How Does Digitization Affect Scholarship?

1. How Does Digitization Affect Scholarship? Mark McCabe University of Michigan Roger Schonfeld Ithaka Christopher Snyder Dartmouth College December 11, 2007

2. What Characteristics Are Important to Authors?

3. Journal Characteristics Important to an Author When it comes to influencing decisions about journals in which to publish an article of yours, how important to you is each of the following possible characteristics of an academic journal? • The journal makes its articles freely available on the Internet, so there is no cost to purchase or to read. • The journal permits scholars to publish articles for free, without paying page or article charges. • Measures have been taken to ensure the protection and safeguarding of the journal’s content for the long term. • The current issues of the journal are circulated widely, and are well read by scholars in your field. • The journal is highly selective; only a small percentage of submitted articles are published. • The journal is available to readers not only in developed nations, but also in developing nations.

4. Preferences for Academic Journals, 2006 Percent of faculty who believe that each characteristic is “very important” in influencing the decisions where to publish their articles

5. Background on the Present Study

6. Objectives • What are the scholarly impacts of various business models for journal publishing? • How do various business models for journal publishing affect the value derived by authors and readers?

7. Natural Experiment • Beginning in 1995 publishers and content aggregators began digitizing current and archival content and placing it online. • However, as late as 2005 (the endpoint of our analysis) backfiles for many journals (and current content in some cases) remained offline. • We exploit this heterogeneous chronology to explore the impact of online access.

8. Previous Studies • Many previous studies of this relationship find large effects • Common flaws: these efforts do not adequately control for potential selection problems affecting article quality, do not use adequate statistical methods, or both • For example, did the best journals, at least in some disciplines, gain an online presence earlier? • This study avoids these problems: Variation in journal quality for content published prior to 1995 is unlikely to be related to online strategies adopted by publishers after 1995.

9. Some Empirical Questions • What is the impact of online access on journal citation rates? • Are the benefits greater for newer or older content? • Are the effects discipline-specific? • Which online “channels” have the greatest impact? • Is the geographic and institutional distribution of citing authors influenced by online access?

10. People, Funding, and Timeline • Researchers • Mark McCabe, Professor of Economics, University of Michigan – Principal Investigator • Chris Snyder, Professor of Economics, Dartmouth – Co-Principal Investigator • Roger Schonfeld, Manager of Research, Ithaka • Funded by a grant from The Andrew W. Mellon Foundation • Data collection is completed, analysis is underway, full findings are expected to become available by mid 2008

11. Our Data

12. Our Data • Three Disciplines • History • Economics and Business • Biological and General Sciences • Hundreds of publishers, aggregators, and archives provided data • 100 journals in each discipline, comparing journal-year by journal-year • 50 that were digitized early on • 50 that were digitized only more recently or not at all • Examine citations TO these journals that appeared in ANY journal from 1980 to 2005 • Complete citation databases obtained from ISI

13. Descriptive Statistics

14. Skewed Distribution of Citation in Economics About 4,700 zeros, one had 771 cites Frequency Citations to journal-publication-year in a year

15. Skewed Distribution of Citations in Science About 5,500 zeros, one had 32,500 cites Frequency Citations to journal-publication-year in a year

16. Online Availability for 1980 Content

17. Geographic Distribution of First Authors of Articles that Cite Other Articles

18. Challenges • ISI data requires extensive clean-up and quality control • Many publishers and aggregators maintain poor records of their journals’ online histories • First authors are confusing and require more consideration

19. Findings

20. Regression Outputs . xtreg lncit1 age* cyr* d2* js2* ow2*, i(articlegroup) fe robust; Fixed-effects (within) regression Number of obs = 54665 Group variable: articlegroup Number of groups = 99 R-sq: within = 0.4435 Obs per group: min = 52 between = 0.0890 avg = 552.2 overall = 0.2605 max = 975 F(102,54464) = 376.66 corr(u_i, Xb) = -0.0774 Prob > F = 0.0000 (Std. Err. adjusted for clustering on articlegroup) ------------------------------------------------------------------------------ | Robust lncit1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1 | .5925995 .0194082 30.53 0.000 .5545593 .6306398 age2 | .9779868 .019672 49.71 0.000 .9394294 1.016544 age3 | 1.132953 .0197409 57.39 0.000 1.09426 1.171645 age4 | 1.146659 .019613 58.46 0.000 1.108217 1.1851 age5 | 1.148625 .0197884 58.05 0.000 1.10984 1.187411 age6 | 1.118217 .0195578 57.18 0.000 1.079883 1.15655 age7 | 1.05887 .0196327 53.93 0.000 1.02039 1.097351 age8 | 1.026378 .0195007 52.63 0.000 .9881561 1.064599 age9 | .9633523 .0196253 49.09 0.000 .9248864 1.001818 age10 | .8995837 .0200633 44.84 0.000 .8602596 .9389078 age11 | .8377198 .0198925 42.11 0.000 .7987304 .8767093 age12 | .7902542 .020094 39.33 0.000 .7508698 .8296386 age13 | .7135656 .020046 35.60 0.000 .6742754 .7528558 age14 | .6518025 .0204853 31.82 0.000 .6116512 .6919538 age15 | .5977419 .020616 28.99 0.000 .5573344 .6381494 age16 | .5455455 .0207872 26.24 0.000 .5048025 .5862885 age17 | .5060501 .020825 24.30 0.000 .465233 .5468672 age18 | .4332353 .0211407 20.49 0.000 .3917994 .4746713 age19 | .3762387 .0215208 17.48 0.000 .3340578 .4184197 age20 | .3139517 .0219721 14.29 0.000 .2708861 .3570172 age21 | .3044858 .022119 13.77 0.000 .2611325 .3478392 age22 | .2190796 .0225092 9.73 0.000 .1749615 .2631978 age23 | .1970334 .0232404 8.48 0.000 .1514821 .2425847 age24 | .1424866 .0237271 6.01 0.000 .0959813 .1889918 age25 | .1347377 .0243322 5.54 0.000 .0870464 .182429 age26 | .0516184 .0250276 2.06 0.039 .002564 .1006727 age27 | .0225138 .0250947 0.90 0.370 -.026672 .0716997 age28 | -.0259718 .0253744 -1.02 0.306 -.0757059 .0237622 age29 | -.0632298 .0264435 -2.39 0.017 -.1150593 -.0114004 age30 | -.1099393 .0276293 -3.98 0.000 -.1640929 -.0557856 age31 | -.142994 .0284942 -5.02 0.000 -.1988429 -.0871451 age32 | -.1782232 .0299062 -5.96 0.000 -.2368396 -.1196067 age33 | -.261908 .030622 -8.55 0.000 -.3219273 -.2018888 age34 | -.2591013 .0324542 -7.98 0.000 -.3227116 -.1954909 age35 | -.3260635 .0341572 -9.55 0.000 -.3930118 -.2591152 age36 | -.3404725 .0353739 -9.62 0.000 -.4098056 -.2711394 age37 | -.3773577 .0378321 -9.97 0.000 -.4515089 -.3032066 age38 | -.4067335 .0402177 -10.11 0.000 -.4855605 -.3279065 age39 | -.4214852 .0412594 -10.22 0.000 -.502354 -.3406164 age40 | -.4806371 .0466054 -10.31 0.000 -.571984 -.3892901 age41 | -.5860335 .0487548 -12.02 0.000 -.6815932 -.4904738 age42 | -.5777724 .0552067 -10.47 0.000 -.685978 -.4695668 age43 | -.6628623 .0573937 -11.55 0.000 -.7753544 -.5503703 age44 | -.657258 .0627245 -10.48 0.000 -.7801984 -.5343176 age45 | -.6902471 .0633513 -10.90 0.000 -.8144161 -.5660781 age46 | -.7091903 .084976 -8.35 0.000 -.8757439 -.5426367 age47 | -.6917692 .0931122 -7.43 0.000 -.8742697 -.5092687 age48 | -.9731208 .1083313 -8.98 0.000 -1.185451 -.7607907 age49 | -.7280118 .1384787 -5.26 0.000 -.9994311 -.4565924 cyr1981 | -.0098747 .0240169 -0.41 0.681 -.056948 .0371985 cyr1982 | .0317948 .0240884 1.32 0.187 -.0154187 .0790083 cyr1983 | .1234897 .0236704 5.22 0.000 .0770956 .1698838 cyr1984 | .180934 .0231046 7.83 0.000 .1356489 .2262192 cyr1985 | .231519 .0232923 9.94 0.000 .1858659 .2771721 cyr1986 | .1728681 .022487 7.69 0.000 .1287933 .2169429 cyr1987 | .2083203 .0221738 9.39 0.000 .1648596 .2517811 cyr1988 | .2496392 .0219732 11.36 0.000 .2065714 .2927069 cyr1989 | .2710759 .0219834 12.33 0.000 .2279883 .3141635 cyr1990 | .3508226 .0216088 16.24 0.000 .3084692 .3931761 cyr1991 | .3260312 .0213618 15.26 0.000 .2841619 .3679004 cyr1992 | .4359447 .0214798 20.30 0.000 .3938441 .4780453 cyr1993 | .4943246 .0214747 23.02 0.000 .4522341 .5364151 cyr1994 | .5531297 .0212418 26.04 0.000 .5114956 .5947639 cyr1995 | .6429404 .02106 30.53 0.000 .6016625 .6842182 cyr1996 | .7298492 .0216921 33.65 0.000 .6873326 .7723658 cyr1997 | .7553296 .0216274 34.92 0.000 .7129399 .7977194 cyr1998 | .8805461 .0220086 40.01 0.000 .8374091 .9236831 cyr1999 | .8970375 .0225007 39.87 0.000 .8529359 .941139 cyr2000 | .95344 .022844 41.74 0.000 .9086656 .9982145 cyr2001 | 1.004546 .0229986 43.68 0.000 .9594687 1.049623 cyr2002 | 1.051948 .0251363 41.85 0.000 1.00268 1.101215 cyr2003 | 1.051039 .0290639 36.16 0.000 .9940739 1.108005 cyr2004 | 1.095085 .0305573 35.84 0.000 1.035192 1.154977 cyr2005 | 1.11283 .0322413 34.52 0.000 1.049637 1.176023 d21995 | -.1711038 .0370271 -4.62 0.000 -.2436773 -.0985304 d21996 | .0334478 .0445991 0.75 0.453 -.0539667 .1208624 d21997 | .0323879 .0398732 0.81 0.417 -.0457639 .1105397 d21998 | .1191668 .038368 3.11 0.002 .0439651 .1943684 d21999 | .1681538 .0369606 4.55 0.000 .0957107 .2405969 d22000 | .2095898 .0342771 6.11 0.000 .1424064 .2767731 d22001 | .1422296 .0349668 4.07 0.000 .0736944 .2107648 d22002 | .1491328 .025983 5.74 0.000 .098206 .2000596 d22003 | .2165946 .026536 8.16 0.000 .1645838 .2686054 d22004 | .2258485 .0260583 8.67 0.000 .1747741 .2769229 d22005 | .1897062 .0264454 7.17 0.000 .137873 .2415394 js21995 | (dropped) js21996 | (dropped) js21997 | .1057016 .0605439 1.75 0.081 -.0129649 .224368 js21998 | .1155885 .0558263 2.07 0.038 .0061685 .2250084 js21999 | .0852402 .0406405 2.10 0.036 .0055846 .1648958 js22000 | .1735513 .0348268 4.98 0.000 .1052906 .241812 js22001 | .20244 .0335772 6.03 0.000 .1366285 .2682516 js22002 | .120797 .0265542 4.55 0.000 .0687506 .1728435 js22003 | .1275872 .0285759 4.46 0.000 .0715783 .1835962 js22004 | .2003938 .028945 6.92 0.000 .1436615 .2571261 js22005 | .1653547 .0308095 5.37 0.000 .1049678 .2257416 ow21995 | (dropped) ow21996 | (dropped) ow21997 | (dropped) ow21998 | .4172081 .1676301 2.49 0.013 .0886518 .7457644 ow21999 | .2048051 .0608046 3.37 0.001 .0856276 .3239825 ow22000 | .0911743 .0503162 1.81 0.070 -.0074459 .1897945 ow22001 | .255593 .0407627 6.27 0.000 .1756977 .3354883 ow22002 | .2183496 .0396038 5.51 0.000 .1407259 .2959733 ow22003 | .121052 .0306477 3.95 0.000 .0609823 .1811217 ow22004 | .1735999 .0302115 5.75 0.000 .1143851 .2328146 ow22005 | .198357 .030743 6.45 0.000 .1381005 .2586135 _cons | -.189991 .0222441 -8.54 0.000 -.2335896 -.1463925 -------------+---------------------------------------------------------------- sigma_u | .63280589 sigma_e | .60905957 rho | .5191145 (fraction of variance due to u_i) ------------------------------------------------------------------------------ USA . xtreg lncit1 age* cyr* d2* js2* ow2*, i(articlegroup) fe robust; Fixed-effects (within) regression Number of obs = 57926 Group variable: articlegroup Number of groups = 99 R-sq: within = 0.5502 Obs per group: min = 136 between = 0.0666 avg = 585.1 overall = 0.2691 max = 975 F(102,57725) = 635.73 corr(u_i, Xb) = -0.0685 Prob > F = 0.0000 (Std. Err. adjusted for clustering on articlegroup) ------------------------------------------------------------------------------ | Robust lncit1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1 | 1.142244 .0209213 54.60 0.000 1.101238 1.18325 age2 | 1.614668 .0203298 79.42 0.000 1.574822 1.654515 age3 | 1.742429 .020097 86.70 0.000 1.703039 1.781819 age4 | 1.758295 .0199514 88.13 0.000 1.71919 1.7974 age5 | 1.722976 .0200796 85.81 0.000 1.68362 1.762332 age6 | 1.670676 .0201464 82.93 0.000 1.631189 1.710163 age7 | 1.58542 .0202174 78.42 0.000 1.545794 1.625047 age8 | 1.501505 .0204184 73.54 0.000 1.461485 1.541526 age9 | 1.431684 .02041 70.15 0.000 1.39168 1.471687 age10 | 1.318883 .0208683 63.20 0.000 1.277981 1.359785 age11 | 1.243756 .0208868 59.55 0.000 1.202818 1.284694 age12 | 1.133539 .0213243 53.16 0.000 1.091744 1.175335 age13 | 1.034687 .021336 48.49 0.000 .9928688 1.076506 age14 | .9489538 .0218776 43.38 0.000 .9060737 .991834 age15 | .8492003 .0221776 38.29 0.000 .8057321 .8926685 age16 | .7603295 .0222371 34.19 0.000 .7167446 .8039144 age17 | .6727317 .022645 29.71 0.000 .6283473 .7171161 age18 | .5801047 .0229048 25.33 0.000 .5352111 .6249983 age19 | .4925934 .0231864 21.24 0.000 .4471479 .5380389 age20 | .4136865 .0240383 17.21 0.000 .3665714 .4608017 age21 | .3147953 .0241193 13.05 0.000 .2675215 .3620692 age22 | .2461616 .0246258 10.00 0.000 .1978949 .2944284 age23 | .1809422 .0252354 7.17 0.000 .1314806 .2304037 age24 | .0807609 .0253864 3.18 0.001 .0310035 .1305183 age25 | .0417641 .0263565 1.58 0.113 -.0098949 .093423 age26 | -.0506222 .0268296 -1.89 0.059 -.1032084 .001964 age27 | -.0976584 .0271571 -3.60 0.000 -.1508864 -.0444304 age28 | -.1720656 .0277512 -6.20 0.000 -.2264582 -.1176731 age29 | -.231436 .0285867 -8.10 0.000 -.2874661 -.175406 age30 | -.2730393 .0300496 -9.09 0.000 -.3319367 -.2141418 age31 | -.3530215 .0318435 -11.09 0.000 -.415435 -.290608 age32 | -.428936 .0323989 -13.24 0.000 -.492438 -.365434 age33 | -.4702224 .0332522 -14.14 0.000 -.5353968 -.4050479 age34 | -.5339165 .0357111 -14.95 0.000 -.6039104 -.4639226 age35 | -.5565653 .0366297 -15.19 0.000 -.6283597 -.484771 age36 | -.6021752 .0392902 -15.33 0.000 -.6791841 -.5251662 age37 | -.6729882 .0410332 -16.40 0.000 -.7534136 -.5925629 age38 | -.6961089 .0430524 -16.17 0.000 -.7804919 -.6117258 age39 | -.7088561 .0456264 -15.54 0.000 -.7982842 -.619428 age40 | -.7603163 .0475862 -15.98 0.000 -.8535854 -.6670472 age41 | -.851268 .0507096 -16.79 0.000 -.9506592 -.7518769 age42 | -.893128 .0542692 -16.46 0.000 -.999496 -.78676 age43 | -.9309673 .059944 -15.53 0.000 -1.048458 -.8134767 age44 | -.9142087 .0656497 -13.93 0.000 -1.042882 -.785535 age45 | -.9691487 .0706603 -13.72 0.000 -1.107643 -.8306541 age46 | -1.00314 .0756524 -13.26 0.000 -1.151419 -.8548613 age47 | -1.075517 .0936534 -11.48 0.000 -1.259078 -.8919558 age48 | -1.030011 .1216722 -8.47 0.000 -1.268489 -.7915328 age49 | -1.116812 .153582 -7.27 0.000 -1.417834 -.8157909 cyr1981 | .0666116 .0274443 2.43 0.015 .0128206 .1204027 cyr1982 | .0927981 .026291 3.53 0.000 .0412676 .1443286 cyr1983 | .187205 .0263629 7.10 0.000 .1355336 .2388763 cyr1984 | .2526398 .0257449 9.81 0.000 .2021796 .3030999 cyr1985 | .3384103 .025345 13.35 0.000 .2887339 .3880866 cyr1986 | .3530945 .0250064 14.12 0.000 .3040818 .4021073 cyr1987 | .3774617 .0245921 15.35 0.000 .329261 .4256624 cyr1988 | .4068968 .0246182 16.53 0.000 .3586451 .4551485 cyr1989 | .4367514 .0240332 18.17 0.000 .3896463 .4838565 cyr1990 | .4906206 .0240033 20.44 0.000 .4435741 .5376671 cyr1991 | .5288997 .0239256 22.11 0.000 .4820053 .5757941 cyr1992 | .5388651 .0237603 22.68 0.000 .4922949 .5854353 cyr1993 | .6240838 .0234526 26.61 0.000 .5781166 .670051 cyr1994 | .6023601 .0232783 25.88 0.000 .5567346 .6479857 cyr1995 | .6319962 .0231628 27.29 0.000 .5865971 .6773954 cyr1996 | .6648641 .0238445 27.88 0.000 .6181289 .7115994 cyr1997 | .6435152 .0240177 26.79 0.000 .5964403 .6905901 cyr1998 | .656988 .0239074 27.48 0.000 .6101293 .7038467 cyr1999 | .6790987 .0241497 28.12 0.000 .6317652 .7264322 cyr2000 | .6968795 .0247525 28.15 0.000 .6483644 .7453946 cyr2001 | .7371999 .024742 29.80 0.000 .6887054 .7856944 cyr2002 | .7553183 .0267213 28.27 0.000 .7029444 .8076922 cyr2003 | .8379202 .0308661 27.15 0.000 .7774226 .8984178 cyr2004 | .8373143 .030719 27.26 0.000 .777105 .8975237 cyr2005 | .8061927 .0328099 24.57 0.000 .7418851 .8705003 d21995 | .0009121 .0333081 0.03 0.978 -.0643719 .066196 d21996 | .1290019 .042289 3.05 0.002 .0461153 .2118886 d21997 | .1280111 .0370225 3.46 0.001 .0554467 .2005755 d21998 | .1095574 .0342414 3.20 0.001 .0424441 .1766706 d21999 | .0858281 .0322034 2.67 0.008 .0227093 .1489469 d22000 | .0941197 .0322918 2.91 0.004 .0308277 .1574117 d22001 | .0497572 .0307 1.62 0.105 -.010415 .1099294 d22002 | .0804476 .0245538 3.28 0.001 .032322 .1285733 d22003 | .0190089 .0252643 0.75 0.452 -.0305093 .068527 d22004 | .0327168 .0242897 1.35 0.178 -.0148912 .0803248 d22005 | -.0098992 .0248985 -0.40 0.691 -.0587004 .038902 js21995 | (dropped) js21996 | (dropped) js21997 | .0204912 .060286 0.34 0.734 -.0976697 .138652 js21998 | .0450672 .0523604 0.86 0.389 -.0575594 .1476939 js21999 | .0715994 .0384573 1.86 0.063 -.0037772 .146976 js22000 | .0426615 .0339351 1.26 0.209 -.0238514 .1091744 js22001 | .0674799 .032501 2.08 0.038 .0037778 .1311821 js22002 | .058473 .0262274 2.23 0.026 .0070671 .1098788 js22003 | .0695892 .0280242 2.48 0.013 .0146618 .1245167 js22004 | .0892428 .0272221 3.28 0.001 .0358874 .1425983 js22005 | .1112909 .0282361 3.94 0.000 .0559479 .1666338 ow21995 | (dropped) ow21996 | (dropped) ow21997 | (dropped) ow21998 | .3257227 .1044066 3.12 0.002 .1210852 .5303601 ow21999 | .0388626 .0487301 0.80 0.425 -.0566486 .1343738 ow22000 | -.0139809 .0446355 -0.31 0.754 -.1014666 .0735049 ow22001 | .0272359 .0363682 0.75 0.454 -.044046 .0985177 ow22002 | .0066044 .034768 0.19 0.849 -.0615409 .0747498 ow22003 | -.0160306 .0291875 -0.55 0.583 -.0732382 .041177 ow22004 | -.0644509 .0272701 -2.36 0.018 -.1179003 -.0110014 ow22005 | -.0826008 .0284924 -2.90 0.004 -.1384461 -.0267556 _cons | .6739908 .0247654 27.22 0.000 .6254505 .7225311 -------------+---------------------------------------------------------------- sigma_u | .92008027 sigma_e | .63682165 rho | .67610797 (fraction of variance due to u_i) ------------------------------------------------------------------------------ Non_USA English . xtreg lncit1 age* cyr* d2* js2* ow2*, i(articlegroup) fe robust; Fixed-effects (within) regression Number of obs = 57160 Group variable: articlegroup Number of groups = 99 R-sq: within = 0.4459 Obs per group: min = 136 between = 0.0471 avg = 577.4 overall = 0.2680 max = 975 F(102,56959) = 437.43 corr(u_i, Xb) = -0.1070 Prob > F = 0.0000 (Std. Err. adjusted for clustering on articlegroup) ------------------------------------------------------------------------------ | Robust lncit1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1 | .8040375 .0200383 40.13 0.000 .7647623 .8433126 age2 | 1.306365 .020054 65.14 0.000 1.267059 1.345671 age3 | 1.441864 .0193271 74.60 0.000 1.403983 1.479745 age4 | 1.442642 .0194947 74.00 0.000 1.404432 1.480852 age5 | 1.443265 .0192586 74.94 0.000 1.405518 1.481013 age6 | 1.395241 .0193099 72.26 0.000 1.357394 1.433089 age7 | 1.335918 .0191769 69.66 0.000 1.298331 1.373504 age8 | 1.289316 .0193719 66.56 0.000 1.251347 1.327285 age9 | 1.206487 .0198721 60.71 0.000 1.167537 1.245436 age10 | 1.134139 .0198899 57.02 0.000 1.095155 1.173124 age11 | 1.040137 .0203035 51.23 0.000 1.000342 1.079932 age12 | .9696013 .0201806 48.05 0.000 .9300472 1.009155 age13 | .8954094 .0203375 44.03 0.000 .8555478 .935271 age14 | .7953303 .0206681 38.48 0.000 .7548207 .8358398 age15 | .7400647 .0206408 35.85 0.000 .6996086 .7805207 age16 | .6828956 .0206237 33.11 0.000 .642473 .7233181 age17 | .6093768 .0208916 29.17 0.000 .5684291 .6503244 age18 | .5484359 .0212505 25.81 0.000 .5067847 .590087 age19 | .4802373 .0213312 22.51 0.000 .4384279 .5220467 age20 | .4207373 .0218698 19.24 0.000 .3778724 .4636023 age21 | .3549788 .0221349 16.04 0.000 .3115942 .3983633 age22 | .3267344 .0225701 14.48 0.000 .282497 .3709718 age23 | .2527689 .0227205 11.13 0.000 .2082366 .2973012 age24 | .2030036 .022417 9.06 0.000 .1590663 .246941 age25 | .1327909 .0234724 5.66 0.000 .0867849 .1787968 age26 | .0845621 .0237398 3.56 0.000 .038032 .1310922 age27 | .0263627 .0242486 1.09 0.277 -.0211648 .0738902 age28 | -.0221393 .0248749 -0.89 0.373 -.0708943 .0266157 age29 | -.064602 .025574 -2.53 0.012 -.1147272 -.0144768 age30 | -.1055199 .0258808 -4.08 0.000 -.1562464 -.0547935 age31 | -.1431767 .0269635 -5.31 0.000 -.1960253 -.0903281 age32 | -.1895377 .0274614 -6.90 0.000 -.2433621 -.1357133 age33 | -.2121815 .0290934 -7.29 0.000 -.2692046 -.1551583 age34 | -.2722889 .0303044 -8.99 0.000 -.3316857 -.2128921 age35 | -.3236192 .0315362 -10.26 0.000 -.3854304 -.2618079 age36 | -.3586663 .0326254 -10.99 0.000 -.4226122 -.2947204 age37 | -.391956 .0319684 -12.26 0.000 -.4546142 -.3292978 age38 | -.4156952 .0355242 -11.70 0.000 -.4853227 -.3460676 age39 | -.3761633 .0383302 -9.81 0.000 -.4512907 -.3010358 age40 | -.4793657 .0399341 -12.00 0.000 -.5576367 -.4010948 age41 | -.4984452 .0439407 -11.34 0.000 -.5845693 -.4123212 age42 | -.4934602 .0490526 -10.06 0.000 -.5896036 -.3973168 age43 | -.5565236 .0517484 -10.75 0.000 -.6579508 -.4550963 age44 | -.5573386 .0499859 -11.15 0.000 -.6553113 -.4593658 age45 | -.647713 .0592516 -10.93 0.000 -.7638465 -.5315794 age46 | -.5664076 .0647024 -8.75 0.000 -.6932247 -.4395904 age47 | -.6715538 .0744939 -9.01 0.000 -.8175623 -.5255452 age48 | -.7706643 .1007963 -7.65 0.000 -.9682256 -.5731029 age49 | -.6571177 .1117111 -5.88 0.000 -.8760721 -.4381633 cyr1981 | -.000454 .0261056 -0.02 0.986 -.051621 .0507131 cyr1982 | .0099397 .025446 0.39 0.696 -.0399346 .059814 cyr1983 | .0996809 .02495 4.00 0.000 .0507787 .1485831 cyr1984 | .1232236 .0244381 5.04 0.000 .0753248 .1711224 cyr1985 | .2059059 .0242248 8.50 0.000 .1584252 .2533866 cyr1986 | .1557467 .0238767 6.52 0.000 .1089482 .2025452 cyr1987 | .2697357 .0242932 11.10 0.000 .2221209 .3173504 cyr1988 | .2164261 .0234411 9.23 0.000 .1704815 .2623707 cyr1989 | .1928685 .0232182 8.31 0.000 .1473607 .2383762 cyr1990 | .2519467 .0230718 10.92 0.000 .2067259 .2971675 cyr1991 | .2447688 .023271 10.52 0.000 .1991574 .2903802 cyr1992 | .3331447 .0227529 14.64 0.000 .2885488 .3777405 cyr1993 | .3488949 .0225777 15.45 0.000 .3046425 .3931472 cyr1994 | .4458527 .0224523 19.86 0.000 .4018461 .4898594 cyr1995 | .5200858 .0224797 23.14 0.000 .4760254 .5641463 cyr1996 | .5938783 .0225517 26.33 0.000 .5496768 .6380798 cyr1997 | .5975654 .0230162 25.96 0.000 .5524535 .6426772 cyr1998 | .6320055 .0230524 27.42 0.000 .5868227 .6771883 cyr1999 | .6569833 .0235202 27.93 0.000 .6108835 .7030831 cyr2000 | .6883455 .023794 28.93 0.000 .6417091 .7349818 cyr2001 | .6768544 .023774 28.47 0.000 .6302572 .7234517 cyr2002 | .6781355 .0256458 26.44 0.000 .6278697 .7284013 cyr2003 | .727104 .0293299 24.79 0.000 .6696172 .7845908 cyr2004 | .7483866 .0298464 25.07 0.000 .6898874 .8068858 cyr2005 | .7976689 .0312425 25.53 0.000 .7364334 .8589044 d21995 | .3747566 .0393274 9.53 0.000 .2976746 .4518385 d21996 | .0076059 .0448179 0.17 0.865 -.0802375 .0954492 d21997 | .0472942 .0430613 1.10 0.272 -.0371061 .1316945 d21998 | .0745453 .040493 1.84 0.066 -.0048212 .1539118 d21999 | .1215005 .0373312 3.25 0.001 .0483311 .1946699 d22000 | .0836343 .035389 2.36 0.018 .0142716 .152997 d22001 | .1063874 .0336693 3.16 0.002 .0403954 .1723795 d22002 | .1665607 .0245534 6.78 0.000 .1184359 .2146855 d22003 | .1237019 .0246153 5.03 0.000 .0754557 .1719481 d22004 | .1113531 .0247987 4.49 0.000 .0627474 .1599588 d22005 | .0831636 .025584 3.25 0.001 .0330189 .1333084 js21995 | (dropped) js21996 | (dropped) js21997 | .0194011 .0584728 0.33 0.740 -.095206 .1340081 js21998 | .0365029 .0561107 0.65 0.515 -.0734744 .1464802 js21999 | -.0320089 .0390562 -0.82 0.412 -.1085593 .0445415 js22000 | .080808 .0310037 2.61 0.009 .0200406 .1415753 js22001 | .0423628 .0311084 1.36 0.173 -.0186098 .1033354 js22002 | -.0091573 .0252636 -0.36 0.717 -.0586742 .0403595 js22003 | .062112 .0267781 2.32 0.020 .0096268 .1145973 js22004 | .0611524 .0274925 2.22 0.026 .007267 .1150378 js22005 | .0655231 .0282626 2.32 0.020 .0101283 .1209179 ow21995 | (dropped) ow21996 | (dropped) ow21997 | (dropped) ow21998 | .5065398 .4027364 1.26 0.208 -.2828259 1.295905 ow21999 | .0772241 .0527526 1.46 0.143 -.0261713 .1806196 ow22000 | .0287958 .0483901 0.60 0.552 -.0660491 .1236406 ow22001 | .0965234 .0397122 2.43 0.015 .0186873 .1743595 ow22002 | .0943641 .0351952 2.68 0.007 .0253812 .163347 ow22003 | .0801613 .0289305 2.77 0.006 .0234574 .1368653 ow22004 | .0743024 .0289919 2.56 0.010 .0174781 .1311268 ow22005 | .0469032 .028618 1.64 0.101 -.0091883 .1029947 _cons | .0866825 .0230417 3.76 0.000 .0415206 .1318444 -------------+---------------------------------------------------------------- sigma_u | .62323578 sigma_e | .6232381 rho | .49999814 (fraction of variance due to u_i) Non_English_Non_Europe . xtreg lncit1 age* cyr* d2* js2* ow2*, i(articlegroup) fe robust; Fixed-effects (within) regression Number of obs = 53339 Group variable: articlegroup Number of groups = 99 R-sq: within = 0.3543 Obs per group: min = 104 between = 0.0260 avg = 538.8 overall = 0.2073 max = 975 F(102,53138) = 254.99 corr(u_i, Xb) = -0.0949 Prob > F = 0.0000 (Std. Err. adjusted for clustering on articlegroup) ------------------------------------------------------------------------------ | Robust lncit1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age1 | .3380092 .0159555 21.18 0.000 .3067364 .369282 age2 | .6266823 .0165165 37.94 0.000 .5943098 .6590547 age3 | .7883217 .016609 47.46 0.000 .7557678 .8208756 age4 | .8277641 .0167855 49.31 0.000 .7948644 .8606637 age5 | .8576919 .016884 50.80 0.000 .8245992 .8907847 age6 | .8461809 .0171128 49.45 0.000 .8126397 .8797221 age7 | .8343981 .0168808 49.43 0.000 .8013116 .8674846 age8 | .8168059 .017029 47.97 0.000 .783429 .8501828 age9 | .7852176 .0173774 45.19 0.000 .7511577 .8192775 age10 | .7513097 .0172813 43.48 0.000 .7174382 .7851813 age11 | .6994383 .0173542 40.30 0.000 .6654238 .7334527 age12 | .6657898 .0176374 37.75 0.000 .6312203 .7003592 age13 | .6411005 .0175182 36.60 0.000 .6067646 .6754364 age14 | .5901127 .017976 32.83 0.000 .5548796 .6253458 age15 | .5371326 .0181277 29.63 0.000 .5016023 .572663 age16 | .5112 .018355 27.85 0.000 .475224 .547176 age17 | .4614197 .0182906 25.23 0.000 .4255701 .4972694 age18 | .4220317 .0185336 22.77 0.000 .3857058 .4583577 age19 | .3647859 .0190418 19.16 0.000 .3274637 .402108 age20 | .32958 .0186139 17.71 0.000 .2930966 .3660633 age21 | .2816502 .0193139 14.58 0.000 .2437948 .3195056 age22 | .2824295 .0194474 14.52 0.000 .2443124 .3205466 age23 | .2359898 .019921 11.85 0.000 .1969444 .2750352 age24 | .1672561 .0203377 8.22 0.000 .1273941 .2071182 age25 | .1406581 .0207119 6.79 0.000 .1000626 .1812536 age26 | .1167943 .022036 5.30 0.000 .0736035 .1599851 age27 | .064554 .0216813 2.98 0.003 .0220585 .1070496 age28 | .0446124 .0226618 1.97 0.049 .0001952 .0890297 age29 | .0034351 .0228618 0.15 0.881 -.0413742 .0482443 age30 | -.0034517 .0247715 -0.14 0.889 -.0520042 .0451007 age31 | -.0534599 .0241998 -2.21 0.027 -.1008917 -.0060281 age32 | -.0663137 .0255304 -2.60 0.009 -.1163536 -.0162738 age33 | -.1041734 .0262499 -3.97 0.000 -.1556234 -.0527234 age34 | -.1300921 .0277284 -4.69 0.000 -.1844399 -.0757442 age35 | -.1534349 .0294404 -5.21 0.000 -.2111384 -.0957315 age36 | -.2272151 .0307721 -7.38 0.000 -.2875287 -.1669014 age37 | -.2397432 .0324992 -7.38 0.000 -.3034418 -.1760445 age38 | -.2445764 .0359357 -6.81 0.000 -.3150106 -.1741422 age39 | -.2998506 .0405069 -7.40 0.000 -.3792444 -.2204569 age40 | -.3264519 .0404981 -8.06 0.000 -.4058284 -.2470753 age41 | -.3701622 .0423991 -8.73 0.000 -.4532648 -.2870596 age42 | -.4063573 .0467689 -8.69 0.000 -.4980247 -.3146899 age43 | -.420763 .052556 -8.01 0.000 -.5237732 -.3177527 age44 | -.4879011 .0585621 -8.33 0.000 -.6026833 -.373119 age45 | -.5606306 .0600689 -9.33 0.000 -.6783662 -.442895 age46 | -.5038485 .0645617 -7.80 0.000 -.63039 -.377307 age47 | -.62336 .0890029 -7.00 0.000 -.7978065 -.4489135 age48 | -.6831456 .1113767 -6.13 0.000 -.9014449 -.4648462 age49 | -.6384979 .1609496 -3.97 0.000 -.9539604 -.3230353 cyr1981 | .0082461 .0228879 0.36 0.719 -.0366144 .0531066 cyr1982 | .054289 .0224047 2.42 0.015 .0103757 .0982024 cyr1983 | .1253487 .022307 5.62 0.000 .0816268 .1690706 cyr1984 | .1027914 .0216741 4.74 0.000 .0603101 .1452727 cyr1985 | .1319299 .021286 6.20 0.000 .0902091 .1736507 cyr1986 | .1797107 .0214419 8.38 0.000 .1376844 .2217371 cyr1987 | .1975117 .0208544 9.47 0.000 .1566368 .2383865 cyr1988 | .1825056 .0205656 8.87 0.000 .1421969 .2228144 cyr1989 | .2048165 .020624 9.93 0.000 .1643933 .2452398 cyr1990 | .2527487 .0204438 12.36 0.000 .2126787 .2928187 cyr1991 | .269059 .0204248 13.17 0.000 .2290261 .3090918 cyr1992 | .25602 .0201985 12.68 0.000 .2164307 .2956093 cyr1993 | .2885954 .0201612 14.31 0.000 .2490793 .3281115 cyr1994 | .3419409 .0200109 17.09 0.000 .3027193 .3811626 cyr1995 | .4405574 .0201105 21.91 0.000 .4011407 .4799741 cyr1996 | .4925645 .0204299 24.11 0.000 .4525217 .5326074 cyr1997 | .5328507 .0206993 25.74 0.000 .4922799 .5734214 cyr1998 | .5642992 .0205527 27.46 0.000 .5240157 .6045828 cyr1999 | .6235817 .021159 29.47 0.000 .5821099 .6650534 cyr2000 | .6493474 .0216308 30.02 0.000 .6069507 .691744 cyr2001 | .7414571 .0221729 33.44 0.000 .6979981 .7849161 cyr2002 | .6902463 .0240668 28.68 0.000 .6430752 .7374174 cyr2003 | .7224713 .0275976 26.18 0.000 .6683798 .7765627 cyr2004 | .7946834 .0289587 27.44 0.000 .7379242 .8514427 cyr2005 | .8077612 .030452 26.53 0.000 .748075 .8674475 d21995 | .4237847 .0349563 12.12 0.000 .35527 .4922994 d21996 | .0691633 .0448006 1.54 0.123 -.0186463 .1569729 d21997 | .106342 .0399121 2.66 0.008 .0281138 .1845701 d21998 | .1630718 .0344816 4.73 0.000 .0954876 .2306561 d21999 | .1952303 .0357098 5.47 0.000 .1252387 .2652219 d22000 | .1407025 .0350373 4.02 0.000 .0720292 .2093759 d22001 | .2123863 .0336829 6.31 0.000 .1463675 .2784052 d22002 | .1607605 .0245616 6.55 0.000 .1126196 .2089014 d22003 | .1724756 .0247533 6.97 0.000 .1239589 .2209923 d22004 | .1925357 .025018 7.70 0.000 .1435003 .2415711 d22005 | .1885599 .025376 7.43 0.000 .1388226 .2382971 js21995 | (dropped) js21996 | (dropped) js21997 | .0362609 .0574777 0.63 0.528 -.076396 .1489177 js21998 | .0655863 .0506122 1.30 0.195 -.0336141 .1647867 js21999 | .1634453 .0349235 4.68 0.000 .0949949 .2318957 js22000 | .1340548 .0303428 4.42 0.000 .0745825 .193527 js22001 | .1175705 .0297903 3.95 0.000 .0591812 .1759599 js22002 | .1703613 .0251861 6.76 0.000 .1209963 .2197264 js22003 | .1965217 .0270895 7.25 0.000 .1434261 .2496174 js22004 | .1620038 .0278002 5.83 0.000 .1075151 .2164924 js22005 | .140304 .0290487 4.83 0.000 .0833684 .1972396 ow21995 | (dropped) ow21996 | (dropped) ow21997 | (dropped) ow21998 | .0952746 .4236361 0.22 0.822 -.7350559 .9256051 ow21999 | .0871446 .0566386 1.54 0.124 -.0238675 .1981567 ow22000 | .2146694 .043485 4.94 0.000 .1294385 .2999003 ow22001 | .2154918 .0392042 5.50 0.000 .1386512 .2923323 ow22002 | .198708 .0365688 5.43 0.000 .1270328 .2703832 ow22003 | .2067046 .0280761 7.36 0.000 .1516753 .261734 ow22004 | .1944047 .028571 6.80 0.000 .1384053 .2504042 ow22005 | .1846608 .0298627 6.18 0.000 .1261296 .2431921 _cons | -.2018852 .019938 -10.13 0.000 -.2409638 -.1628066 -------------+---------------------------------------------------------------- sigma_u | .48810731 sigma_e | .55204062 rho | .43876575 (fraction of variance due to u_i) ------------------------------------------------------------------------------

21. Science Journal Citations Peak in Year Three 95% confidence interval Citations relative to age 49 Years since publication Notes: Results from negative binomial regression with age dummies, digital dummy aggregated across channels for any presence, restricted to 1956-2005 publication years

22. Economics Journal Citations Peak in Year Five 95% confidence interval for science 95% confidence interval for economics Citations relative to age 49 Years since publication Notes: Results from negative binomial regression with age dummies, digital dummy aggregated across channels for any presence, restricted to 1956-2005 publication years

23. Preliminary General Findings • Citation levels more than double in both disciplines over the sample period, 1980-2005. • There is an increase in citations as a result of digitization and online availability. Highly significant, both for pre-1995 content (digitized backfiles) and born-digital periods.

24. Disciplinary Differences • Citation rates peak earlier in science (3 years) than in economics (5 years); the subsequent decline in citations is more rapid in science. • Online access is associated with an average increase in citations of about 10% for economics and 20% for science titles. • However, the changes in citations observed over time is an order of magnitude larger than the measured impact of online access.

25. For Science, Online Access Boosts Citations 20% Overall Online Offline Citations relative to age 49 Years since publication Notes: Results from negative binomial regression with age dummies, digital dummy aggregated across channels for any presence, restricted to 1956-2005 publication years

26. For Economics, Online Access Boosts Citations 10% Overall Citations relative to age 49 Online Offline Years since publication Notes: Results from negative binomial regression with age dummies, digital dummy aggregated across channels for any presence, restricted to 1956-2005 publication years

27. Channel Effects • For Science: JSTOR and publisher portals are important, but not other 3rd party channels (except for the period 95-97). • For Economics, all types of channels have a significant impact. • Longer embargo periods clearly decrease the ability of a given channel to increase citations.

28. HIGHLY PRELIMINARY:Geographic Effects on Citation Growth over Time • Rate of citation growth for biology is much higher (double) in non-English-speaking countries. • Rate of citation growth for economics is moderately higher in non-English-speaking countries. • Implication: Are these disciplines growing faster in non-English-speaking countries?

29. Impact of Digitization for Science – Publisher Website

30. Impact of Digitization for Science – JSTOR

31. Impact of Digitization for Science – Aggregators

32. Impact of Digitization for Economics – Publisher Website

33. Impact of Digitization for Economics – JSTOR

34. Impact of Digitization for Economics – Aggregators

35. HIGHLY PRELIMINARY: Geographic Effects on Citation Patterns • Science: The channel impact is about twice as large in the non-English speaking countries (e.g. overall a 30% increase versus 15%). • Economics: The channel impact is about twice as large outside of the developed English-speaking countries (~20% increase versus less than 10%). • There is much we can learn from various models for the distribution of content and their relative strengths over time.

36. Further Questions and Discussion

37. Further Questions • Does year of source-item publication matter? • Will references to older articles increase more than references to more recently published articles? • Have self-citation patterns changed? • Presumably we will find no effect, an important confirmation of our data and analytical framework.

38. Findings and Discussion • We find a consistent significant impact from digitization. At the same time, it is an order of magnitude less than the changes observed over time. Is the impact “large” or “small” and what implications if any are there? • The impact is greater in science than in economics. Why? What are the implications? • The impact is greater outside of the English-speaking countries. Why? What are the implications? • Channel effects are dramatic. What are the implications?

39. How Does Digitization Affect Scholarship? Roger C. Schonfeld rcs@ithaka.org (212) 500 – 2338 www.ithaka.org/research/citation-analysis