言語処理学会 第21回年次大会 発表論文集 (2015年3月)

Relation between Word Order Characteristics
and Suicide / Homicide Rates (5)語順特徴と自殺率／他殺率との関係(その 5)
Terumasa EHARA
江原暉将
Yamanashi Eiwa College山梨英和大学
http://www.yamanashi-eiwa.ac.jp/~eharate/

1 Introduction
In previous papers (Ehara, 2010, 2011, 2012,2013), we have investigated quantitative relations between the word order characteristics
and suicide / homicide rates. The purpose of ourstudy is to clarify relations between syntactic
structures of a language, especially word order
structures, and people's thought pattern who
use it as a native language.
Inthepreviouspapers,
weusednon-linguistic features: economic feature (GDPper capita) and climate features (average annual temperature and average annual precipitation) in addition to linguistic features to analyze the relation. In this paper, we add more
one economic feature: Gini coefficient. Anothernew thing is to use economic feature values
matched to the measuring year of suicide andhomicide rate data.

2 DataData for the word order characteristics (features) are obtained from the WALS database(Dryer, 2005). In this paper, we use two major
features:(1) Order of Object (O) and Verb (V)(2) Order of Adjective (A) and Noun (N).
We can get feature values of both two features
to 65 languages.
We define feature value "1" if the order issame as Japanese and "－1" if the order is opposite of "1". For the feature-1 (Fea-1), OV isparameterized to 1 and VO is parameterized to－1. For the feature-2 (Fea-2), AN is parameterized to 1 and NA is parameterized to －1.
Suicide rate and homicide rate are obtained
from the WHO's "mortality and burden of disease estimates for WHO member states in2004" (WHO, 2009). We use the base-10 logarithm values of the suicide rate and the homicide rate. They are named as S-rate and H-rate.
We use two economic features. One is base-10
logarithm of GDP per capita (GDP) and theother is Gini coefficient (GINI). These economic
feature values are gathered mainly from WorldBank’s data (World Bank, 2014). In the previous papers, we used economic feature values
from the U.N. data. The reason why we change
the data source is that the World Bank’s data
are time series. The measuring year of theS-rate and H-rate is 2004. So, 2004 is desirable
for the measuring year of GDP and GINI data.
From World Bank’s time series data, we can get
GDP and GINI value at 2004 point, or at least,
we can get interpolated or extrapolated values
at 2004 point. Population data of countries arealso gathered mainly from World Bank. However, World Bank’s data are not exhaustive. We
use several other data sources. Table 1 shows
the country codes of which we gather economic
feature values or population data from otherthan the World Bank.
Average annual temperature (TMP) and average annual precipitation (PRC) data weregathered by the method described in Ehara(2012).
Table 1: Country codes from other than theWorld Bank’s data
Category Data source
UN, 2011
GDP
PIFS, 2012
UN, 2011
Avakov, 2011
CIA, 2004
Wikipedia, 2014
GINI
PIFS, 2012
UNU, 2014
Goodwin, 2009
UNDP, 2009EC, 2008
Population WHO, 2009

Country code
COK, MMR, NRU, PRK
NIU
AFG, QAT
KNA, MCO, MHL, SMR
CYP, NZL, PRK, PRT, SGP, VUT, ZWE
ARE, BHR, GNQ, KWT, LBY, OMN, SAU
COK, KIR, NIU, NRU, PLW, SLB, TON, TUV, WSM
ADO, BHS, BRB, CUB, KOR, LBN, MMR, SOM
ATG
DMA, GRDERICOK, NIU, NRU

We merge GDP, GINI, TMP and PRC datafrom country based data to language based
data by the method of Ehara (2013).
Appendix 1 shows the base data we used inthis paper.

― 16 ―

Copyright(C) 2015 The Association for Natural Language Processing.
All Rights Reserved.

(b) New data sources

3 Analysis and results
We make multiple regression analysis. Criterion variables are S-rate and H-rate. Explanatory variables are GDP, GINI, TMP, PRC,Fea-1 and Fea-2.
At first, we compare the difference of data
sources. Table 2 and Table 3 shows comparison
results using data from previous data sources(mainly UN data) and new data sources(mainly World Bank data). In these experiments, we do not use GINI data. In the following tables, yellow-colored value of probability
means confidence with 1% significance leveland purple-colored value of probability means
confidence with 10% significance level.
The differences by the data sources are small
for both S-rate and H-rate.

Next, results with adding GINI data areshown in Table 4 and Table 5. For S-rate, adding GINI data makes no big change. For H-rate,
it makes several improvements. GINI gives
large contribution to H-rate which is confidencewith 1% significance level. Probability thatpartial regression coefficient of Fea-1 to H-rate
be positive reduces from 7.6% to 6.9%.

Table 2: Comparison result of multiple regression analysis for S-rate

Table 4: Result of multiple regression analysis for S-rate with adding GINI dataH-rate

GDP-0.4029-0.6123-4.17510.0001-0.4921-0.4776

TMP0.0001

0.00200.01180.99070.32140.0015PRC

0.00150.21251.61430.11180.22660.2057Fea-1-0.1256-0.2475-1.7898

0.07860.0745-0.2269-0.1495

0.1034Fea-2

0.05590.1192

0.79830.4279Intercept

1.83700.00004.49280.0000(a) Previous data sources
S-rateStandardized
PartialPartialpartialCorrelation
regression
T-test value Probability
correlation
regression
coefficient
coefficient
coefficient
coefficient

S-rateStandardized
PartialPartialCorrelation
partialcorrelation
T-test value Probability
regression
coefficient
regression
coefficient
coefficient
coefficientGDPStandardized
PartialPartialpartialCorrelation
regression
T-test value Probability
correlation
regression
coefficient
coefficient
coefficient
coefficient-0.0135-0.0260-0.17130.86460.2327-0.0225

GINI-0.0048-0.1031-0.8079

0.4224-0.1730-0.1055

GDP-0.0447-0.0796-0.51450.60880.2124-0.0668

TMP-0.0094-0.1873-1.0489

0.2986-0.2678-0.1364

TMP-0.0115-0.2287-1.27210.2083-0.2675-0.1634

PRC

0.00220.40062.87150.00570.12200.3528PRC

0.00220.38542.86550.00580.12170.3495Fea-1-0.0844-0.2105-1.47620.1453-0.0835-0.1903Fea-1-0.0951-0.2372-1.6718

0.0999-0.0839-0.2127Fea-2

0.15600.42112.69330.00920.36760.3334Fea-2

0.16810.45382.95140.00450.36770.3587

Intercept

1.07500.00002.72750.0084

Intercept

1.05500.00002.77120.0075(b) New data sources

Table 5: Result of multiple regression analysis for H-rate with adding GINI data

Standardized
PartialPartialpartialCorrelation
regression
T-test value Probability
correlation
regression
coefficient
coefficient
coefficient
coefficient

S-rateH-rateStandardized
PartialPartialpartialCorrelation
regression
T-test value Probability
correlation
regression
coefficient
coefficient
coefficient
coefficient-0.3779-0.5743-4.22470.0001-0.4921-0.4851

GDP-0.0082-0.0157-0.10430.91730.2327-0.0136

TMP-0.0100-0.1975-1.11220.2706-0.2678-0.1433

PRC

0.00210.37442.76740.00750.12200.3390GDP

Fea-1-0.0833-0.2077-1.46130.1492-0.0835-0.1869

GINI

0.02250.38203.33990.00150.45800.4016Fea-2

0.16420.44312.88640.00540.3676

0.3518TMP-0.0023-0.0358-0.2238

0.82370.3214-0.0294

Intercept

0.90470.00002.72450.0085

Table 3: Comparison result of multiple regression analysis for H-rate(a) Previous data sources
H-rateStandardized
PartialPartialpartialCorrelation
regression
T-test value Probability
correlation
regression
coefficient
coefficient
coefficientcoefficientGDP-0.4495-0.6307-4.18730.0001-0.4993-0.4786

TMP-0.0013-0.0200-0.11430.90940.3230-0.0149

PRC

0.00140.20331.55230.12590.2261

0.1981Fea-1-0.1269-0.2496-1.8072

0.07580.0735-0.2290Fea-2

0.06430.13690.91450.3642-0.15020.1182

Intercept

2.11700.00004.50220.0000PRC

0.00080.11540.92280.35990.22660.1203Fea-1-0.1204-0.2372-1.8563

0.06850.0745-0.2368-0.1495

0.1848Fea-20.0941

0.20071.43230.1574

Intercept

1.03890.00002.32450.0236

Contribution ratios in these experiments arelisted in Table 6. Adding GINI, contribution
ratio for S-rate rises from 0.2752 to 0.2832 andcontribution ratio for H-rate rises from 0.3139
to 0.4246.

4 Conclusion
We examine the relation between economic,
climate and linguistic features and suicide rate(S-rate) / homicide rate (H-rate) 1 . We makemultiple regression analysis. Explanatory variables are GDP per capita (GDP), Gini coeffi1― 17 ―

Used data are opened at my web site.

Copyright(C) 2015 The Association for Natural Language Processing.
All Rights Reserved.

cient (GINI), average annual temperature(TMP), average annual precipitation (PRC) andtwo word order features. These word orderfeatures are order of Verb and Object noun
(Fea-1) and order of Adjective and Noun(Fea-2).
New two things in this paper are (1) adding
Gini coefficient as an economic feature and (2)changing data sources for economic feature
values and population data.
From the results, we can get similar conclusion of previous papers. However, adding GINI
data, probability that partial regression coefficient of Fea-1 to H-rate be positive reducesfrom 7.9% to 6.9%. We, also, conclude that
GINI contributes with positive partial regression coefficient to H-rate with significance levelwith 1%. Adding GINI data, contribution ratioof explanatory variables to H-rate rises from0.3139 to 0.4246.
Table 6: Contribution ratio(a) S-rate
Experimental setting

Contribution ratio

Previous data sources

0.2779New data sources

0.2752

New data sources + GINI

0.2832(b) H-rate
Experimental settingContribution ratio

Previous data sources

0.3154

New data sources

0.3139New data sources + GINI

0.4246

References
Alexander V. Avakov. 2011. Quality of Life, Balance ofPower, and Nuclear Weapons, pp.57-62, Algora Publishing.
Central Intelligence Agency (CIA). 2004. Distributionof family income - Gini index, The World Factbook,Field 2172.html.
https://www.cia.gov/library/publications/download/download-2004/
Matthew S. Dryer. 2005. Word Order, The World Atlasof Language Structures, Chapter F, pp.330-397, Oxford University Press, 2005.
http://wals.info/Terumasa EHARA. 2010. Relation between the Word
Order Characteristics and Suicide/Homicide Rates,sociation for Natural Language Processing, E4-2,pp.956-959.
Terumasa EHARA. 2011. Relation between the Word
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Association for Natural Language Processing, F4-6,
pp.1037-1040.
Terumasa EHARA. 2012. Relation between the Word
Order Characteristics and Suicide/Homicide Rates(3), Proceedings of The 18th Annual Meeting of The
Association for Natural Language Processing, B1-5,pp.54-57.
Terumasa EHARA. 2013. Relation between the Word
Order Characteristics and Suicide/Homicide Rates(4), Proceedings of The 19th Annual Meeting of The
Association for Natural Language Processing, P6-3,pp.826-829.
European Commission (EC). 2008. 2007 Annual Operational Review ERITREA, page 6.
http://ec.europa.eu/development/icenter/repository/ja
r06_er_en.pdf
J. Yolanda Goodwin. 2009. Millennium Development
Goals Report 2009, page 25.
http://www.undp.org/content/dam/barbados/docs/mdg/Antigua_MDG__2010.pdf
Pacific Islands Forum Secretariat (PIFS). 2012. 2012
Pacific Regional MDGs tracking Report, Figure 2,page 10.
http://www.forumsec.org/resources/uploads/attachme
nts/documents/MDG%20Track%20Rpt%20web%2020
12.pdf
United Nations Development Programme (UNDP).
2009. About Barbados and the OECS.
http://www.bb.undp.org/content/barbados/en/home/countryinfo/
United Nations Statistics Division (UN). 2011. Per
capita GDP at current prices - US dollars, A data
access system to UN databases (UNdata).
http://data.un.org/Default.aspx
United Nations University (UNU). 2014. World Income
Inequality Database, Version 3.0B.
http://www.wider.unu.edu/research/WIID3-0B/en_GB/database/Wikipedia. 2014. GPI Gini, List of countries by incomeequality.
http://en.wikipedia.org/wiki/List_of_countries_by_inc
ome_equality
The World Bank. 2014. World Development Indicators,
Version: 6 November, 2014.
http://data.worldbank.org/data-catalog/world-develop
ment-indicators
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Proceedings of The 16th Annual Meeting of The As-― 18 ―

and burden of disease estimates for WHO member
states in 2004.
http://www.who.int/entity/healthinfo/global_burden_Copyright(C) 2015 The Association for Natural Language Processing.
All Rights Reserved.

disease/gbddeathdalycountryestimates2004.xls― 19 ―

31.09
29.86
32.26
33.06
28.60
35.77
40.81
35.78
16.23
28.73
38.10
28.10
26.82
24.60
40.75
34.27
28.27
40.37
39.78
33.49
41.20
33.15
27.81
32.59
33.79
37.26
32.11
34.07
35.53
52.56
31.44
33.67
34.96
35.20
38.85
42.53
50.88
66.86
42.26
30.17
27.80
39.11
35.43
54.66
30.04
40.77
46.29
30.14
51.92
40.74
31.15
39.70
48.00
36.31
26.36
33.60
42.16
40.00
37.71
41.29
40.77
28.93
30.92
35.19
36.81

-1
1-1
-1
-1-1
-1-1
1-1
1-1
-1
-1
-1
-1
-1
-1
1-1
-1
1-1
-1-1-111-1-11-1-1-1-1-1
1-1
1-1
11-1-1-1-1-1-1-1
1-1
1-1-1-1
1-1
1-1
11-1
1
1-1-1
1-1-1-1-1-1-1
1
1-1-1
1
1
1
11-1
11-1
1
1-1-1-1
1
1-1-1
1-1
1
1
11-1-1
1
11-1
1-1-1
1-1
1-1
1
1-1-1-1
1-1-1
1-1
1
1
1
11-1

PRC
Average annual
precipitation(cm)3.393771
2.128010
3.048024
3.363168
3.137731
3.557097
3.246241
3.152063
3.019127
3.511788
2.579350
4.467936
4.048339
4.655926
4.367388
3.945964
4.558260
3.934029
3.074404
4.315603
4.269251
2.812720
4.656770
3.064688
4.660547
4.478474
4.561596
2.902016
2.608460
2.353630
4.034854
2.618281
3.783960
3.826706
3.422229
3.196846
2.819669
3.725319
2.465188
4.753029
2.342534
3.371794
3.820863
3.575315
3.548176
3.613777
3.3211073.528011
2.809892
3.026599
4.229021
2.161562
3.821655
2.543597
4.604886
2.493528
3.394245
2.376052
3.380264
3.768397
3.163142
3.135880
2.800372
2.667565
2.783113

TMP
Average annual
temperature(degree C)

0.821314
1.286554
0.105408
0.847333
0.865917
1.0631200.380008
0.412644
0.451343
0.478457
1.196518-0.079673
0.126426-0.019535
1.036576
0.949131
0.407808
1.269611
0.564820-0.086086
0.676414
0.738909
0.005114
0.954456-0.138096-0.018780-0.270014
0.511418
1.266511
1.424369
0.891730
0.729125
1.008700
0.959552
0.717185
0.323744
1.180714
1.077837
1.134217-0.081523
0.528509
0.404208
0.207498
1.466058
0.512864
1.472187
0.053785
0.431069
1.122134
0.838138
0.309473
0.521889
1.296117
1.416664
0.091901
0.343362
0.848019
1.201636-0.196905
0.463909
0.946401
1.077609
0.550765
0.545823
0.576957

Fea-1(Feature 2)
Fea-2(Feature 6)

0.880812
0.771504
0.200180
1.061662
1.184603
0.684713
0.359943-0.279631
0.108943
1.136833
0.996099
0.848889
1.230210
1.155397
0.937789
1.399833
1.319829
0.997206
0.287105
0.469537
0.787081
1.227319
1.076481
1.031211
1.092280
0.853405
1.395182
1.059673
0.651690
0.887160
1.334424
1.311896
1.418599
1.632716
0.939178
1.228281
0.988738
0.491204
0.994556
1.064093
0.796098
0.785484
1.229780
0.779586
1.105648
1.562512
0.563156
1.277682
0.717161
1.450317
1.450533
1.398209
0.806887
0.882318
1.138953
0.332681
1.009878
0.694181
0.516362
0.5619781.0341301.429565
1.012720
0.774537
0.999000

GINI(Gini coefficient)GDP
log10(gdp per capita)

Population(10**3)

No. of countries

13014.579
174066.147
170591.288
4 168091.237
126673.536
11 115864.026
232911.245
117676.012
18306.500
17781.161
149875.169
179.060
110197.101
15404.523
42 706106.354
11362.550
15228.172
18 249364.700
14318.300
212071.549
16809.000
1 1110626.108
1292.074
1 221293.797
14070.262
257714.617
1 127761.000
12496.248
113149.386
19254.379
271678.718
15699.11212263.122
13377.075
12085.728
2 1300241.700
15948.461
1
10.092124921.910
14591.910
124018.682
169342.126
138182.222
7 232924.896
121451.748
1 143821.212
1178.794
1614.670
11912.022
119435.000
11997.012
18249.965
20 393990.340
137765.139
18993.531
16663.929
165087.400
14665.522
1100.319
166845.635
14696.876
147451.600
1 155151.394
125864.350
181437.700

H-ratelog10(H)

alb
amh
aeg
arg
arq
ams
amr
asy
aze
bul
brm
ctl
cze
dsh
eng
est
fin
fre
geo
grk
heb
hin
ice
ind
iri
ita
jpn
kha
khm
kin
kor
lao
lat
lit
mcd
mnd
mtu
nau
nep
nor
psh
prs
pol
por
rom
rus
sam
scr
ses
snh
slo
som
spa
swa
swe
tajthatigtng
tur
tkm
ukr
urd
uzb
vie

S-ratelog10(S)1 Albanian
2 Amharic
3 Arabic (Egyptian)
4 Arabic (Gulf)
5 Arabic (Iraqi)
6 Arabic (Modern Standard)
7 Arabic (Moroccan)
8 Arabic (Syrian)
10 Azerbaijani
12 Bulgarian
13 Burmese
14 Catalan
15 Czech
16 Danish
19 English
20 Estonian
21 Finnish
22 French
23 Georgian
25 Greek (Modern)
26 Hebrew (Modern)
27 Hindi
29 Icelandic
30 Indonesian
31 Irish
32 Italian
33 Japanese
34 Khalkha
35 Khmer
36 Kinyarwanda
37 Korean
38 Lao39 Latvian
40 Lithuanian
41 Macedonian
42 Mandarin
43 Motu
44 Nauruan
45 Nepali
46 Norwegian
47 Pashto
48 Persian
49 Polish
50 Portuguese
51 Romanian
53 Russian
54 Samoan
55 Serbian-Croatian
56 Sesotho
57 Sinhala
58 Slovene
59 Somali
60 Spanish
61 Swahili
62 Swedish
64 Tajik
65 Thai
66 Tigrinya
67 Tongan
68 Turkish
69 Turkmen
70 Ukrainian
71 Urdu72 Uzbek
73 Vietnamese

Language code

Base data used in this paper
Language name

No.

Appendix 1

15.2
16.6
21.7
25.8
22.7
23.7
18.3
16.9
14.2
10.3
27.4
11.0
8.4
9.1
18.3
6.0
5.3
22.0
12.7
18.9
15.9
25.2
4.7
28.0
9.8
15.4
16.3
-0.1
27.8
20.5
11.9
26.7
6.0
7.1
12.0
12.9
26.8
27.8
19.0
4.8
13.1
18.4
8.4
21.3
10.8
5.8
26.5
20.5
15.5
27.7
10.7
22.5
17.8
26.0
6.7
15.5
28.9
16.0
23.5
12.1
16.8
8.4
23.0
14.8
24.5

145.9
114.5
3.5
207.5
15.5
40.3
47.7
17.6
36.2
58.6
210.8
107.2
49.9
58.2
120.7
67.9
67.9
117.9
53.1
37.1
63.2
76.8
84.7
182.1
77.5
70.7
152.9
28.1
140.7
106.0
130.4
168.2
70.2
63.1
57.153.9
323.6
205.1
147.6
85.0
28.9
24.1
53.0
131.5
60.0
70.7
271.1
1.3
65.0
232.2
140.1
50.0
93.1
107.2
53.6
56.8
165.3
35.4
163.8
40.8
20.7
61.1
47.0
45.1
164.5

Copyright(C) 2015 The Association for Natural Language Processing.
All Rights Reserved.