Bailey, T., Hughes, K., & Barr, T. (1998). Achieving scale and quality in school-to-work internships: Findings from an employer survey (MDS-902). Berkeley: National Center for Research in Vocational Education, University of California.

THE QUALITY OF INTERNSHIPS

• Industry and Occupational Distribution
• Program Characteristics
• Internship Duration and Learning Time

       Setting up work-based learning experiences involves much more than simply recruiting an adequate number of employers. The internships that those employers provide must have some educational value. After all, a majority of high school students already have jobs, so for work-based learning to be worth the considerable investment in time and resources that it will require, the internships must have greater educational value than the jobs that teenagers already have. Even if enough employers can be recruited, if they participate reluctantly, program operators will not have much leverage to work with the employers to guarantee the educational value of the placements.

       School-to-work and work-based learning developers have not as yet been able to create reliable and systematic measures of internship quality. Formal assessments of learning on the job would allow a rigorous analysis of the most desirable characteristics of internship placements. Alternatively, a small number of studies do document the nature of the experiences that interns have on the job (Moore, 1981; Stasz & Kaganoff, 1997), but many more such studies would be needed to begin to be able to evaluate work-based learning design. Our survey does provide some data that can be used to examine the quality of internships. Here we will examine three measures: (1) the occupational and industrial distribution of internships, (2) some design characteristics of the internships, and (3) the length of time it takes to learn the tasks that the interns are carrying out. These will be explained in more detail later.

Industry and Occupational Distribution

       Table 8 provides a general picture of the concentration of internships by industry and occupation in this sample. The top chart displays the industrial distribution for (1) internships in the sample, (2) employment in nonparticipating firms in the sample, and (3) youth employment. The bottom chart displays the occupational distribution of the same categories. The goal of this analysis is to determine whether the internships are primarily concentrated in typical youth jobs. It may be possible to design useful learning experiences in fast food and retail positions. Furthermore, jobs typically held by adults may have little educational value. Nevertheless, if internships were primarily in the types of jobs that many teenagers already have, then it is reasonable to conclude that the chances would be reduced that they will have an experience that is more educationally valuable than the experiences that they would have without the school-to-work initiative.

       The most important observation for our purposes is that the internships are not concentrated in retail trade, the sector with the most youth employment concentration. The majority of the internships are in the service sector, but this is a very diverse group that includes health, educational, and business services. In general, youth are most concentrated in service occupations, while nearly one-half of all of the internships are in administrative support positions. These are the entry-level jobs in office and business employment. Interns are also overrepresented in technical occupations, while relatively few are in production machine operative positions--an area of youth concentration.

       What conclusions can be drawn from these distributions? First it does not appear that programs are relying on the typical youth jobs. The overrepresentation in technical jobs is encouraging since these are the positions that employers often have difficulty filling; thus, this may represent an effort on the part of some employers to strengthen their pool of available labor.

Program Characteristics

       To assess the quality of the internships, the survey asked about a number of program components that are often considered part of the school-to-work model (Table 9). Each one of these ten components is believed to strengthen the quality of a work-based learning effort. The first two--a written agreement between the school and the student (#1) and a customized plan for each student (#2)--indicate that students, teachers, and employers have thought carefully about the nature of the placement and made a specific plan. A system for documenting and assessing student learning (#3) should help evaluate whether students are actually learning anything. If a student has a specific mentor on the job (#4) and if students have a chance to experience several jobs (#5), then they should have more opportunities to learn a variety of skills. Mentors who receive some training (#6) will be better able to teach and help the interns. By providing a classroom at the workplace (#7), the participating company demonstrates particular commitment to the program and facilitates closer integration between classroom and on-the-job learning. If the company serves on the program's advisory board (#8) and if it advises schools on their curriculum (#9), then the managers will have a better understanding of the educational goals of the program and the role of the work-based learning component. Finally, efforts to have company staff teach or make presentations at the school also demonstrate more involvement with the program which could translate into more careful planning and program development (#10).

       Even the presence of these components is not a guarantee of high quality work-based learning experience. For example, our fieldwork indicates that "assessment" of skills often consists of a check-off sheet completed by the student's supervisor, and "customized plans" can be mechanical and superficial. Nevertheless, the presence of these components can potentially indicate a better planned and implemented work-based learning initiative with more considered and committed participation.

       The data presented in Table 9 indicate that the large majority of participating firms provide a mentor and claim to assess and document student learning on the job. Internships in a majority of the participating organizations also involve a written agreement between the student and the school and the rotation of students among several positions. In contrast, many fewer employers engage in active participation with the schools--only a quarter have staff make presentations at the school, a fifth provide classrooms at the work site, and fewer than one-sixth of participants sit on an advisory board to the program.

       The responses to the questions in Table 9 were added together (as zero-one variables) to develop an index, with a value from zero to ten, for the intensity of the internship (hereafter referred to as "intensity"). Table 10 displays the distribution of the intensity index. About 70% of the firms have between 3 and 6 of the practices. The modal number of practices is 4.

Internship Duration and Learning Time

       Our other measures of program quality include the duration of the internships, the amount of time it takes the intern to learn the assigned job, and the percentage of the internship spent learning (the ratio of the learning time to the duration). We are particularly interested in the latter two. The amount of time that it takes to learn the job is a measure of the amount of learning represented by the placement. There is less educational benefit in a job that can be learned in a day than one that takes a month. The percentage of time spent learning is a measure of the efficiency of the learning that takes place at the placement. If an internship lasts a year, but it only takes a month to learn the job, then little learning is taking place during much of the internship. Since internships potentially take time away from other educational experiences, such as doing homework or participating in extracurricular activities, then it is desirable that as much time as possible during the internship be spent learning. The next several tables show several different breakdowns of quality measures between different types of firms.

       Table 11 displays the means for each of the four quality measures. On average, the internships have almost 5 of the 10 program components, the internships last almost 23 weeks, it takes 14 days to learn the jobs, and the interns spend about 14% of the time on the job learning. The table also shows the relationship between the quality measures and whether the internships are paid or unpaid and whether the firms intend to hire the interns as permanent employees. Compared to unpaid internships, paid placements are strongest on all measures. All four quality measures are also higher for those firms who intend to hire their interns.

       Table 12 compares the quality of internships in firms in the three sectors: (1) private for-profit, (2) not-for profit, and (3) government. The government sites have the highest program intensity--the highest number of program characteristics--but the jobs in internships in the private for-profit sector score highest on the duration and learning time variables.

       Table 13 relates the quality of internships to the educational level of workers who would otherwise have the position if interns were not available. Internships at sites where a college-educated worker would otherwise perform the work score lower on these quality measures than at sites where a worker with a high school or two-year college education would otherwise do the work. This might suggest that internships are best at sites where students are not too far behind other workers, rather than sites where the skill differentials are so great that students do separate work entirely. If indeed the jobs would otherwise be filled with college graduates, then the employers probably do not expect the interns to do the same tasks. Not seeing the interns as potentially productive workers in their assigned tasks, the employers may pay less attention to them. The jobs that could otherwise be filled with workers without a high school degree also tend to score lower on the quality measures. These jobs are probably typical teenage jobs that offer few opportunities to learn. Thus, this analysis suggests that internships are most productive when they involve jobs in which the interns could realistically be expected to be productive, but that still demand skills and abilities that the interns do not already have.

       Table 14 presents regressions of the determinants of three of our quality measures: (1) program intensity, (2) learning time, and (3) the learning ratio. (These analyses include controls for the five programs in case there are systematic quality differences among the five programs.) The program intensity regression suggests that public and non-profit organizations and those that hire permanently tend to provide higher quality internships. Firms that pay their interns appear to score higher in terms of the internships learning times (the time it takes to learn the job assigned to the intern) and the not-for-profits have internships with the shortest learning times. Only the not-for-profit sector variable is significant (and it is negative) in the percent-of-learning time regression. One problem with the analysis is that for-profit status, paid internships, and the intention to hire are all positively correlated[13], so the regression has trouble differentiating among them. But it is interesting that the size of the organization is not related to any of the measures of quality. It may be that non-profits in particular do try to provide good learning experiences and therefore tend to follow program guidelines by introducing the types of practices measured by the intensity variable. On the other hand, the nature of the jobs that they have available may not allow them to give interns positions that inherently have a high learning content.

       An interesting pattern emerges when firms claiming philanthropic motivations are compared to those who participate for self-interested purposes (Table 15). The philanthropic firms look better in terms of program features, while the internships in non-philanthropic firms tend to offer more learning opportunities. These differences are all statistically significant.

       Thus, the data presented in the last few tables offers some insight into the controversy concerning the relative value of paid and unpaid internships. During the debate about the School-to-Work Opportunities Act, some proponents held out for a provision that would require that work experience defined by the Act be paid. Their argument was that employers would take the interns more seriously if they were paid. Others argued that it would be too difficult to recruit enough employers if all internships had to be paid. This data does not show a strong relationship between quality and whether the internship is paid. On the other hand, there is some evidence that firms that take their interns more seriously in the sense that they expect to hire them after the internship is over do provide higher quality internships.

       Earlier we found that firms that provided more training for their workers and that had more progressive human resource practices (associated with "high-performance work organization") also were more likely to provide work-based learning. Table 16 indicates whether the internships in the more progressive firms score higher on the quality measures. The statistically significant and positive correlation between the intensity, duration, and learning time variables, and the amount of training (top panel) or the use of progressive human resource practices (bottom panel), indicates that firms that did engage in these practices did provide higher quality internships (on all of our measures except the ratio of learning time to program duration). It also seems roughly true that these policies matter more for firms that hire than for firms that do not, indicating that firms with strong workforce quality programs may be more motivated by self-interest than philanthropy or collective interest.

[13] In Tables 11 and 12, each of these variables when analyzed alone is positively related to the learning time measures.

[14] Philanthropic motivations are defined as participating to help the community or the educational system in Table 6.

[15] Ratio of learning time to program duration.

[16] Ratio of learning time to program duration.

Bailey, T., Hughes, K., & Barr, T. (1998). Achieving scale and quality in school-to-work internships: Findings from an employer survey (MDS-902). Berkeley: National Center for Research in Vocational Education, University of California.