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The effect of educational technology on college students’ labor market performance

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Abstract

This paper presents some of the first evidence on the effect of information and communications technology (ICT) on college students’ labor market performance. Using a large, representative survey of college students in China, we examine outcomes before and after students were exposed to technology-aided instruction, compared with students who were not exposed to such instruction. The results indicate that the ICT program significantly increased students’ likelihood of obtaining a job offer in the labor market and the wage they were offered. The positive effect comes from students’ increased use of computers and the internet for job search. While most previous studies of the use of technology in education focus only on students’ academic achievement and find zero or negative effects, our study demonstrates that technology may be an effective tool for improving college students’ labor market performance, and that the potential benefits of technology might be underestimated if we focus only on test scores and ignore students’ career development.

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Notes

  1. In particular, previous studies have examined the effect of ICT on firm performance (Akerman et al. 2015; DeStefano et al. 2018; Chen et al. 2019), economic growth (Czernich et al. 2011; Kolko 2012), and the labor market (Fabritz 2013; Akerman et al. 2015).

  2. The statistic is obtained from the CCW Research group, and it is available online at: http://www.edu.cn/xxh/fei/sj/201501/t20150129_1226140.shtml

  3. A large strand of literature focuses on the effectiveness of technology-aided instruction on primary and secondary schools, and reaches mixed conclusions. In particular, some studies find positive effects of technology on students’ academic outcomes (Banerjee et al. 2007; Barrow et al. 2009; Muralidharan et al. 2019), while others find zero or negative effects (Goolsbee and Guryan 2006; Belo et al. 2013).

  4. Specifically, the number of students enrolled in higher education institutions (HEIs) has increased by 65 million between 1999 and 2008, and the global demand for higher education is predicted to expand from less than 100 million students in 2000 to over 250 million in 2025 (Bangkok UNESCO 2011).

  5. According to China’s price statistics, the inflation-adjusted price of a computer declined substantially by 60.1% from 1995 to 2015. Statistics is are the author’s calculation from the China Price Statistic Yearbook.

  6. Most existing studies that examine effect of technology on college students’ academic outcomes use data from tests, and there is little dataset to date that links students’ use of technology and their job market performance.

  7. In particular, Malamud and Pop-Eleches (2011) find that children who won a voucher to purchase a computer had significantly lower school grades but showed improved computer skills, and find that the voucher substantially increases children’s use of computer for computer games, and had only limited impact on the use of computer educational software.

  8. The ICT program in college is a part of “education technology project” in China, proposed by MoE, which aims to improve and accelerate the adoption of educational technology. It is available online at: http://old.moe.gov.cn//publicfiles/business/htmlfiles/moe/s3342/201211/144888.html

  9. Relevantly, in their review of studies on technology in education, Escueta et al. (2017) divided the use of technology into four categories: access to technology, computer-assisted learning, technology-based behavioral intervention, and online instructions. The project we study has three of these categories: access to technology, computer-assisted learning, and online instruction.

  10. A limit of the dataset is that it only contains information on the first job students obtained in their career, but lack information about students’ long-term labor market performance.

  11. In particular, while the time range was the same (from the end of May to early June), the exact date and time of the survey was decided by each college. We believe that such differences will not cause a severe estimate bias for two reasons: First, at the time of the survey, most students had already decided on their future plan. In China, most employees and firms send the job offer before April and according to students’ responses at the time of the survey, 95.1% of the undergraduates had plans after graduation. Second, even if the survey time may be correlated with students’ job market performance, given that we use a difference-in-differences setting, the survey time will bias the estimate if and only if it is systematically correlated with the respondent’s treatment status. For example, if compared with the time period before ICT program implementation, after policy implementation, the survey time is earlier in treated colleges than untreated colleges, the estimate will be biased. Otherwise, the estimate will not be biased. In addition, we find no anecdotal evidence indicating the correlation between ICT program and survey time. To this end, we believe that the different survey times will not be a severe threat for estimates.

  12. In the estimation sample, the 36 colleges are located across 20 provinces, and the treated colleges are located across 13 provinces.

  13. A potential concern about the survey dataset is students’ potential misreport—For example, about their job offer or grades. While this is a common question about measurement error when using survey data, and we are unable to ideally address this issue, we believe that this will not be a severe threat in our setting. First, students are assumed to have little incentives to lie. They were informed that it is a survey conducted by the data center at Tsinghua University, it is only for research purposes and is not concerned with their academic or labor market interests. In addition, students are not required to enter their name or ID on the questionnaire. Thus students have no obvious incentive or motivation to lie. Second, the data center compares statistics from the survey for example, students’ self-reported first job wage, job occupation, and industry with statistics for similar age cohorts from other survey data (including the China Family Panel Survey (CFPS) and population census), and find similar mean values and distributions. Furthermore, given that we rely on a difference-in-differences estimation framework, even if some students misreport their academic performance, in our econometric specification such measurement error will bias the estimate only if the misreport systematically correlates with the treatment status. For example, if students from the treated cohort in treated colleges are more likely to overstate their grade than those from non-treated colleges, the estimate will have a upward bias. Otherwise, measurement error will not bias the estimate.

  14. Given that wage is only observed for students who have already obtained a job offer, and the ICT program has a significantly positive effect on students’ likelihood of obtaining a job offer, estimates of the effect on wage may encounter the problem of sample selection issue. We discuss and address this issue in Section 4.3.

  15. The reduced statistical significance of the estimates is likely to be resulted from the reduced observations. To further test this argument, we remove the first treatment cohort to have a similar size of observation and replicate the regressions. We find that, as expected, similar to the estimates in which we remove cohort 2012, the estimates become less significant as well.

  16. Chinese colleges have low attrition rates. First, most Chinese colleges have high enrollment standards but relatively low graduation standards. Second, the return to college degree is high in China, and Chinese students and their families place high value on college study. Very few college students have an incentive to quit, and most parents don’t allow their children to quit college. To this end, the attrition rate is much lower in Chinese colleges compared to those in the US and Europe.

  17. In other words, we exclude the sample of students who decided to pursue graduate degree, and those who had not reported his/her plan at the time of survey.

  18. Lee (2009) provides a detailed discussion of the issue of sample selection.

  19. 4.2% is the estimated effect on the probability of obtaining job offer, as shown in Table 3 column 2.

  20. It is worth noting that while Heckman selection model is also a commonly used approach to the sample selection issue, here we are unable to use it because it requires an exogenous instrument in the first stage, which is not applicable in our setting.

  21. In China, most final exams in college use a pen-and-paper format, and the CET-4 test is a national-level pen-and-paper format test.

  22. A limitation of this question is that it does not separate the number of CVs sent via the internet vs. those sent as hard copies by mail.

  23. A limitation of the survey question is that it does not separate computer games from other entertainment.

  24. Escueta et al. (2017) reviews such studies and divides the educational technology into four categories: access to technology, computer-assisted learning, technology-based behavioral intervention, and online instruction.

  25. Since the differences are not statistically significant from zero, this heterogeneity pattern across family background should be interpreted with caution.

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Acknowledgments

We thank David Figlio, Ofer Malamud, and other scholars and seminar participants from Fudan University, National University of Singapore, and Northwestern University for helpful comments.

Funding

This study was funded by the National Natural Science Foundation of China (71803027), Ministry of Education of the People’s Republic of China (18YJC790139) and Shanghai Chenguang Talent Program (18CG03).

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Authors and Affiliations

  1. School of Economics and Management, Tsinghua University, Beijing, 100084, China

    Yi Lu

  2. School of Economics, Fudan University, Shanghai, 200433, China

    Hong Song

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  1. Yi Lu
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Correspondence to Hong Song.

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Appendix

Appendix

Table 9 Balancing test: ICT program and selection between college and students
Full size table
Table 10 Effect of ICT program on job obtain and wage, including all individual characteristics as presented in balancing test
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Table 11 Other robustness tests: Student attrition and work decision
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Table 12 Effect of ICT program on wage, using Lee (2006)’s trimming procedure
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Lu, Y., Song, H. The effect of educational technology on college students’ labor market performance. J Popul Econ 33, 1101–1126 (2020). https://doi.org/10.1007/s00148-019-00756-3

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