1. 1 The (Teaching) Role of Universities in the Diffusion of the Internet Avi Goldfarb University of Toronto June 16, 2005 CIDE, Mexico City

2. 2 The Internet and Universities I Universities played an important role in the invention of the Internet and its applications. –The browser was invented at the University of Illinois (Netscape) –Gopher was created at the University of Minnesota –Google & Yahoo were created at Stanford –Akamai was created at MIT –Broadcom was created at UCLA

3. 3 The Internet and Universities II Students in the mid 1990s often got their first exposure to the Internet at school. Students needed to use the Internet in order to succeed (for research & communication). Students then brought their knowledge of the Internet to their homes (and workplaces) after graduation.

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5. 5 Key Points 1.Students in the mid-1990s are more likely to use the Internet than others, controlling for age and education. 2.Those who live with people in this cohort are also more likely to use the Internet 3.These effects do not hold for other computer applications 4.Former students adopt because of higher communications benefits 5.Household members adopt because of lower adoption costs

6. 6 How I will proceed: 1.Data 2.Are former students (and those who live with them) more likely to adopt the Internet than others? 3.Why are they more likely to adopt the Internet? 4.Conclusions

7. 7 Data US Census Current Population Survey Sept. 2001 Computer and Internet Use Supplement –142,667 individuals in 2001 –Survey asks whether use the Internet and a variety of Internet applications, also includes demographics –Asks about use of other computing technologies –Results on all household members –Former student defined by college attendees in the age cohort that attended school between 1993 and 1997 (i.e. born 1971-79).

8. 8 Data Nielsen Panel of Canadian Households –5,519 individuals in 2000, but have information on occupation going from 1995 –Therefore accurately identify student status –Survey asks whether use the Internet and a variety of Internet applications, also includes demographics –Weakness is that this is a strange population

9. 9 Method Are former students (and those who live with them) more likely to adopt than others? –Straightforward probit model of adoption –Difference-in-difference identification –Controlling for observables (including income, education, occupation, industry, etc), are former students and those who live with them more likely to adopt the Internet? –Look at other computing applications to see if this is a result of being technology savvy, owning a computer, or something Internet-specific

10. 10 Results: Factors Driving Internet Use CENSUS DATACoefficientMarg. Effect Former Student0.0919**0.0352** Lives with Former Student0.133**0.0506** Born 1971-79-0.131**-0.0512** Age-0.0239**-0.00925** Postsecondary Graduate0.582**0.216** Current Student0.841**0.272** Also income, race, marital status, gender, citizen, metropolitan, homeowner, employment status, state, occupation, and industry. NIELSEN DATA Former Student0.235*0.0835* Also current student, income, age, education, location, gender, etc. **significant at 99% confidence; *significant at 95% confidence

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13. 13 So postsecondary educational institutions likely played a role in influencing Internet adoption. What kind of role did they play? 1.Who benefited? 2.What did universities do?

14. 14 Low Income Households and Non- Citizens Benefited Most ALLIncome <$40k Income >=60k Non- Citizens Former Student0.0352**0.0602**0.01350.0922* Lives with Former Student 0.0506**0.129**-0.002270.0595* Also current student, age, born 1971-79, education, gender, race, employment status, homeowner, metropolitan, marital status, citizen, income

15. 15 – Did they teach that the Internet is a library? higher skills at accessing online information –Or a shopping mall? trust –Or a communications medium? network effects, comfort with electronic communication, etc –Or do they merely reduce the overall barriers to adoption? Rogers’ (1995) trialability & complexity; Stoneman’s (2002) risk, uncertainty and learning-by-doing The Internet has no value to the user without complementary software applications Internet adoption will depend on overall adoption costs and application net benefits What did Universities Do?

16. 16 Nested Adoption Process –Choose to adopt the Internet, and choose which applications to adopt simultaneously. Adopt Internet Adopt E-commerceAdopt E-communicationAdopt E-Information X  in X  an

17. 17 Identification Simultaneously estimate adoption of the Internet and adoption of applications Therefore for each application –Estimate parameters of the relationship between former student status and adoption, controlling for overall adoption propensity and adoption propensity of other applications. Therefore, the methodology allows for student status to have a different effect on the adoption of different applications.

18. 18 Identification I interpret these different effects as follows: –If former student status affects the adoption of a particular application, then it is likely that universities played a role in increasing the net benefit of that application. –If former student status affects the adoption of the Internet in general, then it is likely that universities played a role in decreasing the costs associated with adoption (as the Internet brings no utility without applications)

19. 19 VariableOverallE-CommerceE-InformationE-Communication Former student^ -0.00529-0.000743-0.02620.250** Former student in HH^ 0.0431**0.135**0.119**0.167** Student 1.03**0.222**0.295**0.970** Born 1971-79 -0.336**0.548**0.588**0.476** Postsec. education 0.715**0.801**0.652**0.715** Age -0.0230**0.0158**0.00792**0.0250** HH inc. <US$20K # -0.518**-0.354**-0.246**-0.201** HH inc. US$20-60K # -0.0155-0.109**0.0145-0.156** HH inc. >US$60K # 0.364**0.402**0.270**0.303** Employed ## 0.600** Unemployed ## 0.552** Female -0.316 ** Metropolitan area -0.0801** US citizen 0.0430+ White 0.227** E-commerce -5.40** E-information 592.1** E-communication 879.0** Results: Nested Diffusion Regression

20. 20 Network Effects The results strongly suggest that universities created a network effect. Two results point in this direction: 1.The benefit of university attendance is driven by online communications 2.Students passed the knowledge to those living in the same household.

21. 21 Conclusion The effect of university education on Internet adoption is particularly strong for the cohort born 1971-79. This is not true of other computing technologies The effect appears to be transferred to other members of the household For the former students, this effect is driven by online communication. For those who live with them, it is driven by a reduction in the cost of adoption. Universities appear to have created a network externality.

22. 22 Discussion In addition to creating knowledge and educating the workforce, universities help new innovations diffuse. Microsoft Donations Digital divide implications

23. 23 Some Unanswered Questions Did educational institutions play a key role in diffusing other technologies as well as the Internet? Which technologies? How important was the diffusion of the Internet through educational institutions to economic growth in the 1990s? What are the most effective ways to diffuse technology through educational institutions?