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1 of 12 Education Policy Analysis Archives Volume 9 Number 43October 22, 2001ISSN 1068-2341 A peer-reviewed scholarly journal Editor: Gene V Glass, College of Education Arizona State University Copyright 2001, the EDUCATION POLICY ANALYSIS ARCHIVES Permission is hereby granted to copy any article if EPAA is credited and copies are not sold. Articles appearing in EPAA are abstracted in the Current Index to Journals in Education by the ERIC Clearinghouse on Assessment and Evaluation and are permanently archived in Resources in Education .Public versus Private Education in Hawaii and Its R ole in the State's Economy Antonina Espiritu Hawaii Pacific UniversityCitation: Espiritu, A. (2001, October 22). Public v ersus Private Education in Hawaii and Its Role in the State's Economy. Education Policy Analysis Archives 9 (43). Retrieved [date] from http://epaa.asu.edu/epaa/v9n43.html.AbstractThis study presents a time-series evidence on the t iming and degree of feedback relationship between participation in educ ation and income growth in Hawaii. Using the unrestricted vector aut oregression approach and two related measures of linear dependence and f eedback, the results suggest that across all educational levels, i.e., K -12 and tertiary, participation in public education could be a good p redictor of income growth in Hawaii. However, decomposing the feedback effect by frequency suggests that the dominance of public edu cation over private education in explaining the variation in income gro wth to be concentrated mainly on the short-run to medium-run for tertiary level and long-run to permanent effect for K-12 level. Ha waii state legislature and educators should perhaps take these results as a motivation not to ignore the problems plaguing Hawaii's public school s but should work towards greater improvement and support for public education given its

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2 of 12predicted significant overall contribution to the H awaiian economy. Introduction The lackluster condition of the Hawaiian economy wh en compared with the economic expansion in the mainland state economies since late 1991 led the Hawaiian legislature to reassess the economy's traditional s ole dependence on the tourism industry. To help revive the economy, the state government fo cused on educational reform as one of their priorities. Hawaii needs to build its huma n capital stock to be an active player in the new information or knowledge-based global econo my. To help ensure the availability of educated and skilled human resource s, the presence of dynamic research and teaching institutions is eminent. However, desp ite the pronounced good intentions and plans made by the state government, a growing n umber of Hawaii residents realize that not enough is being done. Based on a statewide survey, residents are generally disappointed about the economy and the condition of education. In fact, with the dreary statewide economic performance comes difficult choi ces and the need for re-allocation of resources. So, where does public and private edu cation stand in all this? In this study, an empirical investigation is done to assess and compare the relative contribution of public and private schools to Hawai i's economy. This paper presents a time-series evidence on the timing and degree of fe edback relationship between participation in education and income growth in Haw aii. The empirical investigation uses two feedback methods to measure the degree of dependence or the extent of feedback between data series and a related measure to distinguish between short-run and long-run effects of a given innovation or shock. Th is study is intended to contribute to a better understanding of the condition and quality o f the educational system in Hawaii. Also, the findings of this study may have important implications for directing resources or investment in education and shaping of Hawaii's educational policy in the future.Education in Hawaii: An Overview The establishment of early schools in Hawaii was d ue to the efforts of missionaries in the 1840s. Public education was fir st instituted on October 15, 1840 with mandatory attendance of children from ages four to fourteen. The upkeep of earlier statistics on education in Hawaii was difficult bec ause of numerous changes on its classifications. For instance, the compulsory age f or school attendance went through six changes: ages four to fourteen in 1840, six to sixt een in 1859, six to fifteen in 1865, six to fourteen in 1923, six to sixteen in 1937 and fin ally six to eighteen in 1965. Secondary education during the early monarchy years in Hawaii was also limited and left largely to government-subsidized private schools while, higher education was developed only in the twentieth century. Hawaii became the 50th state on Aug 21, 1959. In 19 60, 46% of the population had four years or more of high school training whil e only 9% had four years or more of college training. As of 1998, 84% of the population are high school graduates while 24% have bachelor's or advanced degree.Overview of School Enrollment and Educational Resou rces As summarized in Table 1, the participation in Haw aii's formal public education

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3 of 12 at the level of kindergarten to grade 12 had its bi ggest growth increase in the 1960s; while private schools had its biggest increase in e nrollment in the following decade. Enrollment in K-12 exhibited contrasting trend for public and private schools, i.e., when public institutions experienced positive growth, th e private institutions suffered a negative growth and vice versa.Table 1 Average Growth Rate in K-12 and Tertiary Enrollment Number of Schools and Teachers 1960-691970-791980-891990-99 Public K-12 Institutions Enrollment2.42-0.580.0950.91Schools0.440.590.3890.669*Teachers4.491.051.342.051* Private K-12 Institutions Enrollment1.121.32-0.4030.18Schools2.631.38-0.25-1.064**Teachers3.363.032.620.177** Tertiary Enrollment Public 10.371.31-1.13-0.455 Private 9.439.655.59 2.60 Note: Figures indicated with refer only to 1990-9 7 while those with ** refer only to 1990-96. For some years, the number of K-12 schools establis hed does not seem to follow the enrollment trend. In particular, the number of public schools in the island posted an increase of 0.6% in the 1970s at a time when it exp erienced a comparable 0.6% decline in enrollment. Conversely, at a time of recovery in enrollment, the number of public schools established continued to decline. The numbe r of private schools recorded big increases during the 1960s and 1970s but was drasti cally reversed in the 1980s and 1990s. In terms of school resources, both public an d private schools had their biggest growth increase in hiring teachers during the 1960s However, in terms of average number of pupil per teacher, private schools do a b etter job than public schools in providing small classes due in part to private scho ols continued bigger increases in hiring teachers. The public school system also cont inue to be plagued by other problems or concerns such as low test scores, aging faciliti es and low teacher morale. For tertiary level, private universities exhibited continuous positive growth in enrollment from 1960 to 1999. In contrast, the publ ic university suffered a drastic drop in enrollment in the 1970s relative to the previous decade, and turned into a negative growth in the 1980s and 1990s. This downward trend in enrollment may not seem surprising given that the state funding for the pub lic university system dropped 19% in

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4 of 12the past ten years. In fact, a national survey spot lighted Hawaii as the state with the largest loss in state support for higher education in 1998-99. Budget cuts have forced some programs to close or cease operation. A state law that sets a $352 million floor in state funding for the University of Hawaii (UH) was amended by the legislature wherein they are now to provide only for an appropriation r anging from 60-80% of funds required in addition to tuition. Beginning in 19951996, UH was allowed to keep tuition fees which formerly go into state general fund. Des pite this change, the state university system still finds their resources constrained that they have to resort to increasing tuition fees which took a toll in their enrollment.Data and Description of Methodology Data on school enrollment and per capita Gross Stat e Product were taken from The State of Hawaii Data Book, various issues, Dept of Planning and Economic Development. Earliest available data for private un iversities were recorded in 1955 and were taken from various sources such as Historical Statistics of Hawaii by Robert Schmitt (1977) and Hawaii State Department of Educa tion records. Private universities in Hawaii primarily consists of Bringham Young Univ ersity of Honolulu, Chaminade University and Hawaii Pacific University. In this s tudy, data on public university account only for enrollment at the University of Ha waii at Manoa which is the biggest institution in the state university system. Data on the number of K-12 schools and teachers for both public and private institutions w ere taken from the Hawaii State Department of Education records. Given the availabi lity of relevant data, this study covers the period of 1958 to 1999. Given that a number of models are consistent with observed correlation between human capital and income growth, I used the unrestr icted vector autoregression (VAR) approach to model the dynamic relationship among pe rtinent variables in order to minimize specification error. The VAR approach avoi ds the need for tight structural modeling by treating variables in a system as a fun ction of all lagged values of all of the endogenous variables in the system (Hamilton, 1994) It uses only past regularities and historical patterns in the data as a basis for fore casting. In this study, a three-variable autoregressive system is used. The variables includ e income growth as proxied by the growth rate of real gross state product per capita, enrollment figures at different levels, i.e., K to 12 and higher education from both public and private schools to serve as proxies for human capital stock. A lag length of fo ur years is used for all variables as suggested by the likelihood ratio test done. Also, based on the unit root tests conducted (Dickey, D. & Fuller, 1979; Kwiatkowski, D., Philli ps, Schmidt & Shin, 1992; Phillips & Perron, 1988), the stationarity of some data seri es are inconclusive. Hence, the empirical investigation uses the data series in bot h levels and first differences or in percentage change. The details of the two related measures of linear dependence and feedback used in this study can be found in the Appendix. To meas ure the degree of dependence or the extent of various kinds of feedback between income growth and participation in education as measured by school enrollment, I used Geweke's (1982) bi-variate feedback method. The feedback measures are non-negative and zero only when feedback or causality of the relevant type is not present. A si mple transformation of each feedback measure gives the reduction in the prediction error variance. Also, to distinguish between short-run and long-run effects of a given s hock, I decomposed the feedback by frequency using McGarvey's (1985) methodology. I us ed this method on an expanded three-variable VAR system.

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5 of 12 Empirical Results and Data Analysis The bi-variate feedback results using Geweke's met hod are shown in Table 2. The results suggest that both in terms of levels and fi rst differences, the magnitude of linear feedback from participation in K-12 private educati on to income growth to be about five times greater than the feedback from public enrollm ent to income growth. However, in terms of higher education, the magnitude of feedbac k from the public university is bigger than the feedback from participation in priv ate universities. Also, at all educational levels (i.e., K-12 and tertiary), the f eedback from public education to income growth remains bigger than the feedback from privat e education. This result may suggest that in Hawaii, participation in public education c ould be a good predictor of income growth.Table 2 Feedback from Participation in Education to Income GrowthK to 12 In levelsIn Percentage Change Public0.0852 (8.17%)0.0994 (9.46%)Private0.4997 (39.33%)0.5033 (39.55%)Higher EducationPublic0.2477 (21.94%)0.0743 (7.16%)Private0.0496 (4.84%)0.0459 (4.49%)All educational LevelsPublic0.1683 (15.49%)0.0832 (7.98%)Private0.0788 (7.57%)0.0782 (7.52%) In order to have a better idea of an innovation's short-run versus long-run effects, the feedback measure is decomposed by frequency ban ds. Also, the bi-variate system is extended to a three-variable system and uses the or dering of 'growth prior to public education prior to private education' in the Choles ki decomposition. Although the feedback measure is consistent, McGarvey showed tha t, in small samples, the feedback measure is biased upward. Hence, the Monte Carlo si mulation method is used to derive bias-adjusted feedback estimates. Table 3 summarize s the adjusted estimates and figures enclosed in parentheses pertain to the proportion o f variance explained by a corresponding shock to a series.Table 3 Feedback from Participation in Education to Growth by Frequency LevelsIn levelsPrivate K-12Public K-12 Permanent0.0002 (0.02%)0.0015 (0.15%)

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6 of 12 Long-run0.024 (2.36%)0.132 (12.40%)Medium-run0.174 (15.97%)0.056 (5.49%)Short-run0.832 (56.48%)0.043 (4.24%)Overall0.271 (23.74%)0.061 (5.90%) In Percentage ChangePrivate K-12Public K-12 Permanent0.222 (19.91%)1.124 (67.51%)Long-run0.1281 (12.03%)0.376 (31.32%)Medium-run0.4108 (33.69%)0.0068 (0.68%)Short-run0.8790 (58.48%)0.0893 (8.54%)Overall0.516 (40.29%)0.045 (4.40%) In LevelsPrivate UniversitiesPublic University Permanent0.0966 (9.21%)0.0492 (4.79%)Long-run0.0547 (5.32%)0.2792 (24.36%)Medium-run0.0104 (1.03%)0.1525 (14.15%)Short-run0.0114 (1.13%)0.1087 (10.30%)Overall0.0161 (1.59%)0.1526 (14.15%) In Percentage ChangePrivate UniversitiesPublic Univ ersity Permanent0.0379 (3.72%)0.0028 (0.28%)Long-run0.0417 (4.08%)0.0123 (1.22%)Medium-run0.0612 (5.94%)0.0797 (7.66%)Short-run0.0078 (0.78%)0.0709 (6.85%)Overall0.0405 (3.96%)0.0664 (6.43%) In LevelsPrivatePublic Permanent0.00012 (0.012%)0.1537 (14.24%)Long-run0.0230 (2.28%)0.1461 (13.60%)Medium-run0.0048 (0.48%)0.1256 (11.80%)Short-run0.0568 (5.52%)0.0564 (5.48%)Overall0.024 (2.41%)0.1038 (9.86%) In Percentage ChangePrivatePublic Permanent0.0081 (0.81%)0.0072 (0.72%)Long-run0.0137 (1.36%)0.0201 (1.99%)Medium-run0.0256 (2.52%)0.055 (5.37%)

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7 of 12 Short-run0.0791 (7.60%)0.064 (6.22%)Overall0.0396 (3.88%)0.055 (5.35%) In terms of K-12 enrollment, the results suggest t hat private schools exhibit bigger overall effect on Hawaii's income growth rel ative to that of public schools, confirming the previous result under the bi-variate feedback method. However, the feedback effect is concentrated mainly in the short -run (2-3 years) to medium-run (4-12 years). Conversely, participation in K-12 public ed ucation exhibited a significant long-run to permanent effect on Hawaii's income gro wth relative to that of private education. This result may be explained by the grow ing number of high school graduates migrating out of the state. For example in 1992, th e net out-migration of high school graduates was recorded to be around 690 and increas ed to 958 four years after. Apparently, families who could afford to send their children to private schools are willing to spend a little more to send them out of state in anticipation of more and better choices in education available in the mainland. In terms of tertiary level, the overall contributi on of public school enrollment to Hawaii's income growth is bigger than that of priva te universities. However, decomposing the feedback effect by frequency sugges t that this dominance of public enrollment in explaining the variation in income gr owth seem to be concentrated mainly in the short-run to medium-run. Conversely, private universities exhibit a permanent and long-run effect in explaining the variance in Hawai i's income growth relative to that of the public university. This finding might suggests that private tertiary education may be the key to promoting long-run growth in Hawaii. Sim ilarly, one cannot ignore the significant contribution of the public university i n building Hawaii's human capital stock in the short to medium-run. In terms of all educational levels, i.e. primary, secondary and tertiary level combined, participation in public education tend to explain a greater proportion of variance in Hawaii's income growth relative to priv ate education across almost all frequency levels. Again, this finding confirms the previous result found in the bi-variate feedback method.Concluding Remarks In this study, an empirical investigation is done to assess and compare the relative contribution of public and private schools to Hawai i' economy. I employed the unrestricted vector autoregression (VAR) model that uses only past regularities and historical patterns in the data to examine the dyna mic feedback relationship between participation in education and income growth. The r esults suggest that across all educational levels, i.e., K-12 and tertiary, partic ipation in public education could be a good predictor of income growth in Hawaii. However, decomposing the feedback effect by frequency suggests that the dominance of public education in explaining the variation in income growth to be concentrated mainly on the s hort-run to medium-run for tertiary level and long-run to permanent effect for K-12 lev el. Hawaii state legislature and educators should perhaps take these results as a mo tivation not to ignore the problems plaguing Hawaii's public schools but should work to wards greater improvement and support for public education given its predicted si gnificant overall contribution to the economy. Similarly, the presence of significant con tribution of K-12 private schools in the short-run to medium-run and private universitie s' long-run to permanent effect on Hawaii's income growth should serve as a driving fo rce that could help bring about

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8 of 12healthy competition and greater efficiency in the p rovision of educational services in Hawaii.ReferencesDickey, D. and W.A. Fuller. (1979). Distribution of the estimators for time series regressions with a unit root. Journal of the American Statistical Association ,74, 427-431.Geweke, John. (1982). Measurement of linear depende nce and feedback between multiple time series. Journal of the American Statistical Association 77, no.378, 304-313.Hamilton, James.(1994). Time Series Analysis Princeton University Press. Hawaii State Department of Business, Economic Devel opment and Tourism. Hawaii Data Book. Various issues. Kwiatkowski, D., P.C. Phillips, P. Schmidt and Y. S hin. (1992).Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root ?. Journal of Econometrics 54, 159-78. McGarvey, Mary. (1985). US evidence on linear feedb ack from money growth shocks to relative price changes, 1954 to 1979. The Review of Economics and Statistics 67, no.4, 675-680.National Center for Education Statistics. Education in States & Nations: Indicators Comparing US with the other Industrialized Countrie s. Various issues. National Center for Education Statistics. The Digest of Education Statistics Various issues.National Center for Education Statistics. The Condition of Education Various issues. Phillips, P.C. and P. Perron. (1988).Testing for a unit root in time series regression. Biometrika 65, 335-346. Schmitt, R. (1977). Historical Statistics of Hawaii University Press of Hawaii.About the AuthorAntonina Espiritu, Ph.D.Hawaii Pacific University 1060 Bishop St. LB 402A Honolulu, HI 96813Phone: (808) 544-0892 Fax: (808) 544-0862 Email: aespirit@hpu.eduAntonina Espiritu is an Assistant Professor of Econ omics at Hawaii Pacific University.

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9 of 12She earned her PhD in Economics at the University o f Nebraska-Lincoln under the NSF Economic Education Scholarship and her MA in Econom ics at the University of Hawaii at Manoa under the East-West Center Scholarship. He r current educational research interests include learning assessment of undergradu ate and graduate economics students and the role of education to productivity growth.Appendix Two Related Linear Dependence and Feedback Measures A. Geweke's (1982) method is used to measure the de gree of dependence or the extent of various kinds of feedback between data se ries. He defined the measures of linear dependence between say, X and Y wide-sense s tationary series in terms of the following linear projections,(1) Yt = S s=1 a 1s Yt-s+ S s=1 a 2s Xt-s + u1t(2) Yt = S s=1 b 1s Yt-s+ S s=0 b 2s Xt-s + u2t(3) Yt = S s=1 g 1s Yt-s + u3t where the linear feedback measure from X to Y is de fined as FX Y = log [var (u3t)/ var(u1t)] while the measure of contemporaneous feedback betwe en X and Y is defined as FX Y = log [var (u1t)/ var(u2t)]. So, the measure of linear dependence between X and Y or FX,Y is the sum of linear feedback from X to Y, FX Y linear feedback from Y to X, FY X and instantaneous linear feedback F X Y. FX,Y = FX Y + F Y X + F X Y where FY X is found by switching X and Y in equations (1) and (3) and in the definition of directional feedback. B. Building on Geweke's feedback measure, McGarvey( 1985) developed a useful alternative summary measure by decomposing the feed back by frequency in order to distinguish between short-run and long-run effects of a given innovation or shock. In the context of this study, the MA representation of the 3-variable orthogonalized autoregressive system is as follows:Xt C11(L) C12(L) C13(L) ut Yt = C21(L) C22(L) C23(L) wt Zt C31(L) C32(L) C33(L) ht

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10 of 12 where for example, C21(L) gives the response of Yt to innovations in Xt and, the overall feedback from X to Y is defined as FX Y = log [var (Yt) / var(Yt) S s=0 c21 (s)2var( u t )] The transformation ( 1-exp[-FX Y ]) gives the proportion of Y's variance explained b y shocks to X.To distinguish between short-run and long-run effec ts, the overall feedback is decomposed frequency bands. Feedback from X to Y ov er the interval ( l 1 l 2 ) is defined as fX Y ( l 1 l 2 ) = log [( I l 1 l 2 SY( l ) d l ) / (I l 1 l 2 SY( l ) C 21( l )2 s u 2 ) d l )] since var(Y) = (1/2 p ) I p p SY( l ) d l and SY( l ) = C 21( l )2 s u 2 + C 22( l )2 s w 2 + C 22( l )2s h 2 So, if u t contributes nothing to the variance of Y at freque ncy l the ratio will be one and the feedback measure will be zero. Note tha t a period of a cycle is defined as the ratio of 2 p to the frequency.Copyright 2001 by the Education Policy Analysis ArchivesThe World Wide Web address for the Education Policy Analysis Archives is epaa.asu.edu General questions about appropriateness of topics o r particular articles may be addressed to the Editor, Gene V Glass, glass@asu.edu or reach him at College of Education, Arizona State University, Tempe, AZ 8 5287-0211. (602-965-9644). The Commentary Editor is Casey D. C obb: casey.cobb@unh.edu .EPAA Editorial Board Michael W. Apple University of Wisconsin Greg Camilli Rutgers University John Covaleskie Northern Michigan University Alan Davis University of Colorado, Denver Sherman Dorn University of South Florida Mark E. Fetler California Commission on Teacher Credentialing Richard Garlikov hmwkhelp@scott.net Thomas F. Green Syracuse University Alison I. Griffith York University Arlen Gullickson Western Michigan University Ernest R. House University of Colorado Aimee Howley Ohio University

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11 of 12 Craig B. Howley Appalachia Educational Laboratory William Hunter University of Calgary Daniel Kalls Ume University Benjamin Levin University of Manitoba Thomas Mauhs-Pugh Green Mountain College Dewayne Matthews Education Commission of the States William McInerney Purdue University Mary McKeown-Moak MGT of America (Austin, TX) Les McLean University of Toronto Susan Bobbitt Nolen University of Washington Anne L. Pemberton apembert@pen.k12.va.us Hugh G. Petrie SUNY Buffalo Richard C. Richardson New York University Anthony G. Rud Jr. Purdue University Dennis Sayers California State University—Stanislaus Jay D. Scribner University of Texas at Austin Michael Scriven scriven@aol.com Robert E. Stake University of Illinois—UC Robert Stonehill U.S. Department of Education David D. Williams Brigham Young University EPAA Spanish Language Editorial BoardAssociate Editor for Spanish Language Roberto Rodrguez Gmez Universidad Nacional Autnoma de Mxico roberto@servidor.unam.mx Adrin Acosta (Mxico) Universidad de Guadalajaraadrianacosta@compuserve.com J. Flix Angulo Rasco (Spain) Universidad de Cdizfelix.angulo@uca.es Teresa Bracho (Mxico) Centro de Investigacin y DocenciaEconmica-CIDEbracho dis1.cide.mx Alejandro Canales (Mxico) Universidad Nacional Autnoma deMxicocanalesa@servidor.unam.mx Ursula Casanova (U.S.A.) Arizona State Universitycasanova@asu.edu Jos Contreras Domingo Universitat de Barcelona Jose.Contreras@doe.d5.ub.es Erwin Epstein (U.S.A.) Loyola University of ChicagoEepstein@luc.edu Josu Gonzlez (U.S.A.) Arizona State Universityjosue@asu.edu Rollin Kent (Mxico)Departamento de InvestigacinEducativa-DIE/CINVESTAVrkent@gemtel.com.mx kentr@data.net.mx Mara Beatriz Luce (Brazil)Universidad Federal de Rio Grande do Sul-UFRGSlucemb@orion.ufrgs.br

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12 of 12 Javier Mendoza Rojas (Mxico)Universidad Nacional Autnoma deMxicojaviermr@servidor.unam.mxMarcela Mollis (Argentina)Universidad de Buenos Airesmmollis@filo.uba.ar Humberto Muoz Garca (Mxico) Universidad Nacional Autnoma deMxicohumberto@servidor.unam.mxAngel Ignacio Prez Gmez (Spain)Universidad de Mlagaaiperez@uma.es Daniel Schugurensky (Argentina-Canad)OISE/UT, Canadadschugurensky@oise.utoronto.ca Simon Schwartzman (Brazil)Fundao Instituto Brasileiro e Geografiae Estatstica simon@openlink.com.br Jurjo Torres Santom (Spain)Universidad de A Coruajurjo@udc.es Carlos Alberto Torres (U.S.A.)University of California, Los Angelestorres@gseisucla.edu


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