Previous Next Title Page Contents 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.


WHY DO FIRMS PARTICIPATE?

       Why do these firms participate? We have suggested three broad motivations: (1) philanthropic, (2) individual, and (3) collective. This section uses data from the survey to try to differentiate among these motivations focusing primarily on the distinction between philanthropic and self-interested motives.

       What kind of firms participate in work-based learning? Table 2 displays information on the distribution of firm (organization) size among participating and the nonparticipating (comparison) firms.

       Large firms are much more likely to provide internships than smaller firms, although there are still a substantial number of smaller firms that do participate. This conclusion is supported both by the comparison of the characteristics of the participant and nonparticipant samples (Table 2) and analysis of the screening question for the nonparticipant sample (Table 1). The Census Bureau survey also found a strong relationship between size and participation rates (NCPI, 1997).

       It is likely that program operators looking for placements will go to the large firms first since such firms are more likely to be able to provide multiple placements. Does the firm size data have any implications for possible motivations? Large firms are more likely to have specialized community relations departments. Being more visible, large firms might have a stronger incentive to engage in public service activities. On the other hand, small neighborhood establishments might feel a particular commitment to working with a local school and community. Thus, firm size itself does not seem to have strong implications about motivations.[7] Participation rates may also reflect large fixed costs for participation. Whether the motivation is self-interested or philanthropic, there are costs; and if there is a large fixed-cost component, then a large firm could more easily absorb those costs.


Table 2
Selected Characteristics of Participants vs. Nonparticipants

(Standard Errors of Estimates in Parentheses)

Participants Nonparticipants
Firm size 340.5 32.8
(61.8) (9.5)
Types of training for nonmanagerial workers:*
Tuition reimbursement 41.8% 18.8%
Registered apprenticeships 16.9% 11.5%
Paid external training 55.5% 31.4%
In-house training department or staff 57.1% 50.9%
Customized training by colleges 23.5% 10.7%
Remedial math or reading courses 11.3% 2.0%
Average total (0-6) 2.04 1.26
(0.08) (0.07)
Types of Human Resources programs:*
Job rotation 32.8% 13.0%
Self-managed work teams 44.3% 24.9%
Quality circles 44.9% 14.5%
Total Quality Management 34.5% 21.7%
ESOPs or profit sharing 27.9% 15.9%
Average total (0-5) 1.83 0.87
(0.08) (0.06)

* Standard errors of estimates range from 2% to 3%.

       On the other hand, the relationship between the number of interns (rather than whether or not the firm provides at least one internship) and the employment size of the establishment does suggest the importance of philanthropic or public relations motivations. Large firms do tend to take on more interns, although the increase in the number of interns is not proportional to the increase in the employment size.[8] Pauly et al. (1995) have also noted that large firms seem to only take a small number of interns. They suggested that this may reflect a view towards public relations; participating firms can achieve their public relations goals with a handful of interns. It seems reasonable that employers who see a direct self-interested benefit to participation would not stop at a small number of interns, especially if there is a fixed cost component--the marginal cost of additional interns would be low. Moreover, there are strong incentives for the program operators to increase the number of interns within each establishment. Thus, a significant self-interest in employing interns would suggest a stronger relationship between the size of the establishment and the number of interns.

       Tables 2 and 3 present some additional characteristics of participating and nonparticipating firms. Compared to nonparticipants, participants provide more training, tend to be more oriented towards national and international markets, and have more progressive human resource practices such as job rotation, self-managed work teams, quality circles, Total Quality Management, and profit sharing. Many of these characteristics are associated with progressive or "high-performance work organizations." This conclusion is roughly consistent with results by Shapiro and Zemsky (1996) who find that large firms, those with more highly educated workforces, those that report increased skill requirements, and those that provide more training to young workers (this last result is for non-manufacturing firms) are more likely to also provide work-based learning opportunities.

       One interpretation of this is that internships are an integral part of a broad human resource strategy, suggesting that as (or if) firms move towards more progressive strategies, employer recruitment will become easier. Osterman (1994) argues that firms that adopt high-performance work practices tend also to have a more employee-oriented perspective--that is, they have a philosophical perspective towards these practices which goes beyond any narrow cost/benefit calculation for each practice, as they see the whole package as generally beneficial to the firm. Our data is certainly consistent with that argument as it would apply to participation in work-based learning opportunities, although once again, the small number of placements in the larger firms makes it more difficult to see this as a practice that firms see as fundamental to their business strategy.

       Table 3 presents the distribution of participating and nonparticipating establishments by three sectors: (1) private for-profit, (2) private not-for-profit, and (3) public. The most striking issue here is the small relative share of the private for-profit sector. Just under one half of the participating establishments are for-profit while they account for 80% of the comparison firms. While not-for-profit and public sector organizations could certainly be motivated by the cost savings potentially associated with work-based learning, it is reasonable that appeals to such organizations to "help out" the community or the local school system might be more effective than such appeals would be to profit-making firms. On the other hand, not-for-profits in particular are often very short of cash, and interns might be particularly attractive as cheap labor. Cash constraints may simply make it impossible to hire additional employees so such organizations may be faced with the choice of taking an unpaid intern or doing without anyone. Indeed, unpaid internships are very much overrepresented among the not-for-profit participants.[9]


Table 3
Characteristics of Product Markets

Participants vs. Nonparticipants

Participants Nonparticipants
Most important factor in competition is . . .*
Price 18.56% 20.7%
Quality 55.09 55.6
Other (custom, recognition, innovation) 26.35 23.7
The main market for the firm's goods or services is . . .**
The neighborhood 28.79% 34.3%
The metropolitan area 41.52 49.9
National 15.15 12.6
International 14.55 3.1
The firm's sector is . . .***
Private, for-profit 48.0% 80.5%
Private, not-for-profit 32.7 18.8
Government 19.4 0.7

* Standard errors of estimates range from 1% to 4%.
** Standard errors of estimates range from 1% to 3%.
*** Standard errors of estimates range from 1% to 3%. Participants are weighted by number of interns.



       Table 4 presents a probit regression of the determinants of participation. These results confirm the importance of the firm size. After controlling for sector, geographic area, training, and human resource practices, firm size remains a highly significant determinant of participation. The variables for human resources programs and public sector status also remain statistically significant. The training variable loses its significance when size is included in the model. Given that size, the extent of training, and having human resources programs are all moderately correlated, one should not discount the role of any one of them in contributing to the likelihood of employer participation.


Table 4
Determinants of Participation

Probit Regression
(T-Statistics in Parentheses)

Logarithm of establishment
Employment size   0.30*
(0.049)
No. of training programs[10]   0.05
(0.055)
No. of Human Resources programs[11]   0.16*
(0.053)
Not-for-profit sector   0.11
(0.143)
Government sector   1.23*
(0.0337)
Constant -1.00*
(0.158)
No. observations 542
Ln Likelihood -299.46
Model Chi2(5) 146.04
Pseudo-R2 0.20

* Coefficient is significant at the 1% level.

       Table 5 provides another perspective on the possible motivations of employers. Here employers were asked to compare various skill categories for interns and entry-level workers. In all cases, a majority of the respondents suggested that the skills of the interns were at least as good as those of other entry-level workers, although on average, the alternative entry-level workers were preferred (more employers said that they preferred the regular workers). But that preference was weakest for the "soft" or attitudinal skills such as attendance, reliability, and "attitude." Indeed, more of the respondents preferred the "attitudes" of interns than preferred the attitudes of other workers. At first, Table 5 suggests that many firms are taking interns despite lower perceived skills, perhaps suggesting that they are being motivated by philanthropic concerns. Nevertheless, the majority of firms do not perceive that they are compromising on skill levels. Furthermore, using less skilled interns still may be in the interest of the firms if the wages and costs are lower, or if they expect the interns to stay longer and eventually learn more skills.

       Table 5 also compares employer attitudes about skills for employers who pay their interns to those attitudes for employers who provide unpaid internships. There is a sharp difference. Employers who provide paid internships have much more positive views about their interns. Indeed, on average, they find that the interns have better attendance, reliability, and attitude than the alternative workers. This suggests that firms that pay their interns may be more selective in choosing their interns.


Table 5
Comparisons of Skills of Interns and Entry-Level Workers


All Internships
Skill Interns
Are Better
They Are
the Same
Workers
Are Better
Attendance19.0%51.4%29.6%
Reliability17.2%52.2%30.6%
Attitude23.7%55.8%20.5%
Productivity13.8%47.0%39.2%
Training required to learn job11.6%49.4%39.0%
Communication skills 10.7%42.5%46.9%
Writing skills13.1%40.3%46.6%
Math skills17.1%52.1%30.8%
Technical skills15.4%38.7%45.9%
N ~ 290. Standard errors of estimates are under 2%.

Unpaid Internships
Skill Interns
Are Better
They Are
the Same
Workers
Are Better
Attendance9.7%50.7%39.6%
Reliability11.0%49.0%40.0%
Attitude18.2%55.2%26.6%
Productivity11.7%40.0%48.3%
Training required to learn job8.6%45.0%46.4%
Communication skills9.0%38.9%52.1%
Writing skills10.5%35.5%54.0%
Math skills12.4%44.9%42.7%
Technical skills10.4%34.4%55.2%
N ~ 145. Standard errors of estimates are under 3%.

Paid Internships
Skill Interns
Are Better
They Are
the Same
Workers
Are Better
Attendance29.3%52.7%18.0%
Reliability24.8%53.7%21.5%
Attitude30.4%57.4%12.2%
Productivity16.1%55.1%28.9%
Training required to learn job15.2%53.1%31.7%
Communication skills12.8%44.6%42.6%
Writing skills17.0%44.4%38.5%
Math skills20.5%57.4%22.1%
Technical skills20.6%41.1%38.3%
N ~ 145. Standard errors of estimates are under 2.5%.

       Finally, the participants were asked directly to identify the most important factor that motivated them to participate (Table 6). Nonparticipants were asked what factors might motivate them to participate or discourage them from participating if approached. When asked for their most important motivation, more than half of the participants claimed some philanthropic reason. Almost 26% cited an interest in contributing to the community as their primary motivation while 33% stated that their most important reason was a desire to improve the public education system. Nevertheless, over 41% still identified some self-interested reason as their primary basis for participation. This data also shows that private not-for-profit and public sector participants are much more likely than for-profit participants to cite philanthropic motivations. It is perhaps not surprising that the public sector and non-profit employers would respond to requests to contribute to the community.


Table 6
Biggest Motivations for Participation

Participants vs. Nonparticipants

Participants Nonparticipants
Biggest motivation to participate is/would be . . .

Local labor shortage 3.0% 4.3%
Opportunity to test potential employees 5.8% 15.9%
Part-time/short-term hiring 10.3%   24.1% 
Improving public education system 33.1%   9.1%
Encouragement from industry groups 0.6% 1.4%
Reducing benefits expenses 2.7% 1.9%
Contributing to community 25.8%   11.9%
Access to pre-screened applicants 3.7% 5.1%
Increased training is necessary 4.6% 5.0%
Access to pool of qualified workers 10.3%   21.3% 
N = 329 for participants, 295 for nonparticipants. Standard errors of estimates are under 1.3%.

Primary motivation would be helping community
or educational system:
Private, for-profit sites 47.7%   (3.8%) 17.7%     (2.5%)
Private, not-for-profit sites 76.8%   (4.3%) 32.3%     (6.8%)
Government sites 64.2%   (6.7%) 81.4%   (13.0%)
Standard errors in parentheses.

       While the participants emphasized philanthropic motivations, over three-quarters of the nonparticipants hypothetically looked to internships for self-interested reasons. These comparisons should be made with caution since the answers for participants are based on experience while those for the nonparticipants are hypothetical. The experience with interns could change an employer's perspective. Indeed, the "try it, you'll like it" argument suggests that employers get involved for philanthropic reasons but find that they do benefit from participation. On the other hand, this data suggests a movement in the opposite direction. Firms must be convinced to participate on the basis of self interest, but view their participation in more philanthropic terms after some experience. While the appropriate behavioral model that underlies these results is not clear, they do suggest that experience with interns does not improve employer attitudes about their potential productivity.

       This general conclusion seems to be supported by data presented in Table 7 that indicates the most important factors motivating firms not to participate. Participants are actually much more concerned than nonparticipants about students' lack of basic skills (26.9% list this as their biggest concern) and their unreliability or immaturity (which most concerns 22.1%).[12] This conclusion is further supported by an in-depth study of one of our survey sites that demonstrated a very high attrition rate for employer participants. Indeed, one half of all of the employers who participated in the program between 1984 and 1995 participated for only one internship cycle (Wieler & Bailey, 1997). As in previous studies, this one finds that both participating and nonparticipating employers are much more concerned about the indirect costs of training students than they are about the direct costs of paying students (though it should be mentioned that only about one half of the internships are paid). The theoretical work on participation emphasizes that employers may have little incentive to train interns since they may fear that the interns, once trained, will leave. This does appear to be a preoccupation of the nonparticipants but not the participants.


Table 7
Factors That Discourage Participation

Participants vs. Nonparticipants

Participants Nonparticipants
Biggest motivation not to participate
is/would be . . .
Employee resistance 1.4% 5.1%
Lost productivity for trainers 15.4%   23.2%  
Students might leave after training 4.8% 15.0%  
Opposition from unions 3.4% 1.7%
Uncertain economic climate 3.9% 4.1%
Students lack basic skills 26.9%   9.0%
OSHA/child labor law violations 9.6% 10.1%  
Students not always available 9.6% 10.2%  
Students are unreliable or immature 22.1%   15.8%  
Student wages are too costly 1.4% 4.4%
Problems working with schools 1.4% 1.4%

N = 208 for participants, 279 for nonparticipants. Standard errors of estimates are under 1.9%.

       What can we conclude from this data about the motivations of employers? It appears that philanthropic motivations still outweigh a bottom-line perspective. Although the data is certainly open to interpretation, it is hard to argue from this evidence that most firms are participating out of a conviction that it will advance their business in any direct way. To be sure, responses to direct questions about motivations need to be viewed with some skepticism. But in addition to the responses to direct questions, the weak relationship between establishment size and the number of interns and the preponderance of public and not-for-profit firms in the participant sample also suggest a philanthropic emphasis.

       One interpretation might be that these programs have so far been able to recruit organizations that are philosophically oriented towards public service. There is some evidence in our study that such motivations could support a reasonably large school-to-work program. Some of the programs we studied have been able to sustain large programs for many years, even though the employers report a primacy of philanthropic motivations. For example, both the City-as-School and LaGuardia programs place hundreds of students each year and have been doing so for over 15 years. And our data also suggests that a significant minority of establishments in the cities we surveyed are providing internships. On the other hand, public sector and not-for-profit organizations have been the mainstay of the participant pool. In order to penetrate the for-profit world more successfully, program operators will have to convince employers that participation will be in their firms' interests. On a more optimistic note, our data indicates that this problem may be less difficult if there is a strong general trend towards more progressive human resource practices.


[7] Moreover, there is no correlation between firm size and stated motivations. The respondents' direct statements about motivations are discussed later.

[8] A 10% increase in firm size amounts to a 1.1% increase in the number of interns taken (this univariate regression has a t-statistic of 4.58).

[9] Of private for-profit participants, 62% paid their interns (with a standard error of 3.8%) versus 31.5% of not-for-profits (standard error equals 5.0%) and 39.1% of government firms (standard error equals 7.3%).

[10] Of those listed in Table 2.

[11] Of those listed in Table 2.

[12] On the other hand, data from Table 3 suggests that employers are not much more dissatisfied with the skills of interns than they are with those of the alternative labor supply. There are two possible explanations for the apparent discrepancy between the comparison with alternative workers and the fears about intern skills and attitudes. One explanation is that employers are also very dissatisfied with the alternative workers. Alternatively, those firms who express relative satisfaction with the interns' skills and attitudes are not the ones who show up in Table 4 as complaining about those skills.


Previous Next Title Page Contents 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.

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