examination of a few recent studies comparing federal and private sector compensation. The studies reviewed were chosen because they are official government studies (President's Pay Agent, Congressional Budget Office) or have received significant attention in policy debates. The "human capital" approach attempts to account for (control) as many observable characteristics of individual workers as possible that are known to affect individual compensation. When workers with similar observable characteristics are compared, some of the residual differences in an outcome (e.g., earnings) may be attributed to that individual's sector of work and some of the differences may be unexplained, in part because certain individual characteristics cannot be quantified and modeled. Literature on human capital and wages has shown that various forms of human capital, such as educational attainment, job tenure, and credentialing, are positively associated with earnings. Other things being equal, higher levels of certain human capital are associated with higher individual earnings. Other individual characteristics, such as age, sex, and race, tend to affect earnings as well. Human capital approaches do not necessarily justify the impact of some characteristics on earnings but merely incorporate as many explanatory variables in a model as necessary to isolate the effect of the variable of interest, which is sector of employment in the case of federal compensation studies. Studies using a human capital model typically try to capture as many characteristics of individuals as possible to explain earnings. Because of the need to build models based on individual characteristics, data sources for human capital models are typically household surveys, such as the U.S. Census Bureau's Current Population Survey (CPS), which provides extensive self-reported demographic and economic data. The Congressional Budget Office (CBO) study reviewed in this report, as well as studies from the American Enterprise Institute (AEI) and the Heritage Foundation (Heritage), use the human capital approach to compare compensation of workers in the federal and private sectors. The "jobs analysis" approach focuses on comparing the compensation for similar jobs, based on the actual duties and responsibilities of jobs in different sectors. In this approach, an attempt is made to match comparable jobs in different sectors rather than comparable workers in those sectors. The pay differential is then typically attributed to the pay structures in the different sectors, with the assumption that pay should be equal for equal work, regardless of individual worker traits. Comparing occupation to occupation is an important control in studies of wage differentials. Ideally, a comparison would match federal jobs with jobs in the private sector having identical tasks, responsibilities, skill requirements, and levels of complexity. In practice, this level of job matching may be difficult to achieve, in large part because of data limitations. Available data often make it difficult to look beyond broad occupational categories. This can be a serious limitation, particularly when it comes to trying to gauge actual responsibilities and actual job tasks. Using broad occupational categories, such as "manager," for instance, may conceal a great degree of difference in job function. For example, a manager of a small retail store and a manager of a federal department with complex multibillion dollar programs might both have the occupational title of "manager" but have very different job functions, knowledge, and responsibilities. Even within a sector, similar differences may exist. In the private sector, the manager of an independent store has vastly different duties and complexities than a manager of a manufacturing plant, for example. In addition, there may be some occupations in the federal government that do not have any (or any obvious) counterparts in the private sector, such as jobs in the intelligence community. The President's Pay Agent (PPA) uses the jobs analysis approach in its annual study of pay differentials between federal and private sector workers. Despite the limitations of job matching spelled out above, the PPA arguably goes furthest in the studies under review in measuring actual tasks and responsibilities. Ideally, a compensation comparison study would control for every other factor but the one of interest so that any difference in compensation between two workers may be attributed entirely to the sector in which the workers were employed (federal or private). Given real world data limitations, it is not possible to construct a perfectly controlled study, but it is possible for many demographic controls to match individuals across sectors closely. In other words, there is always some degree of omitted variable bias in studies of this sort, such that not all characteristics (of individuals or jobs) may be measured. For example, it is possible to compare two single white males of the same age with bachelor's degrees working in the same city—one in the federal government and one in the private sector. Data limitations, however, may not allow researchers to compare those same individuals in terms of job tenure (consecutive uninterrupted years in same occupation), motivation, intelligence, aptitude, or other possible explanatory factors. In other words, not all of the observed pay differences are due to premia or penalties from working in a particular sector but might also be attributed to variables not included in the models that researchers construct. Omitted variable bias affects both human capital and jobs analysis studies. In addition to problems associated with omitted variables, even the observable characteristics of individuals or jobs may create error in the models as a result of inexact measurement. Even in relatively straightforward characteristics, such as industry classification, there may be some uncertainty in the data. There is evidence that some private-sector employees might misclassify themselves as federal employees in the March CPS. For example, a federal contractor who is employed by a private-sector firm might classify his sector of work as the federal government. This sort of measurement error also influences any study that compares two groups of workers (e.g., gender, race). As with omitted variable bias, measurement error may affect both human capital and jobs analysis studies. The discussion above shows what the components of a study attempting to isolate the compensation effect of working for the federal government might include. If every factor that affected compensation could be measured and observed, then the premium or penalty of working in the federal government could be quantified. In reality, of course, it is not possible to observe, let alone measure, every factor affecting individual compensation. The differences between the human capital studies (CBO, American Enterprise Institute, Heritage Foundation) and the jobs analysis study (PPA) make it difficult to compare easily across studies. Table 1 below, however, shows the main features of the five studies summarized in this report. Greater detail is provided below on the CBO and PPA studies. As noted previously, to isolate the effect of sector—federal or private—on compensation, many other factors that influence compensation need to be considered (i.e., controlled for). The data in Table 2 show the different control variables in the compensation studies. Table 2 indicates the differences, and thus a large part of the reason for discrepant findings, between the human capital studies and the jobs analysis study. The human capital studies control, to varying degrees, for educational attainment, certain demographic characteristics, firm size, residency, and occupation. Importantly, however, data limitations do not allow a great degree of precision in controlling for occupation or job responsibilities in the human capital studies (see elaboration on this in " The Congressional Budget Office " review below). On the other hand, the PPA study, while not controlling for characteristics of individual workers, focuses in much greater detail than the human capital studies on the content of jobs.