Terrorism and the Rise of Right-Wing Content in Israeli Books

Home , Calcalist, Hadash, Meimad, Meretz, Moledet, Morasha

Supplementary Appendix

Contents

A Automated text analysis: Extracting data on political ideology in popular books 3 A.1 Textual sources ...... 3 A.2 Selection of books into the Google N-gram corpus ...... 4 A.3 Textual pre-processing of Hebrew words ...... 8 A.4 Generating a comprehensive dataset of political phrases in Israeli books . . .9 A.5 Mapping phrases to the ideology of political parties ...... 9 A.6 Measuring phrases’ yearly frequency in Google N-gram ...... 10 A.7 Summary statistics ...... 13

B Empirical strategy and modeling choices 14

C Robustness tests 18

D Additional estimations 22

E Israeli civics textbook comparison 26

F Information on book publishers in Israel 31

1 List of Figures A1 OCR quality of Hebrew books with right and left-wing phrases ...... 5 A2 Phrases most strongly linked the ideology of political blocs ...... 7 A3 Stemming Hebrew Text ...... 12 A4 Residuals over time ...... 15 A5 Year-by-year correlation of the residuals ...... 16 A6 Placebo: Terrorism casualties from other countries ...... 19 A7 Topic Prevalence During the Intifadas vs. Not ...... 21 List of Tables A1 List of political parties by bloc ...... 6 A2 Summary Statistics ...... 13 A3 Placebo: Frequency of phrases by bloc and the number of casualties from other conﬂicts ...... 20 A4 Frequency of phrases by bloc after the peak of terrorism in the First Intifada 23 A5 Frequency of phrases by bloc and the number of casualties from Palestinian Violence (cumulative) ...... 24 A6 Frequency of phrases by bloc and the number of casualties from Palestinian violence (other estimations) ...... 25 A7 Civics book comparison: Book structure ...... 29 A8 Civics book comparison: The roots of the Israeli-Palestinian conﬂict . . . . . 30 A9 The Twenty Major Book Publishing Companies in Israel ...... 32

2 A Automated text analysis: Extracting data on political ideology in popular books A primary contribution of this study is to apply innovative data collection methods— specifically, automated text analysis—to quantitatively measure the popularity of right-wing ideology in books in the wake of terrorist violence. I make use of Hebrew language textual sources to generate a time-series, phrase-level dataset on the readership of right, center, and left-wing political ideas in books published in Israel. The text extraction method involves several steps. First, I obtain three sources of text: Israeli party platforms, the full text of the Hebrew edition of Wikipedia, and the Google N-gram corpus in Hebrew. Second, I pre-process these text corpora using an original algorithm designed to stem political texts in Hebrew. Finally, I generate a comprehensive vocabulary of political phrases extracted from party platforms and Wikipedia, and measure these phrases’ yearly frequency in Israeli books. A.1 Textual sources The first source I use is Israeli political party platforms from 1981 to 2013. I use these platforms to generate a “core vocabulary” that represents political issues on the right-center- left ideological space. I downloaded all available historical election platforms from the website of The Israel Democracy Institute.1 I converted the platforms to textual files using optical character recognition software, and complemented it with manual correction of mistakes. The platforms corpus consists of 51 platforms from right-wing, center, and left-wing parties. The second source I use is the full text of the Hebrew edition of Wikipedia, which consists of 156,531 articles. I divide each Wikipedia article into paragraphs, and calculate the paragraph-level association between “core” phrases coming from party platforms and “associated” phrases appearing in Wikipedia. More details on this process are described below. The third textual source I use is the Google N-gram corpus in Hebrew, which includes a sample of all books published in Israel from 1980 to 2008. The full Google N-gram dataset was created from a corpus of digitized books in eight languages drawn from several dozen universities around the world and direct contributions by publishers. The 2012 version of the dataset consists of more than 8 million books, which is about 6% of books ever published. The textual information was collected by scanning books and digitizing their text with optical character recognition (OCR). Metadata includes information on the year and place

1The platforms can be found at: http://en.idi.org.il/tools-and-data/ israeli-elections-and-parties/

3 of publication (Michel et al., 2011; Lin et al., 2012). In this paper, I use the “2-gram” (i.e., two-word phrases) version of the Google N-gram dataset in Hebrew. The 2012 version of the Hebrew dataset consists of more than 70,000 books and over 8 billion words (Lin et al., 2012). A.2 Selection of books into the Google N-gram corpus One might wonder how books are selected into the Google N-gram corpus, and whether such selection might lead to bias. For example, if only books with certain type of content enter the corpus, such as purely academic publications, that might influence the analysis and interpretation of results. Similarly, if the selection of books varies over time, we might worry about bias in the time-series analysis in this paper. To investigate these possibilities, I examined, first, how Google Books obtained and scanned Hebrew language books — is there a reason to worry about selection of specific content? Second, since the Google N-gram corpus consists of a subset of Google Books, I evaluated the extent to which the selection of books into the N-gram corpus might render the analysis biased. Selection of books into Google Books Some have criticized Google Books in English for heavily representing academic works, as a result of partnering with major American university libraries (Pechenick, Danforth and Dodds, 2015). The critique is centered around the fact that books in universities heavily over-represent academic publications and not enough popular works. In Israel, however, the process of obtaining books has been different. Unlike Google Books in English, the Hebrew version is based on books obtained through contracts with Israeli popular book publishers. In 2010, the company signed contracts with several major Israeli publishers, including Keter and Hakibbutz Hameuchad, as well as smaller publishing houses (Hirschauge, 2010; Lilian, 2010; Kabir, 2010). The inclusion of Hebrew language popular books allows making inferences on the content of books that go beyond pure academic publications. Selection of books into the Google N-gram corpus Since Google N-gram is a subset of Google Books, it is important to understand how books are selected into the N-gram corpus. While the process of selection is not completely transparent, Michel et al. (2011) indicate that books entered the corpus on the basis of “the quality of their OCR and metadata.”2 This is not gold-standard random sampling, but selecting books with good quality OCR is likely orthogonal to the ideological content in books. In other words, it is hard to imagine that books with right-wing or left-wing content would have systematically different OCR

2Michel et al. (2011), p. 176.

4 qualities. In addition, one might worry that OCR quality varies over time, as books published in earlier years might be scanned with poorer quality. However, since the analysis in this paper is based on books published relatively recently — between 1980 and 2008 — the quality of OCR is unlikely to vary over these years. I visually inspected the OCR quality of books with right and left-wing phrases, as well as books published in various years analyzed in this paper. As Figure A1 shows, books with right and left-wing content, published in earlier and later years, have similar levels of OCR quality.

5 Figure A1: OCR quality of Hebrew books with right and left-wing phrases

(A) “People of Israel” (published in 2004) (B) “Star of David” (published in 2000)

(E) “Jewish-Arab’ (published in 1986) (F) “Synagogue’ (published in 1985)

Note: The figure presents screenshots of Hebrew language books that had good quality OCR, included phrases linked to right and left-wing parties, and were published at different points in time. Sources of text: (A) Gamliel (2003, p. 27); (B) Owan and Bruner (2000, p. 13); (C) Golan Agnon (2002, p. 52); (D) Ophir (2001, p. 66); (E) Sela-Shefi and Geretz (1986, p.42); (F) Shiloach (1985, p. 39).

6 Table A1: List of political parties by bloc

Election year Parties in left-wing bloc Parties in center bloc Parties in right-wing bloc

1981 Maarach, Hadash, Ratz* Telem*, Shinui Likud, Mafdal, Agudat Israel*, Hatchiya*, Tami

1984 Maarach, Hadash, Ratz*, Shinui Likud, Mafdal, Agudat Yachad*, Progressive List for Israel*, Hatchiya-Tzomet, Peace*, Shas, Morasha*, Tami*, Kach*, Ometz*

1988 Maarach, Hadash, Ratz*, Shinui Likud, Mafdal, Agudat Mapam*, Progressive List Israel*, Hatchiya*, Tzomet, for Peace*, Mada* Shas, Moledet*, Degel Hatorah*

1992 Labor, Hadash, Meretz, – Likud, Mafdal, Tzomet, Mada*, Shas, Yahadut Hatorah*, Moledet*

1996 Labor, Hadash-Balad, Third Way* Likud, Mafdal, Shas, Yisrael Meretz, Mada-Raam BaAliyah*, Yahadut Hatorah*, Moledet*

1999 Israel Achat, Meretz, Raam, Shinui, Center Party*, Likud, Mafdal, Shas, Hadash, Balad, Am Echad* Yahadut Hatorah*, Yisrael BaAliyah*, National Union, Yisrael Beiteinu

2003 Labor, Meretz, Hadash-Taal, Shinui Likud, Mafdal, Shas, Am Echad*, Balad, Raam National Union, Yahadut Hatorah*, Yisrael BaAliyah*

2006 Labor,Meretz-Yachad, Kadima, Gil* Likud, Shas, Yisrael Raam-Taal, Hadash, Balad Beiteinu, National Union-Mafdal, Yahadut Hatorah*

2009 Labor, Meretz, Meimad*, Kadima Likud, haBait haYehudi, Hadash, Balad*, Raam, haIchud haLeumi, Tzomet*, Daam* Yahadut haTorah*, Shas, Israel Beteinu, Israel haMitchadeshet*

2013 Labor, Meretz, Hadash, Kadima, Hatnua*, Likud-Beitenu, haBait Balad* Yesh-Atid* haYehudi, Shas*, Yahadut haTorah* Note: * Indicates parties whose platforms were missing.

7 Figure A2: Phrases most strongly linked the ideology of political blocs

Center Right−wing Not accurate Change value Traveling person Israel Conversation President of the State Western alliance Children of Israel Separate nameConducted a survey Unify nation Small image Team player Remembrance Day No circumstance Israel Design Nazi Germany Nation Individual cost Jewish temple Spring expense It permitted Turning conflictDoctor House Promotion of issue Wanted to comeEstablished a yeshiva Military Month of Iyar Defense minister Government will Book exist Nation state School Last year Yearly value Man daughter Two names Yitzhak Rabin After war Another partyOther said Religious person Cemetery Held weapons Israel flag Majority British LandRabbi allow British Empire TeamPublic Member State nation Passing week Nation of Israel Jewish people This huge This bookUsage Holy Land According to law According to the ruleRun Israel People of Israel Hospital Depreciation Ben−Zvi Other states Head of party With establishment Synagogue Work Follow song Was subject World Heritage Israel Day During war Other work Tel AvivIsraeli Sea Substantial effectSituation is Second World War Traffic accident Change focus Jewish massacre Education system Beit Midrash Generation of land Large number Jewish democratic Conquest land Book of Samuel Working nation Extensively against After establishmentBritish Mandate Man answered Second Jewish Temple President Bush Yom Kippur War

Left−wing

Religious symbol Arab population Against the Jews Chairman Sexual violence Placed anywhere Rights organization Against settler I suggested Amir PeretzKnown personGolda Meir Second World War Equal opportunity Situation of rights Place in the list The very person Weapons destruction Jewish−Arab Foreign national Arab town Realistic place These people Enact Law Real truth To the people Election Labor party Month of May Star of David Son of nature Against rights Two states Lower House The Basic Law Direct tax Just peace Arabic speaker People of Israel With a party Arab couple Hospital Dead sea People nationPartner with Elections to Knesset Political opinion A law was passed Person profession A citizen of the country

8 A.3 Textual pre-processing of Hebrew words One of the first steps in preparing texts for analysis includes cleaning the files and stemming words (Grimmer and Stewart, 2013). Cleaning text files includes removing stop words, conjunctions, symbols, and numbers. Stemming is a very important part of text analysis, since phrases with the same meaning can appear in multiple forms. In English, stemming includes the removal of endings such as “ing” or “ly.” For example, the words “reading,” “reads,” and “reader” are all stemmed to “read.” Stemming in Hebrew is a more complicated process, since words take a variety of forms which makes the process of transforming them to their roots problematic. As Itai and Wintner (2008) indicate, “Hebrew morphology is rich and complex. The major word formation machinery is root-and-pattern, and inflectional morphology is highly productive and consists of prefixes, suffixes and circumfixes” (p. 2). As such, removing the last few characters of each word does not render an accurate representation of word stems in Hebrew. For this reason, I use an algorithm that I developed with a collaborator, which applies a morphological analyzer constructed by the Technion - Israel Institute of Technology (Itai and Wintner, 2008), to stem Hebrew words. The stemming process is summarized in Figure A3 and is carried out as follows. First, each textual file is processed by a tokenizer, which breaks the text into words while preserving sentence structure, and outputs the result to an XML file. Second, the tokenized files are analyzed by Itai and Wintner’s (2008) morphological analyzer, which takes each token (i.e., each word) and extracts all of its possible interpretations. Each interpretation consists of a core lexicon item—i.e., the stem of the word—and part of speech possibility. The output of the morphological analyzer results in several possible stems for each word in the corpus. To decide which stem is most appropriate, I apply a preference rule which gives a higher priority to proper names and nouns, as political issues in Hebrew usually consist of these forms. The preference rule is as follows:

Proper name > Noun > Adjective > Participle > Verb

After the appropriate stem has been selected for each word, the algorithm generates updated sentences in a stemmed format for each text ﬁle in the corpus. Using the stemmed text, I create vectors of two-word phrases and save them in a document-term-matrix. The stemming process is a computationally intensive task, especially for the Google N- gram corpus. For this reason, I used Amazon Web Services (AWS), a platform for processing large data ﬁles to stem words in Google N-gram. I used Amazon EC2, a virtual server in the cloud, to process the data, and S3, the storage system of AWS, to save the output.

9 I downloaded each of the Google 2-gram zip-files from the central N-gram database, and unzipped them (the database can be found at: http://storage.googleapis.com/books/ ngrams/books/datasetsv2.html). Then, I ran a date filter that selected data from the years 1980 to 2008. Since the 2012 version of the Google N-gram data consists of part of speech tags that are not useful for my analysis, I used a find-and-replace algorithm to filter them out. In the process, the unzipped Google 2-gram files were split into smaller chunks to make them analyzable by the morphological analyzer developed by Itai and Wintner (2008). Then, my algorithm reassembled the split files, which now contained the stemmed versions of the Hebrew 2-grams, and outputted them for further analysis. A.4 Generating a comprehensive dataset of political phrases in Israeli books Using the pre-processing steps described above, I stem the party platforms, Wikipedia, and Google 2-gram corpora. The cleaned and stemmed versions of these texts result in a uniform representation of stemmed Hebrew phrases. The next steps involve mapping phrases to political parties to determine phrases’ association with the ideology of right, center, and left wing blocs; and measuring phrases’ yearly frequency in Google N-gram. These steps are described in Figure 10 in the main text. A.5 Mapping phrases to the ideology of political parties I map phrases to political parties in two stages. First, I weight “core” phrases generated from party platforms using the frequency in which they appear in the platforms. For each political party, my goal is to give more weight to phrases that better represent its political ideology. As terms appearing more frequently in a party’s platforms are arguably closer to its latent ideology, I give higher weight to more frequent phrases. In addition, I transform the raw frequency of each phrase by taking its natural log, since phrases’ appearance in party platforms is highly skewed. Finally, I normalize the log-transformed frequency of each phrase by dividing it by the log of the frequency of the most common phrase in all platforms of a given party, which gives phrases weights that are comparable across parties.

This transformation is captured in equation 1, where fi,p and φi,p respectively represent the raw frequency and normalized weight of phrase i in the platforms of party p, and fmax,p is the frequency of the most common phrase for a given party p.

log(fi,p) φi,p = (1) log(fmax, p) Second, I weight “associated” phrases generated from Wikipedia on the basis of their co- occurrence with core phrases in paragraphs of Wikipedia articles. The goal is to give higher

10 weight to phrases that are more related to the latent ideology captured by the core term. I count the number of times that Wikipedia phrases appear in paragraphs containing core party platform phrases, and assign higher weight to more frequent phrases. After obtaining co-occurrence frequency for each phrase in Wikipedia paragraphs, I log-transform it to smooth the skewness of the frequency distribution. Then, I normalize the logged frequency by dividing it by the logged frequency of the most frequent associated phrase in a given paragraph, which allows for comparing co-occurrence frequencies across Wikipedia paragraphs. The ﬁnal weight for Wikipedia phrases is calculated by multiplying the transformed frequency of each phrase by the weight of the core term with which it is associated. Equation

2 shows this calculation mathematically, where fj,k,i,p and ψj,k,i,p respectively represent the raw frequency and normalized weight of phrase j in Wikipedia paragraph k that includes

core phrase i from the platforms of party p, and where fmax, k,i,p is the frequency of the most frequent associated phrase in paragraph k. log(fj,k,i,p) ψj,k,i,p = φi,p (2) log(fmax, k,i,p) This procedure gives associated phrases the weight of the core phrase to which they are related, by further weighting this weight by the phrases’ relative association with core terms in Wikipedia paragraphs. This process results in an enlarged vocabulary for right-wing, center, and left-wing concepts. I use this enlarged vocabulary to measure popularity of political ideas in books, using ﬁne-grained phrase-level units of analysis. A.6 Measuring phrases’ yearly frequency in Google N-gram To assess changes in the popularity of ideological phrases after a period of terrorism in books read by Israelis, I measure the yearly frequency of each phrase in Google N-gram. The frequency is translated to a percentage of the sum of all phrases in the dataset, which controls for the possibility that not all phrases used in Google N-gram are in my dataset.

This transformation is captured in equation 3, where gi,t and Gi,t stand for the raw and PN transformed Google N-gram frequency of phrase i in year t, and n=1 gn,t reﬂects the sum of the frequencies of all phrases in the dataset at year t.

g G = i,t (3) i,t PN n=1 gn,t Controlling for this possibility ensures that changes in frequencies of phrases outside my dataset do not lead to the appearance of change in frequencies of phrases in my dataset. To

11 ﬁx ideas, here is a simple example. Assume that phrase A’s frequency in the dataset is 5% of the total frequency in Google N-gram in year t, and phrase B’s frequency is 3% of the total frequency in Google N-gram in year t. In year t+1, phrase A’s frequency decreases to 4% and phrase B’s frequency decreases to 2%. However, these changes are not the result of phrases A and B actually decreasing in usage, but because other phrases, not in the dataset, increase in frequency. To neutralize this bias, I scale each year’s frequency to the total frequency of phrases in the dataset for that year, which includes only a subset of the universe of phrases PN in Google N-gram (namely, the core and associated terms). Thus, if n gn,t = 0.95, i.e., PN 95% of the total frequency in Google N-gram in year t, and n gn,t+1 = 0.97, 97% of the total frequency in Google N-gram in year t+1, I divide the percentage of each phrase in year t by 95% and the percentage of each phrase in year t + 1 by 97% to scale the percentages to 100%. The popularity of political ideas, therefore, reﬂects the percentage of the sum of the yearly frequencies of the phrases in my dataset. After obtaining the yearly frequency of each phrase in Google N-gram, I generate a phrase-level time-series cross-section dataset. This dataset includes over a million phrases whose frequency is measured over the course of 28 years, from 1980 to 2008. Each phrase is linked in probability to political parties and therefore to political blocs, measured as a weight ranging from 0 to 1.

12 Figure A3: Stemming Hebrew Text

Corpus of text ﬁles

Tokenization Each text ﬁle is broken into words

Morphological analysis Each word gets a unique ID, word representation in Hebrew script, and possible interpretations. The stems (lexicon items) of each interpretation are saved (Itai and Wintner, 2008).

Selecting stems An algorithm selects the stem based on a preference rule giving higher priority to proper names and nouns

Stemmed text

Generate two-word phrases from stemmed text

13 A.7 Summary statistics

Table A2: Summary Statistics

Year Phrases Frequency Volumes Killed (N) Mean SD Min Max Mean SD Min Max (N) 1980 1008634 0.0000486 0.0003109 2.07E-07 0.0074346 87.24895 279.0374 1 5053 0 1981 1454111 0.0000374 0.000215 1.69E-07 0.0048933 83.67148 258.4522 1 4861 0 1982 1162042 0.0000371 0.0002366 1.79E-07 0.005747 72.29514 249.8572 1 4954 0 1983 1239501 0.0000448 0.0002947 2.41E-07 0.0071962 62.79942 202.3185 1 4478 0 1984 1256329 0.0000452 0.0003039 1.78E-07 0.0060665 79.26538 274.558 1 5089 0 1985 1154575 0.0000468 0.0003473 2.55E-07 0.0083447 61.42195 216.5409 1 4465 0 1986 926586 0.0000665 0.0003999 2.38E-07 0.0074282 79.85955 255.6332 1 4068 0 1987 789774 0.0000611 0.0004048 2.60E-07 0.0074678 73.61474 251.3096 1 4707 0 1988 1210498 0.0000532 0.000344 1.50E-07 0.0067895 100.5409 308.8277 1 5532 12 1989 1179264 0.0000416 0.0002884 1.58E-07 0.0069362 86.60441 279.4332 1 4651 31 1990 1277378 0.0000528 0.00034 1.29E-07 0.0057054 113.3165 370.4312 1 5447 22 1991 1463603 0.00004 0.0002602 9.64E-08 0.0056542 129.5782 391.3454 1 7512 19 1992 1811313 0.0000367 0.0002505 6.49E-08 0.0050182 173.1321 550.703 1 10078 34 1993 1241028 0.0000532 0.0003694 1.35E-07 0.0086834 110.2941 341.8145 1 6457 72 1994 1201529 0.0000633 0.000423 1.21E-07 0.0061274 137.0529 461.9895 1 8418 74 1995 1350444 0.0000366 0.0002428 9.28E-08 0.007403 134.9604 431.9707 1 7757 46 1996 1213148 0.0000438 0.0002674 1.01E-07 0.0050061 142.7983 444.7022 1 8255 75 1997 1740604 0.0000311 0.0001986 6.94E-08 0.00503 149.6641 468.9949 1 7694 29 1998 1643306 0.0000393 0.0002786 7.27E-08 0.0069947 156.139 514.7471 1 8548 12 1999 1261831 0.0000512 0.0003787 7.58E-08 0.0083168 183.8701 598.1633 1 9469 4 2000 1174531 0.0000451 0.0003199 7.32E-08 0.007296 188.3799 671.4685 1 11581 43 2001 1352533 0.0000537 0.0004185 6.98E-08 0.0094087 196.5174 676.8493 1 10503 192 2002 1421839 0.0000504 0.0003827 5.90E-08 0.0080147 215.137 749.6917 1 11881 411 2003 1611913 0.0000329 0.0002033 5.59E-08 0.0071323 195.6324 580.5608 1 11493 171 2004 1525955 0.0000452 0.0003154 5.82E-08 0.0077463 208.9538 638.5593 1 10382 108 2005 1168069 0.0000592 0.0004814 8.97E-08 0.0107891 174.6025 607.7769 1 9779 51 2006 1937119 0.0000395 0.0003024 6.16E-08 0.0072415 176.8622 540.1503 1 10443 23 2007 1226312 0.0000546 0.000428 1.13E-07 0.0097271 125.2397 407.7997 1 6089 13 2008 1779447 0.0000434 0.000325 9.42E-08 0.0088393 124.8737 363.6265 1 6445 31 Note: The table presents summary statistics for the entire dataset by year. The table provides information on the yearly frequency of phrases, as well as the number of volumes in which they appear, for a panel of over a million phrases from 1980 to 2008. The yearly total of phrases changes from year to year, because some phrases never appear in certain years. The table also provides information on the yearly number of casualties from terrorism.

14 B Empirical strategy and modeling choices In this paper I use a variant of a Difference-in-Difference (DD) model, where I interact indicators of phrases’ link with the ideology of political blocs in Israel (right, center, and left) with a variable capturing terrorist violence in the Second Intifada. Thus, I make comparisons in the frequency of phrases before and after years in which terrorist violence peaked. One of the drawbacks of DD models with time-series data is that unmodeled mechanical trends in the dependent variable can lead to understating the standard errors, due to serial correlation in the error term (Bertrand, Duflo and Mullainathan, 2002). Unlike other time-varying dependent variables, however, the frequency of phrases in books is plausibly not driven by mechanical trends not accounted for by the paper’s model. While language is trended in some sense, especially when certain topics become more popular over time, any variation in the frequency of phrases that remains after controlling for time trends, differences across phrases, and terrorist events that might affect the use of language, is likely to be i.i.d white noise. The year fixed effects de-trend the data from any kind of linguistic popularity of a given phrase in a given year; the phrase fixed effects subtract out variation in the frequency across phrases, and the variable capturing terrorist violence accounts for theoretically substantive variation in phrase frequency over time. In addition, the yearly frequency of phrases reflects the number of times in which phrases appear in new published books each year. These books are written by many different independent authors in various types of books. For example, in the year 2003, there were 7,128 new books published in Israel, 82% of them were published by commercial, non-governmental, or private publishers. In 2007, there 5,850 new books were published, of which 86% were printed by commercial, non-governmental, or private publishers (National Library of Israel, 2016b). Thus, it is plausible to assume that any residual variation in the frequency of phrases not accounted for by the model is not systematically trended. Figure A4 plots the residuals from the model in equation 1 (in the main text), when comparing the content of books [6, 1] and [1, 6] from the peak of terrorism in the Second Intifada. The years are plotted on the x-axis and the residuals on the y-axis. It can be seen that the residuals are centered around zero and show no specific pattern over time. Figure A5 plots the residuals of each phrase at each year t against their residuals at year t − 1. Here, as well, there is no systematic relationship between the residuals over time. Statistical t-tests of these relationships show that they are also not statistically significant.

15 Figure A4: Residuals over time 1000 500 Residuals 0 −500

1996 1998 2000 2002 2004 2006 2008

Year

16 Figure A5: Year-by-year correlation of the residuals 400 400 400 200 200 200 0 0 0 Residuals (1997) Residuals (1998) Residuals (1999) −400 −400 −400

−400 −200 0 200 400 −400 −200 0 200 400 −400 −200 0 200 400

Residuals (1996) Residuals (1997) Residuals (1998) 400 400 400 200 200 200 0 0 0 Residuals (2000) Residuals (2001) Residuals (2004) −400 −400 −400

−400 −200 0 200 400 −400 −200 0 200 400 −400 −200 0 200 400

Residuals (1999) Residuals (2000) Residuals (2003) 400 400 400 200 200 200 0 0 0 Residuals (2005) Residuals (2006) Residuals (2007) −400 −400 −400

−400 −200 0 200 400 −400 −200 0 200 400 −400 −200 0 200 400

Residuals (2004) Residuals (2005) Residuals (2006) 400 200 0 Residuals (2008) −400

−400 −200 0 200 400

Residuals (2007)

17 This paper does not use clustered standard errors in its regressions for several reasons. First, as explained above, the standard errors for each phrase are plausibly independent over time, after adjusting for year fixed effects, phrase fixed effects, and patterns of terrorist violence. The yearly frequency of phrases comes from new books published by many different authors each year. Second, aggregating the standard errors to the phrase level by clustering throws away important variation that is crucial for testing this paper’s hypothesis of systematic changes in the right-wing content of books after the Second Intifada. A difference in the frequency of right-wing phrases after the peak of terrorism that remains systematic over time is what this paper aims to test; thus, removing the over time variation in the residuals limits this hypothesis testing. For example, assume that a given phrase’s average frequency in the pre-Intifada years is a, and in the post-Intifada years it is a+x, with little variation over time. In other words, at each consecutive year in the post-Intifada years, the variation around the average frequency of a+x is small. Clustering the standard errors at the phrase level will throw away all of this information, which shows that year-after-year in new books, the frequency of this right-wing phrase remains close to a + x. This low variance increases the efficiency of the statistical test, and is also substantively what this paper aims to test.

18 C Robustness tests This section presents additional robustness tests. First, I carry out several placebo tests to examine whether the frequency of right-wing phrases increases with violence from other contexts. The theoretical idea underlying this study is that right-wing content in popular books results from increased popularity of right-wing ideology in a population targeted by terrorism.3 If terrorism is measured in other conflicts, it should not impact the popularity of right-wing ideology in Israeli books. I use yearly measures of casualties from terrorist attacks taking place in other countries to examine the sensitivity of my results. I utilize data from the Global Terrorism Database (GTD)4 to measure casualties from terrorism around the world. Specifically, I measure the yearly number of people killed in terrorist attacks between 1992-2010 in countries outside the Middle East that experienced casualties in similar or greater magnitude to that experienced in Israel during this period. Using the model described in equation 2, I evaluate the association between a five-year moving average of the casualties in these countries and the frequency of phrases linked to right-wing parties in Israeli books. The results are presented visually in Figure A6 and in more detail in Table A3. It can be seen that across most specifications, the popularity of phrases linked to the ideology of right-wing parties in Israel does not increase with the number of casualties from terrorism in other conflicts. One exception is Turkey, a result which could be explained by the country’s relative proximity to Israel. This evidence suggests that the increase in the popularity of right-wing phrases in the aftermath of terrorism in Israel is systematically related to domestic terrorism, and is not associated with violence from other, unrelated conflicts. In addition, I evaluate the extent to which terrorism was central in Israel during the time period under study. One problem with this study’s empirical strategy is that there could have been other events happening after the peak of violence that could have affected subsequent popularity of political ideas in books. To alleviate this problem, I examine whether terrorism was the most central issue in Israel during these years, rendering other events arguably less impactful. Using newspaper media from this time period, I examine whether the topic of terrorism was covered more than other issues during the Second Intifada. I employ a Structural Topic Model (STM) to examine the salience of terrorism, relative to other issues, in Israel in the years of the years of the uprising. An STM is a topic model that uses the structure of written text to discover underlying themes, or topics, in a large

3Berrebi and Klor (2008); Kibris (2011); Getmansky and Zeitzoﬀ (2014) 4START (2013)

19 Figure A6: Placebo: Terrorism casualties from other countries Placebo: Terrorism casualties from other countries 10 − 8e 10 − 4e

● ● 0 ● ●

10 ● − Change in phrase frequency Change in phrase 4e − ● 10 − 8e −

Algeria India Philippines Russia Somalia Turkey

5−year moving average of casualties

Note: The ﬁgure reports the relationship between 5-year moving averages of the number of casualties from terrorist violence in other countries in the world and the frequency of phrases linked to the ideology of right- wing parties in Israel during the period 1992-2010. It can be seen that an increase in the average number of casualties in other conﬂicts is not associated with an increase in the frequency of phrases linked to right-wing parties in Israeli books.

number of documents.5 Unlike other topic models, the STM allows for using document-level metadata, such as the year in which a document was written, as a covariate in the model, which enables testing how periods of violence escalation relate to the distribution of topics in news articles. For this analysis I use the raw text of 111,848 newswire articles published by Reuters from 1992 to 2005. In an ideal world I would use Hebrew language newspapers published in Israel, but since access to the raw text of these newspapers is limited, I use the Reuters newswire as a second-best source. I obtained the articles from the Factiva database, using only one search term: “israel” and limiting the search to articles originating in Israel. This allows for including all articles published during this period, regardless of the topic covered. I preprocess the articles by removing stop words, conjunctions, numbers, and symbols. Then, I use metadata from each article to code whether it was published during the years of the Palestinian uprisings or not.6 I estimate the STM with this indicator to examine whether

5 Blei, Ng and Jordan (2003); Lucas et al. (2015); Roberts et al. (2014) 6 I code the years 1992-1999 as non-Intifada years and 2000-2005 as Intifada years.

20 Table A3: Placebo: Frequency of phrases by bloc and the number of casualties from other conﬂicts

Dependent variable: yearly frequency of a phrase Algeria India Philippines Russia Somalia Turkey Number of casualties 0.0003*** 0.004*** 0.001 -0.000** 0.01*** 0.0004* (0.000) (0.0006) (0.0008) (0.000) (0.001) (0.0002) Right 0.26*** 3.28*** 0.02 -0.02 0.12* -0.05*** (0.07) (0.36) (0.11) (0.06) (0.07) (0.0002) Left 0.19** 1.41*** 0.82*** -0.04 0.32*** 0.27*** (0.08) (0.42) (0.14) (0.08) (0.09) (0.07) Center 0.69*** 2.9*** -0.78*** -0.42*** 0.36*** -0.06 (0.08) (0.41) (0.13) (0.07) (0.09) (0.06) Number of casualties × Right -0.0002*** -0.01*** -0.0000 0.0001 -0.003* 0.0005** (0.0000) (0.0006) (0.0008) (0.0002) (0.001) (0.0002) Number of casualties × Left -0.0001*** -0.002*** -0.01*** 0.0002 -0.01*** -0.002*** (0.0000) (0.0007) (0.0009) (0.0002) (0.002) (0.0002) Number of casualties × Center -0.0007*** -0.01*** 0.01*** 0.001*** -0.01*** 0.0004 (0.0000) (0.0007) (0.0009) (0.0002) (0.002) (0.0002) Constant 6.88*** 4.66*** 7.02*** 7.36*** 6.95*** 7.11*** (0.07) (0.38) (0.12) (0.08) (0.08) (0.06)

R2 0.5282 0.5282 0.5282 0.5670 0.5282 0.5282 Number of observations: 17,843,459 17,843,459 17,843,459 16,231,546 17,843,459 17,843,459 Phrase fixed effects: Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Total number of casualties in GTD (1992-2010) 10,823 11,098 2,780 3,625 1,917 3,064 Note: The table reports the relationship between 5-year moving averages of the number of casualties from terrorist violence in other countries in the world and the frequency of phrases linked to the ideology of political blocs in Israel during the period 1992-2010. The association between the average number of casualties and frequency of right-wing, left-wing, and center phrases is captured by the interaction terms. Results show that across most models, an increase in the average number of casualties in other conflicts is not associated with an increase in the frequency of phrases linked to right-wing ideology. All coefficients are multiplied by 10,000,000 for easier interpretation. As analysis is conducted on the phrase-level, the magnitudes of the original coefficients are very small. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. articles discussing terrorism and violence are more prevalent than other topics during the years of the Intifada. Figure A7 shows the results from a topic-model with five topics. It can be seen that the topic of terrorism and violence was significantly more prevalent than other topics, such as the economy and peace negotiations, during the Intifada period. This provides evidence in support of the argument that terrorism was a central issue—possibly the most central—in Israel during the period investigated in this study.

21 Figure A7: Topic Prevalence During the Intifadas vs. Not Topic Prevalence During the Intifadas vs. Not

Religion ●

Security/nuclear ●

Economy ●

Peace negotiations ●

Terrorism/violence ●

●

−0.10 −0.05 0.00 0.05 0.10 Not during Intifada During Intifada

22 D Additional estimations This section describes additional analyses. First, I present results for the First Intifada (1987-1991) by examining the content of books surrounding the year 1989, which was the year with the highest number of casualties in that uprising. Second, I present results from several other models to show the the increase in the frequency of phrases relating to the ideology of right-wing parties is robust to a number of additional specifications. Table A4 presents results from the model in equation 1 (in the main text), using the peak of terrorism in the First Intifada as the predictor. The left two columns present the the results obtained when measuring phrase frequency [-6,-1] and [1, 6] years from the peak of violence; the middle columns report the findings when phrase frequency is measured [-6, -4] and [4, 6] years from the peak; and the right two columns show the coefficients estimated using measures of phrase frequency [-3, -1] and [1, 3] years from the peak. Similar to the results obtained for the Second Intifada (reported in the main text), we find that the frequency of phrases linked to right-wing parties increases in popular books in years succeeding the peak of violence in the First Intifada. When comparing the content of books [-6, -1] and [1, 6] years or [-6, -4] and [4, 6] years from the peak, the frequency of phrases reflecting right-wing ideology significantly increases. In substantive terms, it is about 1.7- 7.6% higher in books published after the peak of violence in the First Intifada, compared to the frequency in pre-Intifada years. It should be recalled that there is a limit to the number of times that phrases can appear in a document; hence, this is a substantively significant change. Interestingly, we do not find similar results when comparing phrase frequency [-3, -1] and [1, 3] from the peak of terrorism. Here, the relationship is significantly negative. The results also show that the popularity of phrases linked to center parties significantly increases in all models. At the same time, phrases linked to left-wing parties significantly decrease after the peak of terrorism. Table A5 shows estimates from the model in equation 2 (in the main text) using the cumulative number of casualties in the First and Second Intifada. It can be seen that as Israeli casualties accumulated in the second Palestinian uprising, the popularity of phrases relating to right-wing and center parties increased, and the frequency of phrases relating to left-wing ideology decreased. This result does not hold for the First Intifada, where the accumulated number of casualties was not associated with an increase in the frequency of right-wing phrases in popular Israeli books. One reason for this difference may be that the number of casualties in the First Intifada was significantly smaller than the Second Intifada, as can be visually seen in Figure 5 in the main text.

23 Table A6 reports the results using ﬁve-year lags and moving averages of the number of casualties as predictors. Similar to previous ﬁndings, it can be seen that there is a positive association between the number of casualties and increase in the frequency of right-wing phrases in Israeli books. Columns (3) to (6) show the results broken down to civilians and combatant casualties, and the same pattern holds.

Table A4: Frequency of phrases by bloc after the peak of terrorism in the First Intifada

Before: 1983-1988 Before: 1983-1985 Before: 1986-1988 After: 1990-1995 After: 1993-1995 After: 1990-1992 [-6,-1], [1,6] [-6,-4],[4,6] [-3,-1], [1,3] Estimate SE Estimate SE Estimate SE After peak of terrorism 0.12 (0.11) -0.34** (0.14) -4.54*** (0.13) Right -0.09 (0.08) -0.35*** (0.1) 0.18 (0.12) Left 0.46*** (0.1) 0 (0.13) 1.03*** (0.16) Center -0.52*** (0.09) -0.52*** (0.11) -0.54*** (0.13) After peak of terrorism × Right 0.18** (0.09) 0.75*** (0.13) -0.34*** (0.13) After peak of terrorism × Left -0.86*** (0.11) -0.04 (0.16) -1.73*** (0.16) After peak of terrorism × Center 0.87*** (0.1) 1.02*** (0.15) 0.78*** (0.14) Constant 9.83*** (0.09) 9.83*** (0.11) 12.58*** (0.12) Number of observations 14,922,558 7,443,406 7,479,152 R2 0.62 0.59 0.67 Phrase fixed effects: Year fixed effects: Note: The table reports changes in the frequency of phrases linked to the ideology of political blocs in Israel after the peak of terrorism in the First Intifada (1989). The association between the peak of terrorist attacks and frequency of right-wing, left-wing, and center phrases is captured by the interaction terms. Results show that across almost all estimations, the peak of terrorism in the First Intifada was associated with an increase in the popularity of phrases linked to Right-wing and Center parties and a decrease in the popularity of ideas linked to Left-wing parties. All coefficients are multiplied by 10,000,000 for easier interpretation. As analysis is conducted on the phrase-level, the magnitudes of the original coefficients are very small. * p<0.10,** p<0.05,*** p<0.01.

24 Table A5: Frequency of phrases by bloc and the number of casualties from Palestinian Violence (cumulative)

Dependent variable: Second Intifada First Intifada yearly frequency of a phrase (Years analyzed: 2000-2008) (Years analyzed: 1987-1993)

(1) (2) (3) (4) (5) (6) Cumulative number of casualties -0.002*** -0.002*** -0.004*** -0.02*** -0.02*** -0.03*** (0.0002) (0.0001) (0.0002) (0.001) (0.001) (0.001) Right -0.47** -1.15*** -1.16*** 1.27*** -0.02 0.13 (0.2) (0.14) (0.14) (0.17) (0.12) (0.12) Left 4.33*** 0.35** 0.29* 5.14*** 0.15 0.35** (0.24) (0.18) (0.18) (0.21) (0.15) (0.15) Center -1.97*** -2.25*** -2.26*** -0.58*** -0.78*** -0.57*** (0.23) (0.17) (0.17) (0.18) (0.13) (0.13) Cumulative number of casualties × Right 0.001*** 0.002*** 0.002*** -0.002 0.0001 -0.002 (0.0002) (0.0002) (0.0002) 0.002 (0.001) (0.001) Cumulative number of casualties × Left -0.001*** -0.0004** -0.0003 -0.01*** -0.002 -0.004*** (0.0002) (0.0002) (0.0002) (0.002) (0.001) (0.001) Cumulative number of casualties × Center 0.002*** 0.003*** 0.003*** 0.01*** 0.01*** 0.01*** (0.0003) (0.0002) (0.0002) (0.002) (0.001) (0.001) Constant 9.5*** 10.3*** 10.9*** 9.34*** 10.8*** 15.5*** (0.18) (0.13) (0.14) (0.15) (0.11) (0.17) R2 0.0007 0.5333 0.5336 0.0012 0.6400 0.6409 Number of observations: 13,197,718 13,197,718 13,197,718 8,972,858 8,972,858 8,972,858 Phrase fixed effects: Year fixed effects The table reports the relationship between the cumulative number of casualties from Palestinian violence in the Second and the First Intifadas and the frequency of phrases linked to the ideology of political blocs in Israel. The association between the cumulative number of casualties and frequency of right-wing, left-wing, and center phrases is captured by the interaction terms. Results show that in the Second Intifada, the popularity of phrases linked to the right-wing and center blocs increased as the number of casualties increased, and the popularity of phrases related to left-wing parties decreased with greater number of casualties. In the First Intifada, the cumulative number of casualties was positively related to increase in the frequency of center phrases and a decrease in the popularity of left-wing phrases. All coefficients are multiplied by 10,000,000 for easier interpretation. As analysis is conducted on the phrase-level, the magnitudes of the original coefficients are very small. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01.

25 Table A6: Frequency of phrases by bloc and the number of casualties from Palestinian violence (other estimations)

Dependent variable: (1) (2) (3) (4) (5) (6) yearly frequency of a phrase Moving Lagged Civilians Civilians Soldiers Soldiers average (5 yrs) moving lagged moving lagged (5 yrs) average (5 yrs) average (5 yrs) (5 yrs) (5 yrs)

Number of casualties 0.000 -0.003*** 0.000 -0.004*** -0.0005 -0.01*** (0.0008) (0.0004) (0.001) (0.0006) (0.003) (0.001) Right -0.13** -0.22*** -0.12** -0.25*** -0.14** -0.15*** (0.06) (0.05) (0.05) (0.04) (0.06) (0.04) Left 0.05 -0.21*** 0.05 -0.18*** 0.04 -0.24*** (0.07) (0.06) (0.07) (0.06) (0.07) (0.06) Center -0.37*** -0.44*** -0.34*** -0.46*** -0.41*** -0.35*** (0.07) (0.05) (0.06) (0.05) (0.07) (0.05) Number of casualties × Right 0.002*** 0.003*** 0.003*** 0.01*** 0.01*** 0.01*** (0.0006) (0.0004) (0.0008) (0.0006) (0.002) (0.001) Number of casualties × Left -0.0004 0.003*** -0.0006 0.004*** -0.0009 0.01*** (0.0007) (0.0005) (0.001) (0.0008) (0.002) (0.002) Number of casualties × Center 0.005*** 0.01*** 0.01*** 0.01*** 0.02*** 0.01*** (0.001) (0.0006) (0.001) (0.0008) (0.002) (0.002)

26 Constant 7.25*** 7.39*** 7.24*** 7.4*** 7.26*** 7.35*** (0.056) (0.04) (0.06) (0.04) (0.06) (0.04) R2 0.5395 0.5395 0.5395 0.5395 0.5395 0.5395 Number of observations: 24,660,921 24,660,921 24,660,921 24,660,921 24,660,921 24,660,921 Phrase fixed effects: Year fixed effects The table reports the relationship between 5-year moving averages and 5-year lags of the number of casualties from Palestinian violence and the frequency of phrases linked to the ideology of political blocs in Israel during the period 1987-2008. Columns (1) and (2) use the total number of casualties. Columns (3) and (4) use only civilian casualties. Columns (6) and (7) report the casualties only from the armed forces. The association between the average/lagged number of casualties and frequency of right-wing, left-wing, and center phrases is captured by the interaction terms. Results show that across most models, an increase in the average/lagged number of casualties is associated with an increase in the frequency of phrases linked to right-wing and center parties. All coefficients are multiplied by 10,000,000 for easier interpretation. As analysis is conducted on the phrase-level, the magnitudes of the original coefficients are very small. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01. E Israeli civics textbook comparison In support for the quantitative analysis, I also evaluate the extent to which right-wing, na- tionalistic content increased in Israeli textbooks, which are known to be central in shaping young Israelis’ political attitudes.7 The new civics textbook, To Be Citizens in Israel, provides a useful case, as its latest two versions were published just before and more than a decade after the Second Intifada. One edition was published at the beginning of the second Palestinian uprising in summer 2001, and another was published 15 years later, in 2016. This allows holding the book constant, while observing overtime changes in its content. For space purposes, this section will not include analysis of the entire textbook, but instead will provide key examples from the two editions. To Be Citizens in Israel is one of three required civics textbooks for Israeli high school students and is used as the main source for Israel’s high school matriculation exams. The book covers topics such as the Jewish state, definitions of democracy, government and politics, and the history of the Israeli-Palestinian conflict. In 2011, the Israeli Education Ministry decided that the textbook needs to be updated in light of the many changes that the Israeli society has undergone since publication of the earlier version.8 The process of revision lasted several years, and the new version was published in 2016. The publication of the new edition created a heightened controversy among Israeli educa- tors, academics, and public figures. Critics claimed that the new version strongly advances the political agenda of right-wing groups in Israel, marginalizes the views of the Arab minority and the secular population, and promotes a particular narrative of the history of the Israeli-Palestinian conflict.9 Others have claimed that by being strongly partisan, the new textbook is designed to shape the views of younger generations to support right-wing nationalism.10 In this section, I present a sentence-by-sentence comparison of the two versions of the civics textbook to examine whether there is a noticable change in the language and topics covered in the new edition. Since the Israeli-Palestinian conflict is the main policy area around which the left and the right in Israel disagree, I focus on this topic here. Table A7 shows the overall organization of chpaters and sections in the two editions. It can be seen that the two versions cover similar topics, but there are some additional sections in the 2016 edition that were not covered in 2001, and some sections that existed in the 2001

7Bar-Tal (1998) 8Cotler and Hai (2016) 9Skop (2016a,b); Cook (2016) 10Newman (2016); Gertel (2016)

27 version that were dropped in 2016. Table A8 delves deeper into a sentence-level comparison of a section describing the history of the Israeli-Palestinian conflict. Each row in the table describes a thematic sentence or paragraph from the two versions. Omissions from the 2001 version are marked in blue bold font, and additions to the 2016 version are marked in a red bold font. While both versions describe similar events that relate to the historical roots of the conflict, they markedly differ in their interpretations, especially in topics discussing the Arab minority in Israel, the actions of the Israeli government, and the fate of the Palestinian population. Rows 1 and 2 in Table A8 show two paragraphs that are overall similar between the two editions. Row 2, which displays paragraphs describing the 1948 war, shows that the 2001 edition provided more details on the roots of the war than the 2016 edition. In addition, the 2001 edition used the word “Palestine” to describe the territory on which the state of Israel was later established, a word that was omitted from the 2016 version. In fact, the word “Palestine” is rarely used in the entire new edition. This aligns with right-wing nationalists’ view that Palestine was the historical land of the Jews. Row 3 in Table A8 presents paragraphs describing the roots of the Palestinian refugee problem. In the 2001 version, the book mentions that over 700,000 Palestinians fled and were deported from Palestine, and those who stayed were put under military occupation after the war of 1967. These details were almost completely omitted from the 2016 edition, and replaced with a description of the victory of the Israeli army over the invading armies Arab countries, and the resulting “widespread desertion” of Palestinians from their homes. Row 4 presents paragraphs that continue to describe the fate of Palestinians after the 1948 war. The 2001 edition describes the Palestinian refugee problem as caused by expulsion during the war. The 2016 edition challenges this view, arguing that “Although the Palestinian version claims that most of the refugees were forcibly expelled, the accepted version in Israel today is that most of the refugees fled and a few were expelled.” The 2016 edition therefore interprets the Palestinian refugee problem as being caused by Palestinians’ choice to desert their homes and their refusal to accept the UN partition plan. This is a common view held by the political right in Israel today. Row 5 displays sentences describing the fate of Palestinians who remained in the territory of the state of Israel and were granted Israeli citizenship. While the 2001 version describes their situation as mire and subject to hostile treatment by the Israeli government, the 2016 edition softens these details by stating that these individuals were immediately granted representation in the Israeli government (The Knesset) and by omitting the part mentioning

28 the governments’ hostile policies towards them. The two editions of the Israeli civics texbook are replete with such examples. Since this study focuses on a broader analysis of thousands of Israeli books, it does not provide full qualtative comparison of the two editions. However, the examples presented above strengthen this paper’s ﬁnding that right-wing content has increased in Israel books since the Second Intifada and over time. The civics texbook case supports the argument that elite manipulation drives changes in books, but this mechanism might not hold for non-textbooks. The next section presents an exercise evaluating the extent to which political elites in Israel have inﬂuence on the content of mass produced popular books.

29 Table A7: Civics book comparison: Book structure

2001 2016 Part 1: What is a Jewish State? Part 1: What is a Jewish State? Nationality and nation states Declaration of independence Different approaches to the Israeli state The historical background of the Israeli state The identity of Israeli citizens Nationality and nation states Israel as the state of the Jewish people Justifications for a nation state The Jewish character of the Israeli state The Jewish character of the Israeli state Israel as the state of the Jewish people The identity of Israeli citizens Religious and secular identities in Israeli society The Arabs, Druze, and Cherkess in Israeli society Part 2: What is a democracy? Part 2: What is a democracy? The democratic idea The idea of a democratic state Definitions and approaches to the democratic state Individual, republican, and multicultural democracies The principles of democracy The rule of the people and elections The principle of limited government The majority decision principle The principle of the rule of law in a democratic state Tolerance, pluralism, and consent Limits of/in democracy Human and civic rights Why democracy? Group rights The principle of limited government The principle of the rule of law Political democratic culture Borders and security in a democracy Societal-economic approaches Globalization and its effect on states Challenges in democracies Part 3: Government and politics in Israel Part 3: Government and politics in Israel The constitutional principles of the state of Israel The constitutional principles of the state of Israel The Israeli society, a society with many cleavages Citizenship and return Parties in Israel Parties and interest groups in democracies and in Israel Elections in Israel Elections in Israel Local government Local government in Israel The presidency in Israel The presidency Government authorities in Israel The legislative branch: the Knesset The executive branch: the government Attorney General The judiciary The parliamentary system in Israel Limited government, supervision and auditing Supervision and control of government branches in Israel Human and civic rights, and minority rights in Israel Media and politics in Israel Political culture in Israel The state of Israel: a Jewish and democratic state Part 4: Society and politics in Israel Media and politics in Israel The challenge of shared life in Israel Opinions on the desired character of the state of Israel

30 Table A8: Civics book comparison: The roots of the Israeli-Palestinian conﬂict

2001 2016 1 “The origin of the national cleavage [between Jews and ‘The origin of the national cleavage [between Jews and Arabs] began with the Jewish Zionist settlement in the Arabs] began at the end of the 19th century, with the land of Israel at the end of the nineteenth century.” (p. beginning of Zionist settlement in the land of Israel.” 289) (p. 464) 2 Over the years, the conflict between Arabs and Jews Over the years the conflict escalated, and peaked in in Palestine escalated and became violent. This 1948: with the declaration of the establishment of the conflict intensified with the establishment of the State of Israel, armies of Arab states invaded it and State of Israel and became a violent struggle be- tried to prevent its establishment by force. tween states. The Arab states did not accept the establishment of a Jewish state in the Land of Israel and tried to prevent its establishment by force: the armies of Egypt, Jordan, Syria, Lebanon, and Iraq invaded Israel. 3 During the [1948] war, some 700,000 Arabs During the [1948] war, the Jewish community who lived in Palestine during the British Man- managed to withstand the invasion of Arab date period fled and were deported. They moved countries, which joined the Arabs living in Is- to Arab countries and to Judea, Samaria and the rael in an attempt to prevent the establishment Gaza Strip. This created the problem of the Pales- of the State of Israel. The success of the Israeli tinian refugees in Arab countries. The Six-Day War Defense Forces caused widespread desertion of (1967), during which the IDF conquered Judea, Arabs who left their homes and later found Samaria, and the Gaza Strip, exacerbated the themselves outside the borders of the country. Palestinian problem. A new reality was created Hundreds of thousands became refugees in the Arab in which hundreds of thousands of Palestinian states (some of them in Judea and Samaria, under Arabs found themselves under Israeli military Jordanian control, and in the Gaza Strip, which was control against their will. controlled by Egypt). According to the prevail- ing Zionist view, the tragedy of the refugees is the result of the choice of those who refused to accept the UN partition plan and began a war. 4 Following the expulsion and mass flight of Although the Palestinian version claims that Arabs during the war, only 160,000 Palestinian most of the refugees were forcibly expelled, the Arabs remained in the territory of Israel after the accepted version in Israel today is that most of end of the fighting, about 10% of all the Pales- the refugees fled and a few were expelled ... tinian Arabs who had lived in this area until Either way, the results of the War of Independence then. They remained without political leader- were difficult for the Arab population. Some 160,000 ship. Following the establishment of the state and the Arabs remained in the State of Israel, who received war, the 160,000 Arabs who remained in the State of Israeli citizenship, and some 25,000 of them were Israel were cut off from most of the Palestinian not allowed to return to their homes and lands. Arabs who were residents of the West Bank and the Gaza Strip or refugees in the same territo- ries and neighboring Arab countries. The Arabs in the State of Israel became Israeli citizens. 5 While Arabs within the State of Israel were granted With the establishment of the state, the Arabs who citizenship, the attitude towards them was not as remained in the country became Israeli citizens, and equal citizens. This was because the Arab minority were already represented in the First Knesset. was linked via family, culture, language, history, reli- But the Arab minority was connected to family ties, gion, and nationality to the Arab world, with which culture, language, history, religion and nationalism to the State of Israel was in conflict. For this reason, the Arab world, between which there was a conflict the political and military establishment regarded the between Israel and the State of Israel. The political Arab minority as a security issue, and thus placed the and military establishment treated the Arab minority Arab population under military rule. This situation, in disbelief, and this was manifested in the imposi- in which the Arab minority was perceived as a security tion of a "military government" in areas where a large problem, was reflected in a hostile and suspicious re- Arab population was concentrated. This situation, in lationship between the Arab minority and the Jewish which the Arab minority was perceived as a security public in Israel and the policies that the govern- problem, was also reflected in a hostile and suspicious ment enactred towards the Arab minority. (p. relationship between the Jewish public and the Arab 279) public in the country.

31 F Information on book publishers in Israel In order to evaluate the extent to which book content might be manipulated by political leaders, I collected information on the political and governmental links of book publishers in Israel. The National Library of Israel maintains a database of Israeli publishing houses.11 The database includes information on 1,857 publishing companies that publish books, newspapers, and magazines, among other media. The publishers range from public companies owned by the government to small and relatively unknown businesses. Relevant information includes the companies’ histories, identity of owners and managers, languages of published media, and more. This database is considered the most comprehensive source of publishers in Israel. One of the beneﬁts of using this database is that it includes information on every publishing house ever existed in Israel. According to the Israeli Books Law, anyone who publishes media in Israel must submit information about the publication to the National Library of Israel. As stated in its website:

“The National Library receives legal deposits irrespective of language, format, theme, or quality, and without making ideological judgments. The fact that the library receives the publications, and does not acquire them, neutralizes ﬁnancial constraints that could potentially dictate the selection of certain titles and the disqualiﬁcation of others. Obligating publishers to deposit free copies ensures the principle of freedom of speech and prevents library censorship . . . The library preserves the publications for future generations as a documentation of written Israeli culture, and guarantees that they will be accessible to the public for many years to come, long after they stop being available for purchase.” (National Library of Israel, 2016a).

I selected a random sample of publishers for which I made comprehensive searchers for political links. If a publisher was either a governmental agency or its owner had clear affiliation with political parties, I coded it as being potentially vulnerable to elite manipulation; if not, I coded it as not being prone to manipulation. In the random sample, I found evidence for political or governmental affiliation for only 10% of publishers. In addition, since the majority of volumes in the Israeli book market are published by twenty major publishers, I investigated the extent to which these companies had political affiliations. Table A9 shows that only four of the twenty publishers had clear links to politicians or governmental organizations.

11For more information, see: http://web.nli.org.il/sites/NLI/English/library/depositing/ Pages/lgd-publishers.aspx

32 Table A9: The Twenty Major Book Publishing Companies in Israel

Publisher Established Language Types Political Notes name links Kinneret 1924/1940a Hebrew Books, 1 Ohad Zmora, who established the company, Zmora- magazines was a close friend of Shimon Peres, an Israeli Bitan Dvir politician. Keter 1958 Hebrew, Books 1 The publisher was established as a government English company, by Prime Minister David Ben Gu- rion and the politician Teddy Kollek. Am Oved 1942 Hebrew, Books, 1 The company was estbalished by Berl Katznel- English, magazines son, one of the founding fathers of Israel’s Russian workers union, on of the largest institutions in the country. Yediot 1939 Hebrew, Books 0 Spharim English Hakibbutz 1939/1940b Hebrew, Books 0 Hameuchad English, Arabic Modan 1970 Hebrew Books 0 Achuzat 2004 Hebrew Books, 1 One of the editors, Dana Olmert, is the daugh- Bayit magazines ter of Ehud Olmert, who was Israel’s prime Books minister from 2006 to 2009. Xargol 1998 Hebrew Books 0 Matar 1985 Hebrew Books 0 Books Keshet 1996 Hebrew Books 0 Gvanim 1958 Hebrew, Books, 0 Books English magazines Pardes 2000 Hebrew Books, 0 Publishing magazines House Babel 1995 Hebrew, Books 0 English Resling 2000 Hebrew Books, 0 magazines Even 1994 Hebrew Books, 0 Hoshen magazines Rimonim 2004 Hebrew Books 0 Publishing Opus 1989 Hebrew Books 0 Zikit Books 2012 Hebrew Books 0 Sela Books 2008 Hebrew Books 0 Arie Nir 1995 Hebrew Books 0 a The company is a merger of Dvir, which was established in 1924, and Zmora-Bitan, which was established in 1940. b The company is a merger of Sifriat Poalim, which was established in 1939 and Hakibbutz Hameuchad, which was established in 1940.

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