Educational policy analysis archives

Educational policy analysis archives

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Educational policy analysis archives
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Educational policy analysis archives.
n Vol. 12, no. 30 (June 29, 2004).
Tempe, Ariz. :
b Arizona State University ;
Tampa, Fla. :
University of South Florida.
c June 29, 2004
Modeling school choice : a comparison of public, private-independent, private-religious and home-schooled students / Clive R. Belfield.
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University of South Florida.
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1 of 18 A peer-reviewed scholarly journal Editor: Gene V Glass College of Education Arizona State University Copyright is retained by the first or sole author, who grants right of first publication to the EDUCATION POLICY ANALYSIS ARCHIVES EPAA is a project of the Education Policy Studies Laboratory. Articles published in EPAA are indexed in the Directory of Open Access Journals. Volume 12 Number 30June 29, 2004ISSN 1068-2341Modeling School Choice: A Comparison of Public, Private-Independent, Privat e-Religious And Home-Schooled Students Clive R. Belfield National Center for the Study of Privatization in E ducation Teachers College, Columbia UniversityCitation: Belfield, C., (2004, June 29). Modeling s chool choice: A comparison of public, private-indep endent, private-religious and home-schooled students. Education Policy Analysis Archives, 12 (30). Retrieved [Date] from students now have four choices of schooling: p ublic schooling, private–religious schooling, private–ind ependent schooling, and home-schooling. Of these, home-schoo ling is the most novel: since legalization across the states in the last few decades, it has grown in importance and legitimacy as an alternative choice. Thus, it is now possible to inv estigate the motivation for home-schooling, relative to the othe r schooling options. Here, we use two recent large-scale datase ts to assess the school enrollment decision: the first is the Na tional Household Expenditure Survey (1999), and the second is microdata on SAT test-takers in 2001. We find that, generally, famil ies with home-schoolers have similar characteristics to thos e with children at other types of school, but mother’s characterist ics – specifically, her employment status – have a strong influence on the decision to home-school. Plausibly, religious b elief has an important influence on the schooling decision, not only for Catholic students, but also those of other faiths.Introduction


2 of 18 A sense of disaffection with public schooling – bot h its effectiveness and efficiency – has been emphatically catalogued by academic economists (Hanushek, 1998; Hoxby, 2000; Friedman, 1993). The general population is somewhat more ambivalent (Moe, 2001), and indeed the proportions of students in private schoo ls have remained stable over recent decades (Kenny and Schmidt, 1994). However, private schooling – either religious or sectarian – is not the only outlet for those dissat isfied with public schooling: home-schooling is now a viable option.The recent growth and development of home-schooling has been described in detail by numerous authors (Lines, 2000; Welner and Welner, 1 999; Hammons, 2001; Somerville, 2000; Stevens, 2001; Bauman, 2002). These authors e mphasize the legal and civic aspects of home-schooling, but there has been little quanti tative assessment or economic treatment. This is surprising: home-schooling represents an ex treme form of education privatization, affecting the expenditure patterns, time allocation and labor force participation of the families involved. Furthermore, home-schooling exte nds the school choice decision to four alternatives.Here, we report the determinants of school choice d ecisions by US families, contrasting each schooling option. Such school choices are easily ex pressed using economic calculus. For example, home-schooling may be more effective than public schools, and possibly less costly (if there are either high transport costs or additional expenditures mandated by schools, e.g. uniforms, learning materials). Simila rly, home-schooling may be more effective than private schools (if these are ‘elitist’, and a ppear hostile to outsiders), and possibly less costly (with no direct tuition fees). More generall y, home-schooling may meet the needs of families with particular educational preferences th at are not catered for by available institutions (typically for morality-based schoolin g, James, 1987). In this case the appropriate comparison is between home-schooling and religious schools. Although precise numbers are hard to obtain, NCES ( 2001) estimates – based on weighted interpretation of the NHES99 – indicate approximate ly 850,000 home-schoolers aged from 5 to 17 (1.7% of all US students). And, the number of home-schooled children in the US is growing (Lines, 2000): using the CPS, NHES96, and N HES99, Bauman (2002) charts the number of home-schoolers at: 356,000 in 1994, 636,0 00 in 1996, and 791,000 by 1999. This figure is still small compared to the 5.1 million s tudents in private schools, but home-schooling has only been legal since the 1970s. Moreover, home-schooling might be a possibility for all families during at least some p art of childrearing, with potentially important ramifications. For parents, allocations of time wit hin the household may be changed and labor market supply reduced; consumption of educati onal materials will be affected, as will consumption of public goods and housing (via attenu ated Tiebout effects). For children, academic achievement may be affected, insofar as pa rents differ as adequate substitutes for trained teachers. Also likely to be affected are ch ildren’s welfare; their social skills; and their labor market participation (if home-schoolers are s creened differently by employers). The motivation to home-school therefore merits investig ation. Our inquiry is structured as follows: In the next s ection, we model the school choice decision across school types. Following that we describe the datasets available to us. Next we report the empirical evidence on the determinants of the s chool choice decision. We conclude by referring back to the relevance of home-schooling w ithin the current system of US schooling.The Economics of School ChoicePrior research on school choice in the US has mainl y focused on binary options: students decide to exit public schools for the single altern ative, typically held to be Catholic schooling (but see Figlio and Stone, 1999). However, this sty lization elides religious and non-religious


3 of 18 schooling, even though these are unlikely to be clo se substitutes. It is also out-of-date, given: changes in the teaching staff and student compositi on in religious schools (on the evolution of Catholic schooling, see Sander, 2001; Grogger an d Neal, 2000); the growth of other types of private school; and greater choice in the public sector (e.g. charter schooling). Instead, the school enrollment decision is best articulated as a four-way choice: public schooling, private–religious schooling, private–independent sc hooling, and home-schooling. A straightforward way to infer school choice motiva tion is to compare tabulations of characteristics by school type. For home-schooling, aggregate comparisons show families that are: more likely to be white and non-Hispanic; have income levels comparable to the national average (but with a more leptokurtic distr ibution); and have parents who were more highly educated than the average for the US. It is not necessarily the case that families who decide to home-school possess highly idiosyncratic attributes (see Bauman, 2002; NHES, 2001). However, we are interested in the more gener al question as to what motivates the decision to choose a particular school type.In deciding between the schooling options, househol ds can be assumed to maximize utility. Neal (1997) specifies a utility function for househ old i where: (1) Ui = U(Yi, ECi, Mi) In (1), Y denotes the educational outcomes from sch ooling; EC denotes the unobserved consumption goods from schooling (e.g. religiosity, dutifulness to parents); and M denotes a composite commodity with a price normalized to one. We generalize Neal’s (1997) choice model to include the j =4 different types of schooling where p is public schooling, d is private–independent schooling, r is private–religious schooling, and h is home-schooling. Educational outcomes are therefore determined acros s each of the choices as: (2) Yip = Xip + i(3) Yid = Xid + id + d + i(4) Yir = Xir + ir + r + i(5) Yih = Xih + ih + h + iIn (2)–(5), X denotes a vector of input and control variables. T he ij parameters identify the match between household i and the selected school type; it is assumed that E ( ij| Xi)=0. The j parameters represent the mean outcome effect for s chool type j relative to public schooling. The i term is a household effect (error term) and again by assumption E( i| Xi)=0. Such modeling is necessary to estimate the treatmen t effects across school types, as well as exogenous instruments that may serve to identify th e school choice match (see Evans and Schwab, 1995). However, our inquiry is restricted, first, to specifying the variables to be included in X and then, second, to giving some indication of th e match between household types and school types ( ij). Such inquiry may therefore guide the search for appropriate instruments for the school choice decision (see the discussion in Card, 1999). We therefore estimate a multinomial logit model, wh ere school choice is a function of the characteristics of the household, the child, the mo ther/father, and the local community: (6) Pr(Choice j=1..4) = f( Household Child Mother/father Community ) Our aim is to identify the statistical and substant ive characteristics that motivate the choice of one school type over another. Variables capturing t he child’s characteristics may indicate which children (in terms of ability, gender, and ma turity) favor particular school types.


4 of 18 Parental variables may capture not only intergenera tional transfers of educational attributes, but also parental capacity for home-schooling. Of p articular interest are the household and community characteristics that influence the school choice decision, and their relative importance across each school type. The household v ariables capture the resources available within the home for educational purposes, as well as social differences across students (see Lareau, 2000). The community variable s are likely to capture the local public resources available for schooling, and the importan ce of neighborhood in schooling decisions. This estimation therefore yields several policy-useful questions. For example, how important are household compositions (such as two-p arent families) compared to the education level of the parents? Also, do families w ith special learning needs seek home-schooling as an alternative to public schools, rather than private schools? What school types do students of religions other than Catholici sm choose? Using two similar datasets, we are able to estimate equation (6) to answer such qu estions, as well as triangulate the results.DataTwo recent datasets are available to estimate equat ion (6). These are the National Household Expenditure Survey (NHES99) and micro-dat a from SAT test-participants in 2001 (ETS01). NHES99 is a random-digit dialing telephone survey, with a nationally representative sample of all civilian, non-institutionalized US persons. Screening interviews were administered to 57,278 households (74% response rate), and then par ental interviews were conducted, where children were found to be in the household (8 8% response rate post-screening). The relevant sample of parent respondents is 17,640. To compensate for bias (arising from lack of telephone, non-response, or ethnicity), weights are applied to the data. Whereas public and private school distinctions are relatively stra ightforward, the identification of home-schooling is less clear. Here, home-schooling is identified using the NCES (2001) definition, which is derived from questions: ‘Is ch ild being schooled at home?’; ‘Is child getting all of his/her instruction at home?’ and ‘H ow many hours each week does child usually go to school for instruction?’; and ‘What a re the main reasons you decided to school child at home?’ So, home-schooling is identified wh ere the child is being schooled at home; where any public schooling did not exceed 25 hours per week; and where the child is not being schooled at home for temporary reasons of hea lth. This definition yields 270 (1.5%) students who are home-schooled (unweighted number). The rest of the sample is: 1,530 (8.7%) students who attend religious school; 560 (3 .2%) who attend a private–independent school; and 15,280 (86.6%) who attend public school s. ETS01 is the population of individuals who took the SAT college-entry examination in 2001. Before taking the SAT, each individual is required to complete a background questionnaire which requests information about the household, the individual, and the family. This information is similar to, but not exactly the same as, the information collected in NHES99. One advantage of the ETS01 data, for instance, is t hat individuals report their religion. For the characteristics of the community, county-level data are merged into the core dataset through the individual’s school location. The count y-level variable is the proportion of children aged 5 to 17 who are defined as ‘poor’ in the Censu s, taken from the US Census Small Area Income and Poverty Estimates 1998, State and County 1998 ( Importantly, each test-participant declares their t ype of schooling; and in 2001 the questionnaire included the option ‘home-schooling’. Based on the self-reported school types, the sample includes: 4,653 (0.01%) students who are home-schooled; 109,135 (11.3%) students who attend religious school; 32,469 (3.4%) who attend a private–independent school; and 822,967 (84.9%) who attend public schoo ls. Both datasets are recent, large-scale, and include an array of similar variables; they are also sufficiently up-to-date to include a home-schooling indicator (although we recognize that


5 of 18 home-schoolers may be relatively disinclined to com plete government surveys). The important difference is that the ETS01 data refer e ssentially to only one cohort of students, aged between 14 and 18 in 2001, who are attempting to gain entry to college. Given the relative novelty of home-schooling, those who appea r in ETS01 are the ‘first-movers’ into home-schooling. Moreover, school choice and desire to gain entry to college may be endogenously determined. Notwithstanding, the fact that the ETS01 test-participants are all of similar ability, ages and motivations, may serve as a control for unobservable characteristics motivating the school choice decisi on. Thus, the school choice decision can be interpreted more specifically using the ETS01 da ta: given a student who wishes to go to college, what factors motivate the choice of school type?Estimation of School ChoiceThe multinomial logit estimates for equation (6) ar e given in Tables 1 and 2, using NHES99 and ETS01. The reported coefficients are marginal e ffects, i.e. differentiation of the dependent variable with respect to the independent variable (or transformation of a dummy variable from zero to one). Frequencies for each of the independent variables are given in Appendix Table A1. Table 1 Determinants of the Decision to Home-School or to E nroll at Religious or Non-Religious Private School versus Public Schoolin g: NHES Data (Multinomial Logit Estimation Marginal Effects) Public School Home School Private Religious School Private Independent School Marginal Coeff. (SE)Marginal Coeff. (SE)Marginal Coeff. (SE)Marginal Coeff. (SE) Household characteristics:Owns home -0.0228(0.0063)***0.0000(0.0018)0.0240(0.0018)***-0 .0011(0.0018) Ln (Family Income) -0.0364(0.0046)***-0.0006(0.0009)0.0284(0.0009)***0 .0085(0.0009)*** Adultsa: 2 (both parents) 0.0134(0.0081)*0.0039(0.0026)-0.0123(0.0026)***-0.0 050(0.0026)* Adultsa: 2 (one parent) 0.0108(0.0103)0.0045(0.0057)-0.0111(0.0057)*-0.0042 (0.0057) Adultsa: 3 or more 0.0149(0.0084)*0.0049(0.0038)-0.0156(0.0038)***-0.0 041(0.0038) Siblings for child -0.0020(0.0026)0.0029(0.0006)***0.0009(0.0006)-0.00 18(0.0006)*** Student characteristics:


6 of 18 Male 0.0033(0.0047)0.0001(0.0013)-0.0041(0.0013)***0.000 7(0.0013) Ethnicityb: African Amer. 0.0496(0.0060)***-0.0028(0.0022)-0.0330(0.0022)***0.0139(0.0022)*** Ethnicityb: Asian 0.0128(0.0122)-0.0002(0.0052)-0.0113(0.0052)**-0.00 13(0.0052) Ethnicityb: Hispanic 0.0489(0.0062)***-0.0038(0.0020)*-0.0289(0.0020)*** -0.0162(0.0020)*** Born outside US 0.0195(0.0112)*-0.0031(0.0028)-0.0158(0.0028)***-0. 0006(0.0028) Agec: 10 to 12 years 0.0108(0.0058)*0.0004(0.0019)-0.0062(0.0019)***-0.0 050(0.0019)*** Agec: 13 to 18 years 0.0315(0.0054)***0.0015(0.0017)-0.0262(0.0017)***-0 .0068(0.0017)*** Special Learning Needs 0.0123(0.0058)**-0.0004(0.0015)-0.0145(0.0015)***0. 0027(0.0015)* Mothers' characteristics:Educ.d: High School -0.0257(0.0137)*0.0241(0.0081)***0.0046(0.0081)-0.0 031(0.0081) Educ.d: Some College -0.0554(0.0177)***0.0370(0.0136)***0.0169(0.0136)0. 0015(0.0136) Educ.d: (Higher) Degree -0.1131(0.0213)***0.0473(0.0170)***0.0420(0.0170)** 0.0239(0.0170) Mother: Employed 0.0409(0.0064)***-0.0161(0.0027)***-0.0121(0.0027)* **-0.0128(0.0027)*** Community characteristics:ZIP poverty linee: >10% -0.0086(0.0070)0.0004(0.0017)0.0069(0.0017)***0.001 3(0.0017) ZIP Hisp-Blackf: 0–15% 0.0487(0.0091)***0.0037(0.0028)-0.0320(0.0028)***-0 .0204(0.0028)*** ZIP Hisp-Blackf: 0.0126(0.0077)0.0077(0.0040)*-0.0150(0.0040)***-0.0 053(0.0040)


7 of 18 16–40%Regiong: North East -0.0534(0.0106)***-0.0041(0.0019)**0.0450(0.0019)** *0.0125(0.0019)*** Regiong: South -0.0315(0.0075)***0.0011(0.0018)0.0267(0.0018)***0. 0037(0.0018)** Regiong: Midwest -0.0473(0.0104)***-0.0030(0.0017)*0.0565(0.0017)*** -0.0061(0.0017)*** Areah: Urban -0.0651(0.0063)***-0.0020(0.0016)0.0536(0.0016)***0 .0135(0.0016)*** Areah: Suburban -0.0346(0.0128)***-0.0006(0.0019)0.0338(0.0019)***0 .0014(0.0019) PredictedProb. 0.90900.00860.06230.0200 Pseudo R Squared 0.0992 Log Likelihood 7338.80 Wald Chi-square (75) 1071.57 N 17,640Notes: Parent Sample, National Household Education Survey (NHES, 1999). Robust standard errors in parentheses. aDefault adults: 1 parent only. bDefault ethnicity: white. cDefault age: 5–9 years. dDefault education level: less than High school. eDefault ZIP poverty line: >19%. fDefault ZIP Hisp-Black: >40%. gDefault region: west. hDefault area: rural. ***significance at 0.01 level; **significanc e at 0.05 level; *significance at 0.10 level. The results for both the NHES99 and ETS01 are plaus ible and suggestive (and these results correspond broadly with those of Figlio and Stone, 1999). For household characteristics, common to both equations is a measure of wealth, ei ther the log of family income, or dummy variables for home-ownership, a high-income family, or the expectation of financial aid at college. The results across the two surveys are con sistent: family financial resources are strongly positively correlated with private schooli ng as opposed to public schooling, and home schooling is adopted inversely with family res ource levels. Interestingly, these financing variables show the same magnitude of effe ct for both independent and religious private schooling.The NHES99 includes further details about the house hold: larger numbers of adults in the household are negatively associated with religious schooling, being associated with a shift toward public schooling. However, more children in the household are associated essentially with a switch between private–independent and homeschooling. Student characteristics play a strong role in influ encing the school choice decision. However, the results are discrepant in some cases. So, the N HES99 shows male children are less likely to attend private-religious school, whereas the ETS01 estimation indicates the


8 of 18 opposite. For ethnicity, the results are more in ac cord: both African American and Latino students are more likely to attend public school, a nd least likely to attend private religious school; Asian students are spread more evenly acros s the options, although they too are least likely to attend private religious school. Si milarly, private-religious schools are least likely to enroll US (immigrant) citizens. The age v ariable in the NHES99 survey shows that private schools – particularly religious ones – pri marily serve younger students; home-schooling appears to be prevalent across all a ges. Of special interest in the debate about choice is the disability of the child: oppone nts of choice have argued that private schools will subtly dissuade children with addition al learning needs from enrollment (see the discussion in Howell and Peterson, 2002). For the p rivate independent schools, there is no evidence of such dissuasion: students with disabili ties or special learning needs are more likely to be in these schools. Again, home-schoolin g appears as a neutral option, whereas (according to NHES99, but not ETS01) private-religi ous schools do enroll fewer disabled students. Finally, the ETS01 data includes informat ion on religious status. This variable has a strong effect: students who profess any religion are more likely to be in private-religious schooling, but less likely to be in private-indepen dent schooling. The results for home-schooling are mixed: those following the Catho lic faith are less likely to be home-schooled, but other religions do dispose the f amily toward this choice. Overall, however, the marginal coefficients for religion as a determinant of school choice is between 2 and 10 times that of any other factors. Table 2 Determinants of the Decision to Home-School or to E nroll at Religious or Non-Religious Private School versus Public Schoolin g: ETS01 (Multinomial Logit Estimation Marginal Effects) Public School Home School Private Religious School Private Independent School MarginalCoeff. (SE)Marginal Coeff. (SE)Marginal Coeff. (SE)Marginal Coeff. (SE) Household characteristics:Familyincome > $100,000 -0.0347(0.0026)***-0.0020(0.0003)***0.0252(0.0023)* **0.0116(0.0012)*** Financialaida0.0349(0.0024)***0.0001(0.0003)-0.0134(0.0021)***-0 .0215(0.0013)*** Student characteristics:Male-0.0130(0.0020)***0.0005(0.0003)*0.0105(0.0018) ***0.0020(0.0009)** Ethnicityb: African Amer. 0.0205(0.0034)***-0.0027(0.0003)***-0.0097(0.0032)* **-0.0081(0.0013)*** Ethnicityb: Asian 0.0079(0.0040)**-0.0025(0.0003)***-0.0095(0.0035)** *0.0041(0.0019)** Ethnicityb: Hispanic 0.0194(0.0034)***-0.0016(0.0004)***-0.0098(0.0030)* **-0.0079(0.0016)*** Bornoutside US 0.0615(0.0039)***-0.0020(0.0006)***-0.0503(0.0034)* **-0.0092(0.0020)*** Disability-0.0158(0.0037)***-0.0007(0.0004)*0.0009( 0.0032)0.0156(0.0020)***


9 of 18 Religionc: Catholic -0.1870(0.0047)***-0.0019(0.0004)***0.2017(0.0047)* **-0.0128(0.0009)*** Religionc: Other faiths -0.0246(0.0028)***0.0026(0.0004)***0.0261(0.0027)** *-0.0041(0.0010)*** Mothers' characteristics:Educ.d: High School -0.0607(0.0079)***0.0075(0.0044)*0.0505(0.0065)***0 .0027(0.0033) Educ.d: Some College -0.0811(0.0077)***0.0100(0.0045)**0.0558(0.0060)*** 0.0153(0.0037)*** Educ.d: (Higher) Degree -0.1245(0.0074)***0.0082(0.0035)**0.0810(0.0060)*** 0.0352(0.0043)*** Community characteristics:Countypoverty ratee-0.0031(0.0001)***-0.0000(0.0000)0.0025(0.0001)***0 .0006(0.0001)*** Regionf: North East -0.0135(0.0029)***-0.0013(0.0004)***0.0033(0.0025)0 .0114(0.0015)*** Regionf: South 0.0123(0.0028)***0.0007(0.0004)*-0.0236(0.0024)***0 .0106(0.0014)*** Regionf: Midwest -0.0403(0.0043)***0.0011(0.0006)*0.0352(0.0040)***0 .0040(0.0020)** PredictedProb. 0.88560.00280.08870.0229 Pseudo RSquared 0.0966 LogLikelihood 45062.90 WaldChi-square (51) 9636.54 N 969,223Notes: Education Testing Service (ETS, 2001). Robus t standard errors in parentheses.aDummy variable indicating student anticipates obtai ning financial aid for higher education.bDefault ethnicity: white. cDefault: no religion (or preferred not to answer). dDefault education level: less than High school. eCensus data. fDefault region: west. ***significance at 0.01 level; **significance at 0.05 level; *significance at 0.10 level. Maternal characteristics are identified by educatio n levels, and by whether the mother is employed or not (NHES99 only). Relative to mothers who had not obtained a high school equivalency, the effect of more education is to swi tch enrolment away from public schools toward the other three options. The NHES99 results show higher maternal education is a strong influence on home-schooling, and this findin g is to some extent supported by the ETS01 estimation. Again, however, these educational influences are strongest in causing a switch toward private religious schooling. Similarl y, if the mother is employed, the child is


10 of 18 much more likely to be in public school, with the o ther three options being equally affected positively.Finally, Tables 1 and 2 show the effects of communi ty characteristics. Higher rates of poverty are more likely to encourage private schooling (pre sumably amongst those who are not below the poverty line themselves). (Median househo ld income at the county level – an alternative community income measure for the ETS01 survey – is highly correlated with the poverty rate, and so is omitted from the analysis). Plausibly, home schooling is less common in the North East, and urban areas; these are areas where private schooling options are more common.Before concluding, it is worthwhile noting some of the possible caveats to these findings. The first is the difficulty of measuring home-schooling and finding out precisely what type of educational choice it represents. Some parents may temporarily home-school, e.g. for a single academic year; others may home-school part-t ime, e.g. enrolling only half-days at public school. For the NHES99 data, there is a reas onably agreed definition for home-schooling, but the ETS01 data includes self-re ports of school type. The second caveat is that the sample of home-schoolers is too small; certainly, more efficient estimates would be obtained with larger samples (both absolute and relative to the high proportions enrolled in the other school types). Nevertheless, the ETS01 data includes over 5,000 home-schoolers in its sample. Finally, a third poss ible caveat is that the multinomial logit estimation may be improperly conceived. However, te sting for the ‘irrelevance of independent assumptions’ – by pooling religious and independent students – does not materially influence the coefficients (on the other two school types).ConclusionHere, a simple model of choice is used to explore t he determinants of school choice, represented through the four options now available to parents. The aim has been modest: to see what factors are important when school choice i s being decided on. In this respect the results are not surprising. However, the evidence has a more purposeful applica tion. First, it shows how different factors motivate different switches. So, families a re more disposed toward home-schooling and away from private-independent schooling when th ere are more children in the house; but they are more disposed away from home schooling and toward public schooling when the mother works. Income variables and community povert y rates tend to sway parents toward private schooling, but not toward home-schooling. S econd, the evidence can elucidate which type of schooling is most divergent from the public school norm, i.e., which school type has the strongest “independent” characteristics. Based on Tables 1 and 2, it appears that the families who use private-religious schools have spe cial characteristics, strongly attracting them to this choice. Therefore, it is the religious schools – and not the home-schoolers – that appear the “most different” from public schools, at least along the vector of characteristics for which there are data. Finally, this inquiry may be useful for directing the search for instrumental variables for school choice. Religious belief appears as the most substantively powerful influence in choosing private schooling. I n magnitude, the influence of religious persuasion far outweighs that of family resources o r maternal education levels. Notwithstanding the criticisms leveled at such a va riable (Altonji et al., 1999), it may still be appropriate to model the supply of religious school ing within treatment equations such as (2)-(5) above. For home-schooling decisions, instru mental variables might be derived from the opportunities for, or the need for, mothers to enter the labor market.AcknowledgementsThe authors appreciate the effort of Drew Gitomer a nd the Educational Testing Service for


11 of 18 making the SAT data available.Notes 1. Another method for inference is to look at what sch ools are chosen by families whose choice set is expanded, e.g. through voucher progra ms. This literature has been summarized by Teske and Schneider (2000). 2. We derive the idea of dutifulness via home-schoolin g from Adam Smith: “Do you wish to educate your children to be dutiful [?]… educate th em in your own house. From their parent’s house they may, with propriety and advantage, go ou t every day to attend public schools: but let their dwelling be always at home... Surely no a cquirement, which can possibly be derived from what is called a public education, can make an y sort of compensation for what is almost certainly and necessarily lost by it. Domestic educ ation is the institution of nature; public education, the contrivance of man. It is surely unn ecessary to say, which is likely to be the wisest” (2000 [1759], VI.II.13). 3. The option to declare as a home-schooler was also a vailable to test-participants in the year 2000. However, based on personal communication s with ETS staff, we were persuaded that the home-schooling indicator for 2000 may not be reliable.ReferencesBauman, K. J. (2002). Home-schooling in the United States: Trends and characteristics. Education Policy Analysis Archives 10 (26). Card, D. (1999). The causal effect of education on earnings. In Ashenfelter O and D Card. (Eds). Handbook of Labor Economics Volume 3C. North-Holland: New York. Evans, W. N. and Schwab, R. M. (1995). Finishing hi gh school and starting college: do Catholic schools make a difference? Quarterly Journal of Economics CX 941–974. Figlio, D. N. and Stone, J. A. (1999). Are private schools really better? Research in Labor Economics, 18, 115–140. Friedman, M. (1993). Public schools, make them priv ate. Education Economics 1 25–45. Grogger, J. and Neal, D. (2000). Further evidence o n the benefits of Catholic secondary schooling. Brookings–Wharton Papers on Urban Affairs Brookings: Washington, D. C. Hammons, C. W. (2001). School@home. Education Next Winter, 48–55. Hanushek, E. A. (1998). Conclusions and controversi es about the effectiveness of schools. Federal Reserve Bank of New York Economic Policy Re view 4 1–22. Hoxby, C. M. (2000). Does competition among public schools benefit students and tax–payers? American Economic Review 90 1209-1238. Kenny, L. W. and Schmidt, A. B. (1994). The decline in the number of school districts in the US: 1950-1980. Public Choice 79 1–18. Laneau, R. (2000). Home Advantage. Rowman & Littlefield: New York. Lines, P. 2000. Home-schooling comes of age. The Public Interest 140 74–85. Ludwig, J. and Ladd, H. F. and Duncan, G. J. (2001) Urban poverty and educational


12 of 18 outcomes. Brookings-Wharton Papers on Urban Affairs pp. 147-202. Moe, T. M. (2001). Schools, Vouchers and the American Public Brookings Institution: Washington.National Center for Education Statistics (NCES). 20 01. Homeschooling in the United States: 1999., D. (1997). The effects of Catholic secondary schooling on educational achievement. Journal of Labor Economics, 15 98–123. Rudner, L. M. (1999). Scholastic achievement and de mographic characteristics of home-school students in 1998. Education Policy Analysis Archives, 7 (8). Sander, W. (2001). The effects of Catholic schools on religiosity, education, and competition. Occasional Paper, National Center for the Study of Privatization in Education,, A. (2000). [1759]. The Theory of Moral Sentiments. Prometheus Books: New York. Somerville, S. 2001. Legalizing home-schooling in A merica. A quiet but persistent revolution. Mimeo. Home-school Legal Defense Association.Stevens, M. L. (2001). Kingdom of Children. Culture and Controversy in the Home-Schooling Movement Princeton University Press: Princeton. Teske, P. and Schneider, M. (2000). What research c an tell policymakers about school choice. Journal of Policy Analysis and Management 20 609-631. Welner, K. M. and Welner, K. (1999). Contextualisin g home-schooling data. A response to Rudner. Education Policy Analysis Archives, 7 (13).About the AuthorClive R. BelfieldNational Center for the Study of Privatization in E ducation Teachers College, Columbia University525 W120th StreetNew York NY 10027-6696Email:belfield@tc.eduwww.ncspe.orgDr Clive Belfield is the Associate Director of the National Center for the Study of Privatization in Education, at Teachers College, Columbia Univers ity. His research is on the economics of education, vouchers, and cost-effectiveness. Appendix Table A1 Frequencies for Independent Variables NHES99ETS01 Mean(SD)Mean(SD) Household characteristics:


13 of 18 Owns home0.69–Family income49010.00(31150.00)–Adults: 2 (both parents)0.53–Adults: 2 (one parent)0.08–Adults: 3 or more0.20–Siblings for child1.28(0.34)–Family income > $100,000–0.23Financial aid–0.74Student’s characteristics:Male0.510.45Ethnicity: African Amer.0.160.11Ethnicity: Asian0.030.08Ethnicity: Hispanic0.180.09Born outside US0.050.04Age: 10 to 12 years0.23–Age: 13 to 18 years0.45–Special learning needs0.23–Disability–0.08Religion: Catholic–0.24Religion: Other faiths–0.47Mother’s characteristics:Educ.: High school0.390.08Educ.: Some college0.260.32Educ.: (Higher) degree0.250.39Mother employed0.67–Community characteristics:ZIP poverty line: >10%0.32–ZIP Hisp-Black: 0–15%0.51–ZIP Hisp-Black: 16–40%0.26–County poverty line–17.85(7.36)County median income–43083.16(10264.86)Region: North East0.170.32Region: South0.390.35Region: Midwest0.200.11Area: Urban0.660.35Area: Suburban0.130.52


14 of 18 N 17,640969,223 The World Wide Web address for the Education Policy Analysis Archives is Editor: Gene V Glass, Arizona State UniversityProduction Assistant: Chris Murrell, Arizona State University General questions about appropriateness of topics o r particular articles may be addressed to the Editor, Gene V Glass, or reach him at College of Education, Arizona State Un iversity, Tempe, AZ 85287-2411. The Commentary Editor is Casey D. Cobb: .EPAA Editorial Board Michael W. Apple University of Wisconsin David C. Berliner Arizona State University Greg Camilli Rutgers University Linda Darling-Hammond Stanford University Sherman Dorn University of South Florida Mark E. Fetler California Commission on TeacherCredentialing Gustavo E. Fischman Arizona State Univeristy Richard Garlikov Birmingham, Alabama Thomas F. Green Syracuse University Aimee Howley Ohio University Craig B. Howley Appalachia Educational Laboratory William Hunter University of Ontario Institute ofTechnology Patricia Fey Jarvis Seattle, Washington Daniel Kalls Ume University Benjamin Levin University of Manitoba Thomas Mauhs-Pugh Green Mountain College Les McLean University of Toronto Heinrich Mintrop University of California, Los Angeles Michele Moses Arizona State University Gary Orfield Harvard University Anthony G. Rud Jr. Purdue University Jay Paredes Scribner University of Missouri Michael Scriven University of Auckland Lorrie A. Shepard University of Colorado, Boulder Robert E. Stake University of Illinois—UC Kevin Welner University of Colorado, Boulder Terrence G. Wiley Arizona State University John Willinsky University of British Columbia


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