The Effects of Home Computers on Educational Outcomes: Evidence from a Field Experiment with Community College Students

Transcription

1 The Effects of Home Computers on Educational Outcomes: Evidence from a Field Experiment with Community College Students Robert W. Fairlie University of California, Santa Cruz Rebecca A. London Stanford University

2 Background Nearly all instructional classrooms in U.S. public schools have computers with Internet access, with more than one instructional computer for every four schoolchildren. One-to-one laptop programs (Warschauer 2006, Silvernail and Gritter 2007) The federal government has also played an active role in reducing disparities in access to technology. Spending roughly $2 billion per year on the E-rate program, which provides discounts to schools and libraries for the costs of telecommunications services and equipment (Puma, et al. 2000, Universal Services Administration Company 2007). Schools themselves are currently spending more than $5 billion per year on technology (MDR 2004).

3 Background But, 45 million households in the United States, representing 38 percent of all households, do not use the Internet at home (NTIA 2008). Only 46 percent of the 50 million U.S. households with less than $50,000 in annual income have home Internet access Large disparities in home computer and Internet access by race, ethnicity, and income (Fairlie 2006, NTIA 2008, Goldfarb and Prince 2008, Ono and Zavodny 2007)

4 Home Internet Access by Income Level Current Population Survey (2007) 100% 80% 60% 40% 20% 0% Under $5000 5,000-9,999 10,000-14,999 15,000-19,999 20,000-24,999 25,000-34,999 35,000-49,999 50,000-74,999 75,000-99, , , ,000+

5 Home Internet Access by Race/Ethnicity Current Population Survey (2007) 100% 80% 74% 79% 60% 40% 50% 47% 44% 20% 0% White, non-latino African-American Latino Native American Asian

6 100% Home Internet Use (Ages 18+) by Race/Ethnicity Currrent Population Survey ( ) 80% 60% White non-latino 40% Black 20% Latino 0%

7 Introduction These disparities in access to home computers known as the Digital Divide may contribute to educational inequality. There is no clear theoretical prediction, however, regarding whether home computers are likely to have a negative or positive effect on educational outcomes. Increases and improves flexibility in access time to a computer for writing papers, conducting research, and making calculations for course assignments. Computers are used for games, networking, and communicating with friends potentially crowding out schoolwork time. Mixed evidence from the few studies that examine the relationship between home computers and educational outcomes (Attewell and Battle 1999, Schmitt and Wadsworth 2004, Fuchs and Woessmann 2004, Fairlie 2005, Beltran, Das and Fairlie 2008, and Malamud and Pop-Eleches 2008).

8 Goals of Study Most of the previous research may suffer from omitted variable bias -- specifically that the most educationally motivated families are the ones that purchase computers for their children. In this paper, we conduct the first-ever field experiment involving the provision of free computers to students. Participating students were randomly selected to receive free computers and were followed for two years. The random-assignment evaluation is conducted with 286 entering students receiving financial aid at a large community college in Northern California. Key Questions: Are home computers an important input in educational production? What are the potential mechanisms exerting positive and negative influences on educational outcomes?

9 Theory The educational production function commonly estimated in the literature models student performance as a function of student, family, teacher, and school inputs. The personal computer is an example of one of these inputs in the educational production process. The use of computers in U.S. schools is now universal and has been studied extensively, but the role of home computers as an input in educational production is not well understood. Potentially important, but unregulated use.

10 Positive Influences Personal computers make it easier to complete course assignments through the use of word processors, the Internet, spreadsheets, and other software (Lenhart, et al. 2001). Home access represents the highest quality access in terms of availability, flexibility and autonomy compared to schools and libraries (DiMaggio and Hargittai 2001). Almost all teenagers using home computers use these computers to complete school assignments and nearly three out of every four use them for word processing (Beltran, Das and Fairlie 2009). May also improve familiarity with software increasing the effectiveness of computer use for completing school assignments and the returns to computer use at school (Underwood, et al. 1994, Mitchell Institute 2004). Enhanced computer skills from owning a personal computer may also alter the economic returns to education, especially in fields in which computers are used extensively. The social distractions of using a computer in a crowded computer lab on campus may be avoided by using a computer at home.

11 Negative Influences Home computers are often used for games, networking, downloading music and videos, communicating with friends, and other forms of entertainment potentially displacing time for schoolwork. Nearly three-quarters of home computer users use their computers for games (Beltran, Das and Fairlie 2009). Social networking sites such as Facebook and Myspace and other entertainment sites such as Youtube and itunes have grown rapidly in recent years.

12 Number of Facebook Users 160,000, ,000, ,000, ,000,000 80,000,000 60,000,000 40,000,000 20,000,

13 Negative Influences Computers are also often criticized for displacing other more active and effective forms of learning and by emphasizing presentation (e.g. graphics) over content (Giacquinta, et al. 1993, Stoll 1995 and Fuchs and Woessmann 2004). Computers and the Internet also facilitate cheating and plagiarism and make it easier to find information from non-credible sources (Rainie and Hitlin 2005). There is no clear theoretical prediction on the sign or magnitude of the effects of home computers on educational achievement. Empirical question

14 Identification Strategies used in Previous Studies Regress educational outcomes on the presence of a home computer controlling for detailed student, family and parental characteristics. Large positive effects, but also evidence of negative effects (Attewell and Battle 1999, Schmitt and Wadsworth 2006, Beltran, Das and Fairlie 2008, Fuchs and Woessmann 2004) If the most educationally motivated families are the ones that are the most likely to purchase computers, then potential positive selection bias. Instrumental variable techniques and individual-student fixed effects Bivariate probit models (Fairlie 2005 and Beltran, Das and Fairlie 2008) Falsification tests: future computer ownership is not found to have a positive relationship with educational outcomes (Schmidt and Wadsworth 2006 and Beltran, Das and Fairlie 2008)

15 Identification Strategies used in Previous Studies Natural experiment: created by a government program that allocated a fixed number of vouchers for computers to low-income children in Romanian public schools. (Malamud and Pop-Eleches 2008) Discontinuity created by the allocation of computer vouchers by a ranking of family income indicate that Romanian children winning vouchers have lower grades and educational aspirations. First random-assignment field experiment providing free computers to students. The only previous study that randomly assigned free computers to individuals is Servon and Kaestner (2008). Through a program with a major bank, computers with Internet service were randomly assigned to low- and moderate-income families to determine how they affect the use of financial services. No previous research explores the impact of home computers on the educational outcomes of college students.

16 The Field Experiment We randomly assigned free computers to entering community college students receiving financial aid in Fall Butte College is located in Northern California and has a total enrollment of over 15,000 students. The computers were provided to us by Computers for Classrooms, Inc. a computer refurbisher located in Chico, California. The program was advertised to all students receiving financial aid (1,042 students). A total of 286 students participated in the study, with 141 selected for the treatment group (received a free home computer) and 145 for the control group (did not receive a free home computer). Participation in the program involved returning a baseline questionnaire and consent form releasing future academic records from the college for the study.

17 The Field Experiment Winners were notified by mail and instructed to pick up their computers at the Computers for Classrooms warehouse. More than 90 percent of winning students picked up their free computers near the end of November All correspondence with students was conducted through the office of financial aid. We conducted a follow-up survey of study participants (treatment and control) in late Spring/Summer 2008 with a response rate of 65 percent. Butte College provided us with detailed administrative data on all students in July 2008.

18 Table 1 Application Information for Study Participants, Financial Aid Students, and All Students Study Participants All Financial Aid Students All Students Gender Female Male Missing Race/Ethnicity White Asian and Pacific Islander African-American Latino Native American Other Unknown English language English Not English Unknown/Uncollected Sample size 286 1,042 6,681 Note: Based on administrative data provided by Butte College for entering students in Fall 2006.

19 Table 2 Background Characteristics of Study Participants All Study Treatment: Computer Participants Eligible Ineligible Difference Female 63.3% 64.5% 62.1% Latino 17.8% 15.6% 20.0% Other Minority 18.2% 21.3% 15.2% Age Parent some college 37.8% 41.8% 33.8% Parent college graduate 22.0% 18.4% 25.5% High school grades As and Bs 30.4% 32.6% 28.3% High school grades Bs and Cs 56.6% 55.3% 57.9% Live with own children 27.3% 27.7% 26.9% Live with parents 34.6% 31.2% 37.9% Household income: $10,000-19, % 30.5% 32.4% Household income: $20,000-39, % 27.7% 24.1% Household income: $40,000 or more 16.8% 14.9% 18.6% Takes most classes at Chico Center 25.2% 25.5% 24.8% Takes most classes at Glen/Other 8.4% 7.8% 9.0% Has job 55.0% 52.2% 57.6% Sample size Note: Based on baseline survey administered to all study participants. Control: Computer P-Value for Treatment/ Control

20 Table 3 Computer Use among Study Participants Treatment-Control Treatment Group Control Group Difference Std. Err. Total number of hours of computer use per week Times of day for computer use to complete school assignments Early morning (6:00AM-8:00AM) 18.3% 19.5% -1.3% 5.9% Daytime (8:00AM-5:00PM) 55.9% 49.4% 6.5% 7.5% Early evening (5:00PM-10:00PM) 61.3% 62.1% -0.8% 7.3% Late evening (10:00PM-12:00AM) 37.6% 29.9% 7.7% 7.1% Nighttime (12:00AM-6:00AM) 14.0% 6.9% 7.1% 4.6% Sample size Note: Based on follow-up survey conducted in spring 2008.

21 Figure 1 Grade Distribution for Study Participants 40.0% 36.6% 36.1% 35.0% 30.0% 25.0% 23.9% 21.7% 20.0% 15.0% 15.9% 17.4% Control Group Treatment Group 11.7% 11.3% 10.0% 5.0% 5.7% 5.8% 7.0% 4.6% 0.0% A B C D F CR NC 1.9% 0.4%

22 Table 4 Educational Outcomes of Study Participants All Study Treatment Control Treatment-Control Participants Group Group Difference Std. Err. Take Course for Grade Rate 93.0% 95.1% 91.1% 4.0% 1.2% GPA Credit vs. No Credit 83.3% 92.9% 78.6% 14.3% 7.0% Course Success Rate 81.6% 82.6% 80.7% 1.9% 1.8% Course Completion Rate 74.9% 75.9% 74.0% 1.9% 2.0% Note: Based on administrative data provided by Butte College for study participants.

23 Regression Specification (4.1) y it = α + βx i + δt i + λ t + λ d + u i + ε it, where y it is the course outcome, X i includes baseline characteristics, T i is treatment indicator, λ t are quarter fixed effects, and λ d are department fixed effects. δ captures the treatment versus control effect.

24 Table 6 Course Success Rate Regressions OLS (Intent-to-Treat) (1) (2) (3) (4) (5) Treatment (0.0301) (0.0291) (0.0286) (0.0286) (0.0286) Main controls No Yes Yes Yes Yes Quarter and course department fixed effects No No Yes Yes Yes Campus and job activity No No No Yes Yes Assessments and English language (administrative data) No No No No Yes Mean of dependent variable Sample Size 1,792 1,792 1,792 1,762 1,762 Notes: (1) The dependent variable is whether the grade was a C, CR or better. (2) Standard errors are adjusted for multiple courses taken by study participants. (3) Main controls include gender, race/ethnicity, age, parents' highest education level, high school grades, presence of own children, live with parents, and family income. (4) Assessments include math, English and reading. (5) The dependent variable in the first-stage regression in the IV model is obtaining a new computer. See text for more details on lower and upper bounds.

25 Treatment-on-the-Treated Specification First-stage regression for the probability of computer receipt is: (4.2) C i = α 1 + β 1 X i + πt i + u i + ε it. The second-stage regression is: (4.3) y it = α 2 + β 2 X i + ΔĈ i + u i + ε it, where Ĉ i is the predicted value of computer ownership from (4.2). Δ provides an estimate of the "treatment-on-the-treated" effect.

26 Table 6 Course Success Rate Regressions OLS Estimates IV Estimates (1) (2) (3) (4) (5) (6) Treatment (0.0301) (0.0291) (0.0286) (0.0286) (0.0286) (0.0312) Main controls No Yes Yes Yes Yes Yes Quarter and course department fixed effects No No Yes Yes Yes Yes Campus and job activity No No No Yes Yes Yes Assessments and English language (administrative data) No No No No Yes Yes Mean of dependent variable Sample Size 1,792 1,792 1,792 1,762 1,762 1,762 Notes: (1) The dependent variable is whether the grade was a C, CR or better. (2) Robust standard errors are reported and adjusted for multiple courses taken by study participants. (3) Main controls include gender, race/ethnicity, age, parents' highest education level, high school grades, presence of own children, live with parents, and family income. (4) Assessments include math, English and reading. (5) The dependent variable in the first-stage regression in the IV model is obtaining a new computer. The lower (upper) bound estimate assumes that all control group non-compliers obtained computers at the end (beginning) of the survey period.

27 Table 6 Course Success Rate Regressions OLS Estimates IV Estimates Lower Bound Upper Bound (1) (2) (3) (4) (5) (6) (7) Treatment (0.0301) (0.0291) (0.0286) (0.0286) (0.0286) (0.0312) (0.0389) Main controls No Yes Yes Yes Yes Yes Yes Quarter and course department fixed effects No No Yes Yes Yes Yes Yes Campus and job activity No No No Yes Yes Yes Yes Assessments and English language (administrative data) No No No No Yes Yes Yes Mean of dependent variable Sample Size 1,792 1,792 1,792 1,762 1,762 1,762 1,762 Notes: (1) The dependent variable is whether the grade was a C, CR or better. (2) Robust standard errors are reported and adjusted for multiple courses taken by study participants. (3) Main controls include gender, race/ethnicity, age, parents' highest education level, high school grades, presence of own children, live with parents, and family income. (4) Assessments include math, English and reading. (5) The dependent variable in the first-stage regression in the IV model is obtaining a new computer. The lower (upper) bound estimate assumes that all control group non-compliers obtained computers at the end (beginning) of the survey period.

28 Table 7 Regression Results for Outcomes Using Full Sample Course Success Rate Course Success Rate Graduation Rate (1) (2) (3) Treatment (0.0283) (0.0196) (0.0277) Main controls (administrative data) Yes Yes Yes Quarter and course department fixed effects Yes No No Assessments and English language (administrative data) Yes Yes No Course Fixed Effects No Yes Mean of dependent variable Sample Size 24,460 24,460 6,939 Notes: (1) The dependent variable is whether the grade was a C, CR or better relative to all grades including withdrawals in Specifications 1 and 2. (2) Standard errors are adjusted for multiple courses taken by study participants. (3) Main controls include gender and race/ethnicity from administrative data. (4) Assessments include math, English and reading.

29 Table 8 Regression Results for Various Course Outcomes Completition Rate Take for Grade Rate GPA Credit vs. No Credit (1) (2) (3) (4) Treatment (0.0317) (0.0135) (0.1135) (0.0730) Main controls Yes Yes Yes Yes Quarter and course department fixed effects Yes Yes Yes No Campus and job activity Yes Yes Yes No Assessments and English language (administrative data) Yes Yes Yes No Mean of dependent variable Sample Size 1,919 1,762 1, Notes: (1) The dependent variable is whether the grade was a C, CR or better relative to all grades including withdrawals in Specification 1. (2) Standard errors are adjusted for multiple courses taken by study participants. (3) Main controls include gender, race/ethnicity, age, parents' highest education level, high school grades, presence of own children, live with parents, and family income. (4) Assessments include math, English and reading.

30 Figure 4 Graduation Rates for Study Participants 25.0% 20.0% 18.4% 15.9% 15.0% Control Group Treatment Group 10.0% 5.0% 0.0% Graduation Rate

31 Table 9 Regression Results for the Graduation Rate OLS Estimates IV Estimates Lower Bound Upper Bound (1) (2) (3) (4) (5) (6) Treatment (0.0447) (0.0437) (0.0444) (0.0450) (0.0498) (0.0642) Main controls No Yes Yes Yes Yes Yes Campus and job activity No No Yes Yes Yes Yes Assessments and English language (administrative data) No No No Yes Yes Yes Mean of dependent variable Sample Size Notes: (1) The dependent variable is whether the student received an associates degree, vocational degree or vocational certificate. (2) Robust standard errors are reported. (3) Main controls include gender, race/ethnicity, age, parents' highest education level, high school grades, presence of own children, live with parents, and family income. (4) Assessments include math, English and reading. (5) The dependent variable in the first-stage regression in the IV model is obtaining a new computer. The lower (upper) bound estimate assumes that all control group non-compliers obtained computers at the end (beginning) of the survey period.

32 Value of Computer Assume discount rate of 5 percent, tax rate of 35 percent, and average earnings of $31,000 (U.S. Census 2008) Return to obtaining an associate's degree of roughly 8 percent or $2,500 per year relative to not obtaining one (Kane and Rouse 1999) implies gain in the present value of lifetime earnings of $28,000. The average LATE estimate on the graduation rate of implies that the computer is worth $585 in present value of lifetime earnings. If the value of the computers is $500 then there may be some underinvestment of personal computers although it does not appear to be large.

33 Table 10 Regression Results for Course Success and Graduation Rate by Gender, Race and Income Course Success Rate Course Success Rate Course Success Rate Graduation Rate Graduation Rate Graduation Rate (1) (2) (3) (4) (5) (6) Treatment*female (0.0346) (0.0561) Treatment*male (0.0531) (0.0748) Treatment*minority (0.0507) (0.0752) Treatment*nonminority (0.0345) (0.0563) Treatment*high income (0.0418) (0.0686) Treatment*low income (0.0417) (0.0594) Mean of dependent variable Sample Size 1, , , Notes: (1) The dependent variable in Specifcations 1-2 is whether the grade was a C, CR or better, and the depedent variable in Specifications 3-4 is whether the student received an associates degree, vocational degree or vocational certificate. (2) Standard errors are adjusted for multiple courses taken by study participants. (3) Controls include gender, race/ethnicity, age, parents' highest education level, high school grades, presence of own children, live with parents, and family income.

34 Table 11 Computer Use among Study Participants Treatment Group Treatment - Control Control Group Difference Std. Err. Total number of hours of computer use per week Hours of computer use for completing school assignments Hours of computer use for games, networking, and other entertainment Hours of computer use for and other activities Self rating of computer skills Excellent 19.6% 17.0% 2.5% 5.7% Very good 44.3% 33.0% 11.4% 7.2% Good 27.8% 36.4% -8.5% 6.9% Satisfactory 8.2% 13.6% -5.4% 4.6% Sample size Notes: (1) Estimates are from follow-up survey conducted in spring and summer (2) Games, networking and other entertainment include games, chat rooms, videos (YouTube), networking (Facebook, MySpace), music and entertainment.

35 Table 12 Regression Results for Course Success and Graduation Rate by Initial Distance to Campus Course Success Rate Course Success Rate Graduation Rate Graduation Rate (1) (2) (3) (4) Treatment (0.0541) (0.0792) Treatment*distance to campus (0.0023) (0.0039) Treatment*far from campus (0.0337) (0.0568) Treatment*close to campus (0.0511) (0.0703) Mean of dependent variable Sample Size 1, ,

36 Table 13 Regression Results for Course Success and Graduation Rate for Non- Game/Entertainment Users Course Success Rate Course Success Rate Graduation Graduation Rate Rate (1) (2) (3) (4) Treatment (0.0435) (0.0418) (0.0887) (0.0927) Treatment*infrequent use of computer for games/entertainment (0.0622) (0.0624) (0.1241) (0.1294) Main controls No Yes No Yes Mean of dependent variable Sample Size 1,380 1,

37 Non-Experimental Estimates Previous non-experimental studies generally indicate large positive effects of home computers on educational outcomes, but difficult to compare to these modest-sized effects. Estimate regressions for community college graduation using the latest Computer and Internet Supplement from the Current Population Survey (October 2007) Create longitudinal data by linking the October 2007 CPS to the October 2008 CPS. Households in the CPS are interviewed each month over a 4- month period. Eight months later they are re-interviewed in each month of a second 4-month period. Create two-year panel data. Computer ownership in October 2007 and graduation from two-year college in October 2008.

38 Table 14 Non-Experimental Regression Results for the Graduation Rate Matched Current Population Surveys, October (1) (2) (3) (4) Home Computer with Internet (0.0506) (0.0544) (0.0560) (0.0565) Age controls Yes Yes Yes Yes Gender, race, nativity, region and urbanicity controls No Yes Yes Yes Income and home ownership controls No No Yes Yes Mean of dependent variable Sample Size Notes: (1) The sample includes individuals enrolled in 2-year colleges in the first-survey year in the matched CPS data. (2) The dependent variable is whether the student received an associates degree in the second survey year. (3) Robust standard errors are reported. (4) The sample used in Specification 4 excludes students living in households with $75,000 or more in income.

39 Summary The randomly selected group of students receiving free computers has modestly better educational outcomes than the control group. Although most estimates lacked sufficient precision, the evidence of positive effects is remarkably consistent across different measures, samples, and included controls. Minority and low-income students may benefit more The estimated effects are smaller than those from using non-experimental techniques. Home computers may improve flexibility of use and computer skills, but also increase entertainment-based uses of computers dampening positive impacts on educational outcomes.

40 Policy Conclusions Address the financial, informational and technical constraints limiting the optimal level of investment in personal computers among disadvantaged students Tax breaks, special loans or IDAs for educational computer purchases Expansion of computer refurbishing programs that provide low-cost machines Expand the relatively new programs that allow students to check out school laptop computers for home use Addressing the remaining digital divide in home access is likely to become even more important over time as schools, professors and financial aid sources are increasingly using technology to provide information, communicate with students, and provide course content.