assignment matching exercise 23.01

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Article Contents

Introduction, state creation of categories, burdens in matching to categories, conclusion: studying the matching to categories problem, acknowledgments, data availability.

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Matching to Categories: Learning and Compliance Costs in Administrative Processes

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Donald Moynihan, Eric Giannella, Pamela Herd, Julie Sutherland, Matching to Categories: Learning and Compliance Costs in Administrative Processes, Journal of Public Administration Research and Theory , Volume 32, Issue 4, October 2022, Pages 750–764, https://doi.org/10.1093/jopart/muac002

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A perennial task for the state is the creation and policing of categories. State-created categories have real world impacts on the public. The consequences of racial categorizations, for example, are well-documented. We examine a less studied consequence of state categorization, which are the administrative burdens created when individuals attempt to match themselves to state-created categories. Matching requires time and effort, and failure to match to an advantageous category can mean a loss of material benefits. The matching problem may sometimes result from obscure categories, or an overwhelming number of categories. The matching problem is also amplified when the state uses identity categories—such as self-employed or unemployed, a retiree, parent, spouse or disabled—where individuals hold pre-existing beliefs about such identities that map poorly onto equivalent state categorizations. To study the matching problem and ways to reduce it, we undertook a field experiment in a California welfare program, CalFresh, the state version of the Supplemental Nutrition Assistance Program (SNAP). Claimants often fail to select into the category of “self-employed” even though it would be more favorable for them to do so. We show how a more intuitive presentation of information about the category and its benefits increased the rate of those identifying as self-employed from 8.8% to 12.1%, approximately one-third. We also show that providing a simple self-attestation template to convey information about self-employment status, a means of reducing compliance costs while meeting state documentation requirements, increased the number of claimants providing an acceptable form of documentation to match to the category. The results show that people frequently lack an intuitive understanding of state categories, that the presentation of categories can reduce this matching problem, and that the state can make it easier to document the match.

In Lasswell’s (1936) classic definition of politics—“who gets what, when, how”—a basic task for the state is to establish formal categories that determine the “who” that gets “what.” Determining “who” is disabled, or white, or homosexual is not straightforward, and has significant effects on the individual’s relationship with the state, including the rights and benefits afforded to them.

Administrative determinations of “who” come in two stages. In the first stage, the state develops formal categories, as well as criteria that assign people to those categories. A great deal of research has focused on this stage, which involves the social construction of categories, often reflecting contemporaneous state goals, scientific theories, and lobbying by affected groups ( Loveman 2014 ; Merry 2016 ; Nobles 2000 ).

In the second stage, the individual is matched to the appropriate category. In many cases, this can be straightforward, particularly in cases where individuals do not contest the category or the placement of themselves in it. For example, this is often the case for gender assignment (though see Nisar 2018 ). But in some cases, the categories and criteria are ambiguous, and it is left to the individual to judge which category they fit into. Such cases are complicated if the individual has pre-existing beliefs about their personal identities that may not match the state’s definition of equivalent categories. Here, the individual’s capacity to match to categories may matter a great deal, putting them at risk of losing rights and benefits to which they are entitled.

The matching to categories problem is common across many government programs. For example, the benefit size for Supplemental Nutrition Assistance Program (SNAP), Medicaid, housing and child care assistance can hinge on whether individuals classify themselves as self-employed or not. However, “self-employed” as a category has become less clear as the gig economy has expanded the number of people with self-employment income ( Katz and Krueger 2019 ): the number of people claiming self-employment under SNAP has increased from about 365,000 in 2006 to 939,000 in 2018 (Maneely and Ross-Eisenberg 2020). There are similar category matching challenges in programs ranging from the Earned Income Tax Credit (EITC) (who qualifies as a parent?) to unemployment insurance (who qualifies as unemployed?) or Social Security (what does it mean to be a retiree?). In short, across a wide range of policies, there is a disconnect between how people define themselves, versus how administrative language defines them. Yet, this second stage of the process—the matching to categories—has received little attention. Instead, these problems are viewed as policy specific, rather than systematic, and studied in isolation from each other rather than as an aspect of administrative burdens.

In this article, we investigate the process of matching to categories and the administrative burdens that make such matching processes more or less difficult. The work represents a study of citizen–state interactions, a historically understudied area of public administration research, but one gaining more attention ( Jakobsen et al. 2019 ; Jilke and Tummers 2018 ; Zacka 2017 ) with a particular focus on the administrative burdens arising from those interactions ( Barnes 2021 ; Heinrich 2016 ; Herd and Moynihan 2018 ; Jenkins and Nguyen 2022 ; Masood and Nisar 2021 ; Peeters 2020 ). 1

Specifically, we examine how people match to the category of “self-employed” when applying for social welfare benefits. The category may seem mundane. It is certainly less contested and studied than, for example, state classifications of race or disability. However, individuals often fail to select into this category even though it is financially advantageous for them to do so, as well as accurately reflects the state’s definition of their employment status. Such difficulties reflect administrative burdens, most obviously the learning costs of identifying, understanding and matching oneself to official categories ( Barnes and Henly 2018 ; Herd and Moynihan 2018 ). A variety of approaches exists to fix the matching problem. The state could try to simplify its formal categories to be more consistent with public understanding of associated terms, or try to better educate citizens as to administrative categories. We examine another alternative, which is to use information technology interfaces to reduce learning and compliance costs.

Our empirical example comes in the form of a two-part experiment with CalFresh, the California version of SNAP, which provides benefits to about 11.5% of California residents as of July 2020. Applicants who are self-employed can receive more benefits by claiming a special deduction, because self-employment income is assumed to include costs not typically borne by regular employed workers. But many of those who are eligible do not understand they fall into the category of self-employed, or provide simple documentation to attest to their status, thereby leaving money on the table. For example, many gig workers who work for app-based companies like Uber or GrubHub may not see themselves as “sole proprietors”, which is how California describes the self-employed.

The field experiment contained two treatments administered to actual applicants in the GetCalFresh online application process. The first treatment sought to reduce learning costs, making the category of “self-employed” more intuitive for users, using terms and examples that applicants identified as being more understandable. This treatment increased the rate of those declaring as self-employed from 8.8% to 12.1%, or about one-third.

The second treatment provided a self-attestation template to help individuals describe their self-employment status. This treatment sought to reduce learning costs—since many self-employed individuals would not know what serves as acceptable documentation—but was primarily intended to reduce the compliance costs of providing documentation. Such self-attestation documents are a simple way to claim the deduction, but most applicants struggle to develop the appropriate language. Of those offered the template, 22.8% used it, making them significantly more likely to provide an acceptable form of documentation to state officials. Importantly, the treated group’s applications were approved by state officials at the same rate as the nontreated groups, implying that the treatments increased the success of claimant’s ability to match with state categories. In other words, the results of the experiment were not a function of subjects incorrectly claiming the wrong category.

Our article makes the following contributions. First, we conceptualize matching to categories as a frequent and recurring policy implementation problem. We document how this problem arises across multiple policy settings—we point to cases of matching problems around state-defined categories of parent, disabled, spouse, retiree, unemployed, and even public servant—making it a salient but understudied aspect of administration. Second, we connect the administrative burden framework to the broader literature on state categorization in disciplinary fields such as sociology or political science. Doing so underlines that while we have myriad studies of conflicts about racial classifications, we have much less evidence on the matching problem in other domains that are crucial to the public’s experience of the state. Third, we offer empirical evidence based on mixed methods research on how to minimize the matching problem. We show that learning and compliance costs of matching to categories can be reduced—or increased—depending on how the state presents information and offers help, with the effect of altering the success of the individual in matching to a favorable category. Fourth, while the administrative burden framework has directed attention to the costs of burdens, and behavioral science has pointed to the power of nudges and framing, our work builds on these approaches to direct public sector practitioners to examine the construction and effects of administrative categories. The practical implication of our work is that applying relatively simple user-centered design principles to the presentation of state categories can have potentially large and meaningful effects. In our case, no policies were changed, or expensive outreach programs undertaken. Instead, the changes involved a simplification of terms, more pertinent examples, and provision of helpful templates. In other words, it was quite a simple and feasible administrative fix. The nature of our practical contributions recalls Kurt Lewin’s old adage that “nothing is so practical as a good theory,” that is, good theory illuminates crucial professional questions in a way that prior theory left unattended ( Van de Ven 1989 ).

In the next section, we offer an overview of how the administrative state constructs categories, and theorize about the difficulties individuals face in matching to categories. We then describe our methods, before presenting our data and analysis.

There is a vast and rich literature on processes of state categorization from disciplines such as anthropology, philosophy, history, sociology, and multiple subfields of political science. Our purpose here is not to review this literature in its entirety, but to draw out some essential points before focusing more narrowly on the process by which individuals try to match to state-created categories, and the administrative burdens involved in this process.

What are some of the key lessons from this literature? First, one benchmark of the developed administrative state is its capacity and desire to categorize and measure and to enforce its categorizations and measurements upon the populace ( Alonso and Starr 1989 ; Foucault 1980 ). The development of state-defined standards for categories such as property, crops, or weights make the subjects of the state legible to their rulers, and thereby made it easier to tax those subjects and facilitate other goals such as trade ( Scott 1997 ). Indeed, the etymology of the word “statistics” reflects its connection to the state.

The affixing of categories across states is not uniform, since states developed at different times and with different concerns. As such, categories deemed more pressing at a time of state development are more likely to capture administrative attention. For example, in seeking to explain why gay people in the United States were more subject to administrative detection and punishment, Canaday (2009) deploys a state development argument: the quasi-scientific “discovery” and subsequent demonization of “the homosexual” as a category occurred in the late 19th and 20th centuries, the period when key components of the US administrative state—governing military, immigration, and welfare functions—were maturing.

Second, state categories are constructed, rather than found in nature ( Stone 1988 ; Yanow 1996 ), even as they seek to capture real phenomena ( Desrosières 2002 ). Categories are, therefore, mutable, reflecting values and political pressures of the particular time of their creation. Perhaps the most obvious example of this is the construction of racial and ethnic categories by governments. Consider some of the options for those living in North America over the last 250 years. In the late 19th century, Blacks or Indians living in portions of New Spain that became much of modern United States could purchase certificates issued by the Spanish empire that changed their racial status, allowing them to claim whiteness as a category ( Taylor 1999 , 36). In the United States itself, categorizations ranged from the dubious “one drop of blood” standard to determine Blackness, to the construction of Hispanic and Latino as distinct categories, to the de-ethnicized standard of “white” as a category ( Haney Lopez 1997 ; Thompson 2016 ). Once these classifications are asserted by the administrative state, they become increasingly perceived as the natural order of things, disguising the choices, trade-offs, and decisions that went into their creation ( Nobles 2000 ; Thompson 2016 ).

Third, the construction of categories is generally aligned with the perceived interests of the state, which tends to overlap with the dominant power structure ( Nobles 2000 ). For example, the erasure and re-introduction of racial administrative categories in Latin America reflected goals of first building nationalist communal identities, and later using demographic information as part of economic development goals ( Loveman 2014 ). Such categories therefore served as a source of confusion to those who were unused to them, and many responded by using more familiar national categories—Brazilian, Mexican, etc.

Political pressure, of course, alters the perception of what state interests are, and the evolution of census categories in the United States reflects lobbying by different groups ( Thompson 2016 ). But the essential point for our purposes is that the state construction of categories serves perceived state interests and interpretations; a very different dynamic from one where the state constructs categories to reflect colloquial understandings of categories by the public. As a result, formal state categories may not be intuitive to those being categorized ( Merry 2016 ).

Fourth, classifications can have significant consequences for the subjects of the state. The consequences come via different mechanisms. The category might exclude the individual. The category of a “regular voter”—those who have voted in one of the last three elections, for example—serves to exclude people from the voting rolls ( Herd and Moynihan 2018 ). The exclusion of categories that an individual can match to—for example, gender nonconforming individuals unable to receive forms of ID—can amount to a form of exclusion from civic and economic opportunities ( Nisar 2018 ).

The categorization process might put the individual into an unfavorable category. An obvious example is the racialized categories of segregated societies, where, for example those of mixed race are placed into the category of Black, and therefore subject to significant legal restrictions. In segregated South Africa, racial categories were officially assigned at birth and had lasting effect for all kinds of economic and educational opportunities. As Hacking (1986) puts it, official categories have a way of “making up people” into legal—and punishable—classifications such as pervert, prostitute, vagrant, loiterer, or homosexual.

While the most obvious examples of state categories center on racial, ethnic, gender, or sexual preference, the state creates a myriad of other categories that butt up against individual identities. These categories may appear mundane and thus easier to overlook, even as they represent crucial aspects of the routine provision of benefits or collection of resources ( Peeters and Widlak 2018 ). Relative to the literature on the creation of state categories, evidence on how citizens, in practice, actually match themselves to those categories is scarce.

To better situate the matching problem in policy implementation, and to understand how it functions for a wider array of administrative categories, we turn to the administrative burden literature. The administrative burden framework describes different forms of costs that individuals face in interactions with the state: learning, compliance, and psychological costs ( Moynihan, Herd, and Harvey 2015 ). How might the process of matching to categories give rise to such costs, and in turn make it more likely that people fail to make the match?

Categories can create psychological costs as individuals feel compelled to match those categories. For example, bureaucratic procedures may demand acts that emphasize the lack of autonomy the individual has if they are to satisfy the requirements of a category: a Muslim woman asked to remove a headscarf for a state identification, or a welfare recipient asked to urinate into a cup for a drug test. Those completing the census may complain that the categories do not reflect their perception of their own identities ( Yanow 1996 ).

In this article, we focus on learning and compliance costs of matching to categories. One aspect of learning costs is simply knowing if a program—or in this case a category of program beneficiary—is helpful, to what degree, and therefore if it is worth matching to the category ( Herd and Moynihan 2018 ). Such costs can be reduced in various ways, such as outreach efforts by individuals and nonprofits ( Heinrich 2016 ). It is not just costs of getting on a program: staying on a program brings its own costs ( Barnes 2021 ).

The matching problem is related to, but distinct enough from consideration of general learning costs, to demand special consideration. Since the bureaucratic categories are primarily designed for administrative purposes, there will often be some mismatch between state-created categories and public understanding of those categories. The matching problem may be worsened for several reasons. Administrative burden research points to the role of poor state communication, or excessively complex language ( Herd and Moynihan 2018 ). In the case of complex social programs, the relative benefits of one category over another may be murky, leading individuals not to apply or to stick with whatever is the default category rather than investigating if another would be more beneficial to them. Confusion about categories may also be tied to confusion about programs. For example, intergovernmental programs such as Medicaid, the Children’s Health Insurance Program, and SNAP can be relabeled by state governments. Thus, a California resident might be understandably confused that SNAP, CalFresh, food stamps, and Electronic Benefit Cards are the same program. There are also multiple welfare programs with similar criteria. In some cases, states seek to reduce such confusion by combining such programs into single identifiable brand and application process ( Herd et al. 2013 ). The matching problem may also be worsened because the categories themselves are obscure. Should immigrant family members of US citizens apply for the IR3, IH3, IR4, or IH4 visas? Without careful investigation and expert help, it is impossible for the immigrant to know.

In some cases, the matching process hinges on identity categories—such as whether one qualifies as a parent, employed, unemployed, disabled, or a retiree. Such categories do not seem abstruse. People hold pre-existing beliefs about whether they occupy such identities. But those beliefs may be wrong in the eyes of a state. Let us consider some examples.

The category of unemployed seems unambiguous at first glance. But not having a job does not necessarily mean one is unemployed in the eyes of the state, which has its own criteria to determine eligibility, criteria that can be complex and vary enormously from state to state. As a result, many who do not have a job mistakenly apply for unemployment benefits they are ineligible for, even as others who are formally eligible do not apply ( Wandner and Stettner 2000 ). A system featuring such confusion leads to frustration for those who mistakenly apply, missed benefits for the quarter of those who are eligible but mistakenly do not apply, and contributes to enormous variation in take-up ( Wandner and Stettner 2000 ): the fraction of the unemployed receiving unemployment benefits ranges from 10% in North Carolina to 57% in New Jersey ( U.S. Department of Labor 2020 ).

What does it mean to be an eligible parent? Or a qualifying child? These categories shape many benefits, such as a Child Tax Credit and the EITC in the United States, or children’s allowances in other countries. For the EITC, miscategorization has large impacts. Category mismatches around who qualifies as a “parent” explains the majority of EITC improper payments, which constitute 24% of the program’s expenditures ( U.S. Department of Treasury 2017 ). Nonresident biological parents, resident or nonresident boyfriends, and even grandparents and aunts claim the status to increase deductions ( Edin, Tach, and Halperin-Meekin 2014 ). For applicants, such claiming of categories might reflect the realities of caregiving in complex family situations. Confusion may also arise from the complexity of the tax code: the 2020 IRS EITC guide runs to 41 pages, and it is possible that a child might be categorized as a “dependent” but not a “qualifying child.” Such misclaiming frequently triggers concerns about fraud, such that EITC recipients face higher audit rates than wealthier taxpayers ( Herd and Moynihan 2018 ).

For another anti-poverty program, the Child Tax Credit, pandemic stimulus laws in 2021 changed the notion of what is a qualifying parent or child, so that receipt of benefit is no longer conditional on the parent meeting a certain earnings threshold to qualify. This creates a situation where millions of parents and children who did not qualify before may now access this beneficial category. However, if the expansion is not extended, they will be again deemed ineligible in the future. Such changes increase learning costs and the risk of missed, or erroneously claimed, benefits.

The status of retiree seems straightforward enough. The work of defining eligibility for Social Security retiree benefits is largely undertaken by the Social Security Administration based on administrative data, minimizing most learning costs. But the presentation of retirement categories still affects how people think about their retirement decision. More specifically, participants understandably confuse “Full Retirement Age” with the age when Social Security retirement benefits are maximized, which is in fact “Delayed Retirement Credits.” An experiment that relabeled those categories to Standard Benefit Age and Maximum Benefit Age respectively resulted in not just a more accurate understanding of the categories but also made people more likely to express an intent to delay retirement ( Perez-Arce et al. 2019 ). Social Security also offers an example of how the category of “married” may misalign between popular understandings and state categories: those claiming survivors or spousal benefits must have been married at least 10 years, a legacy of Congressional concerns about “designing women” intent on marrying “old fools” to claim their Social Security benefits ( Kessler Harris 1994 ).

Governments may also choose to compensate certain professions or status. For example, the federal government promises student loan forgiveness based on categories such as disability or serving the public. The Public Service Loan Forgiveness Program is an example of one such program that has been exceptionally unsuccessful. Per one report, just 55 of 1.17 million applications succeeded in claiming the benefit ( U.S. GAO 2018 ). In other words, the vast majority of those who thought of themselves as eligible public servants found out that they were not for the purposes of claiming the benefit. Some of the confusion is about the terms of the program—what is a qualifying loan, whether repayments were regularly made—but much of the problem is as simple as whether their work qualifies as public service. For the purposes of the program, “public service” excludes work in for-profit hospitals but includes work in nonprofit hospitals. At the same time, some type of nonprofit work is excluded, such as working for labor unions. The category also excludes government contractors who work alongside eligible government employees.

While we focus on program services, these are not the only such ways categories matter, as the literature review testifies. At an earlier age someone might try to claim the category of homosexual to avoid military service, while in more recent times might lobby to have that status recognized within the armed forces. Someone might seek a category status, such as nongender conforming or transgender that the state refuses to recognize ( Nisar 2018 ). Someone might seek the category of eligible voter that a street-level bureaucrat refuses to extend for discriminatory reasons.

The preceding examples add to our knowledge of matching processes in multiple ways, beyond the literature reviewed in the prior section. First, categories are a salient feature of policy implementation in multiple policy domains. The process may create confusion and frustration, and a loss of valued services for the eligible who never applies or matches to the wrong categories. Even so, we have little sense of the scale the matching problem because the topic has not been given systemic attention.

Second, category matching requires some measure of interpretative skill on the part of the individual, as well as skill on the part of the state to make state definitions legible to the individual. The person seeking to match to state categories must devote time and effort to learn what those categories are, and to decide which categories reflect their identity or situation. We can expect that mismatches, and consequent learning costs, will grow when people lack the skills to decode the category, or when the phenomena that the category is intended to capture is itself sprawling and rapidly changing. Many of these conditions apply in the case we study. The term “self-employed” is straightforward enough it would seem. But perhaps less so if you have a high school education, or if English is not your first language, or if you are trying to figure out if driving for Uber qualifies as self-employment.

Third, matching problems arise not just from confusing categories and programs, but also the construction of identities and the mismatch between the individual’s belief about their identity and the state’s definition of it. Paying attention to the intersection of identity constructs pushes researchers to overlay a map of how members of the public conceive of different identity categories—while acknowledging that there may be multiple such conceptions—with how the state presents those categories.

Fourth, the learning costs involved in identifying and selecting the appropriate category are not the only type of administrative burden involved. Category matching also requires documenting status, especially so if the category confers some benefits. The provision of documentation may be simple, such as a self-attestation where the individual declares information about their status, or complex, with independent verification via documents produced by third parties. Either way, it is a compliance cost.

While we traditionally think of citizen–state encounters as between two individuals—a member of the public and the street-level bureaucrat—the latter has been increasingly replaced or supplemented by a machine (Peeters and Widlaak 2018). This is especially true for the seemingly simple routine interaction of filling out and submitting forms. For example, 46 of 50 states offer online SNAP applications. All offer online Medicaid applications. Thirty-nine states offer online TANF applications and just seven offer such access for Nutrition for Women, Infant and Children (WIC) (Code for Ameri ca 2019 ).

The machine does not have discretion to discriminate (although discrimination may be built into its algorithms). Nor can it easily recognize if someone needs additional help. If someone is struggling with categories, the machine cannot recognize (so far) their confusion, or provide help beyond the categories and information already coded into its design. The machine can be reprogrammed however, and the explanation of the categories can be made more intuitive, and provided in a variety of formats. Our experiment centers on this approach, examining how modifications to a website interface affected the capacity of people to solve the matching problem.

Theoretical Expectations

While the matching to categories problem offers a relatively novel venue to explore, the prior discussion generates two major insights that help to inform our theoretical expectations. First, people often struggle with matching problems, leaving money on the table even when seemingly low-cost actions would be advantageous to them. There is good reason to believe therefore that people will struggle to match to categories that are confusing to them and where the benefits are unclear. A straightforward extension of this point leads to the next insight, drawing from behavioral science: a “nudge” that makes it easier for individuals to see how their interests aligns with a certain choice (in this case adopting a self-employment category) can change behavior to improve outcomes ( Thaler and Sunstein 2008 ). The empirical evidence on nudges is mixed and much depends on contextual factors—such as the types of interventions, the stakes involved, the target population, and the types of barriers faced. For example, Linos et al. (Forthcoming) show that a variety of low-touch outreach efforts—such as text or mail—have no effect on participation among those eligible for the EITC for a population of low-income Californians similar to the one we study. Nonetheless, a review of 126 interventions undertaken by DellaVigna and Linos (2022) conclude that nudges in general do generate positive effects, though these effects are more modest than is reflected in the published literature.

Our theoretical expectations address two types of costs: learning and compliance costs. First, we expect that treatments that present the category in intuitive and plain terms, including potential benefits of the category, will reduce learning costs, and in doing so increase the ability of individuals to accurately select into an advantageous category. The context for self-employed is that people tend to under-select into this category relative to the true rate of self-employed, and so our expectation is that a reduction in learning costs will increase the selection of the self-employed category. In a context where many people mistakenly over-select into a category, the provision of more intuitive terms can be expected to reduce selection to this category. The accuracy of selection is based on whether the applications of those in the treatment category are rejected by administrators at a rate different than the control group (they are not; we took other precautions to ensure applicants were accurately describing their self-employment status, described in the Data and Methods section).

Second, we expect that a template that allows the individual to efficiently document their status will increase the rate of documentation. Here, we draw directly from the administrative burden literature, and other studies of frictions, to claim that additional documentation requirements make it harder for individuals to access benefits ( Madsen, Mikkelsen, and Moynihan 2020 ), and by extension, finding ways to minimize such compliance costs are useful. Self-attestation forms are one way of satisfying requirements with low compliance costs. They involve declarations of status but without providing third-party documentation. Many social welfare programs, ranging from the SNAP to Medicaid, allow some types of self-attestation. In some cases, as with income, they may be subsequently verified with administrative records. In other cases, as with household size, they are not uniformly verified. They are often used to speed up access to benefits. For example, some states implemented greater use of self-attestation to increase the use of Medicaid real-time eligibility determinations ( Wishner et al. 2018 ). When pandemic rental relief programs stalled, the Biden administration pushed state governments to use self-attestation forms to reduce burdens ( Thrush and Rappeport 2021 ). Importantly, however, while they are widely used, and often promoted as a way to reduce burdens and increase benefit access, there is limited evidence on their effects ( Wishner et al. 2018 ).

We expand upon the nature of the treatments in greater detail below. The goal of the template is not to increase participation rates, but to ease the burdens of providing information, thereby making it more likely that participants can provide an acceptable level of documentation to claim the desired and accurate category. While the outcome variable for the simplifying information is to increase the percentage of those who select into self-employed status, the outcome of interest for the document template is to increase the necessary documentation provided.

Case: Matching to Self-Employed Status

Our analysis examines an effort to encourage matching to the category of self-employed for CalFresh. Matching to the category is complicated by the fact that both more people are self-employed than was the case in the recent past and that new types of jobs are being created in the gig economy that blur the lines between self-employment and traditional employment ( Katz and Krueger 2019 ). For example, Uber has been sued by drivers who claim that the company is their employer, but the company characterizes its workers as independent contractors. Indeed, many jobs, ranging from driving for Uber or Lyft or doing delivery for Grub Hub or DoorDash, count as self-employment under CalFresh rules. Under such circumstances, the potential for learning costs is great.

Who is self-employed, and what can they get? For CalFresh, which is based on federal rules applicable in all states, people are considered self-employed if they operate a business as a sole proprietor or independent contractor, offer their services or merchandise for sale, provide their own supplies, materials or merchandise, are responsible for expenses normally covered by an employer, or are a member of a partnership. An analysis of California administrative data estimated that 17% of SNAP tax-reporting units had self-employment income ( Iselin, MacKay, and Unrath 2021 ). States can decide to provide additional exemptions for the self-employed with SNAP. Most do, but how states manage the documentation and application of self-employment vary. Twenty-three states have sought to reduce burdens by adopting simplified means of claiming benefits related to self-employment ( Maneely and Eisenberg 2020 ). California allows self-employed individuals two options to deduct their expenses. The first is a simple 40% deduction from their income, decreasing their eligible income. The second option is to do a detailed deduction of expenses. The self-employed also face additional compliance costs because, unlike traditional employees, they lack employers that can provide a standardized form of income verification. So different documentation is needed. In the case of CalFresh, if the claimant submits a self-attestation, that is, a statement of estimated hours and income, this typically suffices as documentation for claiming the 40% deduction, although caseworkers may ask for additional proof of income (e.g., checks paid to the claimant, direct deposits from rideshare apps).

Data and Methods

The research took place via a multi-stage process using a mixed-method design. The experiment was delivered on the GetCalFresh website, which provides digital assistance to applicants seeking to receive CalFresh, under a data-use agreement with the California Department of Social Services and an IRB exemption from the state. The website was developed by a civic tech nonprofit, Code for America, and served about 2.6 million people in 2020. Code for America incrementally experiments with process changes drawing on principles of user-centered design to make the application process quicker and more intuitive than the traditional paper or online process. As a result, while applicants apply for benefits via other interfaces, GetCalFresh takes less time compared to other services. 2

User Consultation

The first stage of the process applied user-centered design principles, through a series of qualitative research techniques, to understand how the target population understood self-employment ( Abras et al. 2004 ). First, three eligibility workers were interviewed, highlighting perceived problems that clients encounter with administrative systems, and identifying what represents an acceptable form of documentation to match to the self-employment category. Second, we spoke to three individuals who help people to sign up for CalFresh (two from nonprofit foodbanks, one from local government in California), to understand barriers that self-employed people face. Third, we examined approximately 500 actual income verification documents that were submitted on GetCalFresh over the course of a month in 2018. Of 37 cases where it was clear that people had self-employment income, 13 did not register as self-employed. Some were providing more documentation than was necessary to verify their status. To be clear, however, we could not determine, based on these applications, all of those who may have failed to register as self-employed even though they were eligible to do so. But paystub information further reinforced the concern that people were not claiming their self-employment status.

Fourth, we consulted users directly, using an online card-sorting exercise that asked 20 individuals to associate terms with self-employed status. Participants were recruited from Usertesting.com, a usability study platform typically used by software companies to recruit users and run online studies in order to test or improve technology products. The selection criteria mirrored those we targeted in the experiment: California residents, a mixture of those who identified self-employed and those who did not, working age, and low income (less than $40,000).

In the card-sorting exercise, participants were presented eight terms—independent contractor, freelance worker, self-employed, online merchant, entrepreneur, sole proprietor, part time business, app-based work—and asked (1) “If you had to explain this term to a friend, what would you say?” and (2) “Can you think of any jobs or professions that might fit in this category?” The responses made clear that to these individuals, “self-employed” was confusing, since it implied “not working for a company,” “you don’t have a boss,” “don’t work for an employer,” and “working for themselves” in the words of respondents. Other terms were also confusing. For example, for self-proprietor, few understood the term and many assumed “proprietor” had something to do with building ownership. The terms “freelance” or “independent” best evoked in the mind of participants the wide variety of jobs that the state recognized as self-employed. The user feedback also made clear that providing specific examples helped to broaden a relatively narrow understanding of what counted. In short, our qualitative work identified sources of confusion people had with the category of self-employed, and potential solutions to present the category in a more intuitive way which were implemented in our experimental treatments.

Design of Treatments

Treatment 1 : Subjects were offered an alternative web page in the online application process with different languages and examples to reduce learning costs, and to convey the value of the self-employed category. Figure 1 provides a contrast between the control group (which is the status quo for the online application) and the treatment. In the control (the left panel in figure 1 ), a client signals they are self-employed by answering yes to the “Are you self-employed?” question. The treatment (below, center) provides more terms (independent contractor, freelance) drawn from our qualitative research as well as specific examples based on the review of submitted income documents, including Doordash, barber, dog walker, online selling, and provides a help button providing more information (on the right). As noted above, another aspect of the learning costs is understanding what benefits are on the table. In this context, this means understanding that the category of being self-employed is beneficial to the individual. The treatment therefore communicates that self-employed status “can lead to more benefits,” though of course we cannot be certain they read this clarification.

Treatment 1—Explanation of Self-employment.

Treatment 2 : The second treatment was to offer a simple but effective means of documentation for those who select the self-employed category. A self-attestation represents the most minimal form of acceptable documentation that will, on balance, reduce other types of compliance costs, such as more complex forms of documentation (e.g., verification of income from third parties), or additional interactions with state employees. To reduce compliance costs, we therefore generated a template that allowed claimants to provide a self-attestation about their hours and earnings, and additional documentation if they wished (see figure 2 ). Since there is no status quo version of this, the control group received no page to review, while the treatment groups were presented with the following page. The logic of our design aligns with findings by Linos, Quan, and Kirkman (2020) : that an additional step in an administrative process can sometimes reduce administrative burdens and generate better outcomes.

Treatment 2—Self-attestation Template.

Respondents are randomly assigned to one of four different treatment groups, representing a 2 × 2 between-subject design. The 2 × 2 treatment matrix varies on (a) whether self-employment is better explained (treatment 1) and (b) whether subjects are offered a self-attestation template (treatment 2). There is a 50% chance that subjects will be assigned to one of the two treatments. That meant about a quarter of applicants would receive both treatments, a quarter would receive the self-employment explanation treatment, a quarter would receive the self-attestation template treatment, and a quarter would receive neither. The final sample numbers are provided in appendix table A1 . Appendix table A2 provides a randomization check that shows that the subjects are similar on observable characteristics across assignment to treatment and controls, as well as that application completions were similar across all groups. The race items were not self-reported, but inferred using Imai and Khanna’s (2016) method for inferring race based on voter registration records. We were able to cross-check the accuracy of these inferences with language preferences the clients selected. So, for example, 96.5% of those who preferred an East-Asian language were inferred to be Asian.

Data were collected in January through April of 2019. At the time, about one quarter of all California SNAP applications were submitted via GetCalFresh.org. GetCalFresh users were slightly younger, more likely to be applying only for themselves, and more likely to prefer English than applicants coming from other sources. Since the pandemic, more applicants have shifted to online applications, such that in 2020 and 2021, GetCalFresh accounted for 53% and 56% of all applicants, respectively. Whether online or offline the application process is the same; applicants apply, the materials are reviewed, and then an interview is scheduled. There are some instances where those applying off-line may access some additional help, from nonprofits for example, but this is unusual. In sum, the data that we have suggests only small differences between those applying online versus those applying via other means. The fact that online users are slightly younger and have access to the internet suggest that they may be slightly more able to manage the application process. The implication would be that this might slightly suppress some of the effects we see as less-skilled online users migrate to GetCalFresh, and so we may have a lower bound estimate.

While the project was not formally preregistered, Code for America did prepare a pre-experiment plan that identified the basic treatments and theoretical expectations, completed on January 16, 2019. Detailed and time-stamped guidance consistent with the plan were provided to engineers to implement, which they completed on January 30. We provide the plan in online supplementary material . The main difference between the research plan and the final experiment is that the final experiment collected a larger N . The original plan called for an N of 19,000 completed applications. As it turned out, there were no engineering constraints on running the experiment longer and given the low baseline rate of self-employment and the resulting possible challenge of measuring the effect of the self-attestation template, Code for America allowed the experiment to run for longer, increasing the N to 57,073 (three times the original planned size). The larger N increases the statistical power, but we also provide the results of the initial 19,000, which are equivalent in terms of effects sizes and statistical significance (see appendix table A3 ).

One ethical concern is that the treatment might induce error and worsen outcomes for participants. The qualitative research provided some reassurance that this would not be the case, offering good reason to believe the new terms would benefit users. Nevertheless, members of the research team reviewed documents submitted by the treated subjects on the first day of the experiment. These immediate checks gave no cause for concern—individuals in the explanation treatment appeared to be correctly identifying themselves as self-employed and the self-attestations contained the necessary information. One month later, we reviewed subsequent documents pertaining to income that participants submitted to see whether there had been any issues with those self-attestations and we found only a few documents to clarify or add details about that income based on case worker requests. As noted earlier, there was no difference between treatment and control groups in terms of benefit approval rates, which eliminates the concern that the experiment made participants worse off. Finally, we also observed no difference in application completion rates between those subject to the treatment and controls.

Because having a job and self-employment are both self-reported in the application, the experimental results examine the effects of the treatments on all applicants. 3 For the first treatment, we see a 3.3-percentage-point absolute increase and 36.8% relative increase in applicants describing themselves as self-employed ( p  < .001, reported statistics are based on two-sided chi-square tests for proportions and Welch’s t -tests for means). Of the control group seeing the status-quo presentation of self-employment categories, 8.8% described themselves as self-employed. By contrast, 12.1% of applicants who saw the new screens selected to the self-employed category (see figure 3 ). In other words, the treatment increased the matching to the self-employment category by about one-third. When we modeled the effect of the experiment, we found no evidence that the treatment is moderated by race or ethnicity (appendix table A4 ).

Clearer Explanation Increases Claiming of Self-Employment Category.

Given the positive results of the experiment, GetCalFresh adopted the new explanation for self-employment treatment as the default. This gives us an additional opportunity to judge if the change made a difference by examining trend-lines before and after the adoption of the treatment for the entire population. This approach is less precise than our experiment in estimating causal effects, because it cannot fully account for unobserved external factors affecting the population, and therefore we rely upon it as a secondary form of evidence, and limit the population studied to those with a job to account for changes in the labor market. 4 For the pre-post estimate, we compare the three months prior to our experiment, and then the equivalent 3-month time period to account for seasonal differences. The pre-treatment time period is 26 October 2018–25 January 2019, the post-treatment comparison is 26 October 2019–25 January 2020.

Importantly, because the percentage of people with any reported earnings decreased from 46.2% to 38.8% over this period, we report the change in self-employment as a percentage of people with any earnings (as opposed to the experiment results, which reflected a percentage of all applicants, but where the groups were contemporaneous). While the base rate for our population is different from the experiment because this secondary analysis focuses on those with any type of earnings, the trend is the same, as illustrated in figure 4 .

Percent of Applicants with Any Earnings Who Select Self-Employment Category After Self-employment Treatment Becomes Default.

In the pretreatment time period, 18.8% of the population reporting job income ( n  = 32,181) identified themselves as self-employed. In the post treatment period, 22.5% of the population reporting job income ( n  = 45,999) identified themselves as self-employed. As such, this represents a significant increase in the number of people reporting as self-employed having used the treatment provided by the GetCalFresh website. One obvious concern is that there were simply more self-employed individuals in the general population. However, the fraction of employed Californians reporting primarily self-employed status actually declined over this period ( California Center for Jobs & the Economy 2019 ). These findings provide support for the hypothesis that the change in policy resulted in a larger number of individuals claiming the self-employed benefit.

For the second treatment, the goal of the self-attestation was not to encourage people to select into self-employment category, since the provision of documentation is conditional on that selection. Instead, the goal is to increase the provision of necessary documentation, since undocumented claims are likely to result in requests from state employees for additional information or simply be denied. The timely provision of documentation therefore benefits the claimant. We find that 22.8% of 2,998 people who identified themselves as self-employed submitted a self-attestation (see appendix table A5 ). The control group were not offered the opportunity to use the template, so it makes more sense to compare the outcome to a pre-established target (which was 10% of use in the experimental plan) and the general provision of documentation among self-employed. Using a different measure, 31.4% of people who were exposed to the self-attestation guide submitted at least one type of job-related verification (a self-attestation or other proof of earned income), compared to 23.0% of people who did not see it, a 36.3% relative increase ( p  < .001) (see appendix table A6 ). Meanwhile, if we compare the total number of earned-income related documents submitted by self-employed people, we see that people who saw the guidance submit an average of 40.8% more than did those who did not see the guidance ( p  < .001, mean was .51 without the guidance and .72 with the guidance).

Our pre-experiment plan sought to track speed of determination of applications by counties, on the assumption the self-attestation document would speed up this process, since it would provide case-workers with an acceptable form of documentation. For the general sample, we discovered that we could not get fine-grained enough detail on this outcome to accurately track variation for the entire sample. However, data on time to approval were available from Los Angeles and 17 other counties for a limited period of time ( N  = 1,043). Using this data, the mean time for receipt of benefits was 23.1 days for those who were not offered the self-attestation ( N  = 505) and 21.6 days for those who were offered the self-attestation ( N  = 538) (see figure 5 ).

Results for Self-attestation Experiment: Days to Approval of Eligibility.

This difference is consistent with expectations, but at marginal level of levels of statistical significance ( p  = .064). Since not all subjects used the self-attestation, we can also compare those who were offered and actually used it ( N  = 177) with those that did not ( N  = 866). Those who used the self-attestation form had an average wait time of 19.4 days. This is a significantly lower wait time compared with those not offered the self-attestation (average wait time 23.1 days) and those who were offered the self-attestation and did not use it (average wait time of 22.6 days) ( p  = .001). Though there may be other differences between those who did and did not choose self-attestation if offered which affected the wait times, this latter comparison still offers general support for the claim that wait times are shortened using self-attestation templates. The differences also hold if we separate Los Angeles from less populous counties. Ideally, higher statistical power would resolve this question, but the evidence is suggestive that offering the self-attestation template increases speed to determination. Consistent with this pattern, we also find that the time for approval for anyone submitting at least one verification document, including the self-attestation, is 20.3 days, versus 22.6 days for those who do not ( p  = .033).

The increases in matching to the self-employment categories and provision of documentation suggests a positive outcome. However, the results cannot be considered successful if people mistakenly select the self-employment category, and subsequently face rejection of their claim by the state. To address this question, we examined the state approval rates for applications from those in the control groups and treatment groups. There was no difference between the two, implying, in the eyes of the state at least, a true increase in claimants successfully matching to the self-employment category. On the other hand, our discussions with and direct observation of caseworkers suggest that if an applicant fails to make the correct match to the most beneficial category, it is easily missed by the caseworker. Though caseworkers do ask clients about general job income in the CalFresh interview, they are stretched thin and navigating challenging conditions. For instance, workers need to interpret the client’s situation amid constantly changing policy and new occupations like gig economy jobs, while not escalating tensions in an interview that covers many different personal matters about a client’s situation. In addition, there are competing incentives within the system around regulations, timeliness, and error rates that might prevent any extra conversations outside of a single interview.

There is no interactive effect between our two experimental treatments. This finding is unsurprising when we consider the mechanics of the experiment, and the fact we are dealing with multiple dependent variables. Those who receive the second (template) treatment have already selected into self-employment status, meaning that treatment cannot affect the selection into the self-employment category, the dependent variable for the first treatment. Thus, the only possible interaction effect is for the second dependent variable of providing additional documentation. And here, there is no clear reason to believe that a treatment that helps people to understand they can match to the self-employment category would make them more likely to provide documentation than those who selected into that category without prompting.

One limitation is that we do not observe benefit amounts, so we cannot estimate the benefit increase for those who claim the 40% deduction, which limits our ability to estimate the financial value of the treatment. But to provide a sense of magnitude, here is a hypothetical but representative back-of-the-envelope estimate. In a two-person household with gross monthly self-employed earnings of $1,000 (or about 69% of the federal poverty level), the benefit size, applying the standard 40% deduction, would be $281, when compared with $191 for those whose earnings did not come from self-employment. For such a household, successfully claiming the deduction changes the value of the CalFresh benefit from the equivalent of 19.1%–28.1% of their income. In general, for CalFresh beneficiaries, benefit levels increase by roughly 50% if income is classified as self-employment income. Thus, for those below the poverty line, these are large differences. 5

Another limitation of our analysis is that our treatment combines two theoretical treatments in the first experiment: providing more intuitive presentation of the category, and more information about the benefit. Both types of information reduce learning costs, but work in different ways. Given limited N , it makes sense, and is more ethically justifiable, to start with a combination of changes expected to generate the greatest positive effect. In practice, this is also a common strategy of nudge units as they prioritize generating the greatest positive effect of interventions ( DellaVigna and Linos 2022 ). Future research could separate out the effects of either mechanism in practice.

We also cannot rule out the possibility that the treatment may also reduce psychological costs. However, this seems unlikely to be a causal factor in our design for a number of reasons. While there is evidence that stigma is an issue for SNAP, all of our subjects have overcome such stigma to the point that they have submitted an application. Furthermore, the experience of stigma in SNAP seems to be larger for in-person applications and use in stores ( Herd and Moynihan 2018 , 152–3), which is not something our treatment—which considers variation in an online application process—can address. Finally, our treatment does not attempt to convey messages about deservingness of different categories (for a contrast, see Bhargava and Manoli 2015 ). It is possible that some applicants who might now recognize themselves as self-employed as a result of the treatment may experience some heightened ex-post sense of well-being if they value that category, but this does not explain why they selected into that category.

From the perspective of administrative theory, we map out the relevance of state categories, particularly those that seem more mundane or everyday categories, but that have important implications for peoples’ access to state resources. The construction of identity categories is a widely studied topic across multiple disciplines, especially categories centered on racial identities. It has not, however, been a central area for public administration research, even as identity categories significantly influence peoples’ experiences with the state, including access to basic rights and benefits. Furthermore, even though the matching to category problem is frequently found across programs, it is typically conceptualized in narrow ways and as program specific. Our theoretical frame provides a way to understand these seemingly disparate and disconnected problems as a general administrative problem.

Our approach builds on an administrative burden framework that encourages researchers and practitioners to look for bottlenecks in people’s struggle with administrative processes ( Herd and Moynihan 2018 ). This framework typically presents such processes as a path where travelers may easily stumble and fall off the road, one which is captured by measures of eligible populations not taking up benefits. The matching to categories problem suggests a slightly different metaphor, where two roads diverge, the traveler takes the wrong one, and it makes all the difference. 6 This problem is easier to miss, since the individual may still complete the process and claim a benefit, even if the benefit is not the one most advantageous to them. Indeed, as detailed above the benefit differences can be large.

Public administration and policy studies can contribute in a number of ways to this topic. An important starting point is to direct attention to more mundane state categories that nonetheless have significant potential effects on individual outcomes, descriptively document the severity of this matching problem, and the costs involved. While there is a rich research tradition on the construction of categories, our approach points to the value in considering how people respond to them, and in particular whether they are able to understand bureaucratic categories that can dictate whether or not they can access needed resources and benefits. The mismatch between how state agencies and bureaucrats speak about these categories can vary significantly with how people outside of these organizations think about them. The difference in how people understood the terms “self-proprietor” and “freelance worker” provides an excellent example. While our study focused on simple administrative changes to reduce this problem by minimizing learning and compliance costs, future work could also consider other factors, such as human capital or other factors that result in some individuals being more likely to match correctly to the most advantageous category ( Christensen et al. 2020 ; Masood and Nisar 2021 ).

In looking for solutions to the matching problem, one promising route is to exploit the fact that while administrative categories themselves might be relatively fixed, the presentation of categories is not something that typically requires changes to the law or rules, but can be modified with relative ease. In other words, it is an underappreciated aspect of bureaucratic discretion, where enterprising administrators could invest some time and effort to solve the matching problem. We demonstrate how simple changes to an online interface can make it more likely that people can match to a favorable category, and provide appropriate documentation to claim those benefits. There is nothing technically difficult about our treatment, which involved talking to caseworkers and recipients about what categories mean, and testing whether different presentation of categories were easier to process. Our treatment of the self-attestation may be more difficult, since it depends on what level of documentation is required by law. In some cases, administrators may impose more documentation when self-attestation is sufficient. But in other cases, the law may require detailed documentation, or bureaucrats might feel nervous about reducing compliance costs if it is perceived to lead to more fraud.

While our research examined the framing of categories in the context of online applications, additional research on category matching could further examine the process when it involves direct interactions between members of the public and street-level bureaucrats ( Jilke and Tummers 2018 ; Zacka 2017 ). To what extent does bureaucratic help extend to helping people find the most beneficial category? To what extent does discriminatory behavior involve imposing burdens in order to make it difficult for the individual to claim a favorable category?

More broadly, the idea of category matching might be considered to be part of what is an emerging second-generation agenda of theorizing about administrative burdens. The first generation directed attention to the fact that administrative costs had relatively large effects, and could have a distributive effect. More recent work offers deeper consideration of, for example the skills people require to succeed in their interactions with the state ( Christensen et al. 2020 ; Masood and Nisar 2021 ), or particular subcategories of costs deserving of more attention such as “redemption costs” required to actually utilize benefits once they are accessed ( Barnes 2021 ). It is perhaps no coincidence that such second-generation theorizing, like the idea of matching to categories we present here, comes from direct and close observation of people’s experiences with government. While the broad framework of administrative burdens is now firmly in place, and still leaves much empirical work to pursue, the application of the framework will inevitably lead to more nuanced theoretical constructs and insights. In particular, partnerships with service providers to reduce burdens, such as those pursued here, will cast a light not just on practical problems, but on theoretical constructs that will make such problems more legible in advance, and as a result more likely to be solved.

Beyond the particular results, it is also worth considering the ethos of improvement that underpinned them. While the virtues of incrementalism as a model of decision making are foundational in public administration theory ( Lindblom 1959 ), too often this has translated into a constructive resistance to overly ambitious reforms but also a preference for the default process rather than an active investigation into alternatives. To a great degree, the rise of nudge units, civic tech, and behavioral public administration has institutionalized the tools for a more informed model of incrementalism, one that actively and regularly experiments with better approaches. Our study joins other work that shows that small changes can have large positive effects, but finding those changes requires an informed understanding of the process, a theory of change that highlights potential burdens, and a user-informed approach that identifies the most likely ways to reduce those burdens.

The research reported herein was performed with the permission of the California Department of Social Services. The opinions and conclusions expressed herein are solely those of the authors and should not be considered as representing the policy of the collaborating agency or of any agency of the California government. The Code for America authors thank Cesar Paredes, Francesca Costa, Monica Beas for essential background and feedback, as well as Ash Campo, Jenny Heath, and Travis Grathwell for implementing the experiment. Professor Moynihan’s time on this project was supported by the Psychology of Administrative Burden (POAB) project, funded by the European Research Council, under the Horizon 2020 program (grant agreement no. 802244).

The data underlying this article were collected by GetCalFresh under a data-use agreement with the California Department of Social Services. Data-use is contingent upon agreement with the California Department of Social Services. To begin the process of requesting the data, please contact [email protected] and [email protected], mentioning the title of the article. Code for the analysis can be found at https://osf.io/8sxbf/ .

We use the term citizen not to describe a specific legal status, but in more a generic sense to represent an individual resident of a state (see Roberts 2021 on such distinctions). The population we study—CalFresh participants—include individuals who are and are not US citizens, but who are eligible for the benefits nonetheless.

GetCalFresh takes about 10 min, compared with 30–60 min for other online applications in California. Source: https://codeforamerica.org/news/california-launches-code-for-americas-getcalfresh-in-all-58-counties/ .

Of the entire sample, 46.3% of the sample had earnings (i.e., a job or self-employment income)—or 26,409 of 57,073 households in the sample.

For example, California changed its policy to allow Social Security Insurance recipients to claim SNAP between the two time points we examine, significantly changing the population. We therefore excluded those recipients from the estimates reported here, which means the population are not precisely comparable to those in our experiment.

Estimates based on a benefit calculator ( LSNC Guide to CalFresh Benefits 2019 ). The federal poverty level for a two-person household is $17,420.

With apologies to Robert Frost.

Number of Submitted Applications by Condition

Balance Tests by Condition

Comparison of Self-employment Explanation Experiment Results for Planned and Final Sample Sizes

Two-sided chi-squared test.

Explanation of Self-employment Treatment Is Not Moderated by Race or Ethnicity

Note: p -Values in parentheses; binomial logistic regression; omitted category is white.

Effects of Self-attestation Treatment

Two-sided chi-square test for any earnings verification, two-sided t -test for means of verifications submitted.

Comparison of Self-attestation Template Experiment Results for Planned and Final Sample Sizes

Note: Results are included for both the planned sample and the final sample. The methods section detailed that the experiment was extended beyond the initial planned enrollment. The table demonstrates that the effects are present in both the initial planned sample as well as the larger sample resulting from the extended fielding period for the experiment.

We cannot examine these differences in the case of unfinished applications because document submission is only available after completing the application.

Two-sided chi-square test.

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Army Medical NCOA Virtual Instructor

Mahalo for your interest in this role! Please see the full position description below and click Start Your Application when ready. For more information about DAWSON, please visit dawsonohana.com.

Summary: Provide instructor services at Fort Sam Houston, Texas, in support of the Army Medical MED), Noncommissioned Officers Academy (NCOA). Perform services for NCOA Advance Leader Course (ALC) and Senior Leader Course (SLC).

Job Description

Plan, coordinate, implement, and evaluate student education competencies.

Ensure that critical tasks and 21st Century Leader Competencies are facilitated in a blended environment and incorporated into the learning activities.

Manage distribution and use of materials supplied by lead training site

Review LPs/TSPs, PEs, discussion board, homework assignments, videos (usually two days prior to training); observe all practical exercises (PEs) and ensure student interaction during PEs.

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Qualifications

Must have a minimum of 3 years� experience as a faculty instructor.

Experienced in the use of a Learning Management System (LMS), such as Blackboard, recognized in the academic community. And must be knowledgeable in concepts of adult learning, Bloom�s Taxonomy, Seminar Discourse, Seminar Team Building, and Reflective Learning.

Have served at least three years of active Federal service.

Must be a graduate of the Army Medical Department Advance Leader Course and Senior Leader Course

Graduate of the Army Basic Instructor Course (ABICC) - 8 training days for a total of 80 academic Hours, Foundation Instruction Facilitator Course (FIFC) 10 day / 80 hours blended learning 2 Phases: PH I � DL, 16 hours of self-paced interactive multimedia instruction PH II � Resident, 64 hours of learner centric, small group, facilitated instructional methods or Faculty Development Program Phase 1 and 2 (FDP 1/2)

Graduate of Small Group Instructor Training Course (SGITC), Intermediate Facilitation Skills Course (IFSC) or the Common Faculty Development Instructor Course which combines ABIC and SGITC requirements.

Graduate of the NCOES/Functional course they're instructing and awarded the appropriate SQI or ASI.

Graduate of Master Resiliency Training (MRT) Course

Complete Instructor/Facilitator Certification (For DL)

*This Position is Contingent upon the Award

DAWSON is an Equal Opportunity/Affirmative Action/VEVRAA federal contractor. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, national origin, disability status, protected veteran status, or any other characteristic protected by law.

DAWSON offers a best-in-class benefits program including medical, dental, and vision insurance; a 401(k) program with employer match; paid vacation and sick leave; employer-paid basic life and AD&D insurance; an Employee Assistance Program; and a flexible work environment. Additionally, employees can choose from several voluntary benefits including critical illness coverage; accident insurance; identity theft coverage; pet insurance, and more.

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SPSS Syntax for Common NSSE Analyses

Common data recodes.

Syntax for collapsing response options on survey items or otherwise reconfiguring existing data into new variables.

Collapsing Response Options

Estimating hours per week at the group level, estimating numbers of pages written at the group level, first-generation status.

There are several ways to define first-generation status. This SPSS syntax linked below recodes parental education to meet two distinct definitions: Highest level of education for either parent is less than a bachelor's degree (parents may have some postsecondary education). Highest level of education for either parent is a high school diploma or less (parents have no experience in postsecondary education). *********************************************************. ***** RECODES FOR PARENTAL EDUCATION--->>FIRST-GENERATION STATUS. *********************************************************. ***** FIRST-GENERATION, OPTION 1: Creates a dummy variable for first-generation status where the highest level of parent education is less than a bachelor's degree. ***** FIRST-GENERATION, OPTION 2: Creates a dummy variable for first-generation status where the parents have no experience in higher education. *********************************************************. ***** FIRST-GENERATION, OPTION 1. ***** First-generation status is defined as neither parent having earned a bachelor's degree (parents may have some postsecondary experience). ***** Thus, the highest level of education for either parent is less than a bachelor's degree. ***** This is the first-generation variable included in the NSSE dataset. ***** This definition is used by the U.S. Department of Education for TRIO programs (34 C.F.R. § 646.7). RECODE parented (1 thru 4=1) (5 thru 7=0) INTO firstgen. VARIABLE LABELS firstgen "First-Generation Status (neither parent/guardian holds a bachelor's degree)". FORMATS firstgen(F4.0). VALUE LABELS firstgen 0 'No' 1 'Yes'. EXECUTE. FREQUENCIES firstgen. **********************************************. **** FOR CANADIAN INSTITUTIONS. **** RECODE PARENTED_CA TO FIRSTGEN. ****IF (parented_ca LE 4) firstgen = 1. ****IF (parented_ca GT 4) firstgen = 0. ****EXECUTE. ***** FIRST-GENERATION, OPTION 2. ***** First-generation status is defined as neither parent having any postsecondary experience. ***** Thus, the highest level of education for either parent is a high school diploma. ***** This is an alternate definition of first-generation status sometimes used (see Chen, 2005). ***** Chen, X. (2005). First Generation Students in Postsecondary Education: A Look at Their College Transcripts (NCES 2005–171). U.S. Department of Education, National Center for Education Statistics. Washington, DC: U.S. Government Printing Office. RECODE parented (1 thru 2=1) (3 thru 7=0) INTO firstgenHS. VARIABLE LABELS firstgenHS 'First-Generation Status (no parent/guardian has any postsecondary experience)'. FORMATS firstgenHS (F4.0). VALUE LABELS firstgenHS 0 'Not first-generation (at least one parent/guardian has some postsecondary experience)' 1 'First-generation (no parent/guardian has any postsecondary experience)'. EXECUTE. FREQUENCIES firstgenHS. FREQUENCIES firstgenHS.

NSSE Majors, CIP Crosswalk, And Related Syntax

The Classification of Instructional Programs (CIP) was developed by the National Center for Education Statistics to track enrollment and degree completion by field of study. To better understand how NSSE's major fields relate to CIP codes, or to add CIP codes into a NSSE data file, the syntax below recodes NSSE majors into the four and six-digit CIP coding scheme. We also have an excel crosswalk table available.

*********************************************************. numeric CIP6 (F8.4). numeric CIP4 (F8.2). variable level CIP4 CIP6 (nominal). variable labels CIP4 '4-digit CIP code associated with primary NSSE major code' /CIP6 '6-digit CIP code associated with primary NSSE major code'. *********************Arts & Humanities. *Arts, fine and applied. if (MAJfirstcode eq 1) CIP6 = 50.0701. add value labels CIP6 50.0701 'Art/Art Studies, General'. if (MAJfirstcode eq 1) CIP4 = 50.07. add value labels CIP4 50.07 'Fine and Studio Arts'. *if (MAJfirstcode eq 1) CIP6 = 50.0702. *add value labels CIP6 50.0702 'Fine/Studio Arts, General'. *if (MAJfirstcode eq 1) CIP4 = 50.04. *add value labels CIP4 50.04 'Design and Applied Arts'. *if (MAJfirstcode eq 1) CIP6 = 50.0409. *add value labels CIP6 50.0409 'Graphic Design'. *Architecture. if (MAJfirstcode eq 2) CIP6 = 4.0201. add value labels CIP6 4.0201 'Architecture'. if (MAJfirstcode eq 2) CIP4 = 4.02. add value labels CIP4 4.02 'Architecture'. *Art history. if (MAJfirstcode eq 3) CIP6 = 50.0703. add value labels CIP6 50.0703 'Art History, Criticism and Conservation'. if (MAJfirstcode eq 3) CIP4 = 50.07. add value labels CIP4 50.07 'Fine and Studio Arts'. *English (language and literature). if (MAJfirstcode eq 4) CIP6 = 23.0101. add value labels CIP6 23.0101 'English Language and Literature, General'. if (MAJfirstcode eq 4) CIP4 = 23.01. add value labels CIP4 23.01 'English Language and Literature, General'. *French (language and literature). if (MAJfirstcode eq 5) CIP6 = 16.0901. add value labels CIP6 16.0901 'French Language and Literature'. if (MAJfirstcode eq 5) CIP4 = 16.09. add value labels CIP4 16.09 'Romance Languages, Literatures, and Linguistics'. *Spanish (language and literature). if (MAJfirstcode eq 6) CIP6 = 16.0905. add value labels CIP6 16.0905 'Spanish Language and Literature'. if (MAJfirstcode eq 6) CIP4 = 16.09. add value labels CIP4 16.09 'Romance Languages, Literatures, and Linguistics'. *Other language and literature. if (MAJfirstcode eq 7) CIP6 = 16.0101. add value labels CIP6 16.0101 'Foreign Languages and Literatures, General'. if (MAJfirstcode eq 7) CIP4 = 16.01. add value labels CIP4 16.01 'Linguistic, Comparative, and Related Language Studies and Services'. *History. if (MAJfirstcode eq 8) CIP6 = 54.0101. add value labels CIP6 54.0101 'History, General'. if (MAJfirstcode eq 8) CIP4 = 54.01. add value labels CIP4 54.01 'History'. *Humanities (general). if (MAJfirstcode eq 9) CIP6 = 24.0103. add value labels CIP6 24.0103 'Humanities/Humanistic Studies'. if (MAJfirstcode eq 9) CIP4 = 24.01. add value labels CIP4 24.01 'Liberal Arts and Sciences, General Studies and Humanities'. *Music. if (MAJfirstcode eq 10) CIP6 = 50.0901. add value labels CIP6 50.0901 'Music, General'. if (MAJfirstcode eq 10) CIP4 = 50.09. add value labels CIP4 50.09 'Music'. *if (MAJfirstcode eq 10) CIP6 = 50.0903. *add value labels CIP6 50.0903 'Music Performance, General'. *Philosophy. if (MAJfirstcode eq 11) CIP6 = 38.0101. add value labels CIP6 38.0101 'Philosophy'. if (MAJfirstcode eq 11) CIP4 = 38.01. add value labels CIP4 38.01 'Philosophy'. *Religion. if (MAJfirstcode eq 12) CIP6 = 38.0201. add value labels CIP6 38.0201 'Religion/Religious Studies'. if (MAJfirstcode eq 12) CIP4 = 38.02. add value labels CIP4 38.02 'Religion/Religious Studies'. *Theater or drama. if (MAJfirstcode eq 13) CIP6 = 50.0501. add value labels CIP6 50.0501 'Drama and Dramatics/Theatre Arts, General'. if (MAJfirstcode eq 13) CIP4 = 50.05. add value labels CIP4 50.05 'Drama/Theatre Arts and Stagecraft'. *if (MAJfirstcode eq 13) CIP6 = 50.0506. *add value labels CIP6 50.0506 'Acting'. *if (MAJfirstcode eq 13) CIP4 = 50.01. *add value labels CIP4 50.01 'Visual and Performing Arts, General'. *if (MAJfirstcode eq 13) CIP6 = 50.0101. *add value labels CIP6 50.0101 'Visual and Performing Arts, General'. *Other fine and performing arts. if (MAJfirstcode eq 14) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 14) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 14) CIP6 = 50.0408. *add value labels CIP6 50.0408 'Interior Design'. *if (MAJfirstcode eq 14) CIP4 = 50.04. *add value labels CIP4 50.04 'Design and Applied Arts'. *if (MAJfirstcode eq 14) CIP6 = 50.0301. *add value labels CIP6 50.0301 'Dance, General'. *if (MAJfirstcode eq 14) CIP4 = 50.03. *add value labels CIP4 50.03 'Dance'. *Other humanities. if (MAJfirstcode eq 15) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 15) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 15) CIP6 = 5.0102. *add value labels CIP6 5.0102 'American/United States Studies/Civilization'. *if (MAJfirstcode eq 15) CIP4 = 5.01. *add value labels CIP4 5.01 'Area Studies'. *if (MAJfirstcode eq 15) CIP6 = 5.0103. *add value labels CIP6 5.0103 'Asian Studies/Civilization'. *********************Biological Sciences, Agriculture, & Natural Resources. *Biology (general). if (MAJfirstcode eq 16) CIP6 = 26.0101. add value labels CIP6 26.0101 'Biology/Biological Sciences, General'. if (MAJfirstcode eq 16) CIP4 = 26.01. add value labels CIP4 26.01 'Biology, General'. *Agriculture. if (MAJfirstcode eq 17) CIP6 = 1. add value labels CIP6 1 'Agriculture, General'. if (MAJfirstcode eq 17) CIP4 = 1. add value labels CIP4 1 'Agriculture, General'. *Biochemistry or biophysics. if (MAJfirstcode eq 18) CIP6 = 26.0202. add value labels CIP6 26.0202 'Biochemistry'. if (MAJfirstcode eq 18) CIP4 = 26.02. add value labels CIP4 26.02 'Biochemistry, Biophysics and Molecular Biology'. *if (MAJfirstcode eq 18) CIP6 = 26.0203. *add value labels CIP6 26.0203 'Biophysics'. *Biomedical science. if (MAJfirstcode eq 19) CIP6 = 26.0102. add value labels CIP6 26.0102 'Biomedical Sciences, General'. if (MAJfirstcode eq 19) CIP4 = 26.01. add value labels CIP4 26.01 'Biology, General'. *Botany. if (MAJfirstcode eq 20) CIP6 = 26.0301. add value labels CIP6 26.0301 'Botany/Plant Biology'. if (MAJfirstcode eq 20) CIP4 = 26.03. add value labels CIP4 26.03 'Botany/Plant Biology'. *Cell and molecular biology. if (MAJfirstcode eq 21) CIP6 = 26.0401. add value labels CIP6 26.0401 'Cell/Cellular Biology and Histology'. if (MAJfirstcode eq 21) CIP4 = 26.04. add value labels CIP4 26.04 'Cell/Cellular Biology and Anatomical Sciences'. *Environmental science/studies. if (MAJfirstcode eq 22) CIP6 = 3.0103. add value labels CIP6 3.0103 'Environmental Studies'. if (MAJfirstcode eq 22) CIP4 = 3.01. add value labels CIP4 3.01 'Natural Resources Conservation and Research'. *if (MAJfirstcode eq 22) CIP6 = 3.0104. *add value labels CIP6 3.0104 'Environmental Science'. *Marine science. if (MAJfirstcode eq 23) CIP6 = 30.3201. add value labels CIP6 30.3201 'Marine Sciences'. if (MAJfirstcode eq 23) CIP4 = 30.32. add value labels CIP4 30.32 'Marine Sciences'. *if (MAJfirstcode eq 23) CIP6 = 26.1302. *add value labels CIP6 26.1302 'Marine Biology and Biological Oceanography'. *if (MAJfirstcode eq 23) CIP4 = 26.13. *add value labels CIP4 26.13 'Ecology, Evolution, Systematics, and Population Biology'. *Microbiology or bacteriology. if (MAJfirstcode eq 24) CIP6 = 26.0502. add value labels CIP6 26.0502 'Microbiology, General'. if (MAJfirstcode eq 24) CIP4 = 26.05. add value labels CIP4 26.05 'Microbiological Sciences and Immunology'. *if (MAJfirstcode eq 24) CIP6 = 26.0503. *add value labels CIP6 26.0503 'Medical Microbiology and Bacteriology'. *Natural resources and conservation. if (MAJfirstcode eq 25) CIP6 = 3.0101. add value labels CIP6 3.0101 'Natural Resources/Conservation, General'. if (MAJfirstcode eq 25) CIP4 = 3.01. add value labels CIP4 3.01 'Natural Resources Conservation and Research'. *if (MAJfirstcode eq 25) CIP6 = 3.0601. *add value labels CIP6 3.0601 'Wildlife, Fish and Wildlands Science and Management'. *if (MAJfirstcode eq 25) CIP4 = 3.06. *add value labels CIP4 3.06 'Wildlife and Wildlands Science and Management'. *if (MAJfirstcode eq 25) CIP6 = 3.0501. *add value labels CIP6 3.0501 'Forestry, General'. *if (MAJfirstcode eq 25) CIP4 = 3.05. *add value labels CIP4 3.05 'Forestry'. *Natural science. if (MAJfirstcode eq 26) CIP6 = 30.1801. add value labels CIP6 30.1801 'Natural Sciences'. if (MAJfirstcode eq 26) CIP4 = 30.18. add value labels CIP4 30.18 'Natural Sciences'. *Neuroscience. if (MAJfirstcode eq 27) CIP6 = 26.1501. add value labels CIP6 26.1501 'Neuroscience'. if (MAJfirstcode eq 27) CIP4 = 26.15. add value labels CIP4 26.15 'Neurobiology and Neurosciences'. *Physiology and developmental biology. if (MAJfirstcode eq 28) CIP6 = 26.0901. add value labels CIP6 26.0901 'Physiology, General'. if (MAJfirstcode eq 28) CIP4 = 26.09. add value labels CIP4 26.09 'Physiology, Pathology and Related Sciences'. *Zoology. if (MAJfirstcode eq 29) CIP6 = 26.0701. add value labels CIP6 26.0701 'Zoology/Animal Biology'. if (MAJfirstcode eq 29) CIP4 = 26.07. add value labels CIP4 26.07 'Zoology/Animal Biology'. *Other agriculture and natural resources. if (MAJfirstcode eq 30) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 30) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 30) CIP6 = 1.0102. *add value labels CIP6 1.0102 'Agribusiness/Agricultural Business Operations'. *if (MAJfirstcode eq 30) CIP4 = 1.01. *add value labels CIP4 1.01 'Agricultural Business and Management'. *if (MAJfirstcode eq 30) CIP6 = 1.0601. *add value labels CIP6 1.0601 'Applied Horticulture/Horticulture Operations, General'. *if (MAJfirstcode eq 30) CIP4 = 1.06. *add value labels CIP4 1.06 'Applied Horticulture and Horticultural Business Services'. *Other biological sciences. if (MAJfirstcode eq 31) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 31) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 31) CIP6 = 26.1301. *add value labels CIP6 26.1301 'Ecology'. *if (MAJfirstcode eq 31) CIP4 = 26.13. *add value labels CIP4 26.13 'Ecology, Evolution, Systematics, and Population Biology'. *if (MAJfirstcode eq 31) CIP6 = 26.1201. *add value labels CIP6 26.1201 'Biotechnology'. *if (MAJfirstcode eq 31) CIP4 = 26.12. *add value labels CIP4 26.12 'Biotechnology'. *********************Physical Sciences, Mathematics, & Computer Science. *Physical sciences (general). if (MAJfirstcode eq 32) CIP6 = 40.0101. add value labels CIP6 40.0101 'Physical Sciences'. if (MAJfirstcode eq 32) CIP4 = 40.01. add value labels CIP4 40.01 'Physical Sciences'. *Astronomy. if (MAJfirstcode eq 33) CIP6 = 40.0201. add value labels CIP6 40.0201 'Astronomy'. if (MAJfirstcode eq 33) CIP4 = 40.02. add value labels CIP4 40.02 'Astronomy and Astrophysics'. *Atmospheric science (including meteorology). if (MAJfirstcode eq 34) CIP6 = 40.0401. add value labels CIP6 40.0401 'Atmospheric Sciences and Meteorology, General'. if (MAJfirstcode eq 34) CIP4 = 40.04. add value labels CIP4 40.04 'Atmospheric Sciences and Meteorology'. *Chemistry. if (MAJfirstcode eq 35) CIP6 = 40.0501. add value labels CIP6 40.0501 'Chemistry, General'. if (MAJfirstcode eq 35) CIP4 = 40.05. add value labels CIP4 40.05 'Chemistry'. *Computer science. if (MAJfirstcode eq 36) CIP6 = 11.0701. add value labels CIP6 11.0701 'Computer Science'. if (MAJfirstcode eq 36) CIP4 = 11.07. add value labels CIP4 11.07 'Computer Science'. *Earth science (including geology). if (MAJfirstcode eq 37) CIP6 = 40.0601. add value labels CIP6 40.0601 'Geology/Earth Science, General'. if (MAJfirstcode eq 37) CIP4 = 40.06. add value labels CIP4 40.06 'Geological and Earth Sciences/Geosciences'. *Mathematics. if (MAJfirstcode eq 38) CIP6 = 27.0101. add value labels CIP6 27.0101 'Mathematics, General'. if (MAJfirstcode eq 38) CIP4 = 27.01. add value labels CIP4 27.01 'Mathematics'. *Physics. if (MAJfirstcode eq 39) CIP6 = 40.0801. add value labels CIP6 40.0801 'Physics, General'. if (MAJfirstcode eq 39) CIP4 = 40.08. add value labels CIP4 40.08 'Physics'. *Statistics. if (MAJfirstcode eq 40) CIP6 = 27.0501. add value labels CIP6 27.0501 'Statistics, General'. if (MAJfirstcode eq 40) CIP4 = 27.05. add value labels CIP4 27.05 'Statistics'. *Other physical sciences. if (MAJfirstcode eq 41) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 41) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 41) CIP6 = 40.0202. *add value labels CIP6 40.0202 'Astrophysics'. *if (MAJfirstcode eq 41) CIP4 = 40.02. *add value labels CIP4 40.02 'Astronomy and Astrophysics'. *if (MAJfirstcode eq 41) CIP6 = 40.1001. *add value labels CIP6 40.1001 'Materials Science'. *if (MAJfirstcode eq 41) CIP4 = 40.1. *add value labels CIP4 40.1 'Materials Sciences'. *********************Social Sciences. *Social sciences (general). if (MAJfirstcode eq 42) CIP6 = 45.0101. add value labels CIP6 45.0101 'Social Sciences, General'. if (MAJfirstcode eq 42) CIP4 = 45.01. add value labels CIP4 45.01 'Social Sciences, General'. *Anthropology. if (MAJfirstcode eq 43) CIP6 = 45.0201. add value labels CIP6 45.0201 'Anthropology'. if (MAJfirstcode eq 43) CIP4 = 45.02. add value labels CIP4 45.02 'Anthropology'. *Economics. if (MAJfirstcode eq 44) CIP6 = 45.0601. add value labels CIP6 45.0601 'Economics, General'. if (MAJfirstcode eq 44) CIP4 = 45.06. add value labels CIP4 45.06 'Economics'. *Ethnic studies. if (MAJfirstcode eq 45) CIP6 = 5.02. add value labels CIP6 5.02 'Ethnic Studies'. if (MAJfirstcode eq 45) CIP4 = 5.02. add value labels CIP4 5.02 'Ethnic, Cultural Minority, Gender, and Group Studies'. *Gender studies. if (MAJfirstcode eq 46) CIP6 = 5.0207. add value labels CIP6 5.0207 "Women's Studies". if (MAJfirstcode eq 46) CIP4 = 5.02. add value labels CIP4 5.02 'Ethnic, Cultural Minority, Gender, and Group Studies'. *if (MAJfirstcode eq 46) CIP6 = 5.0299. *add value labels CIP6 5.0299 'Ethnic, Cultural Minority, Gender, and Group Studies, Other'. *Geography. if (MAJfirstcode eq 47) CIP6 = 45.0701. add value labels CIP6 45.0701 'Geography'. if (MAJfirstcode eq 47) CIP4 = 45.07. add value labels CIP4 45.07 'Geography and Cartography'. *International relations. if (MAJfirstcode eq 48) CIP6 = 45.0901. add value labels CIP6 45.0901 'International Relations and Affairs'. if (MAJfirstcode eq 48) CIP4 = 45.09. add value labels CIP4 45.09 'International Relations and National Security Studies'. *Political science. if (MAJfirstcode eq 49) CIP6 = 45.1001. add value labels CIP6 45.1001 'Political Science and Government, General'. if (MAJfirstcode eq 49) CIP4 = 45.1. add value labels CIP4 45.1 'Political Science and Government'. *Psychology. if (MAJfirstcode eq 50) CIP6 = 42.0101. add value labels CIP6 42.0101 'Psychology, General'. if (MAJfirstcode eq 50) CIP4 = 42.01. add value labels CIP4 42.01 'Psychology, General'. *Sociology. if (MAJfirstcode eq 51) CIP6 = 45.1101. add value labels CIP6 45.1101 'Sociology'. if (MAJfirstcode eq 51) CIP4 = 45.11. add value labels CIP4 45.11 'Sociology'. *Other social sciences. if (MAJfirstcode eq 52) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 52) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 52) CIP6 = 30.1701. *add value labels CIP6 30.1701 'Behavioral Science'. *if (MAJfirstcode eq 52) CIP4 = 30.17. *add value labels CIP4 30.17 'Behavioral Sciences'. *if (MAJfirstcode eq 52) CIP6 = 45.9999. *add value labels CIP6 45.9999 'Social sciences, other'. *if (MAJfirstcode eq 52) CIP4 = 45.99. *add value labels CIP4 45.99 'Social Sciences, Other'. *********************Business. *Accounting. if (MAJfirstcode eq 53) CIP6 = 52.0301. add value labels CIP6 52.0301 'Accounting'. if (MAJfirstcode eq 53) CIP4 = 52.03. add value labels CIP4 52.03 'Accounting and Related Services'. *Business administration. if (MAJfirstcode eq 54) CIP6 = 52.0201. add value labels CIP6 52.0201 'Business Administration and Management, General'. if (MAJfirstcode eq 54) CIP4 = 52.02. add value labels CIP4 52.02 'Business Administration, Management and Operations'. *if (MAJfirstcode eq 54) CIP6 = 52.0101. *add value labels CIP6 52.0101 'Business/Commerce, General'. *if (MAJfirstcode eq 54) CIP4 = 52.01. *add value labels CIP4 52.01 'Business/Commerce, General'. *Entrepreneurial studies. if (MAJfirstcode eq 55) CIP6 = 52.0701. add value labels CIP6 52.0701 'Entrepreneurship/Entrepreneurial Studies'. if (MAJfirstcode eq 55) CIP4 = 52.07. add value labels CIP4 52.07 'Entrepreneurial and Small Business Operations'. *Finance. if (MAJfirstcode eq 56) CIP6 = 52.0801. add value labels CIP6 52.0801 'Finance, General'. if (MAJfirstcode eq 56) CIP4 = 52.08. add value labels CIP4 52.08 'Finance and Financial Management Services'. *Hospitality and tourism. if (MAJfirstcode eq 57) CIP6 = 52.0901. add value labels CIP6 52.0901 'Hospitality Administration/Management, General'. if (MAJfirstcode eq 57) CIP4 = 52.09. add value labels CIP4 52.09 'Hospitality Administration/Management'. *International business. if (MAJfirstcode eq 58) CIP6 = 52.1101. add value labels CIP6 52.1101 'International Business/Trade/Commerce'. if (MAJfirstcode eq 58) CIP4 = 52.11. add value labels CIP4 52.11 'International Business'. *Management. if (MAJfirstcode eq 59) CIP6 = 52.0205. add value labels CIP6 52.0205 'Operations Management and Supervision'. if (MAJfirstcode eq 59) CIP4 = 52.02. add value labels CIP4 52.02 'Business Administration, Management and Operations'. *if (MAJfirstcode eq 59) CIP6 = 52.0204. *add value labels CIP6 52.0204 'Office Management and Supervision'. *Management information systems. if (MAJfirstcode eq 60) CIP6 = 52.1201. add value labels CIP6 52.1201 'Management Information Systems, General'. if (MAJfirstcode eq 60) CIP4 = 52.12. add value labels CIP4 52.12 'Management Information Systems and Services'. *Marketing. if (MAJfirstcode eq 61) CIP6 = 52.1401. add value labels CIP6 52.1401 'Marketing/Marketing Management, General'. if (MAJfirstcode eq 61) CIP4 = 52.14. add value labels CIP4 52.14 'Marketing'. *Organizational leadership or behavior. if (MAJfirstcode eq 62) CIP6 = 52.0213. add value labels CIP6 52.0213 'Organizational Leadership'. if (MAJfirstcode eq 62) CIP4 = 52.02. add value labels CIP4 52.02 'Business Administration, Management and Operations'. *Supply chain and operations management. if (MAJfirstcode eq 63) CIP6 = 52.0203. add value labels CIP6 52.0203 'Logistics, Materials, and Supply Chain Management'. if (MAJfirstcode eq 63) CIP4 = 52.02. add value labels CIP4 52.02 'Business Administration, Management and Operations'. *if (MAJfirstcode eq 63) CIP6 = 52.0205. *add value labels CIP6 52.0205 'Operations Management and Supervision'. *Other business. if (MAJfirstcode eq 64) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 64) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 64) CIP6 = 52.1001. *add value labels CIP6 52.1001 'Human Resources Management/Personnel Administration, General'. *if (MAJfirstcode eq 64) CIP4 = 52.1. *add value labels CIP4 52.1 'Human Resources Management and Services'. *if (MAJfirstcode eq 64) CIP6 = 52.1902. *add value labels CIP6 52.1902 'Fashion Merchandising'. *if (MAJfirstcode eq 64) CIP4 = 52.19. *add value labels CIP4 52.19 'Specialized Sales, Merchandising and Marketing Operations'. *********************Communications, Media, & Public Relations. *Communications (general). if (MAJfirstcode eq 65) CIP6 = 9.01. add value labels CIP6 9.01 'Communication, General'. if (MAJfirstcode eq 65) CIP4 = 9.01. add value labels CIP4 9.01 'Communication and Media Studies'. *Broadcast communications. if (MAJfirstcode eq 66) CIP6 = 9.0701. add value labels CIP6 9.0701 'Radio and Television'. if (MAJfirstcode eq 66) CIP4 = 9.07. add value labels CIP4 9.07 'Radio, Television, and Digital Communication'. *if (MAJfirstcode eq 66) CIP6 = 9.0402. *add value labels CIP6 9.0402 'Broadcast Journalism'. *if (MAJfirstcode eq 66) CIP4 = 9.04. *add value labels CIP4 9.04 'Journalism'. *if (MAJfirstcode eq 66) CIP6 = 10.0202. *add value labels CIP6 10.0202 'Radio and Television Broadcasting Technology/Technician'. *if (MAJfirstcode eq 66) CIP4 = 10.02. *add value labels CIP4 10.02 'Audiovisual Communications Technologies/Technicians'. *Journalism. if (MAJfirstcode eq 67) CIP6 = 9.0401. add value labels CIP6 9.0401 'Journalism'. if (MAJfirstcode eq 67) CIP4 = 9.04. add value labels CIP4 9.04 'Journalism'. *Mass communications and media studies. if (MAJfirstcode eq 68) CIP6 = 9.0102. add value labels CIP6 9.0102 'Mass Communication/Media Studies'. if (MAJfirstcode eq 68) CIP4 = 9.01. add value labels CIP4 9.01 'Communication and Media Studies'. *Public relations and advertising. if (MAJfirstcode eq 69) CIP6 = 9.0903. add value labels CIP6 9.0903 'Advertising'. if (MAJfirstcode eq 69) CIP4 = 9.09. add value labels CIP4 9.09 'Public Relations, Advertising, and Applied Communication'. *if (MAJfirstcode eq 69) CIP6 = 9.0902. *add value labels CIP6 9.0902 'Public Relations/Image Management'. *Speech. if (MAJfirstcode eq 70) CIP6 = 9.0101. add value labels CIP6 9.0101 'Speech Communication and Rhetoric'. if (MAJfirstcode eq 70) CIP4 = 9.01. add value labels CIP4 9.01 'Communication and Media Studies'. *Telecommunications. if (MAJfirstcode eq 71) CIP6 = 15.0305. add value labels CIP6 15.0305 'Telecommunications Technology/Technician'. if (MAJfirstcode eq 71) CIP4 = 15.03. add value labels CIP4 15.03 'Electrical Engineering Technologies/Technicians'. *Other communications. if (MAJfirstcode eq 72) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 72) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 72) CIP6 = 9.9999. *add value labels CIP6 9.9999 'Communication, journalism, and related programs, other'. *if (MAJfirstcode eq 72) CIP4 = 9.99. *add value labels CIP4 9.99 'Communication, Journalism, and Related Programs, Other'. *********************Education. *Education (general). if (MAJfirstcode eq 73) CIP6 = 13.0101. add value labels CIP6 13.0101 'Education, General'. if (MAJfirstcode eq 73) CIP4 = 13.01. add value labels CIP4 13.01 'Education, General'. *Business education. if (MAJfirstcode eq 74) CIP6 = 13.1303. add value labels CIP6 13.1303 'Business Teacher Education'. if (MAJfirstcode eq 74) CIP4 = 13.13. add value labels CIP4 13.13 'Teacher Education and Professional Development, Specific Subject Areas'. *Early childhood education. if (MAJfirstcode eq 75) CIP6 = 13.121. add value labels CIP6 13.121 'Early Childhood Education and Teaching'. if (MAJfirstcode eq 75) CIP4 = 13.12. add value labels CIP4 13.12 'Teacher Education and Professional Development, Specific Levels and Methods'. *Elementary, middle school education. if (MAJfirstcode eq 76) CIP6 = 13.1202. add value labels CIP6 13.1202 'Elementary Education and Teaching'. if (MAJfirstcode eq 76) CIP4 = 13.12. add value labels CIP4 13.12 'Teacher Education and Professional Development, Specific Levels and Methods'. *if (MAJfirstcode eq 76) CIP6 = 13.1203. *add value labels CIP6 13.1203 'Junior High/Intermediate/Middle School Education and Teaching'. *Mathematics education. if (MAJfirstcode eq 77) CIP6 = 13.1311. add value labels CIP6 13.1311 'Mathematics Teacher Education'. if (MAJfirstcode eq 77) CIP4 = 13.13. add value labels CIP4 13.13 'Teacher Education and Professional Development, Specific Subject Areas'. *Music or art education. if (MAJfirstcode eq 78) CIP6 = 13.1312. add value labels CIP6 13.1312 'Music Teacher Education'. if (MAJfirstcode eq 78) CIP4 = 13.13. add value labels CIP4 13.13 'Teacher Education and Professional Development, Specific Subject Areas'. *if (MAJfirstcode eq 78) CIP6 = 13.1302. *add value labels CIP6 13.1302 'Art Teacher Education'. *Physical education. if (MAJfirstcode eq 79) CIP6 = 13.1314. add value labels CIP6 13.1314 'Physical Education Teaching and Coaching'. if (MAJfirstcode eq 79) CIP4 = 13.13. add value labels CIP4 13.13 'Teacher Education and Professional Development, Specific Subject Areas'. *if (MAJfirstcode eq 79) CIP6 = 31.0501. *add value labels CIP6 31.0501 'Health and Physical Education/Fitness, General'. *if (MAJfirstcode eq 79) CIP4 = 31.05. *add value labels CIP4 31.05 'Health and Physical Education/Fitness'. *Secondary education. if (MAJfirstcode eq 80) CIP6 = 13.1205. add value labels CIP6 13.1205 'Secondary Education and Teaching'. if (MAJfirstcode eq 80) CIP4 = 13.12. add value labels CIP4 13.12 'Teacher Education and Professional Development, Specific Levels and Methods'. *Social studies education. if (MAJfirstcode eq 81) CIP6 = 13.1317. add value labels CIP6 13.1317 'Social Science Teacher Education'. if (MAJfirstcode eq 81) CIP4 = 13.13. add value labels CIP4 13.13 'Teacher Education and Professional Development, Specific Subject Areas'. *Special education. if (MAJfirstcode eq 82) CIP6 = 13.1001. add value labels CIP6 13.1001 'Special Education and Teaching, General'. if (MAJfirstcode eq 82) CIP4 = 13.1. add value labels CIP4 13.1 'Special Education and Teaching'. *Other education. if (MAJfirstcode eq 83) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 83) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 83) CIP6 = 13.1305. *add value labels CIP6 13.1305 'English/Language Arts Teacher Education'. *if (MAJfirstcode eq 83) CIP4 = 13.13. *add value labels CIP4 13.13 'Teacher Education and Professional Development, Specific Subject Areas'. *********************Engineering. *Engineering (general). if (MAJfirstcode eq 84) CIP6 = 14.0101. add value labels CIP6 14.0101 'Engineering, General'. if (MAJfirstcode eq 84) CIP4 = 14.01. add value labels CIP4 14.01 'Engineering, General'. *Aero-, astronautical engineering. if (MAJfirstcode eq 85) CIP6 = 14.0201. add value labels CIP6 14.0201 'Aerospace, Aeronautical and Astronautical Engineering'. if (MAJfirstcode eq 85) CIP4 = 14.02. add value labels CIP4 14.02 'Aerospace, Aeronautical and Astronautical Engineering'. *Bioengineering. if (MAJfirstcode eq 86) CIP6 = 14.0501. add value labels CIP6 14.0501 'Bioengineering and Biomedical Engineering'. if (MAJfirstcode eq 86) CIP4 = 14.05. add value labels CIP4 14.05 'Biomedical/Medical Engineering'. *Biomedical engineering. if (MAJfirstcode eq 87) CIP6 = 14.0501. add value labels CIP6 14.0501 'Bioengineering and Biomedical Engineering'. if (MAJfirstcode eq 87) CIP4 = 14.05. add value labels CIP4 14.05 'Biomedical/Medical Engineering'. *Chemical engineering. if (MAJfirstcode eq 88) CIP6 = 14.0701. add value labels CIP6 14.0701 'Chemical Engineering'. if (MAJfirstcode eq 88) CIP4 = 14.07. add value labels CIP4 14.07 'Chemical Engineering'. *Civil engineering. if (MAJfirstcode eq 89) CIP6 = 14.0801. add value labels CIP6 14.0801 'Civil Engineering, General'. if (MAJfirstcode eq 89) CIP4 = 14.08. add value labels CIP4 14.08 'Civil Engineering'. *Computer engineering and technology. if (MAJfirstcode eq 90) CIP6 = 14.0901. add value labels CIP6 14.0901 'Computer Engineering, General'. if (MAJfirstcode eq 90) CIP4 = 14.09. add value labels CIP4 14.09 'Computer Engineering'. *if (MAJfirstcode eq 90) CIP6 = 15.1201. *add value labels CIP6 15.1201 'Computer Engineering Technology/Technician'. *if (MAJfirstcode eq 90) CIP4 = 15.12. *add value labels CIP4 15.12 'Computer Engineering Technologies/Technicians'. *Electrical or electronic engineering. if (MAJfirstcode eq 91) CIP6 = 14.1001. add value labels CIP6 14.1001 'Electrical and Electronics Engineering'. if (MAJfirstcode eq 91) CIP4 = 14.1. add value labels CIP4 14.1 'Electrical, Electronics and Communications Engineering'. *Industrial engineering. if (MAJfirstcode eq 92) CIP6 = 14.3501. add value labels CIP6 14.3501 'Industrial Engineering'. if (MAJfirstcode eq 92) CIP4 = 14.35. add value labels CIP4 14.35 'Industrial Engineering'. *Materials engineering. if (MAJfirstcode eq 93) CIP6 = 14.1801. add value labels CIP6 14.1801 'Materials Engineering'. if (MAJfirstcode eq 93) CIP4 = 14.18. add value labels CIP4 14.18 'Materials Engineering'. *Mechanical engineering. if (MAJfirstcode eq 94) CIP6 = 14.1901. add value labels CIP6 14.1901 'Mechanical Engineering'. if (MAJfirstcode eq 94) CIP4 = 14.19. add value labels CIP4 14.19 'Mechanical Engineering'. *Petroleum engineering. if (MAJfirstcode eq 95) CIP6 = 14.2501. add value labels CIP6 14.2501 'Petroleum Engineering'. if (MAJfirstcode eq 95) CIP4 = 14.25. add value labels CIP4 14.25 'Petroleum Engineering'. *Software engineering. if (MAJfirstcode eq 96) CIP6 = 14.0903. add value labels CIP6 14.0903 'Computer Software Engineering'. if (MAJfirstcode eq 96) CIP4 = 14.09. add value labels CIP4 14.09 'Computer Engineering'. *Other engineering. if (MAJfirstcode eq 97) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 97) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 97) CIP6 = 14.0301. *add value labels CIP6 14.0301 'Agricultural Engineering'. *if (MAJfirstcode eq 97) CIP4 = 14.03. *add value labels CIP4 14.03 'Agricultural Engineering'. *if (MAJfirstcode eq 97) CIP6 = 14.1401. *add value labels CIP6 14.1401 'Environmental/Environmental Health Engineering'. *if (MAJfirstcode eq 97) CIP4 = 14.14. *add value labels CIP4 14.14 'Environmental/Environmental Health Engineering'. *********************Health Professions. *Allied health. if (MAJfirstcode eq 98) CIP6 = 51. add value labels CIP6 51 'Health Services/Allied Health/Health Sciences, General'. if (MAJfirstcode eq 98) CIP4 = 51. add value labels CIP4 51 'Health Services/Allied Health/Health Sciences, General'. *Dentistry. if (MAJfirstcode eq 99) CIP6 = 51.1101. add value labels CIP6 51.1101 'Pre-Dentistry Studies'. if (MAJfirstcode eq 99) CIP4 = 51.11. add value labels CIP4 51.11 'Health/Medical Preparatory Programs'. *Health science. if (MAJfirstcode eq 100) CIP6 = 51. add value labels CIP6 51 'Health Services/Allied Health/Health Sciences, General'. if (MAJfirstcode eq 100) CIP4 = 51. add value labels CIP4 51 'Health Services/Allied Health/Health Sciences, General'. *Health technology (medical, dental, laboratory). if (MAJfirstcode eq 101) CIP6 = 51.1005. add value labels CIP6 51.1005 'Clinical Laboratory Science/Medical Technology/Technologist'. if (MAJfirstcode eq 101) CIP4 = 51.1. add value labels CIP4 51.1 'Clinical/Medical Laboratory Science/Research and Allied Professions'. *Healthcare administration and policy. if (MAJfirstcode eq 102) CIP6 = 51.0701. add value labels CIP6 51.0701 'Health/Health Care Administration/Management'. if (MAJfirstcode eq 102) CIP4 = 51.07. add value labels CIP4 51.07 'Health and Medical Administrative Services'. *if (MAJfirstcode eq 102) CIP6 = 51.0702. *add value labels CIP6 51.0702 'Hospital and Health Care Facilities Administration/Management'. *if (MAJfirstcode eq 102) CIP6 = 51.0706. *add value labels CIP6 51.0706 'Health Information/Medical Records Administration/Administrator'. *Kinesiology. if (MAJfirstcode eq 103) CIP6 = 31.0505. add value labels CIP6 31.0505 'Kinesiology and Exercise Science'. if (MAJfirstcode eq 103) CIP4 = 31.05. add value labels CIP4 31.05 'Health and Physical Education/Fitness'. *if (MAJfirstcode eq 103) CIP6 = 26.0908. *add value labels CIP6 26.0908 'Exercise Physiology'. *if (MAJfirstcode eq 103) CIP4 = 26.09. *add value labels CIP4 26.09 'Physiology, Pathology and Related Sciences'. *Medicine. if (MAJfirstcode eq 104) CIP6 = 51.1102. add value labels CIP6 51.1102 'Pre-Medicine/Pre-Medical Studies'. if (MAJfirstcode eq 104) CIP4 = 51.11. add value labels CIP4 51.11 'Health/Medical Preparatory Programs'. *Nursing. if (MAJfirstcode eq 105) CIP6 = 51.3801. add value labels CIP6 51.3801 'Registered nursing/registered nurse'. if (MAJfirstcode eq 105) CIP4 = 51.38. add value labels CIP4 51.38 'Registered Nursing, Nursing Administration, Nursing Research and Clinical Nursing'. *if (MAJfirstcode eq 105) CIP6 = 51.3808. *add value labels CIP6 51.3808 'Nursing Science'. *Nutrition and dietetics. if (MAJfirstcode eq 106) CIP6 = 51.3101. add value labels CIP6 51.3101 'Dietetics/Dietitian'. if (MAJfirstcode eq 106) CIP4 = 51.31. add value labels CIP4 51.31 'Dietetics and Clinical Nutrition Services'. *if (MAJfirstcode eq 106) CIP6 = 19.0501. *add value labels CIP6 19.0501 'Foods, nutrition, and wellness studies, general'. *if (MAJfirstcode eq 106) CIP4 = 19.05. *add value labels CIP4 19.05 'Foods, Nutrition, and Related Services'. *if (MAJfirstcode eq 106) CIP6 = 30.1901. *add value labels CIP6 30.1901 'Nutrition sciences'. *if (MAJfirstcode eq 106) CIP4 = 30.19. *add value labels CIP4 30.19 'Nutrition Sciences'. *Occupational safety and health. if (MAJfirstcode eq 107) CIP6 = 15.0701. add value labels CIP6 15.0701 'Occupational safety and health technology/technician'. if (MAJfirstcode eq 107) CIP4 = 15.07. add value labels CIP4 15.07 'Quality Control and Safety Technologies/Technicians'. *Occupational therapy. if (MAJfirstcode eq 108) CIP6 = 51.2306. add value labels CIP6 51.2306 'Occupational Therapy/Therapist'. if (MAJfirstcode eq 108) CIP4 = 51.23. add value labels CIP4 51.23 'Rehabilitation and Therapeutic Professions'. *if (MAJfirstcode eq 108) CIP6 = 51.1107. *add value labels CIP6 51.1107 'Pre-Occupational Therapy Studies'. *if (MAJfirstcode eq 108) CIP4 = 51.11. *add value labels CIP4 51.11 'Health/Medical Preparatory Programs'. *Pharmacy. if (MAJfirstcode eq 109) CIP6 = 51.2001. add value labels CIP6 51.2001 'Pharmacy'. if (MAJfirstcode eq 109) CIP4 = 51.2. add value labels CIP4 51.2 'Pharmacy, Pharmaceutical Sciences, and Administration'. *if (MAJfirstcode eq 109) CIP6 = 51.201. *add value labels CIP6 51.201 'Pharmaceutical Sciences'. *if (MAJfirstcode eq 109) CIP4 = 51.11. *add value labels CIP4 51.11 'Health/Medical Preparatory Programs'. *if (MAJfirstcode eq 109) CIP6 = 51.1103. *add value labels CIP6 51.1103 'Pre-Pharmacy Studies'. *Physical therapy. if (MAJfirstcode eq 110) CIP6 = 51.2308. add value labels CIP6 51.2308 'Physical Therapy/Therapist'. if (MAJfirstcode eq 110) CIP4 = 51.23. add value labels CIP4 51.23 'Rehabilitation and Therapeutic Professions'. *if (MAJfirstcode eq 110) CIP6 = 51.1109. *add value labels CIP6 51.1109 'Pre-Physical Therapy Studies'. *if (MAJfirstcode eq 110) CIP4 = 51.11. *add value labels CIP4 51.11 'Health/Medical Preparatory Programs'. *Rehabilitation sciences. if (MAJfirstcode eq 111) CIP6 = 51.2314. add value labels CIP6 51.2314 'Rehabilitation Science'. if (MAJfirstcode eq 111) CIP4 = 51.23. add value labels CIP4 51.23 'Rehabilitation and Therapeutic Professions'. *Speech therapy. if (MAJfirstcode eq 112) CIP6 = 51.0204. add value labels CIP6 51.0204 'Audiology/Audiologist and Speech-Language Pathology/Pathologist'. if (MAJfirstcode eq 112) CIP4 = 51.02. add value labels CIP4 51.02 'Communication Disorders Sciences and Services'. *if (MAJfirstcode eq 112) CIP6 = 51.0201. *add value labels CIP6 51.0201 'Communication Sciences and Disorders, General'. *Veterinary science. if (MAJfirstcode eq 113) CIP6 = 51.2501. add value labels CIP6 51.2501 'Veterinary Sciences/Veterinary Clinical Sciences, General'. if (MAJfirstcode eq 113) CIP4 = 51.25. add value labels CIP4 51.25 'Veterinary Biomedical and Clinical Sciences'. *if (MAJfirstcode eq 113) CIP6 = 1.0901. *add value labels CIP6 1.0901 'Animal Sciences, General'. *if (MAJfirstcode eq 113) CIP4 = 1.09. *add value labels CIP4 1.09 'Animal Sciences'. *if (MAJfirstcode eq 113) CIP6 = 51.1104. *add value labels CIP6 51.1104 'Pre-Veterinary Studies'. *if (MAJfirstcode eq 113) CIP4 = 51.11. *add value labels CIP4 51.11 'Health/Medical Preparatory Programs'. *Other health professions. if (MAJfirstcode eq 114) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 114) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 114) CIP6 = 51.2201. *add value labels CIP6 51.2201 'Public Health, General'. *if (MAJfirstcode eq 114) CIP4 = 51.22. *add value labels CIP4 51.22 'Public Health'. *if (MAJfirstcode eq 114) CIP6 = 51.1504. *add value labels CIP6 51.1504 'Community Health Services/Liaison/Counseling'. *if (MAJfirstcode eq 114) CIP4 = 51.15. *add value labels CIP4 51.15 'Mental and Social Health Services and Allied Professions'. *********************Social Service Professions. *Criminal justice. if (MAJfirstcode eq 115) CIP6 = 43.0104. add value labels CIP6 43.0104 'Criminal Justice/Safety Studies'. if (MAJfirstcode eq 115) CIP4 = 43.01. add value labels CIP4 43.01 'Criminal Justice and Corrections'. *if (MAJfirstcode eq 115) CIP6 = 43.0107. *add value labels CIP6 43.0107 'Criminal Justice/Police Science'. *Criminology. if (MAJfirstcode eq 116) CIP6 = 45.0401. add value labels CIP6 45.0401 'Criminology'. if (MAJfirstcode eq 116) CIP4 = 45.04. add value labels CIP4 45.04 'Criminology'. *Forensics. if (MAJfirstcode eq 117) CIP6 = 43.0106. add value labels CIP6 43.0106 'Forensic Science and Technology'. if (MAJfirstcode eq 117) CIP4 = 43.01. add value labels CIP4 43.01 'Criminal Justice and Corrections'. *Justice administration. if (MAJfirstcode eq 118) CIP6 = 43.0103. add value labels CIP6 43.0103 'Criminal Justice/Law Enforcement Administration'. if (MAJfirstcode eq 118) CIP4 = 43.01. add value labels CIP4 43.01 'Criminal Justice and Corrections'. *if (MAJfirstcode eq 118) CIP6 = 43.0112. *add value labels CIP6 43.0112 'Securities Services Administration/Management'. *Law. if (MAJfirstcode eq 119) CIP6 = 22. add value labels CIP6 22 'Legal Studies, General'. if (MAJfirstcode eq 119) CIP4 = 22. add value labels CIP4 22 'Non-Professional General Legal Studies (Undergraduate)'. *if (MAJfirstcode eq 119) CIP6 = 22.0001. *add value labels CIP6 22.0001 'Pre-Law Studies'. *Military science. if (MAJfirstcode eq 120) CIP6 = 29.0399. add value labels CIP6 29.0399 'Military Applied Science, Other'. if (MAJfirstcode eq 120) CIP4 = 29.03. add value labels CIP4 29.03 'Military Technologies'. *if (MAJfirstcode eq 120) CIP6 = 29.9999. *add value labels CIP6 29.9999 'Military Technologies and Applied Sciences, Other'. *if (MAJfirstcode eq 120) CIP4 = 29.99. *add value labels CIP4 29.99 'Military Technologies and Applied Sciences, Other'. *Public administration, policy. if (MAJfirstcode eq 121) CIP6 = 44.0401. add value labels CIP6 44.0401 'Public Administration'. if (MAJfirstcode eq 121) CIP4 = 44.04. add value labels CIP4 44.04 'Public Administration'. *if (MAJfirstcode eq 121) CIP6 = 44.0501. *add value labels CIP6 44.0501 'Public Policy Analysis, General'. *if (MAJfirstcode eq 121) CIP4 = 44.05. *add value labels CIP4 44.05 'Public Policy Analysis'. *Public safety and emergency management. if (MAJfirstcode eq 122) CIP6 = 43.0302. add value labels CIP6 43.0302 'Crisis/Emergency/Disaster Management'. if (MAJfirstcode eq 122) CIP4 = 43.03. add value labels CIP4 43.03 'Homeland Security'. *if (MAJfirstcode eq 122) CIP6 = 43.0203. *add value labels CIP6 43.0203 'Fire Science/Fire-fighting'. *if (MAJfirstcode eq 122) CIP4 = 43.02. *add value labels CIP4 43.02 'Fire Protection'. *if (MAJfirstcode eq 122) CIP6 = 43.0301. *add value labels CIP6 43.0301 'Homeland Security'. *Social work. if (MAJfirstcode eq 123) CIP6 = 44.0701. add value labels CIP6 44.0701 'Social Work'. if (MAJfirstcode eq 123) CIP4 = 44.07. add value labels CIP4 44.07 'Social Work'. *Urban planning. if (MAJfirstcode eq 124) CIP6 = 4.0301. add value labels CIP6 4.0301 'City/Urban, Community and Regional Planning'. if (MAJfirstcode eq 124) CIP4 = 4.03. add value labels CIP4 4.03 'City/Urban, Community and Regional Planning'. *********************All Other. *Computer information systems. if (MAJfirstcode eq 125) CIP6 = 11.0501. add value labels CIP6 11.0501 'Computer Systems Analysis/Analyst'. if (MAJfirstcode eq 125) CIP4 = 11.05. add value labels CIP4 11.05 'Computer Systems Analysis'. *Family and consumer studies. if (MAJfirstcode eq 126) CIP6 = 19.0701. add value labels CIP6 19.0701 'Human Development and Family Studies, General'. if (MAJfirstcode eq 126) CIP4 = 19.07. add value labels CIP4 19.07 'Human Development, Family Studies, and Related Services'. *if (MAJfirstcode eq 126) CIP6 = 19.0101. *add value labels CIP6 19.0101 'Family and Consumer Sciences/Human Sciences, General'. *if (MAJfirstcode eq 126) CIP4 = 19.01. *add value labels CIP4 19.01 'Family and Consumer Sciences/Human Sciences, General'. *General studies. if (MAJfirstcode eq 127) CIP6 = 24.0102. add value labels CIP6 24.0102 'General Studies'. if (MAJfirstcode eq 127) CIP4 = 24.01. add value labels CIP4 24.01 'Liberal Arts and Sciences, General Studies and Humanities'. *Information systems. if (MAJfirstcode eq 128) CIP6 = 11.0401. add value labels CIP6 11.0401 'Information Science/Studies'. if (MAJfirstcode eq 128) CIP4 = 11.04. add value labels CIP4 11.04 'Information Science/Studies'. *if (MAJfirstcode eq 128) CIP6 = 11.0901. *add value labels CIP6 11.0901 'Computer Systems Networking and Telecommunications'. *if (MAJfirstcode eq 128) CIP4 = 11.09. *add value labels CIP4 11.09 'Computer Systems Networking and Telecommunications'. *Information technology. if (MAJfirstcode eq 129) CIP6 = 11.0103. add value labels CIP6 11.0103 'Information Technology'. if (MAJfirstcode eq 129) CIP4 = 11.01. add value labels CIP4 11.01 'Computer and Information Sciences, General'. *Liberal arts and sciences. if (MAJfirstcode eq 130) CIP6 = 24.0101. add value labels CIP6 24.0101 'Liberal Arts and Sciences/Liberal Studies'. if (MAJfirstcode eq 130) CIP4 = 24.01. add value labels CIP4 24.01 'Liberal Arts and Sciences, General Studies and Humanities'. *Multi, Interdisciplinary studies. if (MAJfirstcode eq 131) CIP6 = 30. add value labels CIP6 30 'Multi-/InterdisCIP6linary Studies, General'. if (MAJfirstcode eq 131) CIP4 = 30. add value labels CIP4 30 'Multi-/Interdisciplinary Studies, General'. *Network security and systems. if (MAJfirstcode eq 132) CIP6 = 11.1003. add value labels CIP6 11.1003 'Computer and Information Systems Security/Information Assurance'. if (MAJfirstcode eq 132) CIP4 = 11.1. add value labels CIP4 11.1 'Computer/Information Technology Administration and Management'. *Other computer science and technology. if (MAJfirstcode eq 133) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 133) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 133) CIP6 = 11.0201. *add value labels CIP6 11.0201 'Computer Programming/Programmer, General'. *if (MAJfirstcode eq 133) CIP4 = 11.02. *add value labels CIP4 11.02 'Computer Programming'. *if (MAJfirstcode eq 133) CIP6 = 50.0411. *add value labels CIP6 50.0411 'Game and Interactive Media Design'. *if (MAJfirstcode eq 133) CIP4 = 50.04. *add value labels CIP4 50.04 'Design and Applied Arts'. *Parks, recreation, leisure studies, sports mgmt.. if (MAJfirstcode eq 134) CIP6 = 31.0504. add value labels CIP6 31.0504 'Sport and Fitness Administration/Management'. if (MAJfirstcode eq 134) CIP4 = 31.05. add value labels CIP4 31.05 'Health and Physical Education/Fitness'. *if (MAJfirstcode eq 134) CIP6 = 31.0301. *add value labels CIP6 31.0301 'Parks, Recreation and Leisure Facilities Management, General'. *if (MAJfirstcode eq 134) CIP4 = 31.03. *add value labels CIP4 31.03 'Parks, Recreation and Leisure Facilities Management'. *if (MAJfirstcode eq 134) CIP6 = 31.0101. *add value labels CIP6 31.0101 'Parks, Recreation and Leisure Studies '. *if (MAJfirstcode eq 134) CIP4 = 31.01. *add value labels CIP4 31.01 'Parks, Recreation and Leisure Studies'. *Professional studies (general). if (MAJfirstcode eq 135) CIP6 = 44. add value labels CIP6 44 'Human Services, General'. if (MAJfirstcode eq 135) CIP4 = 44. add value labels CIP4 44 'Human Services, General'. *Technical, vocational studies. if (MAJfirstcode eq 136) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 136) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 136) CIP6 = 15.0803. *add value labels CIP6 15.0803 'Automotive Engineering Technology/Technician'. *if (MAJfirstcode eq 136) CIP4 = 15.08. *add value labels CIP4 15.08 'Mechanical Engineering Related Technologies/Technicians'. *Theological studies, ministry. if (MAJfirstcode eq 137) CIP6 = 39.0601. add value labels CIP6 39.0601 'Theology/Theological Studies'. if (MAJfirstcode eq 137) CIP4 = 39.06. add value labels CIP4 39.06 'Theological and Ministerial Studies'. *if (MAJfirstcode eq 137) CIP6 = 39.0201. *add value labels CIP6 39.0201 'Bible/Biblical Studies'. *if (MAJfirstcode eq 137) CIP4 = 39.02. *add value labels CIP4 39.02 'Bible/Biblical Studies'. *Other, not listed. if (MAJfirstcode eq 138) CIP6 = 88.9999. add value labels CIP6 88.9999 'Inadequate Match'. if (MAJfirstcode eq 138) CIP4 = 88.99. add value labels CIP4 88.99 'Inadequate Match'. *if (MAJfirstcode eq 138) CIP6 = 49.0101. *add value labels CIP6 49.0101 'Aeronautics/Aviation/Aerospace Science and Technology, General'. *if (MAJfirstcode eq 138) CIP4 = 49.01. *add value labels CIP4 49.01 'Air Transportation'. *if (MAJfirstcode eq 138) CIP6 = 12.0504. *add value labels CIP6 12.0504 'Restaurant, Culinary, and Catering Management/Manager'. *if (MAJfirstcode eq 138) CIP4 = 12.05. *add value labels CIP4 12.05 'Culinary Arts and Related Services'. *********************Undecided, undeclared. *Undecided, undeclared. if (MAJfirstcode eq 999) CIP6 = 89.9999. add value labels CIP6 89.9999 'Undecided, Undeclared'. if (MAJfirstcode eq 999) CIP4 = 89.99. add value labels CIP4 89.99 'Undecided, Undeclared'. frequencies CIP6 CIP4.

Engagement by Student Characteristics

Basic approaches to exploring topical areas of engagement by student characteristics.

Engagement with Faculty by Gender and Class Level

Amount of writing by major field category.

To estimate the number of pages of assigned writing, the average number of writing assignments of a given page-length was multiplied by an approximate number of pages for the assignment type. *********************************************************. WRITING BY MAJOR FIELD CATEGORY. *********************************************************. ***** ESTIMATED PAGES OF ASSIGNED WRITING. RECODE wrshort (1=0) (2=4.5) (3=12) (4=24) (5=39) (6=54) (7=69) INTO wr1. RECODE wrmed (1=0) (2=12) (3=32) (4=64) (5=104) (6=144) (7=184) INTO wr2. RECODE wrlong (1=0) (2=22.5) (3=60) (4=120) (5=195) (6=270) (7=345) INTO wr3. EXECUTE. COMPUTE wrpages=wr1+wr2+wr3. VARIABLE LEVEL wrpages (SCALE). VARIABLE LABELS wrpages 'Estimated pages of assigned writing'. EXECUTE. DELETE VARIABLES wr1 wr2 wr3. *****CREATE TABLES BY MAJOR FIELD CATEGORY. CTABLES /VLABELS VARIABLES=MAJfirstcol IRclass wrpages DISPLAY=DEFAULT /TABLE MAJfirstcol [C] BY IRclass [C] > wrpages [S][COUNT MEAN] /CATEGORIES VARIABLES=MAJfirstcol ORDER=A KEY=VALUE EMPTY=INCLUDE /CATEGORIES VARIABLES=IRclass [1, 4] EMPTY=INCLUDE./CATEGORIES VARIABLES=IRclass [1, 4] EMPTY=INCLUDE.

Participation in High-Impact Practices by Student Characteristics

Computing engagement indicators.

Although Engagement Indicators are included in each institution's NSSE data file, an SPSS syntax to compute them on your own is available below. Use the syntax to understand how the indicators are calculated.

Creating Other Scales

Satisfaction.

Two scales help assess overall student satisfaction with the undergraduate experience. Use the SPSS syntax below to create the following Satisfaction Scales: Overall Satisfaction (2 items) Overall Satisfaction plus Quality of Campus Relationships (7 items) *********************************************************. SYNTAX TO CREATE SATISFACTION SCALES 2 satisfaction scales will be created *********************************************************. *******Overall Satisfaction (2 items)***********. COMPUTE evalexpX = (evalexp-1)*20. COMPUTE sameinstX = (sameinst-1)*20. EXECUTE. ***Take the mean of the 2 items. COMPUTE SToverall = mean.2(evalexpX, sameinstX). VARIABLE LABELS SToverall "Overall Satisfaction". EXECUTE. *********************************************************. ****Satisfaction plus Quality of Campus Relationship (7 items). ****A scale will be created only if the students have answered 6 of 7 items. MISSING VALUES QIstudent QIadvisor QIfaculty QIstaff QIadmin (9). COMPUTE QIstudentX = (QIstudent-1)*10. COMPUTE QIadvisorX=(QIadvisor-1)*10. COMPUTE QIfacultyX=(QIfaculty-1)*10. COMPUTE QIstaffX=(QIstaff-1)*10. COMPUTE QIadminX=(QIadmin-1)*10. EXECUTE. ***Take the mean of the 7 items when a respondent has at least 6 of the 7 items. COMPUTE STqcr = mean.6(evalexpX, sameinstX, QIstudentX, QIadvisorX, QIfacultyX, QIstaffX, QIadminX). VARIABLE LABELS STqcr "Satisfaction plus Quality of Campus Relationships". EXECUTE. EXECUTE.

Sense of Belonging

Perceived gains.

A scale is provided which explores the degree to which students reported having made gains in a variety of personal, practical, and general education competency areas as a result of their undergraduate education. *********************************************************. COMPUTE pgwriteX=(pgwrite-1)*20. COMPUTE pgspeakX=(pgspeak-1)*20. COMPUTE pgthinkX=(pgthink-1)*20. COMPUTE pganalyzeX=(pganalyze-1)*20. COMPUTE pgworkX=(pgwork-1)*20. COMPUTE pgothersX=(pgothers-1)*20. COMPUTE pgvaluesX=(pgvalues-1)*20. COMPUTE pgdiverseX=(pgdiverse-1)*20. COMPUTE pgprobsolveX=(pgprobsolve-1)*20. COMPUTE pgcitizenX=(pgcitizen-1)*20. EXECUTE. ***Take the mean of the 10 items when a respondent has at least 9 of the 10 items. COMPUTE pg=mean.9(pgwriteX, pgspeakX, pgthinkX, pganalyzeX, pgworkX, pgothersX, pgvaluesX, pgdiverseX, pgprobsolveX, pgcitizenX). EXECUTE.

Evidence-Based Improvement in Higher Education resources and social media channels

Evidence-Based Improvement in Higher Education

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