Doshisha University Center for the Study of the Creative Economy
Discussion Paper Series No. 2018-02

Elected Officials in the Local Assembly:
Analysis of Prefectural Plenary Session Transcripts
Akihiko Kawaura (Faculty of Policy Studies, Doshisha University)
Yasutomo Kimura (Faculty of Commerce, Otaru University of Commerce)
Keiichi Takamaru (Faculty of City Life Studies, Utsunomiya Kyowa University)
Yuzu Uchidad (Faculty of Engineering, Hokkai-Gakuen University)

Discussion Paper SeriesElected Officials in the Local Assembly:
Analysis of Prefectural Plenary Session Transcripts

Akihiko Kawauraa, Yasutomo Kimurab,
Keiichi Takamaruc and Yuzu Uchidad
a Faculty of Policy Studies, Doshisha University
b Faculty of Commerce, Otaru University of Commercec Faculty of City Life Studies, Utsunomiya Kyowa University
d Faculty of Engineering, Hokkai-Gakuen University(Abstract)
This paper investigates the behavior of elected officials in the local government inJapan. The specific focus of the inquiry is their effort to make themselves recognized in
the local assembly so that they can improve the evaluation by voters. The
methodological innovation is the use of local assembly transcripts from all 47
prefectures in the 2011-2014 period for the empirical analysis. The character count ofremarks in the assembly’s plenary sessions, both of governors and assembly members,
represent their control of the deliberations. The regression analyses suggest that theperformance of the incumbent governors in the immediate gubernatorial election is adeterminant of their character count. A big electoral success gives a governor moresway in the local assembly.

Keywords:

Japan; prefecture; local assembly; governor; transcript

JEL codes:

D72; D78; H89* This research project was supported by the Japan Society for the Promotion of
Science [Grants-in-Aid for Scientific Research #16H02912 and #17K03785]. There areno conflicts of interest to declare.

I. Introduction
The separation of powers is an important element of democracy. In the trias

politica model, a government is divided into three branches, i.e., a legislature, an
executive, and a judiciary, so that checks and balances of powers can be provided. The
legislative and executive branches usually have the primary authority to determine theallocation of public resources, and their decision-makers are elected through popular
votes in most democracies either through a direct or indirect manner. For these reasons
there is an extensive body of political economy literature that has investigated their
incentives and behavior that reflects them.
One example in this field of research is the inquiry into the determinants ofgovernment budget allocation that involves legislators and officials in the executive
body. The pioneering work was in the examination of New Deal budget allocation in theUS, which includes Fleck (2001), Wallis (1987), and Wright (1974). Kawaura (2003,2011) investigated locational budget allocation in Japan, Thailand and the U.S. byexamining whether local legislative representatives belong to parties that control the
executive branch.
This paper constitutes an attempt to take the analysis to the local government
level to investigate the interplay of executive-legislative decision-makers. To the extent
that elected local politicians face the same re-election incentives, their behavior must
exhibit similar patterns in the local government as in the central government. The restof the paper is as follows. The next Section describes methodology and data for theempirical analysis. Section III reports results of the regression analyses. The
concluding Section discusses scopes of future research.
II. Methodology and Data
This research seeks to empirically examine politicians’ behavior in the local
assembly. The executive leader of the local government, i.e., governors and mayors,
engage in policy debate with legislative members in the assembly. Bothgovernors/mayors and assembly members have their constituents, and are concerned
how they are evaluated by the voters. One means for them to improve their evaluation
is to make themselves recognized by speaking in the assembly, which increases theirvisibility. This is more effective when voters can observe assembly sessions and when1

sessions are broadcast on mass media and/or internet. As deliberation time is usuallylimited by assembly schedules, however, they have to compete for the speaking time.
This is where the relationship between governors/mayors and assembly members could
come into play to determine their relative space in the deliberations. Hence theanalyses in this paper employ assembly records, i.e., plenary session transcripts, toidentify their amount of remarks.
This use of the assembly transcripts is the methodological innovation in thisresearch. The data source is Japan’s 47 prefectures, each of which publishes the fulltranscripts of the plenary sessions of its assembly. These transcripts record all theutterances registered during the sessions together with the identity of their speakers.
The sample period is 2011-2014. The character count data tabulated from thesetranscripts is the principal variable in this research. 1
Figure 1 shows the average total character count by prefecture for the fouryear period. 2 One can observe that there is a large variation in the character countacross prefectures. While the count average is 1,318 thousand, the range is between
555 thousands for Yamagata and 2,711 thousand for Tottori. The size of the largest
value relative to the smallest is 4.89 (=2,711,017.25/554855.5). The other observation isthat the total character count is not necessarily correlated with the assembly size interms of its member population. For example, the prefecture of Tottori with the largest
character count has the smallest assembly of 35 members. The next highest charactercount takes place at Shiga, which also has a small assembly with 47 members.
Although Tokyo has the biggest assembly with 127 members on the other hand, its
character count of 1,437 thousand stands at the 18th among prefectures. The averagecount of five prefectures with more than 100 assembly members (Tokyo, Osaka,
Kanagawa, Hokkaido, and Aichi) is 1,405 thousand and not very different from theoverall prefecture mean. This is reflected on a small value (0.065) of the coefficient ofThis count data is part of the information created in a project to prepare theJapanese political corpus. The corpus is available at [ http://local-politics.jp/ ], which isdesigned to provide various information contained in the prefectural assemblytranscripts and is also equipped with a search function by key words. For example, it
allows an investigation of specific policy issues that are discussed in individual
prefectures to the extent that they are reflected in the assembly deliberations. For
more information, please see Kimura, et al. (2016).
2 The size of remarks/statements is represented by the “character count”. This isdifferent from the “word count” in the English text analysis. The Japanese language
writing does not require a space between individual words, which makes the “wordcount” of Japanese transcripts extremely hard to prepare.
12Figure 1. Prefectural Assembly Transcript Character Count: 2011-2014 Average

Note: In parentheses after the prefecture name is the number of members in each assembly.

3correlation between the assembly’s total character count and the number of its
members.
The total character count itself, however, contains factors that are notparticularly informative for the inquiry into the behavior of local politicians. For
example, transcripts include verbal description/explanation of individual documents
distributed for the assembly sessions. Prefectural assemblies do not have a common
convention regarding the documents preparation, and the total character count may
simply reflect their procedural difference. There are also remarks by the local
government bureaucrats to describe administrative rules and regulations to facilitate
deliberations by the elected officials. In addition, the assembly members may pose
specific questions on behalf of voters in their constituents, which prompts response by
non-elected prefecture officials. Finally, when witnesses are called to the sessions toprovide expert opinions on policy matters, their remarks also constitute the total
character count.
Since the focus of this paper is the behavior of elected officials, it is importantto analyze their character count separately from the total count. Table 1 displays thecharacter count for the remarks by the governors and assembly members, together
with their percentage share in the total count. 3 Governors’ character count shows avariation that is wider than that of total counts. Similarly with the total count, thesmallest value is observed for the prefecture of Yamagata and the largest for Tottori.
The relative size between largest to smallest, however, is very different. While it was
4.89 for the total count, the equivalent value for the governors’ count reaches 13.51(=1,077,644.50/79,738.75). The character count for the governor is determined by the
behavior of a single person, which may explain the pronounced variation in the size ofremarks among governors. As assembly members’ character count is a function ofmultiple members’ behavior, it is less likely to exhibit extreme values. For theircharacter count, the relative value between its maximum and minimum (again for
Yamagata and Tottori) is 3.67 (=1,063,105.75/289,997.00). The collective charactercount by the assembly members usually exceeds that of a single governor. One

The utterances by the chairperson, who is selected from assembly members, is
excluded from the assembly member character count. This is because the chairperson
is heavily engaged in session management, and her character count for this task varies
across prefectures. Inclusion of count attributable to this type may bias the countcomparison among prefectures.

3

4

Table 1. Assembly Transcript Character Count by Prefecture: Governor and Assembly Members

Prefecture

Governor

Character Counts (4-Year Mean)
Assembly-M embers
share
(A)
(%)

share
(%)

Assembly

Assembly

M ember

Charact ers/Member(B)(A)/(B)[rank]

Hokkaido
Aomori
Iwate
Miyagi
Akita
Yamagata
Fukushima
IbarakiTochigi
Gunma

369,255.00
176,374.75
165,996.75
333,285.25
291,817.50
79,738.75
89,980.00
204,066.00
126,402.25
108,692.00

23.96
11.61
12.52
20.22
32.75
14.37
8.76
18.65
12.81
7.88

768,875.00
683,534.50
603,364.75
793,413.75
461,646.25
289,997.00
585,975.25
530,582.25
533,044.75
699,715.50

49.89
45.00
45.50
48.15
51.81
52.27
57.08
48.48
54.01
50.73

104
48
48
59
45
44
58
65
50
50

7,393.03
14,240.30
12,570.10
13,334.68
10,258.81
6,590.84
10,103.02
8,162.80
10,660.90
13,994.31

42
14
23
21
34
44
35
39
32
16

Saitama
Chiba
Tokyo
Kanagawa
NiigataToyamaIshikawa
Fukui
Yamanashi
Nagano252,383.75
141,254.25
151,523.25
388,872.00
346,715.25
308,019.75
264,514.75
238,690.75
217,544.25
362,854.25

17.35
8.16
10.55
26.33
28.73
26.28
24.41
20.90
20.36
19.80

741,732.00
1,038,972.00
579,460.25
880,303.75
634,661.75
428,893.00
446,565.50
518,090.75
473,025.75
809,435.00

50.99
60.01
40.33
59.61
52.59
36.59
41.21
45.36
44.28
44.16

94
95
127
107
53
40
43
37
38
58

7,890.77
10,936.55
4,562.68
8,227.14
11,974.75
10,722.33
10,385.24
14,002.45
12,448.05
13,955.78

41
30
47
38
27
31
33
15
24
17

Gifu
Shizuoka
Aichi
Mie
Shiga
Kyoto
Osaka
Hyogo
Nara
Wakayama

186,508.00
322,809.25
192,941.75
197,171.00
603,798.00
316,647.00
152,928.75
228,759.75
312,526.00
114,737.75

17.42
23.67
13.12
15.62
28.79
25.17
13.89
23.51
29.81
13.21

626,819.50
661,129.50
915,206.75
729,530.75
1,052,182.00
736,547.75
618,292.50
542,340.75
541,970.50
538,848.00

58.54
48.47
62.24
57.81
50.18
58.55
56.17
55.73
51.69
62.06

46
69
103
51
47
60
109
89
44
42

13,626.51
9,581.59
8,885.50
14,304.52
22,386.85
12,275.80
5,672.41
6,093.72
12,317.51
12,829.71

19
36
37
13
3
26
46
45
25
22

Tottori
Shimane
Okayama
Hiroshima
Yamaguchi
Tokushima
KagawaEhime
Kochi
Fukuoka

1,077,644.50
310,848.25
369,688.75
143,162.25
162,232.25
200,842.25
248,104.00
159,345.50
308,176.50
418,648.00

39.75
20.69
23.13
17.06
12.99
22.34
25.41
16.66
21.04
33.95

1,063,105.75
705,126.25
856,638.75
461,418.75
733,692.75
467,533.25
563,310.25
547,411.00
660,486.75
679,654.25

39.21
46.93
53.60
54.99
58.75
51.99
57.69
57.25
45.09
55.11

35
37
56
66
49
41
41
47
39
86

30,374.45
19,057.47
15,297.12
6,991.19
14,973.32
11,403.25
13,739.27
11,647.04
16,935.56
7,902.96

1
5
11
43
12
29
18
28
7
40

Saga
Nagasaki
Kumamoto
Oita
MiyazakiKagoshima
Okinawa

327,186.75
191,925.00
118,447.50
215,487.50
245,377.25
130,874.00
160,191.00

22.73
14.32
9.93
18.84
14.99
7.20
8.54

638,497.00
801,738.25
804,012.50
593,974.25
900,132.25
1,030,127.00
811,417.25

44.35
59.83
67.40
51.92
55.00
56.70
43.27

38
46
49
44
39
51
48

16,802.55
17,429.09
16,408.42
13,499.41
23,080.31
20,198.57
16,904.53

9
6
10
20
2
4
8

5

exception is the prefecture of Tottori, whose governor’s count is larger than the
combined counts of its 35 assembly members.
Since governors and assembly members compete for the speaking time,assembly members’ share in the total character count may reflect their numbers.
Regressing their percentage share on their number, i.e., (B) in Table 1, produces:
Assembly members’ share in the total character count= 48.58+(18.13)

No. of Observation: 47

0.06 × Assembly_Member

Adjusted R2: 0.02

(1.33)F (1, 45): 1.76.

This specification is not statistically significant at the 10 percent level, nor is theestimated coefficient of Assembly_Member variable statistically different from zero.
The result does not support the association between the assembly size and the share ofits member’s speaking time. A large assembly does not necessarily lead to an
opportunity for its members to speak in the sessions.
The result above shows that a member of a large assembly has to share thespeaking opportunity with her fellow assembly members, which may arise from thefact that the deliberation time does not necessarily increase with the assembly size.
This could in turn lead to a smaller character count per assembly member in a large
assembly. Below is the result of the regression analysis of the character count per
assembly member, (A)/(B), on the assembly size in terms of its member, (B) in Table 1.
Character count per assembly member

No. of Observation: 47= 20588.10 - 133.31 × Assembly_Member(13.32)***(5.40)***

Adjusted R2: 0.38
F (1, 45): 29.14.

The specification is significant at the 1 percent level, and the estimated coefficient of
Assembly_Member is negative and statistically significant at the 1 percent level. Thisis consistent with the hypothesis regarding the character count per assembly member.
For an individual assembly member, it takes more effort to gain opportunity to speak
in a large assembly.

6III. Regression Analysis and Results
This section examines the determinants of governors’ character counts.
Governors seek for ways to improve their evaluation by local voters. In the prefecturalassembly, this attempt may take the form of increasing their presence through taking
to the microphone in the sessions. Since the deliberation time is limited, however,success of their effort may depend on the power and support they enjoy as anincumbent. Hence the analysis here investigates if their electoral performance is adeterminant of their character counts. The result of this analysis is expected to provide
a new insight into their behavior in the assembly.
The determinants are identified in the following form:
Governor_Count = α + ∑βM × ELECTM + ∑ γN × Non-ELECTN + ε

where Governor_Count represents character counts of their remarks in the prefecturalassembly’s plenary sessions. The summary statistics of the dependent variable(Governor_Count) and explanatory variables (ELECT and Non-Elect) are in Table 2.
The ELECT variables include two measures that represent governor’s political
capital. The percentage share of the votes cast for the governor in the immediate
gubernatorial election is VoteShare-Cast, and the share in the total voter number(including those who did not vote) is VoteShare-Total. 4 The other variable is governor’s
cumulative term, Term, and its tally in month, Term-Month. Variables in the Non-Elect
category are prefecture size (area, AREA, and population, POP), demographic
composition (percentage share of residents in the age range of 15-64, Productive) andprefecture budget size per resident (Exp-PC).
The dataset is a panel with 47 prefectures for four years. Coefficient estimates
are obtained from the random effects specification. Selection of the random effects overthe fixed effects and pooled cross-section is based on the Breusch and Pagan Lagrangian

4

Multiplying VoteShare-Cast by the voting rate produces VoteShare-Total.
7

Table 2. Summary Statistics―――――――――――――――――――――――――――――――――――――――
Mean

Std. Dev.

Min

Max

―――――――――――――――――――――――――――――――――――――――
Governor_Count

257,443.10

164,817.70

67,763

1,175,142

VoteShare-Cast

69.83

14.98

39.58

91.67

VoteShare-Total

32.65

8.76

17.63

54.19

Term

2.17

1.08

1

6

Term-Month

80.63

52.22

6.5

253.5

AREA

80.02

118.8618.77

834.57POP

2799.942712.70574

13,390

Productive

60.96

2.34

55.6

68.1

Exp-PC

456,820.50

152,961.20

203,800.6

1,121,214(ELECT)(Non-ELECT)―――――――――――――――――――――――――――――――――――――――
Units: VoteShare-Cast, VoteShare-Total, Productive, and Senior (percentage);
POP (thousand); AREA (thousand km2): Exp-PC (yen)Note: The number of observations is 179, which is 188 (47 prefectures x 4 years) minus
9 observations for governors elected uncontested.

multiplier test and the Hausman test. Regression results are reported in Table 3. The
specification I and II use the percentage share of votes for the incumbent among total
votes, VoteShare-Cast, as the performance in the immediate gubernatorial election,
while specification III and IV have the share in the total voter population, VoteShareTotal.
The coefficient estimates of VoteShare-Cast are positive and statisticallysignificant. Its size indicates that a governor with a voting share of extra one
percentage point can record an additional 820-845 utterances in the character count of
the assembly transcript, which corresponds to 0.32-0.33 percent of the sample average.
When the electoral success is alternatively represented by VoteShare-Total, however,its coefficient is not statistically significant. This suggests that the incumbent
8

Table 3. Determinants of Governors’ Character Count in the Assembly: 2011-2014――――――――――――――――――――――――――――――――――――――――
Governor_Count

I

II

III

IV

――――――――――――――――――――――――――――――――――――――――(ELECT)
VoteShare-Cast

820.98*845.71**(1.76)(2.00)VoteShare-Total
Term

1183.30

1402.20(1.05)(1.29)-3424.24-131.33(0.51)(0.02)

Term-Month-187.76-163.91(1.52)(1.30)(Non-ELECT)
AREA

102.71

107.69

88.35

93.52

(0.48)(0.50)(0.44)(0.47)-13.58-13.54-13.00-12.95(1.39)(1.39)(1.41)(1.42)

19268.08***

18266.01***(6.37)(5.95)(6.03)(5.53)

0.06

0.05

0.04

0.03

(0.67)(0.63)(0.49)(0.39)

Adjusted R 2

0.02

0.03

0.05

0.06

Wald chi2 statistic

42.52

45.10

38.07

40.10

(p-value

0.00

0.00

0.00

0.00)

No. of observations

179

179

179

179

POP
Productive
Exp-PC

17783.90***16748.19***――――――――――――――――――――――――――――――――――――――――
Note: t-statistics are in parentheses. ***Statistically significant at the 1% level.
** Statistically significant at the 5% level. *Statistically significant at the 10% level.

9governor’s record at the polls would determine her performance on the assembly floor,
and that the share of her votes among those actually cast is important. The share ofvotes in her favor in the total voter population does not count. This may reflect thetendency for the media to report the winner’s voting share among votes cast on theheadlines, which is registered among prefecture officials as her popularity.
The estimated coefficients of variables for governors’ cumulative tenure, Termand Term-Month, are not statistically different from zero. This could be counterintuitive, as multi-term governors with a solid political base is expected to have a clout
over the deliberations in the assembly. One interpretation is that the potential benefits
in terms of appeal to voters from greater presence in the assembly may diminish as
they accumulate terms. With a wide name recognition among residents, multi-term
incumbent governors may not be concerned with their performance in the assembly.
Among Non-Elect variables, the percentage share of residents in the 15-64 age
range, Productive, has statistically significant coefficient estimates. Their sign ispositive, which implies that governors tend to make more utterances in a prefecturewith a larger working age population. Presence of productive residents demands
deliberations on a wide range of policy issues, which requires the leader in theexecutive branch to take an initiative in the assembly.
The explanatory power of the regression is limited, however, in terms of Rsquared. One way of increasing it could be to include attributes of governors
themselves. The same governor remained in position in 39 out of 47 prefectures
throughout the sample period. Part of the reason is the time-dimension of the panel. It
is four years, which is the same with the length of a governor’s tenure. For the
prefectures whose gubernatorial election cycle coincides with the sample period,
characteristics of a specific governor may determine the governor’s character count.
Even when a gubernatorial election takes place during the sample period, anincumbent may prevail and continue to serve. The analysis in this paper accounted forgovernors’ electoral performance and prefecture’s geographical/fiscal size anddemographic composition. Addition of variables to represent governors themselves tothe determinants of character counts could lead to valuable findings.

10IV. Conclusions
This paper represents an attempt to make use of the Japanese political corpus
developed from the prefectural assembly transcripts for the empirical analysis in thesocial science. The corpus provides a wide range of information contained in theprefectural assembly deliberations across 47 prefectures in Japan. The analysis in thispaper examined if there is a political factor that explains the size of utterances by thegovernors.
Examples of researches made possible by the political corpus include theassessment of the impact of governor’s campaign promises on the assembly
deliberations. If local voters elect a governor based on her policy priorities contained in
the election promises, it is important for these policies to be debated on the assembly
floor. If this link is missing, it may lead to voter disillusion about local democracy.
Another potential theme is the relationship between individual assembly members’
character count and re-election probabilities. If visibility of an assembly member tovoters is a factor to determine the re-election prospect in the next election, there could
be a positive association between these variables.
Past empirical researches have used variables representing outcome, such as
budget allocation and legislation, to investigate behavior of politicians. The corpus
provides opportunities to engage in inquiry into the political process by way of analysisof the deliberations in the assembly. This could lead to the expansion of the scope of
empirical inquiry in the political economy field.

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12