https://orcid.org/0000-0002-5502-0119
![Sample our Politics & International Relations journals, sign in here to start your FREE access for 14 days]({"type":"image" , "src" : "/sda/5947/TOC-Subject-Sample-2021-Politics-International-Relations.jpg"})
Abstract
Though many studies have analyzed public opinion of trade liberalization, they do not fully explain regional disparities in people’s preferences with regard to trade. To explain such a geographic division, this paper focuses on differences in social contexts that exist between regions, which is composed of social networks of different characteristics and distinct news coverage from local media. By utilizing multiple recent public opinion surveys conducted in Japan, we confirm that individuals in rural areas are less likely to support trade liberalization and Japan’s membership in the Trans-Pacific Partnership (TPP) independent of individuals’ economic self-interest or skills. With multiple surveys and data on Japanese national and local newspapers, we investigate the mechanisms, showing that (a) individuals whose communication network includes people working for a sector vulnerable to trade liberalization tend to oppose free trade regardless of the industry they work for and that (b) newspapers circulated in rural and urban places tend to cover TPP differently, which also impacts people’s attitudes toward the agreement. By analyzing the mechanisms of sociotropic considerations, our social context approach could eventually lead to further uncovering the formation of public opinion on trade.
Acknowledgments
Earlier versions of this article were presented at annual meetings of the International Studies Association (Baltimore, MD; February 23, 2017), the Southern Political Science Association (Austin, TX; January 18, 2019), and the Midwest Political Science Association (Chicago; April 5, 2019), and the Northeast Workshop in Japanese Politics (Dartmouth College, Hanover, NH; August 26, 2019). We thank Erin Aeran Chung, Lucy Goodhart, Keisuke Iida, Tomoki Kaneko, Kenneth Mori McElwain, Sayumi Miyano, Di Wang, Qi Zhang, and conference and workshop participants for their helpful comments. We also appreciate Fumisato Hara, Tatsuki Kikugawa, and Kota Yoshimochi for their research assistance. All remaining errors are our own.
Disclosure statement
The authors report there are no competing interests to declare.
Notes
1 The data can be downloaded from the GESIS website at https://dbk.gesis.org/dbksearch/sdesc2.asp?no=5950&db=e&doi=10.4232/1.12312 (Accessed June 18, 2023)
2 Among the 26 OECD countries in the sample, over 50% of the respondents from 14 countries supported import limits. Among 12 countries that had less than 50% support for import limits, five countries had more support than opposition for the policy.
3 With the withdrawal of the U.S., the agreement is also called TPP11.
4 See Fordham and Kleinberg (Citation2012) for the difficulty of empirically separating economic self-interests and other factors influencing opinions on trade policy.
5 For more details, see https://www.pewinternet.org/fact-sheet/social-media/ (Accessed on June 18, 2023).
6 Yomiuri Shimbun, July 11, 2016, p.2
7 It is still possible that people choose whether to read local or national newspapers based on political preferences. We cope with this issue in Section ‘Effect of reading local newspapers’ by examining within-individual changes in trade policy preferences using panel survey data.
8 We obtained this number from the website of Statistics Bureau of Japan at https://www.stat.go.jp/data/topics/topi1131.html (Accessed on June 18, 2023).
9 Information on voter turnout rate can be found from the website of Ministry of Internal Affairs and Communication at http://www.soumu.go.jp/senkyo/senkyo_s/news/sonota/nendaibetu/ (Accessed on June 18, 2023).
10 Data from JES and WFS were provided by the Social Science Japan Data Archive, Center for Social Research and Data Archives, Institute of Social Science, The University of Tokyo. JGSS datasets were downloaded from the website of the Inter-university Consortium for Political and Social Research at https://www.icpsr.umich.edu/icpsrweb/ICPSR/series/209 (Accessed on June 18, 2023), and the information on the prefectures where the respondents lived was provided by the JGSS Research Center. We obtained UTAS data from its official website at http://www.masaki.j.u-tokyo.ac.jp/utas/utasindex.html (Accessed on June 18, 2023).
11 WFS was a monthly survey conducted from October 2011 to September 2013 with both panel respondents and fresh cross-sectional samples. The panel component is composed of surveys conducted from October 2011 to September 2012 and those conducted from October 2012 to September 2013. We only use data from the fresh cross-sectional component in this section and confirm that the results do not change even if we use data from the panel component in Online Appendix D.1.
12 The wording of these questions and their English translations are in Online Appendix A.
13 We obtained information on this measure from the website of the Geospatial Information Authority of Japan. See https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-A16-v2_3.html (Accessed on June 18, 2023).
14 According to the Statistics Bureau of Japan, in Japan, a densely inhabited district is defined as:1) a district containing basic unit blocks, etc. with a population density of 4,000 or more per square kilometer, such districts being adjacent to each other in a municipality 2) a district consisting of the above adjacent basic unit blocks, etc. whose population is 5,000 or more at the time of the Population Census of Japan. See https://www.stat.go.jp/english/data/chiri/did/1-1.htm (Accessed on June 18, 2023).
15 This measure represents the percentage of workers who answered they worked for the agricultural sector to the 2010 Census. We obtained these data from e-Stat, the portal site of Japanese Government Statistics, at https://www.e-stat.go.jp/en/stat-search/files?page=1&toukei=00200521&tstat=000001039448 (Accessed on June 18, 2023).
16 We accessed these data from the website of Economic and Social Research Institute at the Cabinet Office at https://www.esri.cao.go.jp/jp/sna/sonota/kenmin/kenmin_top.html (Accessed on June 18, 2023).
17 Some readers may doubt that our key independent variables in this section do not represent regional differences in social contexts that impact public opinion on international trade but rather serve as a proxy for geographic differences in political attitudes in general or economic situation, even after we control for individuals’ party/ideological self-identification and economic indicators of the areas where they live. For this purpose, we re-estimate the regression models using % primary industry GDP as the key independent variable by adding % TPP product, a variable representing the percentages of agricultural products considered to be significantly affected by trade liberalization, and their interaction. In Online Appendix D.2, we demonstrate that individuals in rural areas tend to oppose TPP especially when they live in places with high % TPP product, which suggests that people in rural areas are more protectionist not because of their economic situations or political attitudes in general but because of the regional differences in social context.
18 In March 2016, DPJ merged with other parties to become the Democratic Party. In this paper, the consolidated party is also referred to as DPJ.
19 We obtained the data on GDP per capita from the website of Economic and Social Research Institute at https://www.esri.cao.go.jp/jp/sna/sonota/kenmin/kenmin_top.html and those on unemployment rate from the website of Statistics Bureau of Japan at https://www.stat.go.jp/data/roudou/pref/index.html (both accessed on June 18, 2023).
20 To more directly compare individuals who live in rural and urban areas, we also employ propensity score matching and estimate the regression models only using the matched observations (Ho et al., Citation2007). See Online Appendix D.3 for more details, where we show that the patterns presented in the next section remain even after using matching method.
21 Full regression results are summarized in Tables A.1 and A.2 in Online Appendix.
22 Concretely, we calculate the first differences in predicted probabilities where respondents selected choice options above (i.e. more protectionist than) the neutral category.
23 Question wordings for these items are in Online Appendix A.
24 Full regression results are in Tables A.3 and A.4 in Online Appendix.
25 Throughout this section, as in the previous section, rural and urban areas are the places where % primary industry GDP are one standard deviation above and below the sample average, respectively. In addition, we marginalize the covariates using the ‘observed value approach’ (Hanmer & Ozan Kalkan, Citation2013) and compute the confidence intervals using the method proposed by King et al. (Citation2000).
26 Furthermore, if our perspective is valid, people whose social network contains agricultural workers should be more protectionist even if they live in urban areas. To see whether this expectation holds, the second column of Table A.4 in Online Appendix report the result from the regression model also including the interaction between % primary industry GDP and the variable representing daily/occasional contacts with farmers. The interaction term is statistically indistinguishable from 0, suggesting that individuals who have daily/occasional contacts with agricultural workers are more likely to support import limit regardless of where they live.
27 Hokkaido Shimbun, March 16, 2013, p.2.
28 We collected article counts using Nifty’s business data, Nikkei Telecom, and Factiva in May-June 2019.
29 We collected information of (i) major national newspapers (Asahi, Mainichi, Nikkei, Sankei, and Yomiuri) and (ii) all local newspapers whose articles are available through the databases we used. The names of the newspapers included in our dataset are in Online Appendix C.1
30 The information on prefectural-level circulation was obtained from the 2012 versions of Shimbun Report published by Japan Audit Bureau of Circulation and Japan’s periodicals in print published by Media Research Center.
31 Full regression results are in Table A.5 in Online Appendix.
32 Kaneko (Citation2021) reports a similar finding from his analyses on Japanese newspapers’ positions on constitutional amendments.
33 We first asked the coders to evaluate three separate editorials as practices before working on the main task. The 86 editorials for the main task were presented to raters in random orders. The list of editorials used for the task is in Online Appendix C.2.
34 Thus the baseline category is the individuals who read national newspapers or those who do not read newspapers at all. We also conduct the analysis omitting observations of individuals who do not read newspapers (thus changing the baseline category to those reading national newspapers) to confirm that the findings of this subsection still hold (Model 2, Online Appendix Table A.6).
35 Full regression results are in Table A.6 in Online Appendix.
36 More specifically, we examine the changes in attitudes (i) between the interviews held November 16–22 and December 19–27, 2012 and (ii) June 21–28 and July 26–August 2, 2013. The two National Diet elections were held on December 16, 2012 and July 21, 2013.
37 This way of operationalization is not without downsides. For example, it cannot deal with the ceiling/floor effects (i.e. individuals who choose ‘1’ or ‘5’ in the pre-election wave cannot become more in favor of/opposed to TPP due to the question format). It is also vulnerable to survey measurement errors. Nonetheless, we consider this to be the best method for the data at hand.
38 Full regression results are summarized in Table A.7 in Online Appendix.
39 As with analysis using UTAS data, we also repeat the analyses omitting observations of individuals who read no newspapers. We summarize the results in the second and fourth columns of Online Appendix Table A.7. For November-December 2012, as expected, the interaction term between the Local Newspaper and % primary industry GDP is positive and statistically significant at 5% confidence level. For June-July 2013, though the interaction term is positive, it is statistically indistinguishable from 0, likely because of the small sample size caused by panel attrition.
40 Feigenbaum and Hall (Citation2015) is an exception on this point, as they show that the U.S. legislators from districts that are negatively influenced by Chinese import competition tend to vote in a more protectionist way on trade bills.
Additional information
Notes on contributors
Hirofumi Kawaguchi
Hirofumi Kawaguchi is an Associate Professor in Politics and International Relations at the Faculty of Humanities and Social Sciences, University of the Ryukyus. He works on political institutions, interest groups, and public opinion with a particular focus on agricultural policy in Japan.
Ikuma Ogura
Ikuma Ogura is an Assistant Professor in the Department of Contemporary Social Studies at Ibaraki University. His research interests are political behaviour, political psychology, and political communication.