                                                

            

     

 

  
  

     
   

           
                                                                     

 

            

 

  

 

               

 

  

 

 

  

                        

                                           
     

    

 

   

 

 

 

 

              
    
     

 

    

              

 

      

 

 

I: 11. ..a 6
. I ... .. . 0 I. .o 0. 0 0 .. . 1.00.. I 0;. 0... .0 .. .0 0 06.0 0' .‘*.0,‘I01%04I."3 m I. . . .30
I0 I .. r 70.1,. k. ... ...6 o 6. . . . 9.- . . . .. ‘_ 0' Q .. I r ..- . .. cm... ..a ...u. 560. . .691... .0.... .0. .. .0 .. . ... 0.0. ... . #0...0J00 .96..00.23$...0 65% .0..0..6..0..0 ..x 9mm . ..
0 I 0.. 0 . 6 .0 .... . 0-10 0 .00..- .. 0. ...0 ...IH...¢.60I I .. 000...... 66 0.00000. ...00 0.0 0 00 .0. . 0..-0 .0. 000..6.06.0.a'=60«.0+0100 000.0304 u.”- 3500-0 L00 0.05“.
'66.. .. .... . . , ... 10036 ... '......Io.s.u..0601 6 0.. .... ..I. .0 ..00. ..00 00.0. 0. 1.0...0....0..... 3.....60 . .600 .330. 000.63 .0. ...‘Irk.63060. 000
6 I . 0 0 I 0 0 00 00.6 0 0! 06 .. O '. . 0.I0.. . 06.. 0” 0.0.6.6 666.0 0 . 6 60 00 ~. . I 0 0 00 t . .0. . .6’I010000. 0.001, . :9 00I000b’0031h" .‘lr 0.
71...... ‘0 ID. .0 . .. .0 I .06 0... . 099. . I . 0 0 ..0 - 0. . I . ..0. ... .I .0 o. L: 0.0.. .. 7.0 .6. .04. $0.00.. .6)... ..u ‘3 ..u 30.9.0. 00.0 0. 000000.000
o 0 . . 0.!- 0 I 6. 0 .0. 6 . 0 . I. .. . .0.. . .06I . . 0-0.. . .. 0 1.0.1 ... . II . . I 0 v .0 (.000 . 000 0 . ...0. 0 - 33.0.0: .... .0...0..0.0 90¢..~,.?..§ 6:
a .6. 6.1.0.. . J. . 0. .0. . . ..9 . . . Q. 0 0..... . 0. ... 0.. II 6 .0... .1 . . .f .9 r .. .6....- I. 000 0.. ...v. I0 00. . 0.80 600000...N.§...II. ..0.000000.6 ...... DI.
. .. . . n. . . . ..0 .....60. . 0 0 I. .. . .0 ..00 .2000... .....I 0 . 0.0 00. 0 . g. .. . . 0.0.0...0..l0u .310. J #0000. .06~ . .000 ... ...-3900.33! ‘3R0’ 0
I 0 0 .0 0. I. . 0.... 0 c 6‘ 0 . ' I‘ . .. I . 0 . .0 I .. .80... . . .0.... 0 O 0... 60.... 0 0.6190106000. 9 ... .. a .. *0‘00‘6 00 6' :0 &§‘0.0wf.000000‘.00 0.01.. 00v
. . I. .0 ... ...... . a. .. .. .. ,. . I . . ... 0. . ....l. .. . . . .. .. .0.. .. ... . . 0 .. I. .0. ..VIIII: .0. 0 . 0.. .0 0 . . .0 0.6.. .. “I. 30.30.10. 30066....6000..‘I.00000 ... ‘0
0.. . I . .. 0 . ... .0. .0 . 0.0. . . 0.0. f -.. I-.. .. 0.0.. 0 . .. 0 0. . .00 ..- .6 .0 0.. .I ..00. 0.60 ll ....6 i .1. .0 . . b... .06 h...» .... .. ... ....00 0006 ...-0 I. .0.
I . . . 0 .0 I. . . . 0o . . ...0 0 0. . . .. 0 6. .0. 0 6.. ... u .0 0 I 60 . I0. . ... . 0. .... .\ .38. .01 500000.00 0. 00.00%.“0- .0310. 0'0 6. I P.
V. .. .0... ...I000 .0. I . . . . . «00-060 .0 . . 006 . .. . . 0.. o . .0.. .0. 0.. . . . . . .0 0.. 0' 0\.. ... . .7361... .6.. 0 . 0..!6 .- o . 0 0’0.I.. .. . 00.0 ... 0 ’60- 100 :00. .00 2000.0 .0.
, I . .0. .. _. 0 J. 0 I 0’ .. 0 . .. . . . .... . 00 . 0 06- 0-3.‘00.0 00.0. .0 0 . ... 00 I9 u . O0. .0. .0. 90 .0. . . . 0.0 ’.'xn.0.r.. 0 o 6.. ..0. 00 09 0 006. 00.0.
0 . I 0 ... ..I .9. — 0. . .06 0. I .9. .0 .0 00. 0... . .00. o. 0 . 6.0 I 0 0.0. .0 00. 6A .0.... Q0 .000. 36.0 I. ... .. .00. p: ...I. 0 l .. . . . .0000 .0006 90" . 0.0.00? ”0‘ 00 .000
1.. .- . .. . ..0 2 ... .0. I... . . . ...; . ...0 » n _ .... .... ...: . _. . .. .... 000. . $03.9. 011.. 3.3.. 601000 ..~0 0..... 53“” 30.0:
. 0 I 0. 0.. .... . . ... . I 00 II.I0. 0.0006 . .... . 6.0 ... ..0 . 0 o. .01...» 1.00.9 .. .0 60'.......0.0.0 ..I.‘ .o 0 0 . .0300. .0" 0.. .6.. 6 . .. 060.0 . o . 00:36.0. ¢I00o0 0‘27".
. . 4 . ..vo ... ..0 I. ..0. .... .... 0. .. . .-. . . ...... . . ........ . ... . 0.0.0. 0 . . . ..
u . H In. .900... ... .‘I 0 -..... 0.. 0 z I? . 20.000 0. . 610 .... .0. .0. .0 .. . 06 0.0 ..0 0L0 ..0. 0 ”0‘ . 00. .0.. 10 . . 60...: 0..00 What" $0h0h0n0§9wnn bir‘nﬁ.’u.u02. luv-mnnmk
I. . ... .-. .0.. .. . .6. . .... I a . . . .... 0 03. . . 0... . ..0 .0. .... .I .... .60 0 . ..0 .0 0 6..0.l0 0.“...01...0 .. 6... .0 .0 . .. . . . . ....I .....r‘In‘ 0.0.0 00.00. .. .ni 0kg
. 0 . ..6 v o . .... . . . . V .. .. ... 0.. . .0 .. .... 0 0 .. ..0 . I L... "0..... .' . .. I. ..0. .6. V .I 0.. 0 . . 000 II‘I. 0. .‘6930. L; 9:”,
o. .. O . . .0 . .-. .000 0 I .36.. . 0.0 . .' ... . .0 . .. . 0 v p. .. l. .00 0 ... ..00. .0.... . 0 .. .0 .0....1.I.'0 0.. 00 . . 030. .6.!0‘0... . . “0‘00 06 I! #0000900... . ‘ ~03 0.573600009W
...0 L 6 .... 0 0... .70. . 6 . I I..- .6. ... .... .. A 0 I... 0.3.6“. .I... X... . .00 ’0. . _. .. ...).60000. 03.1.6.6 £001.00.) .0533!“
0.3.... .. 00.0.: o .0 0 . . I0 . . 0... 0... .. .... .0 .3... .0050. .. ...0. 0... . I . .. I30... 0.0. 06.. . . . . .. . ...0On....000»0ol..3.0.000 {rot-:6 .006”
I ... 0:. .I . 0.. 0. 6.. I 0 .0... 6.. 0 .0.. 30.0. 6. 0.6 0. . -0726... L 0 . 0.. .. 0 .0..0.0V . h . 0 .‘300 003.. a“; 9.3
v I. 00 0. . .00 0.. .0 I .066 0... .0.. 00‘ 0- 6.0 . 9.. 0. . I6. I. 0.00 .0... .0606. .I 0.00.0..00..Iv . . .00.-0“~.06. .... 0.030... Iain-'00 40.0.0
. .. . :5.- . .. ...I. . . .. . . . . . 0.6... . a .60. ... .. s .6 . .. a. .. .00. I. 2 . .0... 066.... 6.0 . . .000. .. .200. 00. 3.0 0.. ...‘460 ‘00 ...‘00.r9.~0~0«~00.00. H06. 0.0uN0 LIII“! II .
I . I .0. x . . .. . . .. . 0.. ..I .. .... . ... .0.. .. .. I .. . 3.61..-- I ... I... . .608... 0. .00 .. ...-VI . .. ...: .... . 060:0; .0 110.. J...... . .00‘0.....‘\.. . .0 .0110. L... .....005. I
0 . 0 . ... ....06 0 . 7 .0001 Q ...-61.0 ... .. . .6 ...0. .0 ...I.‘IL . . .0.... 0 I 0 .0000‘000 0.0‘6 .... . u... .00.- .v 6.0.0...." 3..\.0 :00 6.00 37:0kf00 093‘ 3' ‘00.“.
. .I . . .. 0 .. 0. - ... 0. ... . .....0. . . . 0...... I .0. ... l . . . 0....0. $.33... .0 ...II. .00 . ..II 0.0. .06. .0." 00.10.... ...00! I69. .36....01530307 I..000I-.0I0 .0.-.0.. 0 ....3 00
. . . I 0. .0. . . I 00 . .... ..v .. . .. . ; . . 0. .... 00. n. . II J . . . ... . . . 0.?30. .0.. .0.... ..I... .10. ... 00 . ....»03 . 0.00.66.03I 0 D. .. I .. .0 .. .9006. .0 .00... 3.604.066! \‘6 .10. 30.000 1"
. J. ... ... . I .. '0 . . 0 . ... I . . . ... I. . .... . .. .... , . 0. . . 0.0 .0 . 0... 0 o .0 . .0.. .610: . 3.55.1.1... 6 ..0. ”.00....60 .1006. 00.00.... .00. 3.300.666! .0 0.0 ht. 0..
0 0. 0. .. J 3. . I. I .. . . .. ...6 I (66‘: I .0 0 . 0 .. . . 0.0... 0.... .00... . . 0“ 00.60 .00r0..00.vh'60.0.0 . 02.0 00. 0.100 4-0 .200..—.\.:0‘00 “Siﬁuu‘h. 00$K’30v’g.‘ 5.000 0?.
. . r. o I .. 00 .... . . ... v .. I o .0 . I o .30.]. .0 .0 00 . . . .0. I. .0 . ...I .6 . o 00. 0. .. 01.0 .00.. . . 0 6060...... 6.... 0.00. .00.... .66. 609.11%”? 40....1000058? 6.03. 0.0.0.0.
0 . . . .0 . . . .. .... .0 . 0.0 . . 0.. ... I 20 .. .. .. .... . 0.. I... -.v . . 00... .0. .. .o ... . 0... .... . .0. . 9. .390».\ .6 20.0639. .... 036.0 .. It. 0..-0:10.65.
. . . 0 . I .. 6 . . . 0 0 0 o 0 . . I6... ._ 0:0... 0... .. I InS‘... .. ... 00. . .0 t 0 . 63.0.0023 00.0 .InclSXZ: 00. .000... .0 “0'69.“
. I . .. I . - ... ... I . 0 0 0... 0 .6 .0. 0. . 0 .. I... o . . 0 . 00.) 0 . . 0 . 4 . .0... .. .. 0 0.1.1.60.“ e00 . .. .00”: 0.... 000.0
. 0. . . . . . _ 0 .0. . 6... .. 0 06 .. . 6 ... l I . . 0 :0 . 0 . 1.0 6.6-00.0 .00 I 0. 0 0 ..000 . ...... 00 . 3.00. 0 6.000.0'. 00.0.0 0”... 0.00.7) ”Y0, 000“!“0 .000... 6‘
_. .. . .2: . I ....II .. . ... ... . .. . .6.. .. . ... I . . . . ... . . ... . . .. I .9 ... .. . . 0.. . . . . 0. .... 00.5. I . 3.0620 0 ... 6- .0. ..0. . 3.0. ... .... . .00 6600.06... .. . 0.60.1006. 01.00.00.060! ...000. 0.01.6. 00..
... 0 iv .a. I v. 0 .. 0 0 _ . o 0 0 . . I. 000. . .0 . . 0 0. . . 6... 0 0 . ..... I .. . .. . . . . .0 I0. 0 .0. .0 000.0 .600. .. I oOII (A... I 0900 . a 0 ..00. 0vI . 010006004 I . 0 0‘ .0000! ... 0. 0.7.00.0...3 "a“ 0000
.. . 6 I 0 . . . . 1 . a . 0 .. , - . . -.. 0.. . 0 0 .. Iv . u I .. I .6 I o. 0 .0 0 00 0 0. . .. ... II .0 00. 0 0 .. .0 7:0,. 000.. 0 .006. u 0. l. of o 066 0.0 0.000“ 6,)...00 .‘0 a .00. 0.0 00000-00600‘ 6...".
,. 0 .I I I . 0. .I. . . I . o - .0 00 6. I .. 0. . I :0 .. 0 0 I . c . 0 . 0 .0 .. .. I 0. . . . I . . . 0 ... . . ... 0,000 . .. v 4.0. 9 . 0.0 .. 0 0....00.0....'I0.0. 1.0- u...- 06... 0.. 33.0.0130 0003 . 000. .000! 00.00r..v.§.0 .3000. ,‘O100-0.60‘
. o .06 . . o 0 .v . . 00 . . . . .0 . 0 I 0.0 . .. . .. . I. 600! . . n . ' 0‘0 . 6 .0 I. . 0..-00.10.... 0.. :0. ..- 0 0 . O . 0 .. 0 I... 09.0.; 1.006 0..- 6‘.) . 6 . “.0 .0 .. ”0.0.0. I01." ’00 014.209" . .0 0’.....I.0000.0 I1‘Q00£.0}’1z6
w 0 . . . . .0 .II . o . . . . . . 0. . o I I .0. .. 00 I . 00 I ... 06. . 0 6| . .v. I ..00. 00 . . . .— .. .000 . . . .. .. 0.060 . ..- 0‘. 0 .0.. 0.0 060 . . .. .... ...? .0. 0:00.023: 10.0.01..- 6.0.3060. 93.. 00.00” “W1.QM.
. I I . . p 0 . . ... . .. .I . . I u . . .c . . . . . .3 _ . .. I . . . 0. . a .0.. . . ... I . 0. 2. 0.0 . 0... .... 96 ..0 ......» 00.00 .... .300... ...0. 30.0.00 00... 70.07I009140u000m90..08 «0 p ..
v 0.. 0 I0 0 I . 0 0. 0 0.. 0 0 . .0 .0-.. 0.0 J. .0.. 00 .I I ‘0. 6 . ..0 . . I . .00.. ...0 '6 ...0 . .. . I. . 0' 001 60.. 00. ... 0. ... 0.0 I .0 00.9, 0 .01- .0070: .Rr’0;0=300000 . . ..it. 0'. 0700600.“ 6.. 06M)”
. . 4 . I . . . . 0.0 6 . .o .. . I .\6 .66 .I .. 00 P . 0 I l. 0.. .. . ....I.. ...-0. ...000 . .0.... 0‘. :- 0... 96000 0. 6. 000400. 603.0060. .00 00..- .06.:00 0000.: 0. 1.0080010 Ln I .
.r I. . . I o I 7 . .. . 0 . I 0 .. I. . . . . 0 19. I 0 a . . v .0 . . . . . . ..0 .. . . ..0 006. ...I 06 .96 0\ 06.0. .. 0.. u i.‘ 90.. . ...0 0.0. 0D....0.0...00 . 0 ...0 0.0 000. 0 . .. 0:09.101!“ .10. ..
6 . . I . o I 0 00 2 o. I. O 0 060.0 .. . .0 0 .0 0 . ... . .. 0 . 0. 0 . 0. $0.00.. I .. . 00 .0 .0 0. .. .00 . . a .069... L. 00 00. 0. . II... V. . .... . .000 01000.3... 9.00.00... 000. .0),- 0“00%’..0 I £14m
“. . 00 0 .0. I .0 6 6 6 II 0 0 0 v 00 0 0 . 0 0 .0 I I. 0 0 . 0.0 I0, . 0.. v 00. 7’31. .0. .0 .90. I? 0 00. ’0. 00 [W710 ..‘lu. 0‘6...‘00.0906 0.0.00.3. 0 0600.0: 00x00“ . .‘
. 0 .... . I r 0. x . . . .... . I... I. . I .I - .. . I. . I ... ... :00... .6 3 .43.. ......... .60 ......IIao.000.o...0. .....0...’.0I¢00 ...... 9 .0. II. 5030.. .0. 0700.0. .00 h . -0.).0 ...-... . 0.
_. . I. I A . . I . . I 0 . . I .. . . .0 . . ..0 . . .. ...-.... IQ... .. . .. . . .... . ..0.I I I.000500.lo...000 01.00. .. .x. .310 .00. .1. ..00001...‘ 000... .0 0 L00. 10.500
. . . I ... . 0 . . I I .. .. .. . . ... I 0 . .... . 0. . . . . . . . . .7 . .00 o. .60. .I? .0. . ...... 0. )0 0 00. 0:. J50! 00. .0 . I010 .0000 .0 .000 0.“.90.‘00“ﬂq0
w . . o _ o . 6 0 . . A I 66 . I .. . . .. D 00 . I . ..I .\.....0..\000. .. ..0 .0 .0 .... c. 0. .... ... 600 I .0.. \v 6...... 030.0030 0.. 0‘00. 80 900%....
1 I 0 . I I I ..I .0 .I .6 I V ... 0 III I o 0000 l ... 9 . I .6 . 0.0 .1000...00.0.00..0.0 I ..090 00‘. 00'0.0000. 00 .000... ‘9. .6.. 000... a.r0..0f ‘0 .00.:
.. . I . ... . . I . . . . . .0.. .. III . . . o. . . .0.. .....0. o .6.... 0.. .- €1.60}; 0.. .6.}; 0.0. 0...... 3.69.5560... 63.0.0
. o I. . . _ . 0 . _ I . ... 6: I I. I . I r . . 0 . 4.. . . . . . . I ..I). .2 0. ...0 .3an60. 0 . 60. 0. 0‘. 0.I.. 000.1..1000.0.{60n
.0 0 . 0 o 0. 0 . .l 2- o I I w. .0 . . 0 .0 0 . 0 I O o . . . . i . . o .06 .9'0..1 0600.100 .60. 0.0 I60.~0 7 030.1. .00062‘0060000” l...
.. . o. p I . . I . . . 0 0 r0 .0 . I 0 . . ..- 0 . . 0 . ..I .. . . . .. I . . . . 00600.00 .0.... 00.0 70000 09.0. 0.0 3.0—. 09.. 10410 0.00.00. 500‘?” 6
I I. r . 6 .. n _. . 4 . .II . _. I - . . o . 2. . . 0 . ... . 0 0.6. .. 0 .w 0 .66. I ...60.0.0.. .0.... 0.3... . 0.0 0. 9
0 I o I II. .. . II .. . - ..0. 6.. . Iv . l I . . ... . ... . . a - . .. . . 0 . . 0- v v . .... .0 0.0.0.0. (0800.60... 0 o 05.00. ...!00-.. .0..Y.0. .0..
w . .. . .6 . . 0 0 . 6. I. II 0 . 0. I . I . .9} I :0..- ‘I... I .06‘ lad. .00 60.003.000.00 .71 .0
u 0.. .0. 4 0 0 I ..0 0 . t 0. 0 0 . 0 ....» 0. .... 0 II.’-..0 . . .0 0. ’00 0. 0.00009 0‘00 0.0.00 .- 0 03‘0”0:000 0.. 00 00...
. 0 II o . 0.. I . .. . . .. I 0 . .. .... ..0. . nﬁ . 0 .. .... .«3. :0. .0 . up“ 50.5.3.1! 50:“...00056 . A...“ 0.60; 00.50.33
6 . v . . . I l 0 060 0. 0 .60 _ O .0. 0 l . 0 I . ..I.. _ ... 0 006.0 0 ' .. .0.... 7 0 00 . .0 0.0 .6..- u.
v . ..I. . ... . . - . ..0 \. . . . ..II. . 0 .0 . I . . 0 .I . 2 . 1 . . . 0 I I 110033.“: I... 6.6.. 00 1000.40. 0 urn-.0.. «- ... ..0000000- 60.. ﬂak-”0' 0,3000
. I 2 I I . I . . 0 .. . . 0.. I. I0 ..0 . 4.. 0' 0.0 . . 0 I 0. . 0 . . u A... 3.13/21 0. .. 6 0 ~0;00'0300. ..‘bohx‘ 700.00— 0.0.0.-
I 0 0 I 0 I I. I . .0 .6.... r 6' O . . 0 . . I 0 0 6 o I . . I .6 . 0.6.0.. 0 0 0.0 0 0 ..I 60.. I. 0'00. «.0. 0 ..0000. 0. .000 62.0..0000 0&1. 3.0.6.0...01‘.
. . I 0 . . .. . .. . . . . a .. . - . o . .-.I ...-....13. 4.90.. t .6006... 3 000660 06... ...-660.3... -
.. .. . , ... ... -. . __ .. . . . _ .. .... . I. .... \ . .. . . 5'1...- 3.2.6.... 4...... . .... - :00. .6.... 660......6Lo0..0.m.x.....1.6..0006
. 0. . . . . . I . I 3 . . ... . I0 .. ... I 0. ... I .3 .. II I .0 .I. .00 . .. . . . . . . . .a .. 0 .... .II. I .0 I .Jlﬂ.“ .6. 1‘0000060. . . . .0 0:0. 0... .30 ...0‘0 . 30.0000”: «9...... .0.-‘83
"I .0 . . . o . . _ . . . 1... . . . ...: .. . I . . 0.. . . 0. o I ... . . . I .. . . . . . I II .00 .0.. . I 0 .0 . ..0 10. I 0.0.! ...“Ymo‘totaqu 0: .000... ..9 0.. ..‘IVIO r0. 0 0. 03.0'0
. . . I I 6 II I . . 6 .. . . 0 . . .. . 0. I 00 . .0..0I..I. “0300?.0 . one. 0. . .60 I. .. .000. '9.I . 000 0.00.. .0 0
. . . .0...0. . . . 0. .4 . .l _ . . 0 . 0 . . . .0 .0 .0 ..0 n . 73:00. 0100.. 0.2001300 .0.-.01 .6 .70... 600....“ 28‘0““.066 ...-"6636003005.... 0‘.
v. . I . . . . I o .I . .I t .. . .6... . .. 0 09 o . .000 . 0 . ...... ..0. 0.0.. . ~ I.0 J‘..\. 30‘ 6.000.020.0080... .-
. o. I I. .I. . I. . I . . I . 6 . . ... . . o . .a . . ... ... ..v .. .. . ...: N. .10. 0.0.1.0000: 0 1. .0 . 0 ...Q... ... .0 .2‘6..h0.0010..6 ..00 L0
... 0 . . . . . . o.~ . . 6 .0 0 .. . . 0.. I .o . .0 .. 00 o ... .. . a .. 0 I. 0 ..I 1 0 01.00' .0.-...? n 00. {0.050.0- . .0 .6610003.‘.00
00 .. . 0 . - _ p .. A. A . v . 0 . 00. I. ... I . .00 .000 o ‘ 00 .6 I. .0 )6 300 . 0.6‘6030’.‘00...I .0. 060 000.300. .00."!!J0000v”.
.0 . . ... 9 . . . - . . . . . .. .- . 0 r. . 2. . ... I . .. . . . .0. .. .l . . I ....- 0, 000.0... 00.0 000.. .0 . 2.1.0.0260. 00.100 I 0.03.3.6
K 6 I .9 .0 0 a o _ 0. O 0 6 I 0 0. . . _. 00 I I . 0 0. . . 0 A 9 '. 0 ’0 6.0 0 0 .0... 0.00.0 I 00.3 0,: 906.0 0 ’00 .006. '0 ’ I.“ 5 .l
.. . 0 . . 0 I I . 0. .. ....l... ... I I . . . ..0 . I . _ ..0. .. .. 0 ...fl 0 0I0.00...0..... ...0 ...00 6.0.3.7663. .03... . ......J. «W
F OI .. . . 0 0 6 I .I O I . - .0 5. t 0... o. 000 . . . I 0 0- 6 . .0 . 0 000. 00... .76 .I .0 ..J U‘ «7.0109 040 0 6 0.0 3.0‘ v... ‘000’ (9‘0. 000’.
.0 . . I . . . . . . . l. . . . I .. . . . . . . . . . . . I .... I. .... 00 . 060.. . o 0.“. 6.30.1.3... .0 :.l..00..0...... . 0.3....uebq.»000‘110 ..0
w. 0. 00 . J 0 I0 . .0 . I 6. . I. . 0 a I. 0 . I. 0 . .I 0 .0. . . . o . .. . . . . . . .0 .... .0. .0.‘ . ... 0.. 000...... 00 .‘30 ...-.0.... .4 . 02.00.3003 030103.00 . . . It:‘.‘- .300
0 . .I 0 I . . . . . . .0. . .. ..0 0 .I. . .... 0. 0. . 0 . . . . _ . .. o. 0. 0 o. .0.. _ 0.1.06.6 . (0.0. .. 1.5.6.... 000 .. 60.607000 (6 060. In.0000.0\ ‘0.
I I I 0 . . 00 0. Q . . I . 0 . 0 . . . . . . . .. . . . . . .a 3. 0. . . .. . .0 {.0.}. 0.. 0 .13360..\66L..7.4 10.. 0.. 07000 00.0007...
. v 0 0000 I ... I . I 6.0 . II . . . . I o 0 . .0. 0. .. ..0 [0.000 6 00.0 00 .0 000000 e; 0006 V. ..I.6 0‘1 00 0'00. 30000. 00,009.00 0
. II. x . . .. ..I 6 . I . I 0. I .. .. 0 ... 6. ... I. .. I. 0 . I 0 .. 0 00.. o 0. 0 020.00-.. ..II. 0 a). .. 0065...)... 3.0.6 6.0.6 . .0. .I‘.0 04 300.. 00 .
6 I 6 o 0 0 x ..I .. - 00 0 .- .. .362... I0 00 0 . . 0 0 v .1 o. . ..00 0. . . 06.00. ..-fI.A.0 60’. 0.00 0.000. 0‘. 900 ‘0‘. 00..
O I .0 a 0 0 ... . 0.. 0 I II t. 0 6 . I 6 ‘0 .9.0 6 I 0.006. 00.0.... .0. 0.0". _ 00..’..~..00\00 ‘9.‘C.0. s 0 603.09“ zﬁuoo 00.
. I . I 0. . . 0 7 0 0 I. .0 . . . 0 0.... I. 0. .... I 0 I 6.0.. 0. . ..0 . .0 .. . l .0 6 ....t 0...0.I ...! A . .. v. 3.0 a II. .o . 0.. ... 3600‘..r.00.‘§\ .00 r. ‘9
. 0 I . 9 I . 0 . I 0 6 6n 6 0 . . 0. I. . o 1 0 . 0 C . . .01.‘ 0 0 . 0 0 .0 6.0 ”~00 000000.100. 000. I50. 10000.0. . vm0000.. .0000. ‘0‘IU6
‘6 . _ . I 0 o. . I I . ...I... . ... . -.. 0 . 0.0.. I.0.6 . . ... ..0..0.. .0 II: . ..0. 0.. 0.. .5... . ..0... -66.. ..‘2I0I I . 0L0..09.‘10..L.3..
. .0 . . n . .0 . . .. I 9 .0 . ... . . 00. II. . I . . . .0 . . . . ... 006 . 0 . .360. 6.. I ..0.. I . 0. ‘6 . .0700. ..‘0 90600.0 0..-006.6 0.0 \ 0.0.‘5‘19‘6‘00
w I . I I .0 -. 6 . . . . . o . . I 0 O. . .0 .0. . 0.. _ .0 .. . ..0 . 1.06. 0.. .. . 66.06.. .... a . .00. ....0..l-\n»0’000.m 600060.30,
. I I a I I v 0I 0. II. I 6 6 . - I. . .0 I0 . 00... 00 . . . 000.60. . 0.0-0.0.. . 0.0.... 10.000.130.09 .0 3.00 0.001 ’.0.960.0
. 0 0 l I .0. . II 0. O ._ 0 .0. .0... 0 0 . .0 .0 .6 I ... . . 00. 0. .0 . 0.0..0...6... Jr. 0.. '00:}. . .006 01500.00... 0060.50‘00‘00003.0"000.0000.0"
. . . I v . . - II I . . . . _. It .. . u . .0 . . 0 . v I .I .. . . .. 0 I . .. . . .. 0 v0 . . .0 66 .010 .0. 000.6 . ......0 .. 0 .0... 000 06.00. 000... .300
I . I 00 . 0 I 0 2 . 0. . II . .. . . 0 I .. 0. . 0 . .. . I II. . . o - 00 . 0 ..0 .00.... 0. .... 7 5.0006. 0 . .. . 3.1.30.0 00.0 0070 0.‘00‘b'. 0 ’60
o I a. I .l . 0 . 0 . _ I . I . 0.00 . I 0 . 0 I s . . I. . I . . ... . 00. n 1...... ..0 .00. f0 0. .700. .6. .000. 0‘... ..6 000. 0.003003 0000.. .0"
. L . . . . . . - . . . I. .I. ...... . . ..6..9m.6r .0 . .00... ... . 300.660.0061. ..03..r. .26.... 1.0... . ...-00.6.6.0. 6 .
I - . . I . . I . I . . u. ..0- 06.60 ... .00. 03. .100 0:0 06.. 6 0.00.0.6: 00th... .
“I . .0 . 0 0 v c 0 0060...... .0.! 600‘:- 0.0000 000000900”
0 .‘I 0 . 0 . 0 0 . .1... ...0060...u0000000 000. 0.0.0....1-00 003M0§ﬁ
0 I . ..00 I. 000.I 6.00.60. .0 0.6. 0.
. . . . . . ... - . I . . ... 0 003:... .....uD..II 0 . 6..»W.016i06r56.66‘0.$$.u3u}
I 6 0 . a . . C v. y .. . I . u . . . a. I .0 0.0 6.. 6090., 0“ ed ‘:.90.‘ 0 ~ I O
. 4 .. 0 . o . I . I PI 0. I6 . .. . I .. . . . . . . . . . . . . . . . . .0....30’3‘0 0. Orv-(9‘1.— 00‘0‘3 00.6.00 .‘W0 . .0
. 0 . I . . I I . I . 0 .0 . . . . . . .. . .. ... . 000.00....0n0. 00 .23070.
. 0.. . 0 . . . . . . .. . 60””000' .0.0’060un6?000.10“0'000.0
.. . . I . .. . . 0 07.00.05.“ 90.00-00.06.“ 00..."
. . .0 I I . I . . . . I . . . . . . . . . . . . . . . I . . . 1.000 ...-0... . 000.0: 70.0.0.0: 30.60903 0.0. .090. 00.0.0026”!
v .. _ . I . . . . . . . . . . . . . . . .. . ._ . . . . . . .. . . . .. . if 9.0.10.1 3..Ir0..n0.04.l.0€.i1.|h
. . I: . 0. I . . . . . . 0 .70.? 00 ”.ﬂ00‘3000 . .0000 00§.0-1..0. .
0 0 . . .. 100.. r 0 . 0 a .Q 0?: 00.1-07.3. 3017.16
. I I . 0 I . . .. 300.... .0 1.0.002; I .04. 006 Ill..ol‘k.0.00 0. I040: 0..- ..0.’“00’9.00.0“.0
v . . 4 0 06 . .. . . .0... 0 I .. .0.}. an- .... . ....00 y. .. 0.. o0.00...0...0. I0 . 0.010.760. 00390-070019"! .-
Y . 0. . 0 .6 II . 1 I 0 . . . . 00.0 .. .7 0 0 . 0 0 60 0 6 .. 0. .' 6 I 0 . n. . 0 .I0..6 00. 0. . .0 . 000 .0. 0 0.0600 .91, 6" ..000 600 . . .0 .06.... I. 000000 a .500 .00.0000..Ioc0.;—00t 0. ..000
I I 00 6 I ‘ . 0 00 . 0 0. .0 .- ... 0 . I I .60 0 0. 0 . 0 ... . .0 0‘ u 0. 0 0..-6600 .0 0.0 ..0060. .. .00.. .0 ...:- .0I \. 0.. . .0 .00.”..3900. 0 .0 000.90. 0000.9..0'0v.r 9.‘ 300.01...00.
. 0 0 . . . v 00 I . .0 -6 7 0‘. I I . . o. . v 0 00 .6 I . O .. .0 I 0.0I06'0. ...0 .0 .. .0‘ \ \0 ’I. ..b‘0.0 0 0. 00 u 00-01:... 9 0:0 0.....0. a... 00'. 0000 r, 0.09.11 $09.00..
. I 9 . 0 0. 0 .00 .... I. 0 . . 00. 09. o I 60. . I . 0o . . . 00.0 . . .. 0000. . . .6 . . 0 u. . 1.00.. .3. 36040.. I. 0. .00.... 0..... .L 000 0:0 ’5‘..- 00 .n’a‘ta‘: 0T0” .u.0-1090
I I 0 c . . . I . I 0 0 0 I .u 0. 0 I. .0 u . k I. . ‘ . 0.0.. .I 0. I0 0- 0 .060 0. 0 000200 0. .. 0 .0600 . .. 00... v 0.09.1000... 00.000. .. I' .0 4 000.).5.'0. g0-
. . . 0 . . I I o . 0 . .- 4 . . . I... .. 6 . 0 o . I .14....0. .. . 0. 1...... ‘0 n -0. . ..0 ... I0.- I . . .. 0 I. o .0 Iv .00.. 00 1,. . .. 00. .006 6.0000. . 0060606.. 30
I . . I . -0 I . . a . .. .. . -I . .. 0 - . . . 0. .... .. .9 o . ... . .I. .. . ...... .00- .. o .0... .. .. ... . 01.06. III}. . 91000....03? 03.0 .0.. .00.... 10.0..10L.ﬁl§(9...&.000}2 ... 00667.0”,0030‘0‘8
. I 4 . I . 00 . . . I . .0 0 .. .. . 9 0 0 _. . . . v .0.. . I. . . 00 . . 6 .o .0... . 0 ..00; . 0 o I. . .. .. . 0000 00 a. . ...I. 00 0' o, 0. ..‘ I In. 00,11. ...-0. 00 0 0'33 ’00.00000‘000000.
. I I 6 0. .. .. I , . 2 . . 9. . ... .0 . . .. .... a... .... v.00..'.. n...- ....0..0. ....o ..00000. 9... .0....) {600075 00.0... . .
. . . I .0- . . I .. 2 . .... . .. 0 ._ . . . .\ a . I .. . . .. I. 260.. 0. ...... . . .... .. . . 06. ..06 ... ... . . 3.611.... 22.0.3.3... 9.0.6160. 0.00 ...-060....r..3§—.{16-...-.0€:I0.0. ..9-0006.
0. . . - . . - .00 .. . . 0 l a: . .I . .0 .l . . .. .. ... . . . .. . .6. .u 0.... .0.... 0.6 . 0. 6.... .0 ... . . 0... ..00. .. 0). ..6...va...90.0..I..00..I6000.....-0‘66 {..9’.
v I . I . I I I . 0 I .. . . ....I . .0 o I 03'. 0. .. . 0 .. . . . . . .
\- . 0 1 I6 _ I. . . a o 0 . I ‘00. I 00 . I0 .
I . . . . . I _ I 0. ... . . I 9 I 0 . .. .6 .0 0 . . 0 0. .
V . I . . . I . I 0 .. . 0 0 I I. .I 0. o. I. 0
II 0 0 o 6. I 0 a . 0 l .I . 0 .. I I. .. . . 6 0| 0 . 0 I .0
v . 0 LI I v I . 0- 0 0 . .0 00. .II . .0 0 00 6 .
0 II . I ... 0.0 o 0 I 0 . ... .0 . 900. 0 .0 0.
. . 0 I 6 I . 0 . I . . . I .. 0. o . I 0 0
. . . . I I 3 . . .0 . . .0 0 00 0 0 . .0 . . . 0 ..v 0 .00 0..
W. .0 0 .. I .. . . ....I ..66 0.. 00.0... 2 . 0...... . 0.rlo.0....p0.0..0.. . .
. -. . _ . . -.v . 0 . I . .. . 6 0.. . .0. .....006 0. 0 .0. .
0 . . 0 . 00 0 0 . I I 0 o . I6 .0 0 0. I .. 06. 0
. . 00.. 0 p I o I 6 . 0 0 .0 II ..0 0 3 I .00 I. 0 6..00. . 06 .0 0 . ' 0 . . . .
... I w . 0 . . o n 0 0 0 0 I ' 6 ... 0 II .. .0 0. .0! 0 ..0 v. 0 I .0. 0 I. . 0 0‘ ...0 I I u 0 .0 .. 0'0. 0 0 .00 00 .A 3.0..0‘. N000!
.. . . y I I . . . v 0 I. I 9. . 6 00 . 00 .0 0. ..I 6 . _ .. 0 .0.-00. ..7 ... I ... . 0.0 0.0- 60.6.l'f10.‘I13‘10.0.0.0.J..
Jr 0 0. I I o. .. 0 I . 0‘ I - ...O. ... 0... .00 .0 0 -0 I'I .
I 0 I . I . I I . 0 . 0.. ... 0 . 0.0 . .0 0. .I.0 0... 00.0. .
. I . 00. I 6 I I .I . .00... . Q o ..0.0 ..0 000. r 0 00. 04.60: ‘
w 0 I. . . 0 O I 0 0 6 I _ f . 0 0 o . . ... 0 . .6"! .010. I . o I ... .00 .o o 0 .~ .0. 00 0 .0.. . .0 u 0.0.. 50000"qu .10).". 1000:0068”
. I . . . 0 . ... 0 .. . I 0 II . 0 0. .I. . ... . I . 0.00. .0.... ...}. . . ......n..0. .. $0.00!; .300
.0 v . ... .- o. i. . I . . . . . . . o . . . I . .. ... . . l . . .. . I. . 0.. .0 0 .03.... 6:. . . . ._ .. . .. 10...... .I..... .. .... ..Iovu-TWIS‘Jvthwh .339..0..0.000. .0?.....‘..0.. 0...?!)Mftm‘0ucoo‘rp10duw3630h! LWSM"! .53.: JUIi.00.II“00.0§.I0§L00.'
oI I II . .I I0 0 ..0 . . . .. 0 I .. .. 0 ..I6. ... 5.00....100 0. 00.60. 0.v0‘0000.6..0-. I34, 0003. l0. .. I
w . . . . I I . I . . ..II 6 . . 0 .0 . . I0. I 0 . 90. .6.? .01 . ... ... 0. 0. ... . . 0...) 00. .000 0.00.. 0 ...066 10000900.. 0.000. 000’ .I.0u6 3030‘. ’iOMK-‘h‘. h...‘0”00:01....60u.v.m0 3 ‘7‘"; 0.
. . o . 6. . . v. . 0 6 a I . .0 0 .0.. 0 . It. ..0 .00 .‘.. 0.63.0 . . 0.1. 0.00..6.000.0.00. 06.36.000.05 .00.. 00. I'0vb'0000.00.' ’.0.?. .' 60 .f . .0. 00.1"!
w . . q . I . I 0 I I 0. .. a . . . M? . . I . .ll 0 0 ... _. . .00. . .0r ..0. : . p I r I a .l‘ .l 006 1,090.4... .0....1; ..000’ 0.. 001900.025- 00‘ .N00u. ..l 3600.. "00.00. 0.0.00 .000 500003010 0.30.00 6.036.060. 0‘. I. “ugh. .0005!
. . . 0 . .. 0 . 3 . . . .. .. . 60 . _. . . . 0 . 0. .I . -.. .l 0.. I. .. . . .. . . . . .0606... 6.2.0.4.... .0.. 16.67. 0“?0n.(..06.6646¢ 30......00‘30 .00. 0;. .60.."
. 0 . . . . . . .. I 0.. . . I I I 6. . . . .. I 0 0 . ... .- .0 . . . . . . I .. I 0 0.. . 9 I0.- .600. . 0. I 0 00.0. . . . ..00. ...60000000- 00. 003.060.0000. 07.0.0: ... 0*0.llv)l.0.l¥00.60.'000‘ n, 0|.6. D.“-
. . . I ... . . . 0 0. . I I ... . I. .0 .0 . . .0 . . :00 ..0 . I l . I . I . . . ......0.‘ 0. .0.. .030: 0'... 900.36.370.1'1. .3. 6 v. .0.. 00.66.4593. 3.61.0.0 if 0.. 0,
3 ... . . u .. . .. I . 0.. . . II 0 .0 O . 0 . N. 0... . "6. . 0 0.. . . 2‘6 0.. “.0.. .0600 .0 h .0.. ‘0”.0“. 20.0 0 . . .. . .. .. . . .31.»...«00 .. III 0‘5'00 1.5013000000009600 .0.!»‘0 .It‘. 0.0f... .‘W0J0..‘f!0’0n‘..00 £900.000b"0
. 0 . . . I . ... ... . . . 0. . _ . I. 6.. . I r. . .00 .0. ... .... 06.00.60,. ....‘III. 00.308000030002)9. .. 00 I. 0 .. .
.. . o I I o .0 .I . 0 .. . . .r I . . 6 . . ... 0. 0 0 f .. 0 0 a 0M .. I P. . 0 .... «I .60 . O . 1.06.- 0 ..tsA65 9 IV. {uni-0.6010 .000 .10 36.0.0.0” ..HIP0‘.\.000U.0.0 0.0.0.0“.."0‘010‘300hi0000‘060 Landry.
0 . 0 o v I .I .. I 0.0 ..I I n I. . 0 0 0 .0 . . . I CC. 0. p. 0 00:0. . 0 .70.. .0... i760 . .
v . . I. . I . . . . . 0 . I . I . . 0 0 . .6. u . . -n 6. q ..I . 0. ... «... I. . 06060.... urn-...... a. .0.... 06...!630....00.0.6.0. JVIORI§IJT 0 Inﬁwnwf’.’lﬁt.lrth.00¥i
. 0 . 0 . . . 00. . 0 . . I 0 0 ' . .6 0 . II 3 0 .‘ 1x .0 0‘0.‘ 0 00.0“ ‘00. 0 i‘9.06- 3.00 9. 03100.00" ‘ 6
0 I -0 v . 0 .0. . . . .. .0 . 0 . .0 .. .. 0... .0600 .0000. 6... .0., .000)... . n .00. .. 6.6 .00 1.00
w _ y .. . . . - . . - . . - . . z .. . . . . ...... . . . ...I........ . . .. . . . . ...... ......h....u.......19.....ﬂ_.....Hﬂﬂ. ...: ......I...........I..nawa?...n..I...u~.un.u
I .. . r . n . . 6. . .0 I 00 . 00.9 I . . 0 '0 .0... ..u 0 0.0 .0. 0.. 09. .06. .. 0.0000606 000 06.000 00. 6070’. 06.000 0. III
. . . . . _ . . . . ._ . .. .. . .. . .. .. . : . . - . -. : .. . . . . . .... . . ...... ...... .. . 2...... z- .....- In..." .....ukmﬁﬁuvuhs¢ﬁu.ﬂb:2.....-
00 . 0 . . I . . 0 0 . 9 ... 000 I 0 0 0 0 . 0!...0 . ﬁl I 0 0 o 0 000 “I. 060-9000130400“ 0.000 :00;- 0070 .9‘0 00.0 000‘ :0 7 o 0.0: 6O‘QC.|.00|1 1400?! I‘I'OI
W . 0 . . . 0.. ~ I . I o . . ... . I .06. I . . .0 ... .o '0. 000. . 00» 0.. 0 0.0.0.000 n06. .Z. 0 00... 00.300000 6. .0000 IQ .. . .0000 .0000. -....(QVI‘I’.6.0.6606.00000‘0‘UI‘60.\.003.'
0 . . I I . . . I 0 6. I. . ‘.. o .0 I 7 6 60.. . . I 6. . -0.. 170.61'0. 0.6. .0.. 0 00.06.0030.) 0.00.. 000.0 05.0.00 .9910...) 0 030’. .3.‘z"0010 I}...
w 0 . . . . o , .0 . . . . . ... . 0 . . .. .0 v. .. 0. . . 0. . :0; ...t . . .. . 0.. 0 . ... ...o .0 00.00 . ..6 000..6.$...00.0v...600..06’l 0.0.l0l6.....v..l0.0 {0.660.} #000003! ..9
.. . . . 0.9 o . I - . I .0.. .. .... ..I I .0 . .0.... p . O . .. I 00 . ..0. 0 .62.: N0. ..000600-.06..I000f... 1.0.60 0.96. .10. I. 6 0.0.0006 000 .10.. .0000.0H”.0.0.00df0..0. onxh £100,076”) 0h”
0 o. , . 6.. 0 I 0 . . 0. 0 . 00 .6 - .. . c u I. 0 I 0 . . . 6 0 .... 209 0 .0 . .... .0. . . .0 06,0.0'0l: ..060.0 .f? .0 .0.. 903’. 0.0 ..J’lngo .0‘0.;00 ‘00:. 0 .1000! 0.00.0 .6. a." 0. .5 ‘2‘.
I . . . . . 000 . _ . 0 . I .I..d.. .0 v. I 6 3.. ... ..0 .0.- . ...90. . . . .000 000. 096’... .. .- ..00000.0.....00600.r..530000.6-.0006.'0600.0‘01.31030 6....lo.6 060.6...000‘6.
‘ 0 I . . 6 . o .. 0 00 . .00 ...00. 00 I I .. CI 0 .... .0 0. . 0 .6. I ... .. .I .0 I. .000. s 06.... 0.0306. 9.. 3.00. 0 000 0 4.0. 0..-(06.. 0.1000...‘ 10.3.0.6... 00.... .7060”!!0..70.0 .0 'I.'rr\00.. .00... .0.I\.000. 0.6‘60.
. . . II 0 . O 0 . 0 0 o. . 0 00.00 . I0 J . n. i. 6 .0 0. .0660. .I .. 0.2... .9001..00..0.. .0060..0..006. 0.0 000.6000. 0 .00) 0100..."060.0'600I \‘0063’. 0‘ ..6000fol. 0. .‘b. .‘
. I 0 - . . I. o 4. 0 .6. .. ... I 4 . I 0 . .. .0 .. . .. 0 ...... v. .0... 3.6. .0 0... .. .20.... 160.090.... 9..0..6..o0r.51‘.0.2 1.. 0..... 66 ... ..0.
600 0 .. 0 I .I I. 0 I . 000... .0 9 I ..0 I0 00- ... .0 _ .6 0.0.0II00I.0. 0.!10.‘ 0. 0006.0
V o . 0 I u I I . I I .. . It. 0 .. . o I n 6. 0 0 p C II .. 0 . 0. .6.. 0 IN . .0 0 .0 .... I . . 6 .0. .60. .0 0.0030 6.. 000. ' .6 .
. I . I . . .. o . 0 . . . I. 0 . . 00.0 . ...... .00.. 0 0 .0 .... ... 6.60.... . . .-.. 0. .
. .. . 6 o 6 ..I. 0 . I09 . . .00. . c . .. 0 ...00 0 0..... 0. 0...
A . u . . . I I . . a. _ . . . . I . 7 .o ..
.. 0 a I I . I . 0 . . ....0 . . Io . 0 . . . .. . A. I..0.. .0... 1.0.2..
00 0 I . 0 p . I . 6 I 0. I 0 .
m 0 0 I . . . .. I I . 0 .0 . ..0. . . .. . . I z 60 o .. 0. .. 000
I I I . .0 .. .0 . v. . .I .0 . ... . 6|. 0 . . 00.0!0 . . .6....0
.- I .... . . . . . .0 0 6 0 6. c a. . ...0 0 .0 . .I ..00 s n . l.
_ I . c 1 0 .0 . I 0 6 .I. I 0 - 0 I. . 6 0... I. .0 10.0.6. I 0W!..0.. .6. ‘00... 52.. . 0 I
. I r . .0 . 0 0.6.0 000’ 1.00.00. 0. 0
.w00 . . \ . 0u.. v J” . 0 . 0 6 l 0.. I 0. . 0 .. . 0 .. 0 . 0 6. .. o 0 0000.0 n... .. . . 0 . . 0 .0 .0. .0 0 06.00:». 30.00.06. 0 0. I. 00‘ .0L.‘I......I.0. ....‘lf’. .0.. ."fl§f{ﬂ't§iﬂu.-
. I n . . . I I . I I . . . . . 0 . . . . . .I. o . .0 .. ...06 V. . . I 0.. 0 0.0. .. . 0 0 0 . 1 .6
.. .. . . . 60 . n. .. . . . .a I . . Y 0.0 ... . I , . .... ... ..0. . . .0-.. .. . .I . ... 0.. . . . ...JJO..0.0... .00.. .00; 0...0.... .I._.6 .06.00’.0,00. 0...\000”.67Il6. 66‘00‘I .....10'6.\ 60.
. I I o. I 0 6 I . I I . . ... . .. .. . .0 . 0. . .0 I. .. 0 .0- . II . .0 0 0 0 0. . .
I. 0 . . 0 0 l ..-I 0. .I -.v 0 O I In 60I. O .. 0 .1 . J30 .0. 00 00.. 0
v 0. I .0 00 0 I 0 . I I . 00 I0 0 . .00 . I. ..0 .70 .0 0. II o
0 I . U I I {I . . 0 . 0 0 0 0.0 I 3 . 0 0 00. .0 00 0000 O 0
0 I I. f 0 o 0 0. . I00 0 0 0 . . . . .Jo. .- 0 ...00 . .I 0 0.. . .. .. .0
. . .. . 0 0 . . .. . n . 0 I II .00. 0.0 . . 00 .00 .... .00. o ...0:
n. I .I . . I . r . .0. ..06. . . . a . . .0 .. 6 00 .60 . 0 .. ..0 .. .000..II-.0
. . .0. . 0 0 6 I 6 0 I .. . I . . . . 0 . . ..0 ... . .. .. .... .0.. . u . .n .. . 0. 00 \0
. 0 .. . 0 .o 0 . 0. . I I . . 0 1 . 0 .
. . . 0 . . 0 0 . . .. 00
. 0 I . . c . . ..I0 . V .00
I 0 . I 0 0 0 .. 9 0 o . . 0 n — u. I . .
0 0 . I .6 6 I . I n 00 o . . 0. I I 0
.w. . 0 . .. I . 0 . .. 0 0 I I .00 . 0. I . .
0 . 0 000 . . 0 . 4 . . 6. . A 0 6 . .
.I . - o -I. . . .. .6 I 6 .00. ..0000 n ...-0.0...
w .. I 0 . . . . . . I. J . . I v I . . 6:0 ..0 20.0 . .. 9.0...67 . . . . .
. . I. . 0 . . . . . 00 0.002).. . m6...L0.00'..0..0.60u1.0.. .6.-.0."..100r: . . I 0.602.-.. I. 00!
u . 0. . - .. . 6 n . .. 0..... 0. .. ..O ..0... .00. . .... . 00:0. . 0 .0. . 10.4.6... 0. . 0 0.1.01.6..0. H .0.-.30.“. .. .00) 0..":0 “Mutt
. f . 0 0 . I I . 4 q . In... . .. .0. f .00 0..... .I-./00. .0...6.60.!.66.0.06.. ..9-100'... ‘10 000.00.910.06. 006.766
. I .. . . . . . 0. I . .. I. . . I . I n... . .. 0.0... .6 .. .. .. . .300. .0. 000 r .0 13:60.0. 0. .0. v ... 6.69 20.000606... $0163.63. «0.01.562'030..-
I- l I . I .. . I ...-06.0 0.6 0.3.00 0. 0......6 . o .
.. 0 . . . 0 . . .0 . 00 . .- I ...m..6-.00)L.....0...0 00...... 0 6.0 0 0 .61 .0.... .010. 0. 73,.7 00.00.0“A6060
" . . . . .. . 0 . . . . . . I I. . 6. r .00 .
.0 . I 0 I I I 0 . I I . 0. I I. 6 . D . 0 .. . .000 4 A l ..
V O . .I . 0 0 I a . . . '0 0 . '1 .
0 . o 0 0 0 I I0 0 I . a . 0 0 ..
. - . 0 o 0 I . . I 0 0. o 00
w . I .. o . 6 0 . . O I 0 I. g 0 0 ..000 0 I . . . .
. .. . 0 . I o o -. 6 . . ..I. .1. I.. ..I.-..l.6 ... ‘.x3‘.~0'0.0.2. .006.
06 . 0' I 0 . . 0 I . 6| 0 I 0 ...‘ .00. .a.000r0060.0‘.9.0 .Qaao..-l 0.6. . ,vn'. ’I. I v
. . I . . . . uII . .I . _ . .. .0 . ..r..00¢....0’.090..... ...0 10.6.50]. .0.
1 ._ . . . . I 0 0 0 I . . .- .0.6.~.66.Iv...0.0. '6'04 *‘2....l
6 I a . II 9 I .. m. 0 ... 0.0. on 6. 00 . . .- . 0 . .6I.Iﬁ 0 00... 00.0 I6 0 ..I . .6.-6‘20-
. 0 6 _ 0 I .I 0 0 . . 0 66667.0 0.060.. V I 000‘. '0 060"; 5 00.0 L0...I It‘ll.
v . 0 0 0 6 II o I _ ..6 6 7 016.: ...0 00.6. I I ‘..006000.60000‘Q0I16
I . . I . 6 I 0 . ... . . c I 0 0.20.}.6 I. .6.. .0.}..100t.06660
.. I .0 I O I I . .. 0. . ...-.0. .0.}0‘.r0 000‘ 000.’6.6‘l‘.9000tlo
. . . . . . . I | 0 v I v .0. .0 0.06 00.- .3. 60 0 0.06 .0.. 0.0043. 0.0:).0 ’0'
. 0 . . . .. 0 .0 . . 0. 0....)0600...|... n . 1010.000 .6.!600.000.0ll.0§. 0
0 o I . . o .. I J . I I . II.) . - O. _o6 0.6.06... 60.6...05 2A....010600I0020760‘
w .. . a 0060!? 0’cl.6t .r. 0.0... 6.0 6.00.6.2 I‘g0? ."
0. .5 I06 . I 0 . I 0 I . u 0 I .0 .. 0Il.\0.00 00... . 06.00.. 6 6006 I I 0. ‘36.“ 6‘6 00006. 66I0-
I . . 1 . 0\ ... 0 0.1.66 . I I 0 0‘0
ﬂ . . . . . . .. o. . . . . .. . I
I . o 0
“00.0 I. .
‘ I
00' .

“6010.700. I . . _ ...-00. . . 0300.50.01 I .39.} . II .0001”.-

u .7. 0.. If...u1.0......6 .0.. I 6 6.06.: I. 00 ....H . . . o . 6. ...0.0 .... .II..I.O... ....00l.... ......lloa (”amuko ...... 06 .006 m4... 0.. .00:

w. . .0.?“1.000‘..H0$H 6.0 00.004 .03nu". .. 00.0.0000. ...0 00H...0-f 0VII.’0..00'.mu0I".0b&§.p.dmn.o0~..hr.ua0hu1“0mL)... 0.. .0000‘HI..00..7.9.6.MI.06_...‘0.... 30m.6 ‘00 .HL”LW0W%EI‘.1E.0Q0?VQM .Wgﬂr*00§
6 .. . .. .0 .0 . . 0...

- 0—
.. . 000 .I 06 *saI? 1.0 .lit.
1. .0.. o . .0 ...... {.d. 0. I 0 01.6.. 0 6 .00 .06....» 0. 1‘ .0.-1‘ 03.0... If. I 0.0 _’ 6.0.6... 21.0%.... S.. ..b-JU“60FA.?0“P‘ “L I. 06 ....l ....
0 .0’ .

200%

This is to certify that the
dissertation entitled

THE MICROFOUNDATIONS OF SECURITY AND
IMPLICATIONS FOR GOVERNANCE

presented by
ERIC DONAVEN RAILE

has been accepted towards fulﬁllment
of the requirements for the

Ph.D. degree in Political Science

Whine

v Major Professor's Signature
M a ('11, ?’ WK

Date

MSU is an afﬁrmative-action, equal-opportunity employer

THE MICROFOUNDATIONS OF SECURITY AND
IMPLICATIONS FOR GOVERNANCE

By

Eric Donaven Raile

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁlhnent of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Political Science

2008

ABSTRACT

THE MICROFOUNDATIONS OF SECURITY AND
IMPLICATIONS FOR GOVERNANCE

By
Eric Donaven Raile

Broader notions of “human,” or individual, security have been criticized for their
conceptual ambiguity and lack of clear actionable implications. These shortcomings are
signiﬁcant because human security is advocated as a central concept in international
governance and development. I propose studying the microfoundations of security (i.e.,
individual-level attitudes, perceptions, and behaviors related to security considerations)
as a means of providing a theoretical and empirical basis for the idea of human security.
The microfoundations approach and its focus on methodological individualism provide a
methodology that corresponds closely to the theoretical underpinnings of human security.
A departure point for the theoretical framework for security microfoundations is a
productive literature on individual and social risk perception that has failed to penetrate
the mainstream study of politics and governance. Major components of the security
microfoundations framework developed here include: individual vulnerabilities and
uncertainty, approaches and orientations toward insecurity, informational characteristics
of the individual, and environmental patterns and processes. Empirical tests of the
theoretical framework are carried out in three distinct subject areas: attitudes toward
globalization, evaluation of political corruption as a threat, and the perceived threat from
terrorism. The testing results demonstrate the usefulness of the security microfoundations
framework as well as the importance of t0pical and sociopolitical context in individual

security evaluations. Implications for governance are explicitly discussed.

Copyn'ght by
ERIC DONAVEN RAILE
2008

To Amber and Landon, who supplied extraordinary support and tolerance.

iv

ACKNOWLEDGEMENTS

This project has beneﬁtted substantially from the input and advice of Brian Silver,
Aaron McCright, Eric Chang, Michael Colaresi, Amber Raile, and seminar participants at
both Michigan State University and North Dakota State University. I am also indebted to
the Pew Research Center for making its data publicly available. Any remaining errors are

my own.

TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................... viii

LIST OF FIGURES ........................................................................................................... ix

CHAPTER 1

THE MICROFOUNDATIONS OF SECURITY ................................................................. 1
Introduction .............................................................................................................. 2
Signiﬁcance .............................................................................................................. 6
Deﬁning Human Security ...................................................................................... 17
Risk Perception ...................................................................................................... 21
A Preliminary Framework for Security Microfoundations ................................. '...23
Summary and Discussion ....................................................................................... 34

CHAPTER 2

ATTITUDES TOWARD GLOBALIZATION .................................................................. 38
Introduction ............................................................................................................ 39
Attitudes toward Globalization .............................................................................. 41
Some Remaining Questions ................................................................................... 43
The Microfoundations of Security ......................................................................... 45
Data and Methods .................................................................................................. 53
Results .................................................................................................................... 59
Discussion .............................................................................................................. 71

CHAPTER 3

THE THREAT FROM POLITICAL CORRUPTION ...................................................... 79
Introduction ............................................................................................................ 80
Negative Consequences ......................................................................................... 81
Corruption Measurement ....................................................................................... 85
Corruption and Insecurity ...................................................................................... 87
Data and Methods .................................................................................................. 95
Results .................................................................................................................. 100
Discussion ............................................................................................................ 1 10

CHAPTER 4

THE THREAT FROM TERRORISM ............................................................................. 115
Introduction .......................................................................................................... 1 16
Hypotheses ........................................................................................................... 1 17
Data and Methods ................................................................................................ 119
Results .................................................................................................................. 122
Discussion ............................................................................................................ 127

vi

CHAPTER 5

CONCLUSIONS .............................................................................................................. 130
The Microfoundations of Security ....................................................................... 131
Understanding Risk Perception ............................................................................ 134
Governance and Human Security ........................................................................ 135

APPENDICES
Appendix A: Summary of Variables Used in Main Globalization Analysis ....... 139
Appendix B: Multilateral Anticorruption Agreements and Standards ................ 141
Appendix C: Summary of Variables Used in Main Corruption Analysis ........... 142
Appendix D: Correlations among Country Corruption Indicators ...................... 144
Appendix E: Summary of Variables Used in Main Terrorism Analysis ............. 145

REFERENCES ................................................................................................................ 148

vii

LIST OF TABLES

Table 1. Theorized Compositions of Human Security ...................................................... 18

Table 2. Multilevel Mixed-Effects Linear Regression on Attitudes toward
Globalization ...................................................................................................................... 60

Table 3. Variance Parameters for Variable Slopes and Intercepts .................................... 61

Table 4. Multilevel Mixed-Effects Logistic Regression on Perceived Threat from

Corruption ........................................................................................................................ 102
Table 5. Random Effects and Estimation Information ................................................... 106
Table 6. Interaction of Sex and Literacy ......................................................................... 108
Table 7. Changes in Predicted Probabilities by Proﬁle .................................................. 109
Table 8. Multilevel Mixed-Effects Logistic Regression on Perceived Threat from

Terrorism .......................................................................................................................... 120
Table 9. Estimation Information ..................................................................................... 125
Table 10. Changes in Predicted Probabilities by Proﬁle ................................................ 126

viii

Figure 1.
Figure 2.
Figure 3.
Figure 4.

Figure 5.

LIST OF FIGURES

Distribution of Dependent Variable .................................................................. 56
Impact of Income on Attitudes toward Globalization by GDP per Capita ........ 63
Absolute Standardized Reductions in Support for Globalization ...................... 67
Distribution of the Dependent Variable: Political Corruption as a Problem ..... 96
Comparison of Elite and Mass Perceptions of Corruption ................................ 98

ix

CHAPTER 1

THE MICROFOUNDATIONS OF SECURITY

Introduction

Social and behavioral scientists have spent decades examining the
microfoundations of mass phenomena like purchasing behavior, political participation,
and vote choice, as well as elite phenomena like policy preferences and voting. While
researchers have occasionally acknowledged that individual perceptions of security affect
such phenomena, researchers have not undertaken a full examination of the
microfoundations of security. A long-dominant security paradigm that focused on the
integrity and power of states rather than individuals was perhaps largely responsible for
this gap in understanding. However, the gap is a signiﬁcant one because individual
perceptions of security may underlie and unify many of the attitudes and behaviors
observed in a political community.

The term “microfoundations” is an economic one that emerged from a focus on
methodological individualism and a desire in the 19505 and I960S to better integrate the
aggregate theorizing of macroeconomics and the individual-level (persons, ﬁrms, etc.)
theorizing of microeconomics (J anssen 2006). The worry about analyzing the aggregate
level in isolation from micro—level behavior is that such analysis opens the door to a
number of logical and inferential fallacies. An aggregate-level theory with micro-level
foundations is on much ﬁrmer footing.

The state-based model of security tends to assume that the interests and
preferences of the state are imputed rather homogeneously to all the individuals within
the state. A microfoundations approach to security inverts that thinking by advocating the
idea that collective security concerns and behaviors are (at least in part) a function of

individual security perceptions, attitudes, and behaviors. These two approaches are

neither antagonistic nor mutually exclusive. Certainly, threats to the institutions and
resources of the state are also threats to the individuals within the state. However,
perceptions of threat and behavioral responses to threat also depend on mechanisms
operating at the individual level. Political institutions do not operate autonomously from
human input. Elite governance decisions about security are the result of both elite
perceptions and the perceptions of important constituencies. Similarly, elites who wish
for their security perceptions and policies to win the day must effectively communicate
threats to masses and must get the public to buy in or at least acquiesce to the plan.

A security microfoundations approach examines the inextricable linkages
between individual and collective security through the lens of the individual. In doing so,
it integrates the theoretical approach of economic microfoundations with the attitudinal
and behavioral insights of psychology and sociology. Adopting a microfoundations
approach does not require assuming that all aggregate security phenomena are a simple
sum of their parts; the process of translating from the individual to the aggregate level is
mediated by social forces, events, and institutional frameworks. Nor must such an
approach presume that individual mechanisms are universal and context is
inconsequential. To the contrary, inputs from the individual’s environment are crucial
determinants of individual threat perceptions and responses. The other people in that
environment are particularly important. My security perceptions and resultant behaviors
have implications for your security perceptions and behaviors, and vice versa.

Government as a mechanism for enhancing the actual and perceived security of
the individual is an idea rooted deep in political theory. Thomas Hobbes’ 17‘h-century

governmental “Leviathan” is a powerful force that pulls individuals out of a dangerous

“state of nature,” a place of constant threats to individual security. Of course, the
government itself is a menace to the individual here, as attested to by Hobbes’ reference
to government as a biblical monster. The l8‘h-century government deﬁned by J ean-
Jacques Rousseau, on the other hand, is a mechanism resulting from a “social contract”
that applies the “whole common force” to the defense and protection of individuals.
Similarly, Charles Montesquieu deﬁnes “political liberty” in the 18th century work The
Spirit of the Laws as a “tranquility of spirit” that results when government ensures that
“one citizen cannot fear another citizen.” In sum, government is a mechanism that
reduces conﬂict, improves productivity, and enhances quality of life by allowing
individuals the luxury of not having to fret constantly over the next approaching threat.

A debate over the meaning of “security” generally (see Wolfers 1952; Ullman
1983; Mathews 1989; Haﬁendorn 1991; and see Baldwin 1997 for a more comprehensive
list) revived the idea of individual security. This debate culminated in the development of
the “human security” concept in the early 19908 as the end of the Cold War and the
acceleration of globalization made the state-based model of security seem antiquated.
Historical reviews of the human security concept (e.g., Paris 2001; King and Murray
2001; Bajpai 2003; Henk 2005) typically attribute its ﬁrst ﬁrll articulation to a report
issued by the United Nations Development Program (UNDP 1994). However, a debate
over the ﬁmdamental meaning of human security has stymied attempts to move forward
with a human security agenda (Suhrke 2004). The essential problem is that the broad,
“bottom—up” (Hoogensen and Stuvay 2006) version of human security, which centers on

the individual’s point of view and incorporates a wide range of threats, appears to lack

focus and any practical or analytical core (Suhrke 1999; Paris 2001, 2004; Thomas and
Tow 2002).

Meanwhile, a literature on risk stretching back to the 19705 in psychology,
sociology, and anthropology (see reviews in Kasperson et al. 2003; Adam and van Loon
2000; Rohrmann and Renn 2000; Slovic 2000a; Lupton 1999) has gone relatively
unnoticed by students of politics and governance. This research has focused on such
topics as socio-cultural and social constructionist views of risk (Lupton 1999), the
modern “Risk Society” and its reﬂexive character (Beck 1992, 1999), how individuals
psychologically perceive risks (Slovic 20003), cross-cultural differences in risk
perception (Renn and Rohrmann 2000), the communication of risks (J aeger et al. 2001 ,
127-29), and the way social processes can amplify or downplay particular risks
(Kasperson et al. 2003).

I propose that integrating the insights from the conceptual models for human
security and risk perception is a signiﬁcant step in producing a framework for
understanding the microfoundations of security. The microfoundations approach, in turn,
provides a methodology that aligns with the theoretical underpinnings of both human
security and risk perception. The envisioned framework goes beyond the risk perception
literature, which has not utilized many of the tools of political scientists and has not
addressed many topics of basic political importance (though it has examined issues with
policymaking implications). The framework also carries the potential to answer standing
questions produced by risk perception research. Additionally, the proposed synthesis
offers the promise of building human security from the bottom up in a way that makes the

concept more useful for policymakers.

In what follows in this chapter, I ﬁrst argue for the importance of understanding
the microfoundations of security. I then examine the problems associated. with deﬁning
and acting on human security and discuss the relevant ﬁndings of the risk perception
literature. After covering these two constituent parts, I then propose their integration and
expansion into a broader framework. A ﬁnal section of the chapter summarizes and raises

additional considerations.

Signiﬁcance
An Obligation of Governance

Humanitarian concerns and the desire to craft more effective policies are the
principal reasons for policymakers to care about the microfoundations of security. A
fundamental presupposition of human security is that each human life has an inherent
worth and should be protected. While expending resources to reduce objective threats to
the individual certainly recognizes the worth of human life, feelings of insecurity are also
harmful to the individual. The worry and fear associated with feelings of insecurity take a
psychological and physiological toll. Governments can better achieve humanitarian aims
by using an understanding of the microfoundations of security appropriately to calm their
citizens, thereby reducing pain and suffering.

As mentioned earlier, a core ﬁrnction and obligation of government is to enhance
individual security. This obligation goes beyond the humanitarian imperative, which is
not acknowledged by every regime. Hobbes’ Leviathan is not a government based on
humanitarian concerns. The obligation is clearer in a liberal democratic regime, however.

A government has unﬁnished work if citizens, the “bosses” in representative democracy,

have serious feelings of insecurity. If effective accountability mechanisms are available,
policymakers also have personal stakes in fulﬁlling this obligation — else they be
removed from ofﬁce. The potential for rebellion or revolution is a strong incentive to pay

attention to feelings of insecurity among the people of a state as well, regardless of

regime type.

Negative Ramiﬁcations

Policymakers further have incentive to care about the microfoundations of
security because automatic responses to objective threat stimuli and the more subjective
weighing of a situation can both produce negative ramiﬁcations. Though there is much
room for additional empirical evaluation of such claims, the common wisdom is that
human beings who feel insecure develop attitudes and beliefs and take actions that can be
destructive to themselves and others. When governments fail or are incapable of making
individuals feel secure, negative aspects of human nature show through. People ﬁght, kill,
steal, and board under such circumstances. They struggle to gain a competitive
advantage, and they covet what others possess. They blame other groups for their
misfortunes. They ﬁnd ideological means of coping, such as religious radicalization or
hyper-nationalism. They contribute to collective-action problems and set off situations of
panic and chaos.

Relying on objective measures of risk and governmental proclamations would not
require an understanding of micro-processes and the social inﬂuences on those processes.
However, a core ﬁnding in the risk perception literature is that mass perceptions of threat

do not align with more “objective” risk assessments based on expert analysis or event

probability (Slovic 2000a). Therefore, despite their importance, government studies and
proclamations of threat are not the last word. Compared to the objective risks posed,
people tend to worry excessively about things like ﬂying on airplanes and nuclear power,
while tending to overlook the substantial risks from things like driving in automobiles,
smoking, and heart disease (see Ross 1999; Glassner 1999). These misalignrnents have
implications for a wide range of policy areas, from transportation to public health to the
environment. Improved alignment between objective and subjective risk would allow
governments better to set priorities and to obtain public support for making sacriﬁces
necessary for the collective good.

As Hurricane Katrina approached the Louisiana and Mississippi coasts in August
2005, the National Weather Service warned that the area would be uninhabitable for
weeks or longer and that water shortages would “make human suffering incredible by
modern standar ” (National Weather Service 2005). The warnings also spoke of
household appliances and light vehicles ﬂying around and the certain destruction of all
wood-framed, low-rising apartment buildings. Some individuals in the New Orleans area
likely never received these warnings, and others had no means to ﬂee. However, some
individuals heard these warnings and reﬁrsed to leave anyway. Similar stories emerge
from the September 11, 2001, terrorist attacks on the US. Many people died in the twin
towers of the World Trade Center because they failed to heed evacuation warnings or
were told by ofﬁcials not to worry (National Commission on Terrorist Attacks upon the
United States 2004, 286-89). In both cases, misalignrnents between subjective and

objective security led to signiﬁcant casualties and suffering.

Another key observation is that protecting people from physical threats is
sometimes not enough to prevent conﬂict and other negative consequences. The nation-
building efforts in Iraq and Afghanistan illustrate this point. U.S. efforts to “win the
hearts and minds” of the Iraqi people have faltered in part because such winning also
requires making people feel secure, which means both enjoying physical security and
being able to project future survival and well-being in a relatively conﬁdent manner.
Deep ethnic and religious cleavages and a long history of severe mistreatment and
conﬂict in Iraq tend to negate positive assurances about the ﬁrture, and the inﬁltration of
terrorists has exacerbated the problem. Assurances about future well-being also rely on
development, which actual and prospective violence deny. The “Surge” policy of the US.
in Iraq, an intense deployment of additional troops, was based on such thinking. Also
broadly recognized now is that even if violence in Iraq stopped immediately, long-term
feelings of insecurity would pose problems for development and for keeping the peace.

The civil unrest in France in 2005 is another illustration of this point. While the
accidental deaths of two teenagers supposedly precipitated the riots and violent clashes
with police, feelings of insecurity spurred the violence. High unemployment and low
wages in immigrant communities (Smith 2005) combined with well-intentioned
government policies that tend to ignore ethnicity (Blum 2002) to generate feelings of
insecurity, neglect, and invisibility. Again, while death apparently triggered the violence,

the underlying reasons were not all based on objective risk or mortal danger.

Global Governance

Yet another reason to care about the microfoundations of security is that the
United Nations and other international actors have determined that individual security is
an integral part of many different types of programs. These organizations largely have
adopted broad, bottom-up visions of human security that presumably rely on the
microfoundations of security. As mentioned previously, the UNDP’s report in 1994
established a deﬁnition of human security. In a 1999 speech to the UN. General
Assembly, UN. Secretary-General Koﬁ Annan stated that traditional notions of
sovereignty insufﬁciently addressed the fundamental freedoms of individuals (United
Nations 1999). By questioning traditional ideas of sovereignty, Annan was also implying
that the nation-state model of security was outdated. Speaking at the Millennium Summit
the following year, Annan talked about the global issues of “freedom from want, freedom
from fear, and the freedom of future generations to sustain their lives on this planet”
(United Nations 2000). These three freedoms represent elements of human security as
articulated by the UN. The last item in this list most closely approaches traditional ways
of thinking about security, but it also implicitly acknowledges threats from sources other
than guns and bombs. Accepting his Nobel Peace Prize, Annan in 2001 further stressed
and articulated the global implications of human insecurity in saying, “When States
undermine the rule of law and violate the rights of their individual citizens, they become
a menace not only to their own people, but also to their neighbors, and indeed the world”
(Annan 2001). In keeping with messages from the UN, the Commission on Human

Security said in its 2003 ﬁnal report that human security requires an integrated. approach

10

and that human security necessitates systems that provide for “survival, dignity, and
livelihood” (Commission on Human Security 2003, iv).

The U.N.’s focus on human security seemingly has intensiﬁed over the last few
years. In 2004, the UN. created the Human Security Unit within the Ofﬁce for the
Coordination of Humanitarian Affairs and gave that unit the responsibility of integrating
human secmity into all U.N. activities. Furthermore, the UN. Secretary-General’s Hi gh-
Level Panel on Threats, Challenges, and Change has said that security, development, and
human rights are indivisible (United Nations 2004) and has gone so far as to reinterpret
the United Nations Charter to refocus the organization on matters of human security
(Slaughter 2005).

In the way of example, anti-terrorism efforts represent a policy area in which the
microfoundations of security play an essential role. Governments in terrorist-targeted
countries need their citizens to cope with fear while being responsive to fear messages
when appropriate. Governments also need to build trust in risk communication; people
may eventually act in ways contrary to their interests if they have felt duped by the
government in the past. The George W. Bush Administration in the US, with its color-
coded terror warning scheme, has had to ask how it can effectively warn peOple without
earning a reputation for “crying wolf.” Further, the global community and concerned
policymakers within countries that serve as incubators for terrorists need to understand
the perceived threats and forms of uncertainty that push individuals to use terrorist means
to achieve their goals. There is much talk about “addressing the roots” of terror rather

than simply attacking the problem by militaristic means, but what sources of individual

11

insecurity are responsible? Individual poverty is one standard but inadequate answer, as
destitute and poorly educated individuals make unreliable terrorists.

Policies related to globalization constitute another example of an area in which
the microfoundations of security seem quite important. Anti-globalization sentiment is
found worldwide, even in countries that compose the core of the global economy.
Informed policyrnaking that correctly weighs costs and beneﬁts requires understanding
the reasons for such sentiment. Why do some individuals view globalization as a threat?
Societies that have decided to attempt keeping pace with the irrepressible march of
globalization at times must convince citizens to overcome their fears. The standard
rational-choice answers provided by political economists about attitudes toward
globalization (Scheve and Slaughter 2006) are compelling but incomplete. Other
mechanisms appear to contribute to apprehension about a globally connected world, as
well. Studying security-based apprehension about democratization stands to produce

valuable information for policymakers in much the same way.

A Changing World

The technological advances and economic grth of the modern era are double-
edged swords. In many places life spans have increased dramatically, but we should not
necessarily expect these improvements to produce a corresponding bump in perceptions
of security. Human beings have always lived with fear, worry, and uncertainty. What sets
the current period apart are the types of new threats generated by advanced technology

and the availability of information concerning threats. The overall stakes in terms of

12

human lives and suffering are potentially greater now, as well. These trends argue in
favor of better understanding the microfoundations of security.

The types of threats that religious texts spoke of thousands of years ago — hunger,
pestilence, violence from one’s neighbor, the power of nature — are still everyday
concerns for large swathes of the world’s population. The threat posed by the state for
many people in such situations only compounds the misery. Again, the presumed
consequences in developing areas are devastating. Economic and political deve10pment
are thwarted as people act on threats both perceived and real. Ethnic groups attack one
another as they compete to fend off poverty or simply out of fear of the “other.” Hoarding
responses to threats prevent medicines and food from helping people. Government
ofﬁcials extract rents from the system to fend off threats to themselves and their families,
thereby worsening threats to the general populace. Misinforrnation, superstition, cultural
taboos, and claims of miracle cures facilitate the spread of menacing diseases like
HIV/AIDS.

The vulnerabilities (perceived and real) produced by severe inequalities in wealth
and power further generate conﬂict (Wilkin 1999), rebellion (Gurr 1970), and complex
human emergencies (Auvinen and Nafziger 1999). And while economic and political
modernization ultimately may reduce such problems, transitional periods are also
dangerous times. Unpredictability and the breakdown of routine are marks of these
periods. The ﬂuctuating environment produces both threats and opportunities. Income
inequality may actually increase during economic transitions (Kuznets 1955), though this
is not a foregone conclusion (Acemoglu and Robinson 2002). Public corruption, too, may

increase during democratic transitional periods (Montinola and J ackman 2002), thereby

l3

producing a variety of economic, political, and social ills (Rose-Ackerman 1999).
Furthermore, drastic changes in a society can bring into being extreme attitudes that
generate various forms of destruction (Hoffer 1963).

Given the severity of the consequences, better comprehending the mechanisms
that produce insecurity at the individual level is essential. However, the importance of
comprehension is not limited to the developing world. Threat-based phenomena like
those just enumerated may be more conspicuous in developing countries, but more
developed countries are not immune from them. The roles of threat and uncertainty in the
developed world are subtler but also somewhat perplexing given the extraordinary
standard of living and the way that government regulatory structures have reduced
everyday risks. However, as pointed out by sociologists (e.g., Beck 1992, 1999),
technology is a cause of fear and anxiety in the modern world. Research technologies
allow us to “discover” previously invisible (and often inconsequential) threats (Ross
1999). Information technologies permit the rapid and pervasive spread of information
about risk, as the imperative to maximize proﬁts drives the media to accentuate and dwell
on dangers. Additionally, technological innovations are simultaneously solutions and new
threats. No less than the venerated British physicist Stephen Hawking recently declared
that he was not sure how the human race would survive the next hundred years in the face
of new technologies and their unintended consequences.1

Large-scale environmental changes, some linked at least in part to new
technologies, are also liable to produce insecurity. Rising sea levels will likely threaten

trillions upon trillions of dollars worth of property and infrastructure, while population

 

1 In an audio posting on the Internet, Hawking posed the following question: “How can the human race
survive the next hundred years?” His intention was to stimulate thought and to bring awareness to the
dangers currently facing humanity. See Dr. Hawking’s video posted at YAHOO! Video.

14

growth and migration are putting pressure on water supplies throughout the world.
Climate change could be exacerbating natural phenomena like hurricanes, wildﬁres, and
pandemic, as well. This account is not intended to be alarmist. The point is that precious
little thought and planning have gone into dealing with the individual insecurity and
ramiﬁcations created by such phenomena.2 What happens to societies in the face of such
loss?

Messages about threats are ubiquitous, perhaps especially in the developed world.
Government agencies compete for funding by accentuating the speciﬁc threats they aim
to address, while politicians offer differing interpretations of threats according to their
ideological inclinations. News media make big money by relaying and framing danger,
and journalists have many tools at their disposal for generating fear (Glassner 1999).
Local newscasts in many parts of the US, for example, are little more than a nightly
litany of robberies, stabbings, shootings, kidnappings, and chemical spills. The national
media is no different, and the general public is an enthusiastic accomplice. The sniper
attacks in Washington, DC, were the most closely followed news story in the US. in
2002 (Pew Research Center 2002). Waves of stories about child kidnappings (in 2002)
and shark attacks (just prior to 9/11) produced immense interest as well, despite no actual
change in risk (Wilson, Martins, and Marske 2005; CNN 2002).

The biggest and most signiﬁcant challenge may be getting policymakers in
developed states to care about individual insecurity. As Liotta (2005) notes, intervention
on behalf of individuals in other states still typically occurs only when such intervention

serves the traditional ends of powerﬁrl states. Why should policymakers in the developed

 

2 Encouraging counter-examples are beginning to emerge. For example, Norway recently built a
“doomsday” seed vault inside an Arctic mountain to safeguard the existence and future diversity of
vegetative life.

15

world care about insecurity in other areas of the world? Echoing Annan’s earlier
comments, the “ﬂattening” of the world via globalization means that the negative
ramiﬁcations of feelings of insecurity spread across state borders, thereby making human
suffering an “irrevocable universal concern” (Axworthy 2001, 20). Policymakers in the
developed world must also realize that “developing” areas exist within the borders of
nearly all states. For people living in such areas, the conditions (and consequential
feelings of insecurity) often are not much different from those in the developing world.
These pockets of distress and discontent may grow as income inequality worsens and the
global population swells.

Finally, feelings of insecurity can push people quickly into taking actions that are
harmful to the social good even in more developed states. The 2001-2002 period was an
anxious time in the Washington, DC, metropolitan area. In addition to constant concern
about another large-scale terrorist attack, smaller-scale terrorism in the form of anthrax
mailings and the previously mentioned sniper attacks generated worry for the people of
the region. However, the minute risk level for any one individual did not keep people
from seeking preventive drugs for anthrax even in unaffected parts of the country
(Belongia et al. 2005) or from avoiding their normal contributions to daily commerce
(Schulden et al. 2006). Acts of violence on Muslims in the US. following 9/11 as well as
looting and violence after disasters like Hurricane Katrina further illustrate the negative

implications of fear and insecurity even in the wealthiest of states.

16

Defining Human Security

As mentioned earlier, the idea of “human secruity” ﬂowed directly from a
renewed consideration of the meaning of security that took place primarily in the 19805
and early 19905. Bajpai (2003) discems the contemporary origins of the human security
concept in the attention drawn to the subjects of individual safety and well-being by
economists and multinational commissions in the 19605 and 19705. Differentiation
eventually emerged among such concepts as national security, international security, and
global security (Haﬁendom 1991), and human security would recognize how peace and
development had become “inextricably interrelated” (N ef 1999).

Broadly construed, human security refers to individual safety, well-being, and
freedom from threats. This conceptualization is not uniformly accepted, however. Despite
strong advocacy by the United Nations and others in the intergovernmental community
for human security in recent years, the idea remains a divisive one. The following
elements compose the still deﬁnitive UNDP formulation of human security: economic
security, food security, health security, environmental security, personal security,
community security, and political security (United Nations Development Program 1994).
Several authors have proposed alternative components or deﬁnitions of human security,
however (see Table 1). Not incorporated in the typical history of the human security
concept are the works of Abraham Maslow (1943, 1970) and his theory concerning the
hierarchy of basic human needs, which includes consideration of the various forms of
safety or security important to the individual. “Safety needs” comprise the level of needs

immediately above the ﬁrst level of physiological needs in this theory. Maslow (197 0)

l7

 

contends that both children and adults prefer a “safe, orderly, predictable, lawful,

organized world” (p. 41).

Table 1. Theorized Compositions of Human Security
E

Source Components

 

Maslow (1943, 1970) Following the ﬁrst level of physiological needs are the “safety” needs,
which include (1) security; (2) stability; (3) dependency; (4) protection; (5)
freedom from fear, from anxiety and chaos; (6) need for structure, order,
law, limits; (7) strength in the protector

UNDP (1994) (1) Economic security, (2) food security, (3) health security, (4)
environmental security, (5) personal security, (6) community security, (7)
political security

Axworthy (1997) (1) Absence of military threat, (2) security against economic privation, (3)
acceptable quality of life, (4) guarantee of fundamental human rights

Nef (1999) (1) Environmental, personal, and physical security; (2) economic security;
(3) social security; (4) political security; (5) cultural security

Suhrke (1999) Reduced vulnerability for: (1) victims of war and internal conﬂicts, (2)
those who live close to the subsistence level, and (3) victims of natural
disasters

Thomas (1999) (1) Meeting basic material needs (e.g., food, shelter, education, health

care); (2) realization of human dignity (e.g., meaningful participation in
community, emancipation from oppressive power structures, personal
autonomy, control over one’s life)

King and Murray (1) Income, (2) health, (3) education, (4) political freedom, (5) democracy
(2001)
Bajpai (2003) Security is composed of direct and indirect threats to personal (1) safety

and (2) freedom; security is enhanced by (3) norms and (4) institutions
(preferably representative and democratized ones)

 

Some policymakers and academic researchers have declared that the human
security policy agenda has “stalled” (Suhrke 2004) due to the difﬁculties of deﬁning the

term —- difﬁculties that stem in part from the conﬂicting interests of various state and

18

intergovernmental actors in deﬁning human security (Paris 2001). Paris (2001) proposes
another way forward. He recommends using the term “human security” as one element of
a four-fold classiﬁcatory structure for security studies more broadly. As such, “human
securit)?’ would be an umbrella term for studies generally focused on the security of
societies, groups, and individuals in the face of primarily nonmilitary threats.

Arguments about deﬁning human security often reduce to the question of whether
a broad or narrow conceptualization of individual security is more appropriate (Owen
2004; Roberts 2006). While both conceptualizations center on the security of the
individual, they differ in their visions of how far beyond traditional national security
concerns human security should extend. Narrow versions only extend security a bit, for
example by including violent threats. The narrow approach is appealing to policymakers
looking for practical and actionable implications of human security, especially if the
approach requires little adjustment to existing security policy and infrastructure. Broader
versions (like that of the UNDP), on the other hand, go beyond militarized conﬂict and
violence to include other threats to human survival, well-being, and dignity. A central
feature of broader approaches is that human security unites or is connected to areas such
as development, humanitarian efforts, human rights, and conﬂict resolution (Ogata and
Cels 2003; Axworthy 1997).

Rather than the narrow-versus-broad distinction, Hoogensen and Stuvey (2006)
focus on the differences between “top-down” and “bottom-up” notions of human
security. Government policymakers construct top-down deﬁnitions of security, while
bottom-up deﬁnitions have their locus in individual human beings. Opponents of top-

down approaches complain that despite a purported focus on protecting the individual

19

such approaches do not correct for the problems of traditional state-centric views of
security. Critics argue that the state typically does not recognize the threat it poses to its
own people or recognize that security is highly context dependent and socially
constructed (Mohamed Salih 1999; Bellamy and McDonald 2002; Grayson 2003;
Hoogensen and Stuvey 2006).

If hmnan security is as crucial to governance and policymaking as some suggest,
pushing the deﬁnitional issue is also essential. In particular, the “amorphous” (Thomas
and Tow 2002) broad approach to human security, which is favored by many important
actors in the intergovernmental community and is considered more conceptually accurate
than narrower approaches, requires a ﬁrmer foundation. The central question becomes
whether it is possible to make human security both conceptually accurate and practically
applicable.

I propose that achieving both goals requires extending the broad, bottom-up
version of human security to its subjective individual roots and rethinking policy
implications. For example, embedded in the broad approach is the idea that national
security facilitates development, which in turn contributes to national security. This
“security-development nexus” (Roberts 2006), or connection between “freedom from
fear” and “freedom ﬁom want,” currently involves underdeveloped causal mechanisms.
One could explain this relationship at the state level by claiming that a lack of national
security keeps markets from functioning near optimality and that struggling economies
cannot spend large sums of money on weapons, diplomacy, etc. However, this nexus also
appears to result ﬁ'om causal mechanisms operating at the individual level, with

individual perceptions of security serving as the key to the system of relationships.

20

Feelings of insecurity may create risk aversion that prevents individuals from taking
chances in such areas as furthering their education or trying more difﬁcult jobs. An
individual’s lack of wealth could, in turn, increase feelings of vulnerability and risk
aversion.

A crucial point in extending the broad, bottom-up version of human secruity is
that global deﬁnitions have only limited utility. Alkire (2004) counted over thirty
different deﬁnitions of human security a few years ago. Are some deﬁnitions of human
security better because their creators are smarter, work harder, or have access to
knowledge that others do not? Do the deﬁnitions build carefully on one another in an
incremental way as the prevailing scientiﬁc paradigm prescribes? While a grain of truth
may reside in each of these suggestions, the more likely explanation is that individuals
think about security in different ways, and each author brings a particular self-centered

bias to the deﬁnitional procedure.

Risk Perception

“Risk” is an idea that combines the potential for harm (i.e., threats, hazards,
dangers) with probabilistic uncertainty over whether the conversion to actual harm will
occur. We already know a good deal about the subjective nature of risk and the
generation of risk perceptions. Early research on risk in the social and behavioral sciences
exhibited a bifurcation between “socio-cultural” and “psychometric” approaches. A
prominent example of the socio-cultural approach is cultural anthropological research
like Douglas’ (1992) study of the politicization of dangers. The psychometric approach to

risk, on the other hand, has focused on individual—level factors in risk deﬁnition and

21

perception. Behavioral economics research on decision making (see Kahneman 2003) has
paralleled this psychometric research in important ways. These two literatures have
produced useful information concerning the different cognitive heuristics, biases, and
tendencies that are especially apparent under conditions of threat and uncertainty. These
cognitive phenomena affect both the perception of risk and actions taken under
conditions of risk.

The bifurcation between socio-cultural and psychometric approaches to risk has
given way to various synthetic approaches. The “Risk Society” approach (Beck 1992,
1999), for example, takes the position that objective risks do exist but that one must take
into account both socio-cultural contexts and individual differences when talking about
the deﬁnition and perception of risk. Similarly, a summary of the psychometric paradigm
says that it “encompasses a theoretical framework that assumes risk is subjectively
deﬁned by individuals who may be inﬂuenced by a wide array of psychological, social,
institutional and cultural factors” (Slovic 2000a, xxiii). The recent study of cross-cultural
risk perception has underscored these ideas in showing both differences across cultures
and apparently universal cognitive features of risk perception (Renn and Rohrmann
2000). However, such studies typically include only two or three countries and typically
do not control for differences in objective risk across countries or respondents.3 Weighing
the results of such studies, Renn and Rohrmann (2000) posit that risk perception is
related to four hierarchical “context” levels. The narrowest context is the “heuristics of

information processing” level, which is embedded in a “cognitive-affective factors” level,

 

3 Some sociological and psychological risk researchers adopt the position that no “objective” risks exist,
since risk calculation always involves some sort of theoretical model. While recognizing the subjective side
of risk, I am inclined to believe that rates of occurrence and standard probability modeling often can
provide information about objective risk.

22

which is in turn embedded in a “social-political institutions” level. Finally, the social-
political institutions context is embedded in the broad “cultural background” context.
Another synthetic line of risk research has examined the ways in which risk
undergoes a “social ampliﬁcation” process, with the media playing an important role (see
Kasperson et al. 2003). The “social ampliﬁcation of risk ﬁamewOrk” (SARF) describes
the dynamic social processes that underlie risk perception and response. Sometimes risks
deemed large by experts receive little societal attention, a process called “attenuation,”
while risks judged low by experts are sometimes subjected to a great deal of
sociopolitical concern and activity — the risk “ampliﬁcation” process. SARF, according to
its architects, holds that “risk, risk events, and the characteristics of both become
portrayed through various risk signals (images signs, and symbols), which in turn interact
with a wide range of psychological, social, institutional, or cultural processes in ways that
intensify or attenuate perceptions of risk and its manageability” (Kasperson et al. 2003,

15).

A Preliminary Framework for Security Microfoundations

The following questions motivate a framework for understanding the

microfoundations of security:

0 What do individuals view as sources of insecurity? Why do individuals over-
estimate certain political risks and under-estimate others? Where applicable,
why do views of masses diverge from those of elites? When doing so protects
individuals and contributes to the social good, how can governments achieve

better alignment between subjective and objective security?

23

0 What are the origins of security perceptions? What individual-level (e.g.,
cognitive, experiential) and environmental (e.g., social, institutional,
informational) factors play a role? How do elite (e.g., politicians, media,
bureaucrats) communications contribute to these perceptions?

0 What attitudes and behaviors result from perceptions of insecurity? What
factors are important in translating these perceptions into attitudes and
behaviors?

0 What are the policy prescriptions for reducing feelings of insecurity and their
negative ramifications? How do governments prevent such ramiﬁcations from
arising? How do governments resolve harmful collective action problems that

spring ﬁ'om subjective insecurity?

Theoretical and Practical Beneﬁts

The ﬁrst set of theoretical beneﬁts likely to accrue from a fuller research program
on the microfoundations of security, based on the foregoing questions, is an expansion
and more thorough inspection of the current state of knowledge on risk perception.
Researchers have gathered information on topics like perceived risk from various health,
environmental, and energy threats (see Slovic 2000a, 2000b). While such research is
relevant to public policymaking, it stops far short of exhausting the important topics in
the political domain. Furthermore, the risk perception ﬁeld is one that would beneﬁt
greatly from additional research on contextual inﬂuences; as mentioned earlier, cross-

contextual research has only been conducted in limited ways thus far. Cross-national and

24

other cross-contextual research would illuminate which types of contextual variables are
important and under what conditions.

Additionally, the causal mechanisms for certain ﬁndings in the extant risk
perceptions literature are either unclear or are subject to strong competition from a
number of theories (e. g., the ﬁnding that women tend to view threats across the board as
higher). An expanded, multi-level research agenda could help sort out and unify causal
mechanisms. Finally, the cognitive risk perceptions literature has uncovered evidence that
“worldviews” like fatalism, individualism, and egalitarianism are important determinants
of risk perception (Slovic 2000b). However, this literature has not really examined certain
ideologies (e.g., political ideology, economic ideology), attitudes (e.g., dogrnatism,
nationalism, patriotism), and identities (e.g., ethnic identity, religious identity) that are
central to political analysis. Nor has it fully considered certain features of information
processing thought important to politically based cognition like selective attention
(Deutsch and Deutsch 1963; Norman 1968), cognitive dissonance (F estinger 1957), or
attention to salient information (Entrnan 1989).

A reverse set of theoretical beneﬁts, of course, is that application of the risk
perception model to political phenomena is also likely to enhance our understanding of
political processes. Applying an individual security perspective to political issues like
globalization, corruption, and terrorism could provide greater explanatory power than
current approaches and could provide theoretical coverage of a wide range of political
phenomena. The intention is not to reduce all human behavior and interactions to issues
of security, but rather to see what types of behavior and interactions a security

microfoundations approach could help to explain. In other words, how useful and broadly

25

applicable would a set of theoretical tools focused on the microfoundations of security
be? What kind of explanatory power would they possess?

Yet another anticipated set of theoretical beneﬁts pertains to developing human
security as a concept and its implications more completely. One direct and substantial
beneﬁt is eliminating some of the ambiguity that surrounds broader notions of human
security — ambiguity that stems in no small degree ﬁom a lack of information about
individual security perceptions and reactions. Additionally, a ﬁamework that recognizes
the incongruity between obj ective/expert and mass assessments of risk aims to ﬁll the
very gap that critics of the top-down version of security have decried as problematic.
Another speciﬁc anticipated beneﬁt would be sorting out the connections and causal
endogeneity (or reciprocal causation) that plague human security. Individual security is
partially a function of international and national security, but the reverse may also be
true. Even more importantly, is development a part of human security, a precursor to
human security, or a consequence of human security? Perhaps all the above are true.
Conversely, how do individual feelings of insecurity contribute to attitudes and behaviors
that are harmful to development, human rights, and conﬂict resolution efforts?

These are not just ivory-tower musings. As discussed earlier, a better
comprehension of the microfoundations of security has clear practical implications for
policymaking. Keeping individuals physically safe is undoubtedly an imperative of
governance, but dealing with perceptions of insecurity and their ramiﬁcations is also
quite important. This objective necessitates a ﬁrm understanding of what makes masses

and political elites feel unsafe and what they are likely to do under conditions of

26

perceived insecurity. Also useful would be a better understanding of when and how

leaders manipulate feelings of insecurity to achieve their objectives.

Framework Assumptions

The principal assumption underlying this preliminary framework is that a non-
negligible proportion of important human behaviors in the polis and the marketplace are
based on self-interested security considerations. Stated more strongly, this proportion is
considerably larger than previously believed. The reasoning for this assumption is that
survival and well-being are primary objectives of human life. Human beings generally
prefer a “safe, orderly, predictable, lawful, organized world” (Maslow 1970, 41) because
such a world generally facilitates the objectives of survival and well-being. As for the
importance of self-interest, the idea of security means little when separated from the
interests of the individual, group, state, etc. Security pertains to the interest of some entity
in remaining whole in one sense or another, with survival as the extreme and various
dimensions of well-being composing broader understandings of security.

The anticipated objections to this claim are similar to those typically lodged
against rational choice theory. The ﬁrst anticipated objection to this type of claim is that
the social sciences frequently have turned up evidence of altruistic and group-based
behaviors or other behaviors that seem to provide utility without enhancing either an
individual’s bank account or security. These ﬁndings of other-regarding behavior (e.g.,
Sears et al. 1980; Sears and Funk 1990) include the economic voting literature, which
shows a much stronger linkage between vote choice and macroeconomic conditions than

between vote choice and individual economic conditions, particularly in the US. (see

27

Lewis—Beck and Stegrnaier 2000). A number of different approaches are available for
responding to such an objection. One approach is to acknowledge that human beings are
complex organisms that interact to create complex societies. Consequently, no single
motivating force (e.g., security) is sufﬁcient to describe more than a miniscule proportion
of individual and social behavior.

Other approaches, on the other hand, emphasize how seemingly other-regarding
behaviors appear self-interested if one looks through the appropriate lenses, though such
approaches at times produce hypotheses that are not falsiﬁable. Many of the claims of
evolutionary biology and of the evolutionary social sciences (see Laland and Brown
2002), for example, adopt such an approach. The idea of “inclusive ﬁtness” (Hamilton
1964) or “kin selection” theory (Maynard Smith 1964), which intends to explain altruism
based on genetic relatedness, has been called a foundational idea for modern evolutionary
thought (Cosmides and Tooby 1987). Evolutionary theorists also have attempted to
explain social behavior that extends beyond familial ties. Evolutionary psychology
provides an explanation in the form of evolutionary adaptations for social behavior and
group living (Cosmides and Tooby 1992). Evolutionary game theory approaches also
provide some guidance, showing that a communal-sharing norm may emerge from
autonomous interactions of adaptive and proﬁt-seeking individuals (Karneda et al. 2003).
Most prominently in political science, Axelrod (1984) demonstrated that reciprocity and
cooperation could emerge as an evolutionary stable strategy. Cultural group selection (see
Boyd and Richerson 1985), another approach, proposes that groups of individuals are
evolutionarily selected based on particular culturally learned ideas or behaviors. For

example, social norms, contracts, and institutions that promote other-focused behavior

28

within the group may permit such groups to prosper. A troubling, though intuitively and
empirically appealing, implication of this theory is the selection of genetic
predispositions for in-group altruism and out-group hostility (Richerson and Boyd 1998).

Regardless of which kind of existing approach one ﬁnds most persuasive, the
framework assumption concerning the prevalence of self-interested security-based
behaviors is a testable one that can be reexamined based on empirical tests of the
framework. In some ways, the successfulness of the ﬁamework in explaining empirical
phenomena is a direct test of this assumption. As for altruism-type objections, we can
observe how often individuals incorporate ingroup or outgroup evaluations into their
security perceptions and calculations. We can also observe how frequently attitudes like
concern for the environment or for the well-being of others are associated with security
attitudes. After all, a community with fewer pathogens and fewer unhappy, desperate
individuals is a safer place to live.

Another anticipated objection, again also lodged against rational choice theory, is
that cognitive and informational limitations prevent people from acting in truly self-
interested ways. This is where a ﬁamework for understanding the microfoundations of
security parts ways with “purer” forms of rational choice theory, which tend to be more
productive in high-information environments with elite players whose interests are clear.
A secondary assumption of the framework is that behavior in this domain is “boundedly”
or “intendedly” rational (see Conlisk 1996; Jones 1999). Rather than assuming high
informational and cognitive processing requirements, we merely assume that people want
to assess threats accurately so as to devote appropriate resources to them but often are

unable to do so due to informational and cognitive limitations. This secondary

29

assumption, too, is a testable one, and the extant empirical support is strong. People
frequently use heuristics (i.e., informational shortcuts or rules of thumb) or exhibit biases
in making decisions with regard to conditions of risk or uncertainty (see Rohrmann and
Renn 2000; Slovic 2000a; Kahneman 2003).
A ﬁnal assumption underlying this ﬁamework is that empirical research will be
useful in deﬁning the microfoundations of security. What is being advocated here is a
largely empirical, scientiﬁc approach for answering these questions. The attitudes,
Opinions, and beliefs of individuals that pertain to risk and security can be elicited ﬁom
individuals with appropriate questions. The security-based behaviors of individuals are
also observable. Furthermore, perceptions of security are, in part, a function of the
actions, messages, and rules that ﬂow from government — which are observable. As
always, such efforts will involve some degree of error, but a broader research agenda
carried out by a community of researchers using different approaches would generate
more accurate assessments. While the proposed framework recognizes the importance of
context, it eschews many of the elements of a pure constructivist approach. Various
Contexts seem to matter for individual security, but these contexts — informational
Patterns, environmental inﬂuences, topical areas, etc. - can all be converted into
Variables. Non-empirical work on risk and security informs the testing and evaluation of
cElusal mechanisms, but the belief here is that features of individual perception are
identiﬁable using empirical methods. The fact that security is not a constant across
individuals does not negate the usefulness of empirical approaches to study. Inferential

In(ithods permit the identiﬁcation of patterns and allow us to discern the ways in which

context matters.

30

Framework Components

The risk perception literature is a starting point for building a framework for the
microfoundations of security. Risk perception is about evaluating threats and uncertainty.
Previously identiﬁed biases, heuristics, and cognitive tendencies may hold across an even
broader range of phenomena and situations than those already studied. For example, a
standard ﬁnding is that more vulnerable groups like women, racial minorities, the less
educated, and the less wealthy (see Slovic 2000b; Slovic et al. 2000) tend to inﬂate risk
perceptions beyond any observed increase in actual risk. This ﬁnding is consistent with
ﬁndings that voluntariness and controllability (F ischhoff et a1. 2000) are crucial
components of risk perceptions. This ﬁnding is further consistent with theorizing about
the importance of the power-relationship and identity aspects of human security
(Hoogensen and Stuvay 2006). Also worth exploring is whether such patterns are more
focused on clearly self-interested concerns or on more social ones, as evaluations of
Community well-being may be a heuristic for projecting an individual’s own security. In
line with suggestions made by Bajpai (2003, 2004), a framework for individual
assessments of security should also incorporate capabilities. More speciﬁcally, factors
that mitigate or provide a buffer against threats should enter individual security
Considerations. For example, wealth can serve as a buffer against a variety of threats —
from health risks to criminal acts.
Orientations toward insecurity and approaches for dealing with insecurity

constitute another major component of the preliminary framework. The previously

31

mentioned “worldviews” in the risk perception literature fall into this broader category.

These orientations and approaches guide which information about threats an individual
selects from the environment and how that information is processed. For example, a
person who uses worry as a means of coping with threats may very actively seek out
information about threats, while a person with a negative view of human nature may seek
and process information about human-based threats using this orientation as a guide.
Blame-shifting or blame-assignment is another example of a mechanism for coping with
insecurity. A substantial body of research has found that threats increase intolerance,
prejudice, ethnocentrism, and xenophobia (see Huddy et al. 2005). Attitudes toward out-
groups become negative and often violent. Focusing on the comforts of tradition and
routine is yet another example of an approach for dealing with feelings of insecurity.
People relying on this mechanism will tend to see threats in change.

An individual ’s informational characteristics also matter for security perceptions.
Information cannot easily change durable features like an individual’s orientations or
approaches toward insecurity, but individual informational characteristics — like exposure

to certain kinds of information, the sources and volume of information sought,
information processing capacity, and experiences -— should inﬂuence individual
assessments of risk. For example, an individual’s experiences may serve as a primary
l’esource for evaluating threats — sometimes leading to inﬂation of threat perceptions and
SOtnetimes leading to underestimation. An individual’s exposure to “focusing events” tied
to threats also may shape perceptions of those particular threats. Additionally, individuals
With different social information networks may be exposed to very different messages

about the nature and extent of threats.

32

Environmental patterns constitute a ﬁnal, broad component of the preliminary
framework. Individual perceptions of security are in part the product of dynamic
interaction between people and their environments. Information patterns are the ﬁrst
example of relevant environmental inﬂuences, as they affect what information an
individual can choose from in forming perceptions. The types and amounts of
information available, the informational ﬂows produced by institutions, and the
constraints placed on certain types of information vary by context. The frames and
emphases given to speciﬁc threats by elites are widely variable based on one’s context, as
well. The idea behind “securitization” is that the term “security” is a rhetorical and
agenda-setting tool. Labeling an issue as a “security” issue gives it a special status on the
public agenda and hands it over to the policy apparatuses and policymakers that typically
deal with secruity issues (Buzan, Wmver, and Wilde 1997). Furthermore, issues labeled
as “security” issues lend themselves well to symbolic manipulation. Politicians often are
adept at using symbols to reassure citizens and make them “quiescent,” or passively
acceptant, of particular policies (Edelrnan 1960, 1967; Stone 2002).

Aggregate levels of instability and uncertainty are a second example of relevant
environmental inﬂuences. Assessment of security is largely an exercise in projecting and
forecasting, which introduces a strong element of uncertainty. Furthermore, uncertainty is
“ubiquitous, consequential, and ineradicable in political life” (Ciofﬁ-Revilla 1998, 3), a
signiﬁcant observation as we tie perceptions of security to the more political notion of
human security. The institutions of governance can reduce uncertainty by patterning
interactions and can spread out the costs of harmful events, while the political arena is a

venue for dueling forecasts and arguments about uncertain cause-and-effect relationships.

33

Political behavior often depends on a joint consideration of threat and uncertainty.
Thomas Jefferson explicitly recognized this conﬂuence of threat and uncertainty in the
text of the US. Declaration of Independence: “Prudence, indeed, will dictate that
Governments long established should not be changed for light and transient causes; and
accordingly all experience hath shewn, that mankind are more disposed to suffer, while
evils are sufferable, than to right themselves by abolishing the forms to which they are
accustomed.” In other words, certain threats and suffering may be tolerable so long as
they are accompanied by a particular degree of predictability.

Norms constitute a third example of relevant environmental inﬂuences. The
prevalence of particular approaches toward insecurity may inﬂuence individual security
perceptions. A norm of mistrust, for example, may prejudice individuals toward
perceiving threats from other people. In terms of reactions to uncertainty, Hofstede and
Hofstede (2005) ﬁnd substantial cross-cultural differences in “uncertainty avoidance” or
tolerance of ambiguity. Uncertainty avoidance may determine whether the default

reaction is projecting high or low threat under conditions of uncertainty.

Summary and Discussion

A better understanding of the microfoundations of security is an imperative of
governance in that protecting citizens from threats both perceived and real is a core
purpose of government. The improved policymaking that may result from such
knowledge serves humanitarian aims and the goal of limiting negative consequences of
individual insecurity. The emphasis placed on human security by the United Nations and

other international actors and the challenges posed by a rapidly changing world also call

34

for exploring the microfoundations of security. Enhanced understanding can provide
policymakers with vital information for supporting political and economic development,
reducing internal and external conﬂict, improving disaster preparedness and response,
and overcoming collective action problems in the face of risk.

Empirical research guided by strong theory also has the potential to address
problems associated with the deﬁnition and articulation of human security, as well as the
implementation of human security initiatives. Broader versions of the human security
concept, like those advocated by the United Nations, have met substantial criticism, and a
lack of knowledge about the microfoundations of security is a prime suspect for these
problems. We need to comprehend what individuals view as sources of insecurity and
why. In so doing, we can reduce conceptual ambiguity, increase analytical utility, and
produce clear policy implications.

Students of risk perception and of decision making under conditions of risk and
uncertainty have produced knowledge relevant to the microfoundations of security. These
literatures are useful but require a theoretical expansion of concepts beyond risk and an
extension into new political domains in order to provide a solid foundation for
understanding individual security and for developing practical approaches to human
security. Integrating the insights from the risk perception and human security models can
contribute to building an even more useful body of knowledge about the
microfoundations of security.

An underlying assumption of a framework for the microfoundations of security is
that security-based evaluations are a very important part of political life. While the idea

of security assumes self-interest, the extent of self-interested behavior and the extent to

35

which other-regarding behavior is actually self-interested are both testable questions
embedded in the framework. Boundedly rational assumptions seem appropriate given
previous related research, but the success of the framework as a whole is essentially a test
of these assumptions, as well.

This chapter has proposed a number of important components for the preliminary
construction of a security microfoundations ﬁ'amework. The types of biases, heuristics,
and cognitive tendencies previously identiﬁed with regard to the evaluation of threats and
conditions of uncertainty are crucial, as are the capabilities individuals believe they
possess for warding off threats. Additionally important are the orientations and
approaches individuals use for dealing with insecurity and the individual informational
characteristics that govern the intake and processing of information about threats.
Environmental patterns, too, inﬂuence individual assessments of security. Chief among
these patterns are informational ﬂows, instability and uncertainty, and norms. The major
components outlined here roughly conform to Renn and Rohrrnann’s (2000) suggestion
of thinking in terms of multiple “context levels” of risk perception.

Political researchers have begun examining individual perceptions of security,
particularly with regard to issues like terrorism and disaster management. However, these
efforts have neither been guided by a unifying theoretical framework nor have they aimed
explicitly at improved comprehension of human security. The US. Department of
Homeland Security has a Center of Excellence for the Study of Terrorism and Responses
to Terrorism (START) that conducts research on topics germane to individual
perceptions of security. Other researchers have also explored security perceptions and the

implications for governance in areas such as: the relationship between perceived threat

36

from terrorism and support for civil liberties (Davis and Silver 2004), the implications of
terror threats perceived as either personal or national in character (Huddy et al. 2002),
and the differential implications of fear and anxiety for antiterrorism policies (Huddy et
al. 2005). Researchers have also examined communication-based aspects of security
perceptions like media ﬁ'aming of terror issues (Norris et al. 2003), likely citizen
reactions upon discovering the veracity of fear-based messages provided by government
(Lupia and Menning 2005), and the factors affecting whether individuals would evacuate
if warned of a signiﬁcant threat (Silver and Burton 2006).

Empirical testing is vital to assessment of the framework. As mentioned earlier,
the preliminary framework is subject to revision based on empirical ﬁndings, including
reassessment of the assumptions underlying the framework. This research will supply
information about general tendencies in the face of threat and uncertainty, but it will also
provide details concerning the importance of context and contingencies in generating
security perceptions. Continued theoretical development in this area is also vital. While
this chapter has proposed a set of relationships as they relate to the generation of security
perceptions, less deﬁned are the factors important in translating security perceptions into

action.

37

CHAPTER 2

ATTITUDES TOWARD GLOBALIZATION

38

Introduction

Many elites, particularly in advanced economies, view globalization as a positive
collection of trends. Economists tout the increased efﬁciency and overall welfare that
result from trade liberalization and freer migration of labor. Social and political leaders
praise the multiculturalism, tolerance, and diversity that can result from greater global
interconnectedness. Political scientists see the diffusion of liberal democratic institutions
and values as well as reduced conﬂict due to the existence of shared interests.

Not everyone agrees with this rosy view of globalization. The forces of
globalization have unleashed conﬂict both within states and across state lines. Opposition
to globalization has manifested itself via protests, riots, and militant behavior.
Globalization, rightly or wrongly, has been blamed for religious extremism, terrorism,
and serious political conﬂict. Concentration of wealth is yet another malady prominently
associated with globalization, as are environmental ills and pandemic. Certainly,
messages ﬁ'om some elites — many of them in the “developing” world — emphasize such
negative consequences. Studies of mass attitudes have noted that masses often are
unimpressed with globalization, as well (see Herrmann, Tetlock, and Diascro 2001;
Mayda and Rodrik 2005; Scheve and Slaughter 2006).

Given dueling perspectives and how “globalization” is many different things to
many people, the costs and beneﬁts of globalization as a whole are neither entirely clear
nor irnminently calculable. In the US, the severely under-informed anti-globalization
protestor is an indelible icon of media coverage and is emblematic of the topic’s
complexity. Understandably, a good deal of ambiguity remains concerning the sources of

support for and opposition to globalization. The standard political economy approach for

39

explaining attitudes toward globalization, which relies on a combination of rational
choice assumptions and theories of international trade, is persuasive in some ways. The
informational and cognitive requirements of that approach and the complexity of the
topic suggest that much of the story remains untold, however. We have heard about
phenomena associated with globalization like the erosion of identity and culture, but we
are short on empirical tests of particular mechanisms explaining how or why
globalization is a source of worry for some people. Alternatively, we have little evidence
about why other people adopt pro-globalization attitudes and exhibit little worry. Having
such information would allow us better to predict where support for globalization will be
strongest and where opposition is likely to emerge. Such information also would facilitate
more productive normative discussions about the desirability of globalization and more
effective policies for dealing with citizen unease.

With such goals in mind, I propose thinking about individual attitudes toward
globalization in terms of individual security. Such an approach brings economic and non-
economic explanations of attitudes toward globalization under a single comprehensive
umbrella, which is perhaps more complex in terms of the set of predictions but is less
complex ﬁ'om the perspective of what is expected from the individual actor. Such an
approach further allows globalization attitudes to serve as a fertile testing ground for
better understanding security perceptions. Beyond the integrated theoretical framework,
this analysis makes a number of other contributions to our understanding of attitudes
toward globalization. These other contributions include consideration of broader notions

of globalization, the use of under-utilized and more recent cross-national data, multilevel

40

statistical modeling, and attempts to sort out the true nature of education’s impact on
attitudes toward globalization.

The multilevel statistical analysis provides evidence that the lens of individual
security is a useful tool for understanding attitudes toward globalization. In particular,
these attitudes are a function of: assessments of threats, capabilities, and uncertainty;
approaches for dealing with insecurity and orientations toward insecurity, informational
characteristics of the individual; and environmental patterns and processes. This security
microfoundations approach, built on lesser informational and cognitive requirements,
tells a more comprehensive story than the standard political economy approach to

understanding attitudes toward globalization.

Attitudes toward Globalization

The bulk of the empirical research on attitudes toward globalization has examined
attitudes toward trade or irmnigration, with a distinction typically made between
“economic” and “non-economic” predictors. The theoretical foundation for causal
arguments made by political economists is the idea of comparative advantage, with such
international trade theories as the Heckscher-Ohlin model (Ohlin 1933; Learner 1995)
and the Ricardo-Viner (or speciﬁc-factors) model generating predictions. Political
economy arguments build on rational choice assumptions so that personal preferences are
determined by narrowly self-interested calculations that take into account the impact of
trade or immigration on the individual’s personal economic welfare.

Comparative advantage has wage and employment implications based on the .

relative abundance of factors of production (e.g., skilled vs. unskilled labor) in a country.

41

These factors are either mobile across sectors (per Heckscher-Ohlin assumptions) or are
immobile or partly immobile within particular industries (per Ricardo-Viner
assumptions). Factor incomes vary either by the type of factor (i.e., factor endowments)
in the former view or by the industry of employment (i.e., speciﬁc factors) in the latter.
Overall, the literature seems to produce consistent support for such models with regard to
trade (Scheve and Slaughter 2001; O’Rourke and Sinnott 2001; Mayda and Rodrik 2005;
Scheve and Slaughter 2006) and immigration (Mayda 2005; O’Rourke and Sinnott 2006;
Scheve and Slaughter 2006). Beyond factor-based explanations, authors have also
claimed that asset ownership inﬂuences attitudes toward globalization (Scheve and
Slaughter 2001, 2004), that the type of globalization (e.g., foreign direct investment by
multinational enterprises) matters for preference formation (Noland 2005), and that
individual concerns about state welfare expenditures and redistributive policy are
inﬂuential particularly with regard to immigration (W ellisch and Walz 1999; Hanson,
Scheve, and Slaughter 2007).

Another category of explanations is also economic in nature but deals with
apparent “other-regarding” or “sociotropic” (Meehl 1977; Kinder and Kiewiet 1979,
1981) attitudes, which at ﬁrst glance do not ﬁt well with the individual-level rational
choice assumptions. In this View, individuals develop attitudes toward globalization
based on their assessments of such phenomena as aggregate unemployment rates or
income inequality in their societies. The empirical evidence linking sociotropism and
globalization thus far seems weaker than the evidence for direct individual self-interest.

“Non-economic” determinants of attitudes toward globalization (e.g., identities,

attitudes, attachments, values, ideologies, worldviews) are included as controls in studies

42

with a political economy bent. Authors commonly provide only ad hoc explanations for
these results. Common ﬁndings are that intolerance, nationalism, chauvinism, national

pride, and patriotism negatively affect attitudes toward trade and immigration.

Some Remaining Questions

Despite the important efforts of authors like the foregoing, explanations of
attitudes toward globalization remain incomplete. This paper aims to address a few of the
principal areas of ambiguity. First, with very few exceptions (e.g., Noland 2005; Wolfe
and Mendelsohn 2005), the quantitative empirical literature has not considered broader
notions of globalization in the same way as have some non-quantitative researchers.
Trade and immigration are important components of globalization, but the “woman on
the street” often sees globalization in more general ways. The diffusion of foreign ideas
and norms is another component of globalization, as are the greater communicative
connectedness and international travel that can lead to such diffusion.

Second, as pointed out by Scheve and Slaughter (2006), the literature lacks
coherent theoretical models of the “non-economic” factors that affect attitudes toward
globalization. Rough econometric tests suggest that non-economic determinants are
substantially more important than economic ones (e.g., Mayda 2005). Additionally, while
the ﬁndings of the standard political economy approach appear strong, the assumed
cognitive and informational requirements are quite burdensome. Do individuals
simultaneously and accurately: (1) make calculations based on their particular locations
within the economic infrastructure of their respective countries, (2) understand the

economic infrastructures of relevant trading partners, (3) project the likely impact of

43

international trade or immigration on their own livelihoods based on prominent theories
of international trade, and (4) develop attitudes strictly in accordance with these
calculations? The current state of knowledge about mass decision making strongly throws
such propositions into doubt, perhaps even with the use of heuristics.

Third, the econometric tests in the literature often cover only one country like the
US. (Citrin et al. 1997; Dominitz and Manski 1997; Hemnann, Tetlock, and Diascro
2001; Hanson, Scheve, and Slaughter 2007), Great Britain (Scheve and Slaughter 2004),
France (Hellwig 2007), or Canada (Wolfe and Mendelsohn 2005). While comparative
studies are necessary for untangling macro-level causality, existing cross-country
analyses have used less-than-optimal econometric methods. Additionally, nearly all
comparative studies have used the same two datasets, the 1995 National Identity module
of the International Social Survey Program or the 1995-1997 wave of the World Values
Survey.

Finally, the impact that education and information have on attitudes toward
globalization remains unclear. Part of the problem is that political economy studies have
used education as a proxy for skill level in operationalizing comparative advantage.
While some studies have conducted responsible robustness checks of these particular
ﬁndings (e.g., Scheve and Slaughter 2001; Mayda 2005; Mayda and Rodrik 2005), such
studies cannot wholly rule out the possibility that education and sector of employment

merely determine how much or what a person learns about globalization.

44

The Microfoundations of Security

The explanatory approach employed here centers on the idea of the
microfoundations of security. In following with the economic understanding of the term
“microfoundations,” which refers to the microeconomic phenomena that undergird
observable macroeconomic phenomena, this approach focuses primarily on security-
relevant perceptions, attitudes, and behaviors at the individual level. Such an approach
does not preclude consideration of social forces and patterns that inﬂuence the individual,
however. I propose that attitudes toward globalization — particularly of the less supportive
variety — are largely a function of crude, individual-level security assessments. Four
categorical components of individual security are included in this analysis. The ﬁrst
category includes the elements of an individual’s risk assessment like threats, capabilities
for warding off threats, and uncertainty about projections. The second category includes
approaches for dealing with insecurity and orientations toward insecurity. The third
category includes the individual’s informational characteristics. A ﬁnal category includes
the different environmental patterns and processes that can affect individual security
perceptions.

The approach being advocated here shares some similarities with the standard
political economy model. In particular, thinking about the issue in terms of security
invokes images of a mostly self-interested actor. However, the underlying assumption
here is that the individual is boundedly or intendedly rational. The individual’s security
“calculations” need not be accurate or correct from the standpoint of more objective

measurement tools. The informational requirements also are much reduced. The

45

individual needs to know very little about international trade, immigration, and contexts

other than her own.

Individual Level

The standard political economy model suggests that individual economic
characteristics combine with macroeconomic context to determine the individual’s
orientation toward features of globalization like trade and immigration. A given
individual conducts a cost-beneﬁt analysis that takes into account such items as the
individual’s relative skills and assets within the domestic economy, comparative
advantages and disadvantages with major trading and migration partners, and
implications for the welfare state and redistributive policy. The commonly forwarded
implication of jointly applying international trade theory and rational choice theory is that
high-skilled individuals in national economies with a relative abundance of high-skilled
workers (as compared to low-skilled workers) will adopt a more positive orientation
toward globalization than will their low-skilled fellow citizens. Conversely, high-skilled
individuals in national economies with a relative abundance of low-skilled workers will
adopt a more negative orientation toward globalization than will low-skilled workers in
such countries.

However, looking at attitudes toward globalization through the lens of individual
security perceptions being proposed here produces slightly different predictions. The ﬁrst
hypothesis pertains to an individual’s vulnerability. Research on the perception of risk
consistently has found that certain groups like women, racial minorities, and the less

educated perceive a greater risk from a wide range of threats, regardless of whether such

46

groups actually are at greater risk (Ferraro 1995; Slovic 2000b). A likely culprit for this
pattern of ﬁndings is that the relative lack of power and control felt by disadvantaged
individuals increases feelings of vulnerability. Such an explanation is consistent with
ﬁndings about the centrality of voluntariness and controllability in risk perceptions

(F ischhoff et al. 2000), as well as with theorizing about power-relationship and identity
aspects of human security (Hoogensen and Stuvey 2006).

Cognitive tendencies identiﬁed in decision-making research also seem relevant as
mechanisms here. The status quo bias, which involves people thinking about alternatives
vis-a-vis the current situation and with a heavier weighting for disadvantages (Samuelson
and Zeckhauser 198 8), would lead vulnerable individuals to project the negatives of
globalization as potentially devastating. The margin of error for such individuals is small.
Similarly, the availability heuristic (Tversky and Kahneman 1974; Slovic, Fischhoff, and
Lichtenstein 1979) means that individuals in more tenuous situations should easily be
able to imagine the negative implications of globalization and should evaluate
globalization accordingly.

The other side of this vulnerability hypothesis is that individuals who feel less
vulnerable can evaluate globalization without giving much consideration to risks. Such
assessments need not involve burdensome calculations of comparative advantage, either.
Instead, the less vulnerable simply believe they have a buffer against any hardship that
could result from globalization-based change (for similar arguments in the US. context
see Simon 1987; Dominitz and Manski 1997; Herrmann, Tetlock, and Diascro 2001).

Individual security is also partially a function of the collective’s security.

Perceived threats to the collective (e.g., the social group, community, society) have

47

negative implications for the individual’s safety and well-being. The collective provides
protection from potential “outside” enemies, and a healthy collective can generate
secondary security beneﬁts (e.g., reduced crime). Perceptions of threats to the collective
also should increase feelings of vulnerability, thereby boosting assessments of the threat
from globalization. The collective in this instance is the state, since much globalization
rhetoric focuses on distinctions between the state and the outside and because the state
makes policies that affect globalization. Again, individuals that see their ingroup (i.e.,
state) as strong also may believe that they have a buffer against potential harm.

Feelings of uncertainty also should reduce support for globalization. Uncertainty
can assume multiple guises, including difficulty in projecting future conditions, belief
that future conditions may be more ﬁ'agile and unpredictable, and discomfort with
perceived trends and change. In any case, uncertainty should contribute to feelings of
vulnerability. Furthermore, individuals that perceive undesirable trends or change may
place blame on globalization, which would show up in evaluations of globalization.

The second category of explanatory factors includes approaches for dealing with
insecurity and orientations toward insecurity. These factors govern where individuals
look for threats and how they assess those threats. Worry — as a form of extreme
vigilance — is one approach for dealing with threats and insecurity. Anxious individuals
should have a heightened sense of threat ﬁom globalization, in part due to an increased
risk aversion (see Huddy et al. 2005) that could increase concerns about the unknown
impacts of globalization.

Individuals may also deal with feelings of insecurity with mechanisms that

dampen or redirect these feelings. Shifting responsibility or assigning blame for

48

outcomes, thus removing the burden from the individual, is an example. Such
mechanisms often have elements of group-based behavior, as demonizing outgroups is an
effective blame strategy. Demonization should be clearest when the perceived threats are
from international actors — the “agents” of globalization. Conversely, strategies that place
blame on the individual for his or her fortunes should ease concerns about globalization
and the broad forces that characterize it, since the individual ultimately determines his
own outcomes.

A more nuanced version of ingroup-outgroup approaches manifests itself at the
elite level in isolationism-versus-engagement arguments about foreign policy. Strategic
orientations toward the external are based on whether an individual believes that
engagement with the outside or isolation from the outside is the better security strategy.
This orientation is similar to the distinction between “liberal” and “nationalist” ideologies
of political economy (Gilpin 1987) or the distinction between “intemationalist” and
“isolationist” attitudes (Herrmann, Tetlock, and Diascro 2001). An individual’s
orientation toward the external acts as a powerful heuristic for political decision making
for elites and masses alike. Though unsupported by theory, previous ﬁndings that racism,
chauvinism, intolerance, patriotism, and nationalism inﬂuence attitudes toward
globalization (Mayda 2005; Mayda and Rodrik 2005; O’Rourke and Sinnott 2001, 2006)
are consistent with ingroup-outgroup approaches for dealing with insecurity. Also
consistent with the ingroup-outgroup approach are ﬁndings that the erosion of autonomy
and identity (Fetzer 2000; Noland 2005) inﬂuences attitudes toward immigration and,

trade. Such erosion has a dual effect as it is a threat posed by the outgroup and it

49

constitutes a threat to the existing security infrastructure for those individuals focused on
the safety provided by identiﬁcation with the ingroup.

Yet another approach for dealing with threats and insecurity is to focus on the
comforts of tradition and routine. As with ingroup-emphasizing approaches, a focus on
tradition sticks to what is well known rather than seeking broad exposure to outside
stimuli. Globalization represents both change and exposure to the outside, which means
that individuals who use tradition and routine as an approach for dealing with threats and
insecurity will evaluate globalization more negatively.

The individual’s informational characteristics compose the third category of
explanatory factors. Information contributes to threat deﬁnition and may even inﬂuence
approaches for dealing with insecurity, though the latter are much more durable.
Relatively extensive and unﬁltered exposure to the outside world should lessen concerns
about the outside being dangerous and should produce more positive evaluations of
global interconnectedness. On the other hand, negative attitudes toward the information
sources providing such exposure should produce less supportive views of globalization
by association.

A particularly important source of information is an individual’s formal
education. Upper-level education, in particular, may expose individuals to information
that is approving of globalization (though counter-examples certainly exist). Education
also tends to produce infonnation-seeking behavior and more consistent organization of
information — as acknowledged by political researchers who use education as a proxy for
“political sophistication.” Additionally, education may inﬂuence approaches for dealing

with threats and insecurity. The liberalizing impact of higher education generally

50

(Erikson, Luttbeg, and Tedin 1991; Zaller 1992) may contribute to favoring an approach
of engagement with the outside, which should lead to more positive evaluations of
globalization. Finally, in line with Uncertainty Reduction Theory (Berger and Calabrese
1975), greater information should reduce uncertainty, thereby also easing concerns about

globalization.

Country Level

The individual does not make assessments or develop approaches and orientations
in a vacuum. Individual security is also a matter of adapting to one’s environment and
processing information from that environment. Consequently, environmental patterns and
processes also play a role. Thinking ﬁrst about threats and capabilities, we should expect
that people in countries that have beneﬁted monetarily in a clear way from exchange will
have more positive orientations toward globalization. In this case, the broad, easily
ascertained monetary beneﬁts of globalization are a means of bolstering the security of
the collective. However, this hypothesis requires an assumption that individuals are
getting clear signals about sociotropic conditions, perhaps even ﬁ'om elite cues and
messages, and are acting accordingly.

Additionally, other people tend to constitute a particularly clear and direct type of
threat. Theoretically, everybody beneﬁts in macroeconomic terms from migration, as
labor goes to its highest-valued use. However, some poor countries experience “brain
drains” and wealthy countries end up footing large social services bills for immigrants as
they get settled. Furthermore, many people in wealthier countries have negative reactions

to the native job loss that accompanies an inﬂux of immigrants. Such job loss offends

51

ingroup-focused approaches for dealing with insecurity, and individuals may feel their
own jobs are potentially in danger (though the uneducated and unskilled are generally at
greatest risk). For all these reasons, the inﬂux of outsiders is viewed as a threat and is a
source of less positive views toward globalization.

The non-monetary standard of living in a country is another potential threat or
capability. A simple aggregation of the individual-level vulnerability hypothesis would
suggest that countries with an aggregate low standard of living would be less supportive
of globalization. However, simple aggregation assumes that people are making relative
assessments based on conditions in other countries rather than their own — a proposition
that is rather demanding ﬁom a cognitive and informational perspective.

On the other hand, we observe some elite rhetoric in countries with higher living
standards that points to the negative aspects of globalization, such as increased inequality
and cultural erosion (for both the developed and developing worlds). After controlling for
aggregate monetary beneﬁts, do these messages have an impact on attitudes toward
globalization? Conversely, does elite rhetoric focused on the beneﬁts of globalization
shift attitudes in developing countries? We may also observe a form of loss aversion in
countries with a relatively high standard of living (aside from considerations of wealth
and ﬁnancial beneﬁt): wanting to avoid any threat or uncertainty that could undermine
that standard of living. Of course, such individuals would be overlooking the aspects of
globalization that may have permitted the higher standard of living in the ﬁrst place. Due
to plausible theoretical conjectures in both directions, this hypothesis remains

directionally neutral.

52

Finally, the prevalence in an environment of particular approaches for dealing
with insecurity should affect attitudes toward globalization if we accept that such
approaches constitute cultural norms. For example, orientations toward uncertainty
should inﬂuence attitudes toward globalization and the change it brings. Cultures that are
uncomfortable with uncertainty and that cope with it by avoiding or attempting to resolve

ambiguity should adopt a less positive view of globalization.

Data and Methods

This study uses individual-level data collected by the Pew Global Attitudes
Project between July and October of 2002. The survey was conducted with over 38,000
respondents ﬁ'om a well-diversiﬁed mixture of 44 countries in terms of both development
and geographical region.1 Country—level data used in this study comes from multiple
different sources, as explained in Appendix A.

I have found no study on attitudes toward globalization that has modeled the
multilevel data structure explicitly. A number of other approaches are possible, but all
suffer ﬁom deﬁciencies. The shortcomings of single-country studies are a lack of
generalizability and an inability to assess system-level impacts. Simply pooling all
respondents across countries is problematic because it ignores the impact of macro
processes and assumes that no group-level differentiator inﬂuences patterns of causality.
The use of dichotomous indicator variables for countries is inferior because this mean-

shift approach provides no causal mechanisms for the differences. Modeling at only the

 

' The 44 countries are: Angola, Argentina, Bangladesh, Bolivia, Brazil, Bulgaria, Canada, China, Cote
d’Ivoire, Czech Republic, Egypt, France, Germany, Ghana, Guatemala, Honduras, India, Indonesia, Italy,
Japan, Jordan, Kenya, Lebanon, Mali, Mexico, Nigeria, Pakistan, Peru, the Philippines, Poland, Russia,
Senegal, the Slovak Republic, South Africa, South Korea, Tanzania, Turkey, Uganda, Ukraine, the United
Kingdom, the United States of America, Uzbekistan, Venezuela, and Vietnam.

53

aggregate level is perhaps most suspect, since it raises the potential for serious
underspeciﬁcation and problems with inference and ecological fallacy, as well as not
allowing for individual-level causality and interpretation. Yet another potential approach
is to run two separate regressions — one at the individual level and one at the aggregate
level. The shortcomings with this approach are that variation among groups is overstated
and that it does not allow for interactions between levels. A ﬁnal possibility would be to
use an individual-level model that includes the group-level variables simply replicated
across individuals in each group. This approach ignores group-level variation beyond that
explained by the included group-level variables.

This study uses a multilevel model, which is designed to overcome such
deﬁciencies (see Gehnan and Hill 2007; Bicke12007; Rabe-Hesketh and Skronda12005;
Steenbergen and Jones 2002; Raudenbush and Bryk 2002). Even more importantly,
multilevel models correspond most closely to the theory being advanced here, given the
importance of both individual assessments and environmental signals or conditions, as
well as the potential for interaction between the two. Multilevel models are most effective
when the pattern of results is close to the complete pooling (i.e., ignoring group-level
variation) scenario (Gelman and Hill 2007), and these data meet that condition, as well.

The main dependent variable for the analyses is a Globalization index. This
variable is a composite of attitudes toward interstate trade and business ties; interstate
availability of products; interstate travel and communication; interstate spread of movies,

television, and music; and overall interstate connectedness.2 Pew asked to what extent the

 

2 Factor analysis using a principal axis factoring extraction method for these ﬁve base measures provides an
initial eigenvalue of 2.521, with the dimension accounting for 50.42% of the variance. Variables measuring
attitudes toward irnrrrigration and toward “globalization” itself are not included in this index due to a
complete lack of ﬁt with the former and a desire to avoid connotations attached to the word “globalization”

54

respondent thought that each of these was a: very good thing, somewhat good, somewhat
bad, or a very bad thing. The response options for each question were rescaled to the 0-1
interval and were added together. The ﬁnal variable is a simple, unweighted mean of the
ﬁve component measures.3 See Appendix A for further information about the variables
used in this study.

Figure 1 shows the distribution of the dependent variable, with higher values
representing more negative views of globalization. The ﬁgure shows that responses are
strikingly positive overall when questions are posed in a relatively neutral way. This
result contradicts ﬁndings for speciﬁc questions about trade or immigration in the mid-
and late-19905 (Scheve and Slaughter 2006), especially when questions referenced policy
options or mentioned job loss (Wolfe and Mendelsohn 2005). Pluralities or majorities of
respondents tended to favor trade protectionism and immigration restrictions. The oft-
used trade question from the 1995 National Identity module of the International Social
Survey Program (ISSP) asked whether a respondent’s country “should limit the import of
foreign products in order to protect its national economy,” while the similar question
from the 1995-1997 wave of the World Values Survey (WV S) asked the respondent to
choose between “let goods be imported if people want to buy them” and “imposing
restrictions to protect jobs.” The ISSP question on immigration asked whether
immigration should be reduced or increased, while the WVS question asked under what

conditions immigration should be “permitted.” The Pew questions, on the other hand,

 

in the case of the latter. The factor pattern or “factor loadings” are: travel and communication (0.690);
connectedness (0.668); trade and business ties (0.630); availability of products (0.593); and spread of
movies, television, and music (0.497). I avoid using the single globalization question as the dependent
variable because the index is likely a more reliable measure (Cronbach’s a = 0.734) and because
interpretation of an ordinal multilevel model is much less clear.

3 The simple index is nearly perfectly correlated with the factor scores produced by the previous factor
analysis.

55

aimed toward using more neutral language to elicit general affect toward each of the

globalization components.

 

Proportion of Sample

0.10 015 0.20 0.25 0.30

l l l l l
I

0.05
1

 

 

 

0.00
l

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

NOTE: Higher values are less supportive of globalization.

Figure 1. Distribution of Dependent Variable

The ﬁrst set of independent variables pertains to threats, capabilities, and
uncertainty from the individual’s perspective. This set of variables includes: Unemployed,
which is a dichotomous variable indicating unemployed status; Lacking food, a variable
indicating that an individual did not have enough money at some point during the last
year to buy food for her family; High income, a dichotomous variable indicating whether

an individual was above the observed median income category in her country; and

56

Female, a dichotomous indicator of being of the female sex — a disadvantaged group in
virtually every society. Other independent variables here are directly subjective
assessments, including: Dissatisfaction with income, which measures the level of
satisfaction with current income; Job availability worse, an assessment of the extent to
which the availability of good-paying jobs has gotten worse over the last ﬁve years; and
Macroeconomic situation, a description of the current state of the country’s economy.
These last two variables are sociotropic evaluations.

A few of the variables measure future projections and uncertainty. Personal future
trend is the differential between the individual’s assessment of his future situation and his
assessment of the present. Macroeconomic future is a projection of the country’s ﬁrture
economic conditions. Children ’5 future is a projection of future conditions for children.
All three variables are subjective assessments, and the last two are sociotropic in nature.

The second set of independent variables is composed of variables measuring
approaches for dealing with insecurity and orientations toward insecurity. Worry index
measures the extent to which an individual adopts an anxious orientation toward issues of
security.4 A few other variables measure extemalization of threats to speciﬁc sources and
the placing of blame, including International corporations, International organizations,
and Immigrants. Two other variables look at ingroup-outgroup orientations focused on
protecting the ingroup culture (Protect culture) and claiming superiority of the ingroup
culture (Own country superior). Eﬂicacy measures the extent to which an individual

views success as determined more by outside forces (like globalization) or by her own

 

" The worry index consists of assessments of whether the following are problems, with the factor pattern in
parentheses: spread of HIV/AIDS (0.565), poor drinking water (0.500), poor quality schools (0.463),
crime (0.454), and people leaving for jobs (0.299). As with the dependent variable, the original response
options were rescaled to the 0-1 interval, and the composite variable is an unweighted mean of the original
ﬁve measures.

57

actions — thereby either shifting blame or placing it squarely on herself. A preference for
the stability and comfort of traditional ways is another strategy for coping with
uncertainty and insecurity, as measured by Modern pace and Prefer traditional marriage.
Age is also included here with the observation that older people tend to evidence a greater
dislike for uncertainty and change and tend to prefer traditional ways, due perhaps to
their greater risk aversion (see Halek and Eisenhauer 2001).

The ﬁnal set of independent variables at the individual level measures
informational characteristics like information availability and intake and orientations
toward information sources. No international news is a dichotomous variable indicating
individuals who do not watch international news. No computer is a variable indicating a
lack of computer use. News inﬂuence bad measures affect toward the news media.
Finally, Education level is a measure of an individual’s highest attained level of
education, with the categories standardized across countries.

The model also includes variables at the country level in accordance with the
hypotheses proposed earlier. Macroeconomic beneﬁts are operationalized as: GDP per
capita PPP in US$, a measure of a country’s gross domestic product per capita, adjusted
for purchasing power parity, in US. dollars; and Net trade in $ US billions, a measure of a
country’s exports minus imports in billions of US. dollars. The measure of net migration
of people, Net migration rate, is equivalent to the thousands of people entering a country
minus the thousands leaving. The measure of the non-ﬁnancial standard of living in a
country is the United Nation’s Life expectancy index, and the measure of intolerance of
ambiguity is Hofstede’s Uncertainty avoidance (Hofstede and Hofstede 2005). Finally,

the model also includes one cross-level interaction to assess the standard political

58

economy hypothesis. GDP *income interacts the individual-level measure High income

with the state-level measure GDP per capita PPP in US$.

Results

The results of a multilevel mixed-effects linear regression appear in Table 2. The
table includes coefﬁcients for individual-level variables, country-level variables, and one
cross-level interaction. Table 3 displays the variance parameters for those individual-
level variables whose slopes were permitted to vary across countries (often called
“random effects”). The results provide solid support for the hypotheses. The variables
included in the random effects equation were chosen based on the likelihood that their
effects would vary considerably across countries due to context and conditions not
captured in the speciﬁcation and based on potential inclusion in cross-level interactions.
In each case, the variance parameters for the random slopes and intercepts are several
times larger than their standard errors, suggesting that the random speciﬁcations are

prudent.

59

Table 2. Multilevel Mixed-Eﬂ'ects Linear Regression on Attitudes toward Globalization

Individual-level Variables

 

 

 

 

 

 

 

 

Coefﬁcient Std. Error p S 95% Conﬁdence Interval
Unemployed 0.00571 0.001 88 0.002 0.00201 0.00940
Lacking food 0.00292 0.00104 0.005 0.00089 0.00495
High income -0.01515 0.00504 0.003 -0.02502 -0.00528
Female 0.00972 0.00180 0.001 0.00619 0.01325
Dissatisfaction with income 0.00291 0.00073 0.001 0.00149 0.00434
Job availability worse 0.00860 0.00104 0.001 0.00656 0.01065
Macroeconomic situation 0.00247 0.00081 0.002 0.00088 0.00406
Personal future trend -0.00148 0.00041 0.001 -0.00229 -0.00068
Macroeconomic future 0.00806 0.00098 0.001 0.00614 0.00998
Children’s future 0.01063 0.00106 0.001 0.00855 0.01271
Worry index -0.00213 0.00293 0.467 -0.00788 0.00361
International corporations 0.01625 0.00083 0.001 0.01461 0.01788
International organizations 0.01147 0.00085 0.001 0.00981 0.01314
Immigrants 0.00893 0.00076 0.001 0.00745 0.01042
Protect culture 0.00010 0.00074 0.893 —0.00136 0.00156
Own country superior -0.00019 0.00075 0.802 -0.00165 0.00128
Efﬁcacy -0.00412 0.00066 0.001 —0.00541 -0.00283
Modern pace 0.01657 0.00096 0.001 0.01469 0.01844
Prefer traditional marriage 0.00564 0.00100 0.001 0.00368 0.00759
Age 0.00049 0.00007 0.001 0.00036 0.00062
No international news 0.01011 0.00109 0.001 0.00797 0.01224
No computer 0.00777 0.00119 0.001 0.00543 0.01011
News inﬂuence bad 0.01609 0.00081 0.001 0.01450 0.01768
Education level -0.01049 0.00163 0.001 -0.01369 -0.00729
Constant -0.09199 0.03189 0.004 -0.15449 -0.02950

Country-level Variables
GDP per capita PPP in US$ -0.000004 0.000001 0.001 -0.000006 -0.000002
Net trade in $US billions -0.00018 0.00009 0.037 -0.00035 0.00001
Net migration rate 0.00697 0.00359 0.052 -0.00006 0.01401
Life expectancy index 0.12843 0.04689 0.006 0.03652 0.22034
Uncertainty avoidance 0.00029 0.00037 0.439 —0.00044 0.00102
Cross-level Interaction

GDP*income 0.0000004 0.263

 

 

 

NOTES: The sample size for the analysis is 30,306 respondents and 42 countries. All tests are two tailed.

0.0000004

-0.0000003

0.000001

The log likelihood is 14734.95, with Wald x2 = 4495.75 (p < 0.0001). As a measure of ﬁt, AIC = —29385.9.
The likelihood ratio test between the multilevel model and the equivalent OLS model provides a x2 of

2512.54 (10 DF; p < 0.0001).

60

 

Table 3. Variance Parameters for Variable Slopes and Intercepts

 

Variable

 

95% Conﬁdence Interval

 

Variance Std. Error
High income 0.00045 0.00014 0.00024 0.00084
Worry index 0.00027 0.00008 0.00016 0.00047
Education level 0.00008 0.00002 0.00005 0.00014
Constant 0.00520 0.00149 0.00296 0.00911

 

—
NOTES: The model was ﬁt using maximum likelihood estimation, with distinct estimation of all variances
and covariances of the random effects. The residual variance parameter estimate is 0.02190 (SE = 0.00018;

95% CI = 0.02155 to 0.02225).

A number of results from the model suggest that this is the appropriate estimation
method. Statistically signiﬁcant estimates are obtained at both the individual and country
levels, with pre-estimation analysis showing that 13.75% of the variance is at the country
level. Furthermore, the likelihood ratio test noted in Table 2 provides evidence that this
model is preferable to regular OLS. The results in Table 3 show that the random effects
variables do indeed differ in their impacts across countries.

The standard political economy hypothesis calls for an interaction between an
individual’s skill level and the distribution of skill levels within her country. Evaluation
of this hypothesis requires consideration of three coefﬁcients. As an indicator of skill
level, individuals with higher relative incomes in their countries (High income) have
more positive views of globalization in the “main effect.” Furthermore, the people of
wealthier countries (GDP per capita) are also more positively oriented toward
globalization. However, the real test is that the cross-level interaction (GDP *income) is
not statistically signiﬁcant. The data do not support the view that income has a different
effect on attitudes toward globalization in more advanced economies than it does in less

advanced economies. Figure 2 provides additional evidence by plotting the post-

61

estimation country slopes for the individual income variable against the GDP per capita
for each country. On the left side of the ﬁgure one should see only positive coefﬁcients,
meaning that in poorer countries individuals with higher incomes are less supportive of
globalization. On the right side the coefﬁcients should be negative, meaning that
individuals with higher incomes in wealthier countries are more supportive of
globalization than their lower income compatriots. The points in the ﬁgure are labeled
with their international FIPS codes.

Clearly, many counter-examples to the anticipated pattern exist across the ﬁgure.
A large proportion of the less-developed economies have negative slopes. Higher income
individuals in these countries are more supportive of globalization. The average of the
slopes on the left side of the ﬁgure is about zero. One sees a number of Latin American
countries defying the expected pattern, including Peru, Venezuela, Brazil, Mexico, and
Argentina. The most likely explanation for this result is the popularity of Dependency
Theory and Leftist ideology among the poorer classes in Latin America, both of which
blame globalization (more speciﬁcally the North and its capitalistic imperialism) for
“keeping” these countries in less economically advanced conditions. Another striking
feature of the ﬁgure is the case of Jordan, which is an extreme outlier in the upper left-
hand comer. A conjecture is that hi gher-income individuals in Jordan beneﬁt from having
a more closed society; alternatively, higher-income individuals may be particularly wary
of the erosion of their traditional culture. Finally, counter-examples also exist on the
right-hand side of the ﬁgure. The slopes for the United Kingdom and Japan are in the
“wrong” direction, while the slopes for Italy and Germany are very near to zero. In the

United Kingdom and Japan, higher-income individuals are less supportive of

62

globalization, which may be a reﬂection of greater sensitivity to the negative implications
of globalization among higher-income workers. Even if one were to adopt a more
probabilistic way of thinking about Figure 2 (r = -0.36), it is clear that the standard

political economy model leaves a great deal unexplained.

 

 

 

 

 

O
9 — .
°' ’8
“5’ ...
8 3 d
c o'
'5 0 Standard political economy rediction
g 3 _ for comparatively Iow-skille economy
Q o
E
I. to Id
..9 g _ us 0
*5 o' 12%
.9 91. g
d.) o - , lo . _
8 o 0 bf r O a
$3 tu x g 0 O “o”
E’ g u o
S 0. — 0
8 9’ Standard political economy prediction
for comparatively high-skrl ed economy
5 _
c? 1 1 1 f l I I 1 I
0 5000 10000 15000 20000 25000 30000 35000 40000

GDP per capita

Figure 2. Impact of Income on Attitudes toward Globalization by GDP per Capita

The result for individual income generally ﬁts with the vulnerability hypothesis,
which proposed that more threatening individual conditions would negatively inﬂuence
attitudes toward globalization. Also consistent with this hypothesis are the results
showing that being unemployed, lacking money for food, and being female are all

associated with less supportive attitudes toward globalization. The more subjective

63

assessments of threats and capabilities also point in this direction. Greater dissatisfaction
with income produces more negative attitudes toward globalization. Sociotropic
evaluations, such as belief that job availability has gotten worse and more negative
evaluations of the macroeconomic situation, also detract from support for globalization.
As hypothesized, uncertainty produces less supportive attitudes toward globalization, as
well. More negative assessments of the individual’s own future, of the country’s future,
and of future conditions for children all lead to dimmer views of globalization.5 In sum,
the whole set of variables is supportive of the vulnerability hypothesis.

As for insecurity approaches and orientations, anxiety does not seem to decrease
support for globalization. However, the ingroup-outgroup variables mostly performed as
expected. More negative assessments of international corporations, international
organizations, and immigrants all produce less supportive attitudes toward globalization.
Additionally, greater self-efﬁcacy (which places responsibility on the individual)
increases support for globalization. The two exceptions are that assessments of one’s own
culture as superior and a perceived need to protect one’s culture from foreign inﬂuence
fail to achieve statistical signiﬁcance. Favoring the maintenance of tradition in the form
of greater dislike for the modern pace of life, preference for traditional marriage, and
being older all contributed to less supportive views of globalization, in line with
predictions about the use of tradition and stability as means of countering insecurity fears.

The next set of hypotheses addressed informational characteristics. As
anticipated, lesser exposure to outside information (in the form of not watching

international news and not using a computer) produces more negative views of

‘

s Coding the latter two variables dichotomously so that “don’t know” responses take the value of 1 leads to
Similar results. Therefore, uncertainty works in two ways — in the form of negative views of the future and
in the form of not being sure of future conditions.

64

globalization. Seeing the media as a bad inﬂuence also produces lesser support for
globalization. Additionally, education — a source of greater information seeking and
uncertainty reduction, greater ability to process this information, and perhaps
engagement-oriented strategies for dealing with the outside world — generates more
positive views of globalization.

At the country level, clear macroeconomic beneﬁts — in the form of wealth
production and a more positive trade balance — do indeed have a positive effect on views
of globalization. Additionally, in line with theorizing about the threat posed by other
people, a greater net migration rate produces more negative views of globalization (p 5
0.052 for a two-tailed test). The hypothesis about the non-ﬁnancial standard of living in a
country was directionally agnostic, and the results show a negative impact on support for
globalization. Finally, despite a very strong bivariate correlation, cultural aversion to
ambiguity does not have a statistically signiﬁcant effect on attitudes toward
globalization.6

Figure 3 shows relative effect sizes of the independent variables at the individual
level. The ﬁgure uses the regular coefﬁcients for dichotomous variables and multiplies
the coefﬁcients for other variables by two times the standard deviation of the underlying
independent variable (see Gelman and Hill 2007, 57). While such comparison does not
provide unassailable answers, it does provide some insights, including a better
understanding of the pattern and consistency of the results. Nearly all the individual-level
variables reach statistical signiﬁcance. This statistical signiﬁcance is not too unusual

given the sample size, but the fact that the directionality of the results match with the

 

6 The lack of signiﬁcance may be due in part to the lack of speciﬁc uncertainty avoidance scores for all the
countries included in the analysis. Regional averages were used as proxies for country scores in some
cases, thereby decreasing variance.

65

hypotheses across the board is a noteworthy pattern. Further, the standardized sizes of the
impacts are of decent size when compared to the standard deviation of the dependent
variable (0.176).

Figure 3 suggests that sociotropic evaluations of threat and uncertainty are more
inﬂuential than egocentric ones, in line with ﬁndings elsewhere about phenomena like
economic voting (see Lewis-Beck and Stegrnaier 2000). More inﬂuential yet are
approaches toward insecurity focused on blaming the “agents” of globalization and a
preference for traditionalism evidenced by a dislike for the modern pace of life. Finally,
education level and negative affect for the news media — an intermediary of globalization
of sorts — also have substantial impacts on attitudes toward globalization.

A similar exercise for the country-level variables, which are not directly
comparable with the individual-level variables, produces the following standardized
impacts: GDP per capita (0.076); Net trade (0.033); Net migration rate (0.028); Life
expectancy index (0.045); and Uncertainty avoidance (0.000). The inﬂuence of GDP per
capita is substantially larger than the other variables — a particularly interesting result
given that the bivariate correlation between the dependent variable and GDP per capita is
virtually zero. Controlling for the other factors in the model reveals the relationship. The
life expectancy variable also appears to have a relatively large effect, and one that

countervails the impact of GDP per capita.

66

 

O Umployed
O Lacking Food
0 I-bh hcome (-)
’ Rm“ THREATS,
O ﬁssat. with hcome CAPABILITIES.
UNCERTAINTY

0 Job Availability Worse
O thrmcon. Situation
0 Own Econ. Future (-)

 

 

 

 

 

 

 

 

 

O throeconom'c Future
0 Children's Future
t ...........................................................................
+ Worry hdex
. O ht. Corporations
1 O ht. Organizations
. O rmr'grants
Rotect Oulure INSECURITY APPROACHES
Chauvinism & ORIENTATIONS
O Bficacy H
O Nbdern pace
0 Traditional Nhrriage
’ 0 Age
0 No international news
0 No corrputer
0 Iwas 'nfluence bad
INFORMATIONAL 0 Education level (-)
CHARACTERISTICS
0.00 0.01 0.02 0.03 0.04 0.05

NOTES: The ﬁgure shows the absolute values of standardized impacts on the dependent variable. The x—
axis is in units of the dependent variable. Independent variables are broken into three categories: (1) threats,
capabilities, uncertainty; (2) insecurity approaches and orientations; and (3) informational characteristics.
The ﬁgure uses regular coefﬁcients for dichotomous variables and multiplies coefﬁcients for other variables
by two times the standard deviation of the underlying independent variable. Variables with negative
coefﬁcients are marked accordingly. All variables follow the anticipated pattern. Variables with coefﬁcients
failing to reach statistical signiﬁcance are represented with a score of zero.

Figure 3. Absolute Standardized Reductions in Support for Globalization

67

Robustness checks and diagnostics

The ﬁrst robustness check uses alternative speciﬁcations to further test the
standard political economy hypothesis. Some authors have operationalized skill level
with education rather than income, which is a variable subject to various measurement
problems.7 Replacing the income-GDP interaction with an education-GDP interaction
produces a non-signiﬁcant coefﬁcient for GDP (p 5 0.214) and a substantially smaller
“main” ﬁxed effect size for education level (~-0.006 versus ~-0.010), though a
statistically signiﬁcant interaction (b = -0.000000487, p 5 0.001). The signs of the
education and interaction coefﬁcients mean that higher GDP boosts globalization support
among the well educated, which would accord with the standard political economy
hypothesis. However, even using the minimum value of GDP per capita in the data
($580), the combined effect of education and the interaction term still increases support
for globalization. Therefore, we do not observe greater education producing a more
negative orientation toward globalization in poorer economics; the effect is simply not as
large in the supportive direction.

One must also acknowledge the different dependent variable being used here.
While the attitudes toward trade variable is highly correlated with the globalization index

(r = 0.679), we can also separately try making the trade variable dependent.8 The

 

7 Questions about income frequently suffer from respondent reﬁrsal to answer. In this data, however, 91%
of respondents provided an answer to the income question rather than refusing or saying they did not know.
The bigger potential problem is the issue of cross-country comparability. True comparison would require
exact income ﬁgures (which are almost never available in survey data) and standardizing income by
purchasing power parity. Measuring income relatively within a country, as it is here, is beneﬁcial in that it
recognizes the importance of relative assessments rather than absolute ones.

8 While the technically correct approach here and for the following immigration model would be to use an
ordinal mixed-effects model, I use a linear model as an approximation due to the relative clarity of the
linear model’s results.

68

standard political economy hypothesis also encounters problems here as the income
variable and the cross-level interaction term fail to reach statistical signiﬁcance.

Yet another possible dependent variable, this one not included in the globalization
index due to a lack of ﬁt (the correlation with the globalization index is 0.028), measures
attitudes toward immigration. The income-GDP interaction term fails to reach statistical
signiﬁcance in a multilevel linear model with attitudes toward immigration dependent, as
do several other independent variables. Relatively large effect sizes are observed for
' attitudes toward immigrants, feelings of ingroup superiority, and belief that the culture
must be protected from foreign inﬂuence, with an extremely large relative effect size for
the worry index.

Another potential concern with the core model is that certain results are
endogenous. Though running a two-stage multilevel model is not possible, we can use a
regular two-stage linear regression as an approximation. In separate models, the
“cleaned” versions of the Modern pace, Children ’s future, and International corporations
variables remain statistically signiﬁcant in the second-stage regression with Globalization
index dependent. These variables were identiﬁed as ones perhaps most susceptible to
endogenous causation.

Several other independent variables relevant to the hypotheses are available in the
data, but most would reduce the sample size substantially since they were not asked in all
countries. An expanded equation with these other variables has a sample size of 21,341
(as compared to 30,306) with 37 countries represented (as compared to 42). Further
bolstering the ingroup-outgroup hypothesis are results showing reduced support for

globalization based on a more unfavorable view of the US, a belief that the spread of

69

U.S. customs and ideas is bad, and seeing consumerism as a threat to the ingroup culture.
Further supporting the tradition hypothesis is a ﬁnding that more traditional views of
sexuality (in this case seeing homosexuality as unacceptable) lead to lesser support for
globalization. Other results not connected explicitly to previous hypotheses are that
linking a growing income gap to globalization and seeing the free market as undesirable
both generate less supportive attitudes toward globalization.9 At the country level, the
results show that countries with greater electoral competitiveness have more negative
views of globalization. A greater variety of political parties may enhance the likelihood
of seeing anti-globalization rhetoric among elites, thereby inﬂuencing individual
attitudes.

A few other checks are based on suggestions and speculations made in the
literature. The importance of a country’s unemployment rate as a source of sociotropic
evaluation (Davidson et al. 2006) is not reﬂected in the data, nor is the interacted impact
of aggregate inequality and an individual’s education level on attitudes toward
globalization (O’Rourke and Sinnott 2006). Aggregate inequality alone also does not
have an effect on attitudes toward globalization. The data provide no evidence that
greater social spending (operationalized as either total governmental spending per capita
or public healthcare spending) reduces attitudinal differences toward globalization
between more- and less-skilled workers (Scheve and Slaughter 2006).

Finally, I have performed a number of diagnostic tests for the core model
speciﬁcation. These tests demonstrate no issues with non-normally distributed errors,

non-linear relationships, inﬂuential outlying observations, or multicollinearity (the latter

 

9 Of note here is that other income gap variables not explicitly linking income inequality to globalization do
not achieve statistical signiﬁcance. One alternative variable sirrrply asks whether the income gap has gotten
worse, while the other asks whether a growing income gap is good or bad.

70

via variance inﬂation factors for a regular OLS equation). The robustness checks reduce
concerns about speciﬁcation bias, as well. While the OLS equation contains
heteroskedastic errors, the use of robust standard errors is not an option in the multilevel
equation and the use of weighted least squares is deemed undesirable due to

complications with identiﬁcation and interpretation.

Discussion
Summary

Rather than trade or immigration alone this study has examined globalization as a
conglomeration of trends of growing interconnectedness across states. The results
demonstrate the importance of using different data sources, different variables, and
different questions. Attitudes toward trade and other forms of interconnectedness move
together empirically, but immigration is clearly a different animal. People also exhibit
particular affect toward the term “globalization” itself. Moreover, responses are far more
positive if you characterize globalization using terms like “ties” and “opportunity” and

,9 66

being more “connected” than if terms like “protect, restrict,” and “jobs” are invoked.
These positive replies contradict much of the previous research on the topic.

This study has proposed a coherent theoretical approach centered on the
microfoundations of security as a means of integrating economic and non-economic
determinants of attitudes toward globalization. This approach also offers the ability to
integrate individual-level and contextual factors. The cognitive and informational

requirements of this boundedly rational approach are much reduced as compared to the

rational choice approach. Empirical tests carried out in accordance with this theoretical

71

approach have explicitly modeled the proposed multilevel causal structure and have done
so across dozens of countries rather than just a few.

The examination of individual threats, capabilities, and uncertainty — particularly
as perceived by the individual — has shown that vulnerability in many guises inﬂuences
attitudes toward globalization. Individual evaluations of both egocentric and sociotropic
conditions are important indicators of vulnerability. This study has also proposed that
approaches for dealing with insecurity and orientations toward insecurity are important
elements of the microfoundations of security, thereby providing a theoretical anchor for
both the “worldviews” of the risk perception literature and the “non-economic”
determinants of the political economy literature on attitudes toward globalization.
Especially important are individual security orientations toward outgroups, approaches
for assigning blame, and the use of tradition as a source of comfort and protection in the
face of threats.

The informational proﬁle of the individual also bears on attitudes toward
globalization. This is an under-explored element of such attitudes, but it is one wholly in
ﬁtting with an approach focused on individual perceptions of security, which are
dependent on informational inputs. Exposure to outside information, positive affect
toward outside information sources, and greater education all increase support for
globalization in a consistent manner, thus strengthening the argument that what an
individual hears and learns about globalization is important. Education may also have
secondary effects by reducing feelings of uncertainty and by inﬂuencing approaches

toward insecurity.

72

This study also incorporates environmental (i.e., country-level) impacts on
attitudes toward globalization, using the lens of individual security as a guide. The ﬁscal
vulnerability or strength of the collective, measured by per capita production and net
trade, has an impact similar to individual assessments of macroeconomic strength.
However, given the mismatch between individual assessments and the country level
measures of economic strength (i.e., low bivariate correlations), the causal mechanism
seems to be less direct. Elite messages or a country’s conditions may have a more
subconscious effect in shifting attitudes in countries. Net migration patterns also
inﬂuence attitudes toward globalization. Though we cannot say for sure whether this
result is due to xenophobia and racism and/or concerns about job loss and social
spending, additional analysis does show that overall government spending, per capita
government spending, and public healthcare spending do not affect attitudes toward
globalization. Therefore, this result appears to reinforce the results pertaining to

perceptions of people — especially the “other” — as threats.

Individual Economic Self-Interest?

Many phenomena seem to contradict an assessment that individual economic self-
interest is responsible for attitudes toward globalization. In the US, one can point to
widespread opposition to outsourcing of j obs despite support for the trend by the
president’s chief ﬁnancial advisers, a pervasive dislike for trade in places like Iowa that
beneﬁt disproportionately from global connectedness, and outrage over toxic toys

produced in China.10 The lesser support for globalization among poorer classes in Latin

 

'0 On the outsourcing issue see “Treasury Chief Defends Outsourcing of US. Work,” by Edmund L.
Andrews, New York Times, March 31, 2004. As for anti-globalization sentiment in Iowa, see “A

73

America, mentioned earlier, supplies another example. The loss of the Bharatiya Janata
Party (BJP) in the 2004 parliamentary elections in India also deﬁes expectations, as the
poor (in a less-advanced economy) opposed the party’s pro-globalization platform due to
a belief that they were not beneﬁting from the corporate model of globalization. These
examples all contradict the story of individual economic self-interest, yet they also seem
to contradict the ﬁnding in the Pew data of widespread support for more expansive
notions of globalization. Clearly, attitudes toward globalization are a complex
phenomenon.

Some doubt may remain as to whether the results produced by the individual
security approach contradict or supersede the political economy approach. Working in
favor of the individual security approach are the more feasible cognitive and
informational assumptions and the ﬁt with the whole set of independent variables. The
individual security approach produces a consistent pattern of results. Admittedly, this is
not a sounding of the death knell for the political economy approach. This study uses a
different dependent variable, a different point in time, and a less—than-perfect
operationalization of the key income variable.

While it does not appear that context matters in this data in the way proposed by
the political economy approach, the results in Figure 2, the magnitude of the random-
effects coefﬁcients in Table 3, and the residual variance at the country level all suggest
that context is important. The effects of the subjective vulnerability and uncertainty
variables are more universal across contexts than is the effect of the income variable.

Most likely, an individual’s relative income within her country does not map extremely

 

Globalization Winner Joins in Trade Backlash,” by Deborah Solomon and Greg Hitt, Wall Street Journal,
November 21, 2007.

74

well to feelings of vulnerability, a conjecture supported by a lower-than-expected

correlation between actual income and satisfaction with income (r = 0.23).

Additional Questions

No single study can answer the questions being raised here deﬁnitively, and this
one has engendered a number of new questions. First, as just mentioned, the importance
of context merits greater scrutiny. Second, this study has made certain assumptions about
the impact of elites on attitudes toward globalization. A direct study of elite messages and
actions should prove very useful (as noted by Scheve and Slaughter 2006). Third, the
non-results for some variables have spawned additional questions. These data do not
provide support for the idea of chauvinism and protectionism as approaches for dealing
with insecurity connected to globalization, nor do they suggest that a more negative
orientation toward globalization is a straightforward reaction for individuals prone to
worrying. Such results further emphasize the point that globalization is a complex
phenomenon that is perhaps not very well understood by masses or elites. This apparent
ambivalence or confusion is not the case with attitudes toward immigration. Greater
worry, chauvinism, and protectionism are all strongly associated with concerns about
immigration. The different nature of immigration is clear in how we observe more policy
restrictions on the migration of unskilled labor than we see on trade (Bilal et al. 2003).
Cultural aversion to uncertainty also is not a signiﬁcant predictor of attitudes toward
globalization, but further work may explore whether individual-level measures of

ambiguity intolerance do function in such a manner.

75

Fourth, attitudes toward globalization may exhibit substantial dynamism over
time given the importance of information, how quickly globalization can transform
societies, and how prominent events can inﬂuence these types of evaluations. This study
is a snapshot in time and should be compared against new data as it becomes available.
Fifth, the result concerning the non-economic standard of living in a country is somewhat
puzzling. Though some reasons for such a result were hypothesized, what is the causal
mechanism explaining why better living conditions decrease support for globalization?
Are masses more susceptible to particular types of elite messages about international
conditions based on a country’s non-economic living conditions? This would mean
receptiveness to anti-globalization rhetoric in countries with higher living standards and
receptiveness to pro-globalization messages in countries with lower non-economic living
standards. The ﬁnding that greater political competitiveness decreases support for
globalization bolsters this argument in the case of more developed countries. Some may
also attribute this phenomenon to “post-modernism” (Inglehart 1997; Inglehart and
Welzel 2005). Such an assertion would be correct in one sense: Higher non-economic
living standards remove some worry about daily survival issues, thereby permitting the
individual to worry more about threats posed by people and by cultural erosion. In short,
higher living standards may contribute to a particular type of xenophobia. Sorting this
question out completely will require a study of elite messages in conjunction with the

informational characteristics of individuals.

76

Policy Implications

Concerns about globalization may diversify the demands placed on policymakers
in a way that reduces democratic accountability (Hellwi g 2007) and could threaten the
international pact of “embedded liberalism,” which is the compromise between
international openness and the domestic welfare state (Wolfe and Mendelsohn 2005).
Consequently, policymakers have strong incentives to debate the normative desirability
of globalization and to ensure that policies of interconnectedness have the initial and
continued support of populations when undertaken.

What do the results of this study mean for policymakers? Irnportantly, these
results show extensive support for globalization. The contrast between this pattern and
previously identiﬁed patterns is a strong indication that the naming of globalization
issues has signiﬁcant consequences for public support. The results also provide a means
for understanding the sources of pro-globalization and anti-globalization positions ﬁom
an individual security perspective. Therefore, the results may provide information useful
for easing globalization-based insecurity, facilitating policy transitions, and preventing
dangerous mob reactions. Conversely, the results also provide a guide of sorts for
maintaining support for policies of interconnectedness.

As has been claimed with regard to human security in more general and aggregate
terms (Roberts 2006), these results show a “security—development nexus” at the
individual level. Feelings of insecurity generate an unwillingness to take risks that may
be necessary for improved conditions. However, the results also offer the caveat that poor
living conditions may leave individuals more open to messages about the improvements

globalization brings. Additionally, making people feel less vulnerable and less unsure

77

about the future can decrease concerns about globalization in the developing and
developed worlds alike. After all, the ﬂip side of this vicious circle is a virtuous one.
Ultimately, policymakers may be able to look to the microfoundations of security studied

here for policy approaches for improving “human security” as it relates to globalization.

78

CHAPTER 3

THE THREAT FROM POLITICAL CORRUPTION

79

Introduction

They may be ﬂawed and indirect quantiﬁers, but individual perceptions of public
corruption are important because these perceptions affect fundamental political attitudes
and behaviors. Such resultant attitudes and behaviors include people’s evaluations of
government performance and legitimacy (Anderson and Tverdova 2003) and their
willingness to comply with laws and to assist in law enforcement efforts (Gardiner 2002;
Torgler 2005). These mass evaluations of government in turn serve as obstacles or
resources for those who govem.

Mass perceptions have been relatively overlooked in a large literature that deals
primarily with the causes and consequences of corruption. Examining the sources of mass
perceptions can contribute to answering a number of interrelated questions. Mass
evaluations of corruption as a problem or threat constitute a resource for studying the
bases of individual insecurity and the impact of political messages about threat. Further,
public support for anticorruption programs, often crucial for program success and
effectiveness, is typically built on concerns about the negative consequences of
corruption. Therefore, how the general public learns about or interprets the threat from
public corruption constitutes important information for policymakers interested in
building public support. In addition, most corruption research has relied on aggregate
measures of elite perceptions, thereby raising questions about validity, sampling biases,
and the strength of causal inferences. Examining mass perceptions may help to illuminate
the extent of such problems.

In what follows, I ﬁrst discuss the negative consequences of corruption as posited

in the literature and consider the momentum among political elites to ﬁght corruption

80

over the last decade and a half. Then, I look at the role of perceptions in corruption
measurement. A subsequent section introduces hypotheses based primarily on common
elements of threat perception from the individual’s perspective. Analytical results follow
a discussion of the data and methods of analysis. A consideration of the ﬁndings and their
implications serves to conclude.

The results of statistical analyses, controlling for aggregate estimates of
corruption in a country, provide evidence supportive of the proposition that certain
security-based attitudes, perceptions, and patterns inﬂuence the extent to which an
individual views political corruption as a threat. In particular, more negative evaluations
of sociotropic conditions, more pessimistic outlooks (especially toward other people), and
greater education all increase assessments of political corruption as a threat at the
individual level. At the country level, a freer ﬂow of information increases threat
assessments, as do the aggregate estimates of corruption. The analyses also produce
evidence that individual experiences with bribery are linked to assessments of corruption
in developing countries at least. Greater intercommunal conﬂict and greater national debt
also appear to increase assessments of threat from corruption in the sample of developing

countries. The analyses also delve into the unusual results for sex and income.

Negative Consequences

Worth noting is that the view of corruption as undermining and inverting the
normative purposes of government is not a unanimous one. The “revisionist” perspective
has run against the grain in asserting that corruption can have beneﬁcial consequences

(see discussions in Tanzi 1998; Lancaster and Montinola 2001). Claims of beneﬁcial

81

corruption typically hinge on arguments of increased economic efﬁciency. According to
authors in this literature, corruption removes red tape that slows investment and growth
(Leff 1964; Huntington 1968), lowers the tax burden (Becker and Stigler 1974), indicates
which “customers” of government most value quick service (Lui 1985), directs
government projects to the most efﬁcient ﬁrms (Beck and Maher 1986; Lien 1986), or
facilitates bargaining between the public and private sectors (Shleifer and Vishny 1994).
Beyond increasing economic efﬁciency, corruption also may result in enhanced public
support for politicians (Kurer 2001).

In contrast to the revisionist perspective, the bulk of research makes a strong case
for the negative consequences of public corruption (see reviews in Bardhan 1997; Tanzi
1998; Jain 2001; Lancaster and Montinola 2001; but especially Rose-Ackerman 1999,
2002, 2006). How corrupt ofﬁcials spend their illicit proceeds is an important
consideration when assessing the consequences of corruption (Nye 1989; Hutchcroft
1997), but the predominant view overall is that corruption primarily beneﬁts the corrupt
individual and his or her family, friends, and business associates at the expense of many
others.

Researchers have linked public corruption to worsened economic growth via a
number of intermediate mechanisms, including effects on investment and economic
efﬁciency. Public corruption decreases rational productive investment domestically
(Waterbury 1973; Shleifer and Vishny 1993; Mauro 1995), as well as foreign direct
investment (Wei 2000) and net capital inﬂows (Graf Larnbsdorff 2003). Irnportantly,
overall public investment may increase as corrupt politicians attempt to augment the ﬂow

of rents, but this investment is less productive and shifts ﬁrnding away from critical areas

82

such as operations and maintenance (Tanzi and Davoodi 1997) and education (Mauro
1998). Corruption further can reduce economic efﬁciency through market distortions
(Tanzi 1998), including the production of incentives to maintain monopolies and prevent
entry (Shleifer and Vishny 1993), though efﬁciency effects may depend on the
distribution of power in patron-client networks within a country (Khan 1996). Yet
another avenue for harming economic efﬁciency and grth is that a corrupt system that
makes rent-seeking relatively more proﬁtable will draw talented individuals away from
entrepreneurship (Murphy, Shleifer, and Vishny 1991).

Public corruption also purportedly harms the political system and the public
ﬁnancing and procurement systems. Corruption both darnpens the effects of social
demands (Scott 1972) and spurs on activities like lobbying and bribery that create overall
social welfare costs (Krueger 1974). Compounding the problem of social harms,
corruption also decreases per capita incomes (for treatment of the endogeneity issue see
Kaufrnann and Kraay 2002, 2003), increases income inequality and poverty (Gupta,
Davoodi, and Alonso-Terme 2002), and hurts the “little guy” by increasing operating
expenses for small businesses (Tanzi 1998). In terms of public ﬁnancing, corruption
increases the size of the unofﬁcial economy (Johnson, Kaufrnann, and Zoido-Lobaton
1998) and facilitates the operations of organized crime (Rose-Ackerman 1999), thereby
contributing to reduced tax revenues (Tanzi and Davoodi 1997) and increased ﬁscal
deﬁcits (Tanzi 1998). Corruption also redirects public funds toward areas like military
expenditures and arms procurement in which rent extraction is easier (Gupta, de Mello,
and Sharan 2001) and poses an obstacle to important reforms (Rose-Ackerman 1999).

Corruption furthermore weakens bureaucratic capacity (Theobald 1990) and undermines

83

property rights and the legal system as a whole (Tanzi 1998). Finally, though this account
is far ﬁ'om exhaustive, we ﬁnd claims that corruption has negative consequences for the
ways that citizens interact with their government and with one another. Seligson (2002a)
asserts that direct experiences with corruption erode beliefs in the political system and
reduce interpersonal trust, while Anderson and Tverdova (2003) also show negative
evaluations of the political system and lesser trust in civil servants resulting from
corruption.

The outburst of studies that began in the 19905 detailing the negative
consequences of corruption coincided with the work of multilateral organizations and
individual donor countries to create programs aimed at reducing corruption. Furthering
the connections, many of the individuals involved in these research efforts have been
associated in some way with multilateral donor organizations like the World Bank and
the International Monetary Fund. Donor countries have begun requiring certain
anticorruption standards before supplying funds; a prominent example is the Millennium
Challenge Corporation of the US, which uses corruption control as one of its indicators
for choosing recipient countries. Multilateral funding organizations also began auditing
projects for corruption, though much too late according to critics. The spread of non-
governmental organizations associated with the anticorruption cause occurred during this
time period and contributed to international efforts, as well. The most prominent of these
organizations, Transparency International, now has a network of chapters throughout the
world. Brown (2006) further notes a decreased tolerance for corruption in international

business during this same time period.

84

Multilateral anticorruption agreements and peer-based compliance mechanisms
have constituted the biggest push against corruption in the international governance
arena. The ﬁrst such agreement was the Inter—American Convention against Corruption,
adopted in March of 1996. A ﬂurry of agreements and standard-setting documents
followed over the next eight years (see Appendix B). The United Nations Convention
against Corruption, adopted in October 2003, has been viewed by many as a capstone to

these international efforts.

Corruption Measurement

The deﬁnition and measurement of corruption join the causes and consequences
of corruption as major topics in the literature. The two greatest difﬁculties in
measurement stem from the lack of a common deﬁnition and the concealed nature of
corrupt acts (Sik 2002). Reaching a common deﬁnition is problematic due to the multi-
dirnensionality and context-dependence of corruption (Philip 2006). For example, corrupt
acts differ in severity, the extent and victims of the damage, and the number of parties
involved — among other characteristics. In response to this complexity, Hellman et al.
(2000) split corrupt acts into three categories: inﬂuence, state capture, and administrative
corruption. Similarly, authors often make distinctions between “grand” and “petty”
corruption and between “active” and “passive” corruption (Langseth 2006). The
distribution of opinions conceming the inappropriateness of a particular act can vary from
one act to another, and these distributions can vary ﬁom one geographic context to
another, as well. Heidenheimer (2002) uses the labels “black,” “gray,” and “white” in

describing the tolerance for different types of acts in different contexts. These deﬁnitional

85

issues are substantial enough that the United Nations Convention against Corruption
avoids a direct deﬁnition, instead allowing the acts enumerated within the convention to
compose the deﬁnition of corruption. This decision was the result of considerable
debate. '4

A common dichotomization of corruption measures splits them into perception-
based measures and more “objective” or “proxy” measures not based on perceptions (Sik
2002; Duncan 2006). Though measures in both categories suffer from deﬁciencies, critics
have hammered perception-based measures particularly hard, with the biggest hammer
reserved for the most widely used and cited measure — Transparency Intemational’s
Corruption Perceptions Index (see Lancaster and Montinola 2001; Sik 2002; Galtung
2006; Miller 2006; Philip 2006). Despite these criticisms, and despite the paucity of
corruption deﬁnitions that incorporate perceptions (Johnston 2001), measures that rely
primarily on elite perceptions have remained popular.

The study of mass perceptions at the individual level provides leverage in
discerning the severity of problems associated with perception-based measurement. First,
elite surveys tend to produce a sample biased toward males and relatively wealthy
individuals (Galtung 2006) and may include a substantial number of businesspersons who
self-select into corrupt activities (Sik 2002). Analysis of a more balanced and

representative sample can indicate the extent of the bias problem. Second, elites tend to

 

'4 Countries proposed and argued for a number of different options for deﬁning “corruption” within the
convention. The debate on this issue can be tracked through drafts of the convention. See the following, in
chronological order:
0 Pages 7-8 of United Nations document A/AC.261/3 (Part I) of Dec. 27, 2001;
Footnote 24 of A/AC.261/3/Rev.l of March 26, 2002;
Footnote 21 of A/AC.261/3/Rev.2 of November 19, 2002;
Footnotes 32-36 of A/AC.261/3/Rev.3 of Feb. 5, 2003;
Footnote 30 of A/AC.261/3/Rev.4 of May 12, 2003; and
Footnote 16 of A/AC.26l/Rev.5 of August 15, 2003.

86

inhabit a different informational environment than the average person on the street. A
broader sample may provide insight into the issue of media effects on perceptions and the
issue of non-independence of elite observations (see Sik 2002; Miller 2006).

Third, an underlying assumption of perception-based measures seems to be that
they reﬂect direct respondent experiences as well as, perhaps, accurate second-hand
information about the experiences of others. However, perceptions may be a function of
much more. Analysis at the individual level can help distinguish whether certain
predispositions and attitudes inﬂuence these perceptions about corruption, while at the
same time discerning whether an individual’s social network has informational effects.
Finally, the use of aggregated perception measures in cross-country studies poses the risk
of committing an ecological fallacy if the causal mechanisms operate at least in part at
the individual level (Lancaster and Montinola 2001; Seligson 2002b). The analysis of
individual-level mass data should aid in evaluating whether the relationships uncovered

in aggregate cross-national research are the product of faulty reasoning.

Corruption and Insecurity
The Threat from Political Corruption

This study examines the extent to which respondents believe that “corrupt
political leaders” are a “problem” within their countries. The assignment of signiﬁcant
problem status likely involves two considerations — an evaluation that the phenomenon is
widespread and an assessment that corruption threatens something of value. The latter
condition in particular is a necessary one for problem deﬁnition, since widespread

phenomena that threaten nothing are rarely considered problematic. The ideas of threat

87

and harm and risk are common pieces of public policy theories explaining how particular
“issues” become “problems” and achieve a place on the public agenda, whether driven by
events, media coverage, or issue deﬁnition and framing by political elites (see Edelrnan
1967; Downs 1972; Baumgartner and Jones 1993; Stone 2002; Kingdon 2003). Political
corruption constitutes a threat — according to events, the news media, and many political
and academic elites worldwide — in that it produces a host of negative consequences.
Inasmuch as political corruption prominently threatens the well-being of both individuals
and societies, it also merits designation as a problem that needs ﬁxing.

What, then, drives individual perceptions of threat from political corruption?
Again, the standard measurement assumption is that assessments of the prevalence and
severity of corruption are a function of experience or observation. By extension,
perception of threat additionally would be a function of experiencing or observing the
negative consequences — or harms — of political corruption.

Applying rational choice theory assumptions to the study of patron-client
relationships produces a second (though related) kind of explanation. The idea here is that
patrons (i.e., political ofﬁcials) buy Support from clients (i.e., voters, other important
constituents) using the ill-gotten proceeds of corrupt activities. The resultant prediction is
that the beneﬁciaries of this redistribution of resources in the society would not see
political corruption as a threat, since they are better off as a consequence of the
corruption. Other individuals, however, are doubly harmed in that they lose in relative
terms (since they are not beneﬁting from the corruption) in addition to enduring the social

costs of corruption. Manzetti and Wilson (2007) supply evidence that corrupt political

88

leaders operating within clientelistic networks can maintain support through the
manipulation of government resources.

I instead propose looking at the perceived threat from political corruption through
the more comprehensive lens of security microfoundations. The study of
“microfoundations” in economic theory establishes how causal mechanisms at the
individual level contribute to observed macroeconomic phenomena. Similarly, an
approach centering on the microfoundations of security focuses primarily on security-
relevant perceptions, attitudes, and behaviors at the individual level. Such an approach
does not preclude consideration of social forces and patterns that inﬂuence the individual,
however. In particular, I propose that assessments of the threat from political corruption
are dependent on common risk perception biases and heuristics, approaches and
orientations for dealing with insecurity, informational characteristics of the individual,
and the patterns and processes in an individual’s environment.

The security microfoundations approach is not wholly inconsistent with the
observation/ experience explanatory approach or the patron-client explanatory approach.
Certainly, observations and experiences concerning the negative consequences of
corruption are solid indicators of the level of individual insecurity being produced by
political corruption. Similarly, beneﬁting from patron-client networks can provide
individual security, while exclusion from these networks can serve to heighten
vulnerability. The security microfoundations approach, however, is comprehensive
enough to subsmne both explanations while also explaining a number of other causal

relationships.

89

Individual-level Hypotheses

Literatures on perceived risk and on decision-making heuristics and biases have
revealed patterns in individual risk assessment. For example, a consistent ﬁnding in the
risk perception literature is that women tend to perceive greater risk than do men across a
wide range of potential threats and contexts (see Slovic 2000b; Slovic et al. 2000;
Sjt'iberg et al. 2000). The less educated (Slovic et al. 2000) and racial minorities (Slovic
2000b) also tend to inﬂate risk perceptions. Similarly, the study of perceived risk of
victimization ﬁom crime shows that women and less healthy individuals tend to perceive
greater risk across a range of potential crimes, though women are less likely to be
victimized except in the case of sexual assault (Ferraro 1995). The prototype for low risk
perception in the US. is a white, middle-aged, relatively wealthy, better-educated,
politically conservative male (Slovic 2000b; F inucane et al. 2000b). Taken as a whole,
these results suggests that vulnerable groups in more precarious situations and with lesser
power react to such circumstances with hyper-vigilance and an inﬂated sense of risks
from everywhere. This reaction is a response to a smaller margin for error in everyday
life. Such an interpretation also ﬁts with ﬁndings that voluntariness and controllability
(F ischhoff et al. 2000) are crucial components of risk perceptions. We may expect
assessments of threat from political corruption to function similarly under this
vulnerability hypothesis.

The empirical analysis will also consider a number of issues related to this
vulnerability hypothesis. First, are perceptions or actual conditions of vulnerability more
important? Second, the economic voting literature (see Lewis-Beck and Stegrnaier 2000)

suggests that “sociotropic” (Meehl 1977) criteria, which are “other regarding” or socially

90

focused, are more important as evaluative benchmarks than are individually focused or
“egocentric” criteria. 15 that the case here, as well? Third, does uncertainty or
apprehension about future conditions also contribute to feelings of vulnerability? Finally,
could any identiﬁed effects that align with the vulnerability hypothesis instead be the
result of a different causal mechanism? In particular, could such effects be reﬂective of
the distribution of the negative consequences of corruption or reﬂective of individuals
linking particular conditions to political corruption?

While the literature has identiﬁed “worldviews” like fatalism, individualism, and
egalitarianism as factors in risk perception (Slovic 2000b), it has not provided a unifying
ﬁamework for understanding the impact of such factors. I propose that these worldviews
fall into a broader category of approaches for dealing with insecurity and orientations
toward insecurity. For example, worry — as a form of hyper-vigilance — is one manner of
dealing with an uncertain and potentially threatening world. Anxious individuals should
express worry about many different types of threats, including the threat posed by
political corruption.

An important ﬁnding in the risk perception literature is the existence of an “affect
heuristic” (Finucane et al. 20003), which means that risk evaluations depend upon how
an individual feels toward the source of risk. Accordingly, individuals with negative
feelings toward politics or toward the government or prominent governmental leaders
should evaluate the threat from corruption based on that negative affect, thereby
increasing the perceived threat from corruption.

We should also expect general pessimism to inﬂuence evaluations, related as it is

to both negative affective mood (Marshall et al. 1992) and worry. In other words, some

91

individuals will see a substantial threat from corruption because their general pre-
cognitive orientation toward the world is a negative one. Negative views of human nature
(especially other humans) are a particularly relevant form of pessimism, given
corruption’s status as an “immoral” human activity. Xenophobic attitudes and ingroup-
focusing tendencies are manifestations of such negative views of human nature, as are
attitudes that reﬂect a belief that people get the bad things they deserve or cam for
themselves. Traditional and conservative views also have been associated with
pessimism, hostility, and an overall dim view of human nature (Adomo et al. 1950;
Hoffer 1951; Huntington 1957; McClosky 1958). Individuals who have adopted these
types of orientations should see a greater threat from political corruption.

Finally, informational characteristics of the individual also inﬂuence assessments
of threat. Individuals with access to more information, especially from a greater variety
of sources, should receive more of the messages about the negative consequences of
corruption and about the ability of a society to function without corrupt transactions.
Consequently, such individuals should express greater concern about the threat posed by

political corruption, despite this demographic’s status as a less vulnerable population.

Country-level Hypotheses

In terms of environmental inﬂuences, instability and uncertainty should increase
threat assessments of many types. Projecting secure conditions becomes difﬁcult in an
unpredictable and ambiguous environment. A competing —- and observationally

equivalent -— causal story is that instability creates conditions ripe for political corruption

92

(Lederman, Loayza, and Scares 2005) and that the higher level of actual corruption
subsequently is reﬂected in mass threat evaluations.

A second set of environmental inﬂuences involves social norms of mistrust.
Conﬂict among social groups promotes mistrust in a society, including the xenophobic
and ingroup-focusing attitudes discussed earlier, thereby affecting evaluations of
corruption as a threat. Again, however, a competing causal mechanism exists. Social
conﬂict may create instability in a way that opens opportunities for corruption (Treisman
2000; Adsera, Boix, and Payne 2003; Lederrnan, Loayza, and Soares 2005). Enhanced
opportunities would then lead to greater corruption, and accurate perception by the
masses would result in evaluations of greater threat.

Informational patterns — and particularly the extent of ﬁeely ﬂowing information
— constitute a third environmental source of threat perceptions. These informational
patterns determine the types of inputs available for an individual’s threat assessment. On
the one hand, various authors have proposed that freely ﬂowing information should
reduce actual corruption. Institutional accounts propose that separation of powers
(Persson, Roland, and Tabellini 1997), increased political competition (Alt and Lassen
2003), and free and regular elections (Adsera, Boix, and Payne 2003) all enhance
transparency and information ﬂows, thereby dissuading corruption or permitting more
effective enforcement. More complex accounts claim that the effects of democracy on
corruption are nonlinear, with a quadratic function (Montinola and J ackrnan 2002) or a
cubic function (Sung 2004) best explaining the effects. Greater political competition may
also have the effect of limiting credible commitments in exchange for bribes (Montinola

and Jackman 2002). In terms of transparency, freedom of the press, too, has been

93

associated with lower aggregate levels of corruption (Brunetti and Weder 2003;
Lederman, Loayza, and Scares 2005).

With this third set of patterns the competing hypothesis works in the opposite
direction. Regardless of the impact on actual corruption, an increase in freely ﬂowing
information should increase perceptions of threat given the messages from elites and the
ﬁnancial incentives for the mass media to uncover scandals. Furthermore, greater
political competitiveness should increase incentives for political parties to lob accusations
of corruption at one another. Closed-infonnation environments allow regimes to reduce
perceptions of the problem.

A fourth set of environmental inﬂuences involves the negative consequences
linked to political corruption. The assumption here is that respondents are able to draw
their own conclusions about causality or have received such information ﬁom political or
media elites. This is really just an indirect form of the informational hypotheses posed at
the individual level, this time requiring assumptions about accurate perception and
informational processing to bridge the gap. As mentioned earlier, some have proposed
that bloated government spending is one consequence of corruption as politicians attempt
to increase the ﬂow of rents. The proposed negative consequences for economic grth
were also documented earlier.

Finally, an important control in the empirical speciﬁcation is the elite assessment
of corruption in a country. A total lack of correspondence between elite and mass
perceptions of corruption would raise serious questions about the validity and substantive

importance of elite perception measures.

94

Data and Methods

The individual-level data used in this study are ﬁ'om the Pew Global Attitudes
Project. The Pew Research Center collected data in a well-diversiﬁed sample of 44
countries between July and October of 2002.15 The country—level variables derive from a
number of different sources. Please see Appendix C for details on independent variables
at both levels. The dependent variable asks to what extent corrupt political leaders are a
problem in the respondent’s country. Though mass assessments do not necessarily
translate into committed support for effective anticorruption programs, Figure 4
demonstrates that claims by political elites that masses do not see a problem with political
corruption are dubious at best. Nearly 70% of the pooled cross-national sample sees
corruption as a “very big” problem. Further, the pattern of variation across countries (not
shown here) directly contradicts the claim that developing countries most in need of

anticorruption programs are hampered by apathetic or ignorant populaces.

 

'5 The 44 countries are: Angola, Argentina, Bangladesh, Bolivia, Brazil, Bulgaria, Canada, China, Cote
d’Ivoire, Czech Republic, Egypt, France, Germany, Ghana, Guatemala, Honduras, India, Indonesia, Italy,
Japan, Jordan, Kenya, Lebanon, Mali, Mexico, Nigeria, Pakistan, Peru, the Philippines, Poland, Russia,
Senegal, the Slovak Republic, South Africa, South Korea, Tanzania, Turkey, Uganda, Ukraine, the United
Kingdom, the United States of America, Uzbekistan, Venezuela, and Vietnam.

95

 

69.68%

    

1.53%

 

 

 

Not at all Small Moderately big Very big
Extent of the Problem

NOTES: This is the distribution of the dependent variable prior to dichotomization for
analysis. The sample size for this ﬁgure is 32,462. An additional 931 “don’t know” responses are
added into the “0” category when dichotomizing the variable. The percentage falling into the
“very big” category after dichotomization is 67.74%.

Figure 4. Distribution of the Dependent Variable: Political Corruption as a Problem

Figure 5 shows that at the aggregate level mass perceptions track with elite
perceptions relatively well (r = 0.557). This scatter plot compares aggregated country
percentages for the dependent variable (i.e., the percent of the sample falling into the
“very big” problem category) against a rescaled version of the World Bank Institute
(WBI)’s “Control of Corruption” measure for 2002 (Kaufmann, Kraay, and Mastruzzi
2005). The WBI index is composed primarily of sources that utilize elite perceptions and
experiences. While the WBI index also draws from sources that utilize mass household
surveys (e. g., Aﬁ'obarometer, Latinobarometro), its composite value correlates rather
closely with its constituent parts based on elite perceptions (see Appendix D). The WBI

measure is also highly correlated with Transparency Intemational’s Corruption

96

Perceptions Index but provides better coverage of countries for the year in question and
draws data from a greater number of distinct sources. The clear outliers in Figure 5 are
Japan (in the lower right-hand comer) and Jordan and Uzbekistan (in the upper left-hand
corner), as indicated by the FIPS 10-4 country code labels. Japan’s situation most likely
owes to the corruption scandals that have afﬂicted the Liberal Democratic Party in what
has been largely a single-party system in the post-War era; news of these scandals may
have had a larger impact on masses than on elites and may have inﬂated mass perceptions
of corruption. Lack of transparency is probably a major factor in the cases of Jordan and
Uzbekistan, where masses do not see political corruption as much of a problem.
Widespread trust in the government or mass acceptance of corrupt practices could be
other contributing factors to such a result. The Pew survey also questioned respondents
about the frequency with which they had to provide bribes to public servants in order to
get services to which they were entitled. The bribery measure also tracks rather well with
the WBI measure at the aggregate level (r = 0.538) for a sample limited to developing

countries.

97

 

 

 

 

'- “ ‘3’ a ked H
uz V6 ,2 . b9

” ‘ ° «gg o '6 b' 0 ° 9 r
2 0 a o
3 Q 7 h 3 1" Ii! _
.3 N -' $9 bu 9° mx m| Q00 1—
'o . O o 0
r: 10 O O
— Q _ 0 lo P
: ez sfks
a.

it _
5 ”- ‘ o
2 m. - ja *-
fr 0
N. - 0
US
F: “ Uk ca ng
o -« O o
1 1 1 1 1 1 1 1 1 1 1
0 10 20 30 40 50 60 70 80 90 100

% Saying Political Corruption a Very Big Problem

NOTES: The World Bank lnstitute's Control of Corruption measure has been rescaled to the
0-1 interval and has been inverted so that higher values are indicative of more corruption.
The mass perception measure on the x-axis is calculated using the Pew Global Attitudes
Project data. The bivariate correlation between the two measures is 0.557.

Figure 5. Comparison of Elite and Mass Perceptions of Corruption

As noted earlier, a question asking to what extent political corruption is a

“problem” likely prompts two interrelated evaluations from a respondent — the ﬁrst

dealing with how widespread or severe the corruption is and the second dealing with the

threat or harm from corruption. The question wording, especially in that it asks about

political leaders, points toward “grand” corruption, thereby forestalling certain concems

about respondent differences in deﬁning corruption. The 1995-1997 wave of the World

Values Survey (WVS) also asked a question about corruption that may help determine

how respondents evaluate such questions. The WVS asked: “How widespread do you

think bribe taking and corruption is in this country?” Pooling respondents across

98

countries and using the appropriate weight, the marginal percentages for valid responses
were: 3.6% said almost no public ofﬁcials were engaged in it, 29.5% said a few public
ofﬁcials, 39.3% said most public ofﬁcials, and 27.6% said almost all public ofﬁcials. The
timing of the two surveys may explain some of the distributional difference, given the
international push for anticorruption programs in the late 19905 and early 20005.
However, the difference also suggests that people evaluate questions about a “problem”
or threat differently than a question that asks about how widespread a particular type of
corruption is. Bolstering this claim of different evaluations, the correlation at the
individual level in the Pew data between assessment of political corruption as a problem
and experience with bribery is quite low (r = 0.046).

In terms of methodological choices, multilevel econometric modeling allows
simultaneous consideration of causal mechanisms within the individual and ﬁ'om
environmental inﬂuences, which is crucial based on the hypotheses proposed earlier.
Multilevel models also calculate more appropriate standard errors at the group level
(Gehnan and Hill 2007), which is consequential because aggregate corruption studies
likely underestimate the standard errors. Given the distributional bunching at one end,
dichotomization of the dependent variable seems prudent. Respondents clearly have no
qualms about saying that political corruption is a “very big” problem. The dichotomous
version of the variable (“very big” problem = l) ensures a reasonable distribution of cases
across categories and avoids computational convergence and interpretation difﬁculties
associated with ordinal multilevel models. Multilevel mixed-effects logistic regression is
used as a method appropriate for both the multilevel nature of the hypotheses and the

dichotomous dependent variable.

99

Results

Table 4 presents the results from four different speciﬁcations for a multilevel
mixed-effects logistic regression on perceived threat ﬁom corruption.16 The ﬁrst
speciﬁcation listed is the core speciﬁcation. The vulnerability hypothesis receives mixed
support. The female (Female) and income (Highinc) variables run contrary to the
expected direction. Furthermore, being unemployed (Unemployed status) and an
individual’s egocentric evaluation of his present (Own situation) and ﬁrture (Own future
situation) conditions have no bearing on the dependent variable. However, the
sociotropic variables do have statistically signiﬁcant coefﬁcients in the expected
direction. More negative evaluations of present macroeconomic conditions
(Macroeconomic situation) and future macroeconomic conditions (Macroeconomic
future) both produce greater perceived threat ﬁom corruption. Assessments that the
income gap in the country has grown (Income gap), as an indicator of concerns about
social inequity, also increase the perceived threat from corruption.

However, the actual mechanism relating sociotropic evaluations to threat
evaluations about corruption is not entirely clear. A ﬁrst possibility is that more negative
assessments of sociotropic conditions and of corruption are both a function of pessimistic
orientations toward the world. In this case, pessimism is the real causal factor, though
pessimism also could be due to perceived or actual vulnerability -— in which case
pessimism becomes an intermediary for vulnerability. A second possibility is that
subjective sociotropic evaluations of vulnerability are the easiest heuristic for individuals

to apply in evaluating the threat from corruption. A third possibility is that the individual

 

‘6 Data were unweighted during the analysis. Despite non-random geographic sampling in some countries,
the multilevel statistical model does not permit the application of data weights.

100

links other problems in the society explicitly to political corruption based on observation,
information from social networks, or messages about negative consequences from elites.
Regardless of the causal mechanism (though these causal stories need not be mutually
exclusive), more objective indicators do not seem to function as well as subjective ones.
People’s perceptions of vulnerability or of negative consequences are important,
including perceptions of future uncertainty.

Approaches and orientations toward insecurity perform as anticipated. Anxiety
(Worry index) has a particularly powerful effect. Individuals also evaluate the threat from
corruption based on affect toward the political system (Politics a major problem,
National government) and its representatives (National executive). Furthermore,
generalized pessimism (Dissatisfaction with world) and pessimism aimed more directly at
other people (Immigration, Protect culture) tend to increase perceived threat from
corruption. Blaming the individual (Eﬂicacy) does not have an effect opposite that of
pessimism aimed at other people, however. Finally, individuals focused on the stability of
tradition as a means of dealing with insecurity (Losing tradition) also see a larger threat

from corruption.

101

Table 4. Multilevel Mixed-Effects Logistic Regression on Perceived Threat
from Corruption
E

 

 

 

 

 

 

 

 

Speciﬁcation #1 Speciﬁcation #2
Individual-level Variables (Core) (Interaction)
Female -0.3266*** (0.0454) -0.5750** (0.1919)
High income 0.0738* (0.0335) 00740“ (0.0335)
Unemployed status 0.0256 (0.0561) 0.0256 (0.0561)
Own situation 0.0088 (0.0090) 0.0092 (0.0090)
Macroecon. situation 0.1055*** (0.0143) 0.1057*** (0.0144)
Own future situation 0.0031 (0.0078) 0.0028 (0.0078)
Macroeconomic future 0.0510** (0.0171) 0.0513** (0.0171)
Income gap 0.1231 *** (0.0223) 0.1231*** (0.0223)
Worry index 1.0455*** (0.0263) 1.0437*** (0.0263)
Politics a major problem 0.4374*** (0.0340) 0.4378*** (0.0340)
National government 0.1125*** (0.0160) 0.1127*** (0.0160)
National executive 0.0774*** (0.0158) 0.0778*** (0.0158)
Dissatisfaction w/ world 0.0631 ** (0.0202) 0.0624" (0.0202)
Immigration 0.1520*** (0.0473) 0.1521*** (0.0473)
Protect culture 0.0464*** (0.0125) 0.0462*** (0.0125)
Efﬁcacy 0.0035 (0.0116) 0.0035 (0.0116)
Losing tradition 0.0978*** (0.0186) 0.0971*** (0.0186)
Education 0.0154*** (0.0030) 0.0153*** (0.0031)
Bribery experience
Constant —6.8300*** (0.3932) -6.1502*** (0.6440)
Country-level Variables
Regime durability -0.0027 (0.0029) -0.0028 (0.0028)
Intercommunal conﬂict 0.0574 (0.0403) 0.0537 (0.0399)
Press freedom -0.0155* (0.0064) -0.0155* (0.0063)
National debt per capita 0.00002 (0.00001) 0.00003 (0.00001)
Corruption 2.2912*** (0.5733) 2.1443*** (0.5972)
GDP per capita
Literacy -0.0069 (0.0050)
Cross-level Interaction
Literacy" Female 0.0030 (0.0023)

—
NOTES: Standard errors appear in parentheses. All tests are two tailed. See Table 5 for
details. ***p 5 0.001 "p S 0.01 *p S 0.05

102

     

Table 4. Multilevel Mixed-Effects Logistic Regression on Perceived Threat

 

 

 

 

 

 

 

from Corruption (Continued)
7 i i _ --—— i ’ _eciﬁcation #3 Sperﬁncatio

Individual-level Variables (GDP) (Bribery)
Female -0.3205*** (0.0453) -0.3325*** (0.0499)
High income 0.0738* (0.0335) 0.1030" (0.0380)
Unemployed status 0.0263 (0.0561) 0.0368 (0.0658)
Own situation 0.0090 (0.0090) 0.0141 (0.0099)
Macroecon. situation 0.1059*** (0.0144) 0.1033*** (0.0162)
Own future situation 0.0030 (0.0078) -0.0002 (0.0086)
Macroeconomic future 0.0506M (0.0172) 00438“ (0.0192)
Income gap 0.1230*** (0.0223) 0.0748“ (0.0249)
Worry index 1.0477*** (0.0263) 1.0414*** (0.0292)
Politics a major problem 0.4389*** (0.0340) 0.4219*** (0.0381)
National government 0.1130*** (0.0160) 0.0869*** (0.0181)
National executive 0.0775*** (0.0158) 0.0536“ (0.0184)
Dissatisfaction w/ world 0.0626” (0.0202) 0.0652** (0.0222)
Immigration 0.1509*** (0.0473) 0.1710*** (0.0526)
Protect culture 0.0464*** (0.0125) 0.0489*** (0.0144)
Efﬁcacy 0.0033 (0.0116) -0.0001 (0.0130)
Losing tradition 0.0975*** (0.0186) 0.1064*** (0.0212)
Education 0.0154*** (0.0030) 0.0163*** (0.0032)
Bribery experience 00459“ (0.0190)
Constant -5.2738*** (0.3961) -6.7678*** (0.5742)
Country-level Variables
Regime durability -0.0031 (0.0033) -0.0073 (0.0076)
Intercommunal conﬂict 0.0670 (0.0438) 0.0825* (0.0379)
Press freedom -0.0066 (0.0060) ~0.0154* (0.0065)
National debt per capita 0.00003 (0.00002) 0.0002* (0.0001)
Corruption 2.2049" (0.8309)
GDP per capita -0.00005** (0.00002)
Literacy

 

Cross-level Interaction

 

Literacy*Female

NOTES: Standard errors appear in parentheses. All tests are two tailed. See Table 5 for
details. ***p 5 0.001 "p S 0.01 *p S 0.05

103

The patron-client hypothesis receives mixed support. The vulnerability variables
contradict the hypothesis that those individuals beneﬁting from a patron-client network
are less likely to see corruption as a problem, since higher income individuals see
corruption as more of a problem and subj ective evaluation of one’s own present status
has no bearing on the evaluation of corruption as a threat. The distribution of the
dependent variable also bodes ill for the patron-client hypothesis. A rather small
percentage of respondents overall see political corruption as non-threatening, and that
percentage is roughly the same across both developing and developed countries in the
sample. The beneﬁciaries from the patron-client networks would have to be rather few in
number and would have to appear rather universally regardless of govemment type or
quality. The variables measuring affect toward government do perform in accordance
with the expectations of the patron-client hypothesis, however. Individuals beneﬁting
from a clientelistic network would likely have a more positive orientation toward
government and would also not see corruption as much of a problem —— though the
distribution of the dependent variable remains problematic here, as well.

The information-based hypothesis also receives support in the empirical analysis.
Greater education (Education), as a proxy for information gathering and processing,
increases the perceived threat from corruption. Experiences with bribery (Bribery
experience) constitute a direct source of information about corruption, as well.
Speciﬁcation #4 in Table 4 tests the hypothesis that experience with bribery increases
perceptions of threat from political corruption. The results provide supportive evidence,

though this particular question was not asked in more developed countries, thereby

104

decreasing the number and variety of countries in the sample. Additionally, even a
change over the entire range of the bribery variable only increases the probability of
seeing corruption as a very big problem by 0.0247 (using the same methodology
employed in creating Table 7), a relatively small effect from a substantive point of view.

At the country level, the control for mostly elite assessments of corruption
(Corrupt) functions as expected by paralleling individual evaluations of the threat from
political corruption. However, regime durability (Durability), as an indicator of stability,
does not have a statistically signiﬁcant impact on the dependent variable — except in the
reduced-sample ﬁnal speciﬁcation. The same is true of social norms of mistrust
(Intercommunal conﬂict). The result concerning aggregate informational patterns (Press
Freedom) favors the interpretation that transparency decreases corruption and provides
reassurance to citizens. The idea that social transparency permits the stoking of people’s
fears about corruption or increases perceptions of the incidence of corruption is not
supported.

A series of variables were used in testing whether the negative consequences of
corruption are translated into more negative respondent evaluations, either through
observation or via messages from elites. The ﬁrst was based on the assertion that
government spending can get out of control as politicians attempt to enhance the ﬂow of
rents. However, National debt per capita had a statistically signiﬁcant effect only in the
reduced-sample equation. Speciﬁcation #3 used national productivity (GDP per capita)
as a measure of the impact of corruption on economic growth. Using this variable
necessitated dropping the aggregate corruption measure as a control due to

multicollinearity, however. Higher GDP per capita does produce lesser concern when the

105

corruption control is eliminated (though not otherwise). Military expenditures and total

imports were included in equations not shown here, since military expenditures are

thought to increase because they lend themselves well to additional corruption and

imports are thought to decrease as companies avoid investing in corrupt countries.

However, neither variable achieved statistically signiﬁcant results.

The results from the random-effects portion of the equations appear in Table 5.

The impact of the sex variable varies considerably across countries, suggesting that

contextual factors condition this relationship with corruption perceptions. Table 5 also

contains information about each of the model speciﬁcations. These indicators of model

performance all suggest that the mixed-effects multilevel model was an appropriate

choice for estimation, particularly as compared to regular logistic regression.

 

Table 5. Random Eﬂ'ects and Estimation Information

 

 

Speciﬁcation #1 Specification #2 Speciﬁcation #3 Speciﬁcation #4
Random Effects (Core) (Interaction) (GDP) (Bribery)
Female 0.1910 (0.0403) 0.1737 (0.0430) 0.1903 (0.0406) 0.1843 (0.0440)
Constant 0.5422 (0.0672) 0.5257 (0.0658) 0.5557 (0.0679) 0.5143 (0.0716)

Sex-Constant

-0.5106 (0.1981)

-0.4862 (0.2184)

-0.3216 (0.2297)

-0.6305 (0.1920)

 

 

Correlation

Estimation Specification #1 Speciﬁcation #2 Speciﬁcation #3 Speciﬁcation #4

Information (Core) (Interaction) (GDP) (Bribery)

Sample Sizes 26,31 1 (40) 26,311 (40) 26,31 1 (40) 21,463 (33)

(Countries)

Log Likelihood -13,234.15 43,232.89 -13,237.55 -10,519.96

Wald )8 2,576.79 2,578.62 2,561.99 1,988.89
(p50.0001) (pS0.0001) (p.<_0.0001) (13300001)

LR Test vs. Logistic 819.49 781.06 917.79 570.05

Regression (13500001) (pS0.0001)

(15500001) (15500001)

   

NOTES: Random effects parameters are standard deviations for the ﬁrst two variables and the correlation
between those two variables for the third. Standard errors follow in parentheses. Note that the estimates for
the two variables are several times larger than their standard errors.

106

The results for the female and income variables, which run contrary to a large
body of literature on risk perception, are unusual enough to warrant additional analysis.
Evidently, considerations other than vulnerability are predominant here. Additional
examination of the data provides no evidence that the effect for females (i.e., seeing
corruption as less of a threat) is due to informational differences or to stances on “moral”
issues. The data do suggest, however, an effect based on a certain percentage of women
“trusting the men to run things.” The relationship between sex and corruption perception
is particularly strong among those individuals preferring or indifferent to traditional
marriage roles (r = 0.097 as compared to 0.031 for others). If true, one may expect this
result to be contingent on environmental factors related to modern ideas about women’s
rights.

Speciﬁcation #2 in Table 4 examines whether the sex effect is contingent on
literacy in a country. The coefﬁcients for Literacy and Literacy *Female are not
statistically signiﬁcant by conventional standards. However, the meaningfulness of
standard signiﬁcance tests for interactions in models with categorical dependent variables
is a subject of considerable debate (see Norton, Wang, and Ai 2004; Berry and Rubin
2007; Franzese and Kam 2007), as is the issue of appropriate tests for interactions more
generally (Brarnbor, Clark, and Golder 2006). The use of a multilevel model further
complicates inferences. Table 6 provides one means of ascertaining the signiﬁcance of
the interaction. The difference between males and females in the predicted probability of

saying that corruption is a very big problem is 0.083 when country literacy is at an

107

observed minimum. The equivalent difference is 0.058 when literacy is at a maximum,
meaning that sex differences are smaller in more literate countries, which would ﬁt with
the women’s rights and liberation story. However, the point estimate of this “difference-
in-differences” effect (i.e., 0.083 — 0.058 = 0.025) is substantively small and is unlikely to

be statistically signiﬁcant.

Table 6. Interaction of Sex and Literacy

 

 

 

Sex Value Literacy Value Literacy*Sex Value Predicted Probability
Male (=0) 38.0 (Min) 0.0 0.801
Female (=1) 38.0 (1)1111.) 38.0 0.718
Diﬁ'erence at minimum literacy 0.083
Male (=0) 99.9 (Max) 0.0 0.724
Female (=1) 99.9 (Max) 99.9 0.666
Difference at maximum literacy 0.058

NOTES: All other independent variables were set at their mean values.

The income result, on the other hand, ﬁts with an interpretation that individuals
with more money have greater concern about the ﬁnancial implications of corruption,
since they have more to lose in absolute terms. Perhaps even more plausible is that
higher-income individuals tend to work in white-collar or other industries in which
corruption is more easily understood or information about corruption is more plentiful.

Table 7 presents the results of an analysis designed to provide estimated effect
sizes for the different hypotheses. This analysis also uses changes in predicted probability

for the dependent variable. The analysis shows that more negative evaluations of

108

sociotropic conditions increases seeing corruption as a very big problem by about 6
percentage points (i.e., a change in probability of 0.0563). Similarly, a more pessimistic
outlook (including pessimism about human nature) results in an increase of about 5
percentage points. Even more impressively, negative affect toward the government and
its agents produces an increase of about 9 percentage points. Furthermore, greater
education results in an increase of about 3 percentage points. At the country level (which
is not directly comparable to the individual level), freedom of the press generates an
increase of about 7 percentage points, while primarily elite assessments of corruption
produce an increase of about 11 percentage points. Of course, these effects are not

additive; each is calculated while holding the other variables at their mean values.

Table 7. Changes in Predicted Probabilities by Proﬁle

 

 

Proﬁle Low High Difference
Negative sociotropic 0.6856 0.7419 0.0563
evaluations

Pessimism 0.6895 0.7410 0.0515
Negative affect 0.6536 0.7395 0.0859
Education 0.6999 0.7255 0.0256
Press freedom 0.7327 0.6626 0.0701
Corruption 0.6720 0.7786 0.1066

_
NOTES: Predicted probabilities are for the dependent variable taking the value of 1 (as
opposed to 0). “Difference” is the absolute difference between the two predicted
probabilities for each proﬁle. “Low” values are ‘/2 standard deviation below the mean for
all variables of interest, while “high” values are ‘/2 standard deviation above the mean,
except for dichotomous variables (which are set at 0 and 1). All other variables in each
equation are held at their mean values. This post-estimation analysis is based on
Speciﬁcation #2. The variables included in “Negative sociotropic evaluations” are:
Macroeconomic Jituatt'on, Macroeconomic future, and Income gap. The variables included in
“Pessimism” are Dirratt'g’action with world, Immigration, Protect Culture, and laying tradition. All

other category names are also variable names, with country-level variables appearing
below the line.

109

Discussion
Summary

This study has examined mass perceptions of corruption at the individual level in
a way that has engaged with the major themes in the corruption literature — causes and
consequences, deﬁnition and measurement, and implications for anticorruption programs.
Evaluations of political corruption as a “problem” involve assessments of both the
prevalence of corruption and the extent to which political corruption threatens something
of value to an individual, though the latter is especially important. Understanding the
sources of such evaluations is essential to governance efforts aimed at reducing the
negative and pervasive consequences of corruption.

I have used a theoretical approach focused on the microfoundations of security,
though I have also considered alternative hypotheses and causal mechanisms. The results
of multilevel mixed-effects logistic regression analyses and post-estimation analyses
provide support for the security approach but leave the door open in some circumstances
for the consideration of other causal mechanisms. Vulnerability only appears to affect
corruption perceptions via subjective, sociotropic assessments — a ﬁnding that ﬁts with
other literatures like the economic voting literature. Uncertainty about sociotropic
conditions also contribute to feelings of vulnerability. Standard ﬁndings concerning sex
and income as measures of vulnerability are contradicted by the ﬁndings here. Additional
analysis provides some support for the view that a sub-population of women is less likely
to see corruption as a problem or threat because they leave it to the men to take care of
such things. The ﬁnding for income may be indicative of either loss aversion or exposure

to corruption information. It seems unlikely that these ﬁndings are a result of men and

110

higher-income individuals suffering the negative consequences of political corruption in a
disproportionate way, however.

Approaches and orientations toward insecurity are especially important in
evaluations of corruption as a threat. Negative feelings directed toward politics and
toward the government and its agents inﬂuence corruption. Pessimism, particularly
directed at other people, also increases perceptions of threat from corruption. Negativity
and blame form a close relationship in affecting corruption perceptions.

Greater education, which may indicate enhanced information-seeking and access
to information, serves to increase threat perceptions, as well. Individuals with more
education may receive more messages about the negative consequences of corruption and
may be better able to understand those messages. They may also be more attentive to
particular instances of corruption in the news. Direct exposure to bribery, too, serves as a
source of information. Greater experience with bribery increases perceived threat from
corruption, but only in the sample of developing countries. However, this limitation may
not matter much given that more developed countries are believed to have lower actual
rates of bribery.

As measured here, environmental instability or uncertainty does not affect
corruption perceptions, while conﬂict or social norms of mistrust only matter for
developing countries. The ﬁndings with regard to the free ﬂow of information in a
society favors either the interpretation that transparency reduces (or seems to reduce)
corruption in a way that is subsequently reﬂected in individual evaluations.

A series of variables tested the assertion that the negative aggregate consequences

of corruption shiﬁ mass perceptions in a negative direction. Since the negative

111

consequences are based on studies that primarily use elite perceptions as corruption
measures, these hypotheses also evaluate to some degree how well mass and elite
perceptions of corruption move together (though causality is in opposite directions).
Lesser wealth production affects mass perceptions of corruption only if one does not
control for elite perceptions (which are highly collinear with GDP per capita), while
national debt (a two-pronged consequence of corruption due to spending waste and
politician attempts to enhance the ﬂow of rents) only matters among developing
countries. A number of other variables failed to produce statistically signiﬁcant results.
Elite assessments of corruption were also included in most of the speciﬁcations, though
primarily as a control. The positive, statistically signiﬁcant, and substantively large
results demonstrate a decent correspondence between elite and mass perceptions.

A number of questions ﬂow directly out of the analyses performed here. First, do
these results hold with other types of dependent variables, and to what degree are they
time variant? Second, it is unclear whether the correspondence between elite and mass
perceptions of corruption are spurious (i.e., the perceptions of both elites and other
individuals are subject to the same outside causal factors) or are indicative that elite
messages are reaching the masses. More direct information on elite messages and on
mass attentiveness to these messages is necessary to provide more deﬁnitive answers to
this question. Third, the informational ﬁndings at the individual and aggregate levels
appear to be contradictory. Individuals with more information are more likely to see
corruption as a very big problem, though freer social ﬂows of information decrease

perceptions of threat. Why is this? Finally, the ﬁnding for experiences with bribery is

112

rather unimpressive. Is this because everyday bribery and political corruption are

evaluated in completely different ways, or is some other explanation more likely?

Implications

The use of perception-based measures of corruption makes the sources of those
perceptions important, regardless of what is supposed to generate actual levels of
corruption. As Miller (2006) points out, perceptions of corruption may be a function of
such things as gossip, scandals or allegations in the media, an individual’s own
experience of unfair treatment or own corrupt behavior, and the corruptibility of the
individual. Based on the present study, if elite perceptions arise similarly to those of other
individuals, we know that negative sociotropic evaluations, blame, and other forms of
pessimism can all inﬂuence elite assessments. Educational patterns, freedom of the press,
and other factors could also bias results. Accordingly, aggregate measures based on elite
perceptions are biased to the extent that countries vary in the distribution of such attitudes
and orientations. Furthermore, elite samples are biased toward being male and relatively
wealthy, as mentioned earlier. Again, if the results produced here are also applicable to
elites, the greater proportion of both males and relatively wealthy individuals in the
sample will tend to inﬂate the perceived threat from political corruption. The greater
education among elites further reinforces this inﬂation.

Do these results offer any clues or prescriptions for governance? The widespread
belief that corruption is a “very big” problem certainly indicates that the public will
support (or at least acquiesce to) anticorruption programs, perhaps even if they involve

some initial sacriﬁces. Claims of a widely accepted “culture of corruption” are excuses

113

with little empirical basis. For those cases in which support for tough reforms is not
sufﬁcient, the “ﬁxes” suggested by these results are not necessarily palatable from a
normative standpoint. Strategically sowing the seeds of pessimism and blame and
circumscribing press ﬁeedom are tools typically associated with tyrants and demagogues;
such strategies will backﬁre in the long run, as well. Additionally, the results suggest no
quick ﬁx for those instances when having public perceptions reﬂect corruption
improvements would be helpful. The attitudes, orientations, and patterns underlying
corruption perceptions are not easily adjusted. Getting people to believe in change where
negative patterns exist will be especially difﬁcult.

The results ultimately reﬂect patterns of reinforcing negativity and reinforcing
positivity. Whether these reinforcing patterns contribute to actual corruption levels is
unknown, but it is certainly a reasonable assertion that pessimism causes individuals to
commit corrupt acts. The focus of governors, then, should be on programs that give
people hope and optimism and create feelings of security. These programs are likely to

have multiplicative effects that inﬂuence corruption and much more.

114

CHAPTER 4

THE THREAT FROM TERRORISM

115

Introduction

This chapter follows the design of the previous two in examining the perceived
threat from terrorism using a security microfoundations perspective. In countries facing
threats from terrorists, understanding the sources of threat perceptions among the
populace is essential information for getting citizens to act in their own best interests.
Inappropriately high levels of fear prevent the wheels of commerce from turning and may
induce panicked behavior that is harmful both generally and for antiterrorism activities in
particular. Inappropriately low levels of concern about terrorist attacks detract from
necessary preparation and vigilance and can lead to unnecessary casualties. Information
about individual insecurity as it pertains to terrorism may also help governments craﬁ
effective threat messages that do not alienate citizens or make them distrustful.

Much of the existing research relevant to individual attitudes and terrorism is
psychological in nature and utilizes experiments. Additionally, much of this research has
looked at responses to threat rather than sources of threat perceptions. Huddy et al.
(2002) provide a good overview of the responses to threat found in the literature,
including ethnocentrism, xenophobia, intolerance, closed-mindedness, and increased
attention to and thought about threats. Additionally, Cohen et al. (2005) talk about
experimental tests of Terror Management Theory, which proposes that humans use
cultural worldviews as a source of comfort in a world that is ultimately going to kill
them. The only studies that directly address sources of threat perceptions with regard to
terrorism are Huddy et al. (2002) and Huddy et al. (2005), but this question is only a
preliminary sidelight for the main analysis in each study and is analyzed only for the US.

case. The main question for the former study concerns the differential consequences of

116

perceived personal threat and perceived national threat, while the primary question for
the latter concerns the differential consequences of anxiety and perceived threat. Huddy
et al. (2002) utilize a survey conducted only in the New York City area, while Huddy et

al. (2005) utilize a nationwide survey.

Hypotheses
Individual-level Hypotheses

Though the ﬁndings across the Huddy et al. (2002) and Huddy et al. (2005)
studies differ slightly, in general the authors ﬁnd greater perceived threat among:
women, ethnic minorities, those in trade or labor occupations, the less educated, the less
wealthy, those living nearer to the 9/11 attacks (though these results vary) or who knew
somebody missing after the attacks, and those with more authoritarian personalities.
These ﬁndings, of course, align rather well with the vulnerability hypothesis proposed in
earlier chapters. I propose that these vulnerability relationships should also apply in a
sample with dozens of countries.

The approaches and orientations toward insecurity proposed in earlier chapters
should also apply in the case of terrorism — perhaps even more clearly in the case of
terrorism. To some extent, these approaches and orientations align with the “cultural
worldviews” that Terror Management Theory proposes as sources of terrorism and
responses to terrorism. Xenophobic attitudes in particular should play a role, given the
clear linkages between terrorist attacks and human perpetrators. The media is fond of
talking about the “faces” of terror. Worry also ﬁgures to play a prominent role due to the
serious consequences and horriﬁc nature of terrorism. Finally, the US. is a terrorism

focal point and was especially so in 2002 when the data were collected. As the US. is

117

billed as the primary “opponent” of terror, more negative attitudes toward the US. should
dampen terror concerns while more positive attitudes should accentuate the sense of
threat.

In the case of corruption it was proposed that greater individual information
would increase the sense of threat because it would also increase individual information
about the negative consequences of corruption. The expectation with regard to terrorism
is less clear, however. While some of the messages coming ﬁ'om elites stoke fear, a good
deal of information about the infrequency of terrorist attacks is also available. F ear-based
messages also tend to have short-term impacts. In addition, terrorism is a subject area in
which the vulnerability effect tied to education (i.e., greater perceived threat among the

less educated) could also be clearer than in previous chapters.

Country-level Hypotheses

At the country level, we should expect people in countries targeted by terrorists to
feel a greater sense of threat. Though this study presently does not contain a measure of
terrorist incidents (see the discussion at the end of the chapter), it does contain measures
of power. Country characteristics like larger populations and higher government
expenditures, as indicators of greater power, should also indicate the extent to which a
country is a target of disaffected terrorists. Countervailing such an effect, however, would
be an environmental feature like system stability. Political stability, for example, would
offer hopes both of more effective anti-terror policies and of less terrorism born of

political conﬂict within the country. Equivalently, an unstable environment should

118

enhance feelings of vulnerability, while discomfort with instability or ambiguity should

augment such feelings.

Data and Methods

This chapter utilizes the same data sources and methodologies as the previous
chapter. Appendix B provides a description of all new independent variables at the
individual and country levels in addition to descriptions for all variables used previously.
The dependent variable here is Terror threat, which is a subjective individual assessment
of the extent to which terrorism is a problem in the respondent’s country. The response
options are: a very big problem (54.90%), a moderately big problem (21.34%), a small
problem (14.61%), or not a problem at all (9.15%). The responses have been recoded to
create a dichotomous variable with “very big problem” as the 1 category, with “don’t
know” responses included in the 0 category. The 1 category of the ﬁnal version of the
variable contains 52.90% of respondents. As with the chapter on corruption, this chapter

also uses multilevel mixed-effects logistic regression as its primary method of analysis.

119

Table 8. Multilevel Mixed-Effects Logistic Regression on Perceived Threat

 

Individual-level

Speciﬁcation #2

 

 

 

 

 

 

 

 

Variables (Core) (N o Worry Index)
Children -0.0059 (0.0344) -0.0042 (0.0326)
Female 0.0379 (0.0290) 0.1352*** (0.0275)
High income -0.1097*** (0.0323) —0.1277*** (0.0307)
Own situation 0.0138 (0.0083) 0.0077 (0.0079)
Own future 0.0255*** (0.0072) 0.0292*** (0.0068)
Macroeconomic situation 0.0021 (0.0130) 0.0245* (0.01 23)
Worry index 1.1410*** (0.0242)

Dissatisfaction w/ world 0.0432* (0.0194) 0.0905** * (0.0183)
Restrict entry 0.0849*** (0.0117) 0.1410*** (0.0111)
Chauvinism 0.1194*** (0.0116) 0.1617*** (0.0110)
U.S. ideas and customs

U.S. ﬁght against terror

N 0 computer 0.1177*** (0.0216) 0.1248*** (0.0205)
Education level -0.0258* (0.0118) -0.0276* (0.01 12)
Constant -7.8559*** (0.5775) -3.8711*** (0.5521)
Country-level Variables

Population (in millions) 0.0034*** (0.0007) 0.0031 *** (0.0007)
Government expenditures 0.0001* (0.0001) 0.0001 (0.0001)
per capita

Political stability -0.4962** (0.1801) -0.5461** (0.1746)
Uncertainty avoidance 0.0195** (0.0070) 0.0210** (0.0067)
Random Effects

Constant S.D. 0.7101 (0.0819) 0.6886 (0.0791)

 

—
NOTES: Standard errors appear in parentheses. All tests are two tailed. See Table 9 for details.
The random effects estimate is a standard deviation for the constant. This estimate is several
times larger than its standard error, suggesting that permitting the country intercepts to vary is
prudent.

***p 5 0.001 **p s 0.01 *p s 0.05

120

Table 8. Multilevel Mixed-Eﬂ‘ects Logistic Regression on Perceived Threat

from Terrorism (Continued)
a

 

 

 

 

 

 

 

 

Individual-level Speciﬁcation #3 Speciﬁcation #4
Variables (Views of U.S.) (Alternative DV)
Children -0.0020 (0.0361) 0.0523 (0.0386)
Female 0.0194 (0.0302) 0.1354*** (0.0323)
High income —0.1068*** (0.0336) -0.0721* (0.0361)
Own situation 0.0161 (0.0086) 0.0241* (0.0095)
Own future 0.0230” (0.0074) 0.0168* (0.0082)
Macroeconomic situation -0.0004 (0.0135) -0.0473** * (0.0146)
Worry index 1.1604*** (0.0253) 1.1047*** (0.0253)
Dissatisfaction w/ world 0.0507* (0.0201) 0.0512* (0.0211)
Restrict entry 0.0756*** (0.0121) 0.0703*** (0.0127)
Chauvinism 0.1179*** (0.0121) 0.0954*** (0.0128)
U.S. ideas and customs -0.0593*** (0.0173)

U.S. ﬁght against terror -0.1077*** (0.0183)

No computer 0.1208*** (0.0228) 0.0978*** (0.0243)
Education level -0.0218 (0.0121) -0.0078 (0.0132)
Constant -7.6071*** (0.5981) -6.8283*** (0.6370)
Country-level Variables

Population (in millions) 0.003 5** * (0.0007) 0.0034*** (0.0008)
Government 0.0001* (0.0001) 0.0002*** (0.0001)
expenditures per capita

Political stability -0.5111** (0.1862) -0.5442** (0.1991)
Uncertainty avoidance 0.0195" (0.0072) 0.0248*** (0.0077)
Random Effects

Constant S.D. 0.7327 (0.0857) 0.7855 (0.0916)

NOTES: Standard errors appear in parentheses. All tests are two tailed. See Table 9 for
details. The random effects estimate is a standard deviation for the constant. This estimate is
several times larger than its standard error, suggesting that permitting the country intercepts

to vary is prudent.

***p 5 0.001 **p s 0.01 *p S 0.05

121

Results

Table 8 presents the results of the main analyses, with four different speciﬁcations
for the multilevel mixed-effects logistic regression model. The ﬁrst speciﬁcation is the
core speciﬁcation. The second speciﬁcation removes worry from the equation due to its
overwhelming inﬂuence. The third equation adds affect toward the US. to the core
speciﬁcation (thereby eliminating U.S. respondents from the sample), and the ﬁnal
speciﬁcation utilizes a different form of the dependent variable. Table 9 presents model
characteristics for each speciﬁcation.

The need for Speciﬁcation #2 is worth mentioning at this juncture. The Worry
Index variable is relatively highly correlated with the dependent variable (r = 0.358).
Though this correlation makes sense from a theoretical perspective, the structure of the
survey also likely contributes. The questions for the worry index and the question about
terrorism are part of the same series of questions about problems in the respondent’s
country. Therefore, one would expect a certain degree of correlation due to the similar
question wording, structure, and placement within the survey. However, it seems rather
unlikely that the survey structure is the primary reason for this set of correlations. In any
case, Speciﬁcation #2 removes the worry variable to examine what happens to the
remaining independent variables in the equation, and the substantive effects are minimal.

The vulnerability hypothesis fares relatively well, though not all the variables
produce statistically signiﬁcant results. Having children (Children) does not increase
one’s sense of vulnerability in a way that inﬂuences perceived threat from terrorism, nor
does the conventional wisdom about “terror moms” appear in the data (via an interaction

between having children and being female that is not shown in Table 8). Being female

122

increases perceived threat in a statistically signiﬁcant way in some of the equations but
not in others. Removing the worry variable reveals this relationship (Speciﬁcation #2), as
women are bigger woniers in the data overall. Using the version of the dependent
variable that places both “very big problem” and “moderately big problem” in the 1
category reveals the relationship, as well. The income variable (High income) also
performs in accordance with the vulnerability hypothesis, with lower level individuals
perceiving a greater threat. Different from the result obtained in the previous chapter, the
subjective assessment most important here is the egocentric future one (Own future).
Predictions of one’s own future vulnerability are most closely tied to perceptions of threat
from terrorism.

In terms of approaches and orientations toward insecurity, worry (Worry Index)
has a tremendous effect on threat perception, as already indicated. Table 10 provides
estimates of effect sizes, using the same methodology as in the previous chapter. A
centered standard deviation change in the worry variable generates an increase in the
likelihood of falling into the “very big problem” category equivalent to about 21
percentage points (for Speciﬁcation #3). Generalized pessimism (Dissatisfaction with the
world) increases perceived threat as one would anticipate. Xenophobic attitudes (Restrict
entry, Chauvinism) are relatively important causal factors as predicted, with a centered
standard deviation change producing a change of about 6 percentage points in the
dependent variable. These two variables in particular demonstrate increased predictive
power when worry is removed from the equation, which is reﬂective of the correlation
between anxiety and xenophobia. Also supporting this ﬁnding (but not shown due to a

decreased sample size), is that individuals who dislike the impact that ethnic minorities

123

are having in their countries perceive a greater threat from terrorism. Finally, dislike for
the US. and its policies (U.S. ideas and customs, U.S. ﬁghts against terror) does indeed
decrease concerns about terrorism.

The results favor the interpretation that having information decreases the
perception of threat from terrorism. Individuals without computers (No computer) and
individuals with less education (Education level) tend to see a greater threat from
terrorism. Though not shown here due to a reduced sample size for the question, not
having traveled to other countries in the last ﬁve years also contributes to increased threat
perception. This particular ﬁnding suggests that having information about the outside
world tends to decrease apprehension about the threats emanating from it, supporting
both the informational account and the xenophobic account. Replacing the country-level
population measure with a measure of economic openness (due to high collinearity)
provides similar evidence in that greater openness tends to decrease concerns about
terror.

The country—level variables included in the equations perform as expected. More
powerful target countries, as indicated by Population (in millions) and Government
expenditures per capita, do exhibit higher levels of apprehension about terrorism.
Furthermore, Political stability does have a calming inﬂuence, while discomfort with

ambiguity (Uncertainty avoidance) tends to increase threat perceptions.

124

Table 9. Estimation Information
————=

Speciﬁcation #1 Speciﬁcation #2 Speciﬁcation #3 Speciﬁcation #4

 

(Core) (No Worry Index) (Views of U.S.) (Alternative DV)

Sample Sizes 27,637 (40) 27,735 (40) 26,015 (39) 27,637 (40)
(Countries)
Log Likelihood -14,688.507 -16,031.566 -13,655.877 -12,301.265
Wald )6 2,718.24 684.57 2,567.29 2,295.99

(13500001) (pS0.0001) (p500001) (13500001)
AIC 29,413.01 32,097.13 27,351.75 24,638.53
LR Test vs. 1,834.25 2,076.15 1,824.19 1,625.03
Logistic Regression (p300001) (p500001) (p500001) ((530.0001)

 

Some minor changes in effects are observed across speciﬁcations, especially for

the alternate dependent variable used in Speciﬁcation #4, but overall the results in Table
8 are rather robust to speciﬁcation differences. Variables that lose statistical signiﬁcance
often are still signiﬁcant with a one-tailed test, as well. A logistic regression for the US.
only (eliminating the country-level variables) demonstrates that the US. was not an
unusual case vis-a-vis the overall ﬁndings, even in 2002. Particularly strong predictors
for the US. case are Female, Worry index, Restrict entry, Chauvinism, and Education
level. Furthermore, Speciﬁcation #3, which eliminates U.S. respondents, provides
evidence that the US. sub-sarnple is not driving the overall results. These analyses have
not yet ﬁrlly considered the random effects of individual-level variables across countries.
Preliminary analysis suggests that the impact of socioeconomic assessments does not
differ in an appreciable way across countries, contrary to a “roots of terror” hypothesis.

Sex may have a variable effect as in the previous chapter, however. I have already

125

mentioned the substantively large inﬂuences of worry and xenophobia. Though one
cannot compare across levels, the impact of the country-level variables is also rather
large. This is particularly true of a country’s population, which in Table 10 increases

threat perception by about 23 percentage points for a centered standard-deviation change.

Table 10. Changes in Predicted Probabilities by Proﬁle

 

 

Absolute
Proﬁle Low High Difference
Income 0.5663 0.5399 0.0264
Own future 0.5478 0.5621 0.0143
Worry 0.4497 0.6555 0.2058
Dissatisfaction w/ world 0.5501 0.5598 0.0097
Xenophobia 0.5230 0.5864 0.0634
Views toward U.S. 0.5739 0.5358 0.0381
No computer 0.5428 0.5669 0.0241
Population 0.4372 0.6671 0.2299
Government expenditures 0.5114 0.5977 0.0863
Political stability 0.6063 0.5022 0.1041
Uncertainty avoidance 0.5120 0.5971 0.0851

—
NOTES: Predicted probabilities are for the dependent variable taking the value of 1 (as
opposed to 0). “Low" values are ‘/2 standard deviation below the mean, while “high”
values are ‘/2 standard deviation above the mean. The exception is that dichotomous
variables are changed from 0 to 1. All other variables in each equation are held at their
mean values. This post-estimation analysis is based on Speciﬁcation #3. The variables
included in “Xenophobia” are: Rertrict mtg! and C/ramn'nitrn. The variables included in
“Views toward US.” are US. idea: and carton” and U3. ﬁght againtt terror. All other category
names are also variable names, with country-level variables appearing below the line. The
mean value for the dependent variable for the respondents included in the analysis is
0.5348.

126

Discussion

As in previous chapters, again we see reinforcing patterns of negativity,
xenophobia, threat, and limited information. Authors elsewhere have established the
consequences of greater perceived threat ﬁ'om terrorism, including supportiveness for
anti-terrorism policies that incorporate negative stereotypes (Huddy et al. 2005),
behaviors designed to decrease exposure to risk (Huddy et al. 2002), decreased political
tolerance (Skitka, Bauman, and Mullen 2004), and greater willingness to trade off civil
liberties for anti-terrorism protections, in particular among individuals who are more
trusting of government (Davis and Silver 2004). Terrorist events have also been linked to
behaviors like seeking out unnecessary drug treatment (Belongia et al. 2005) and averting
normal purchases (Schulden et al. 2006). In sum, perceived threat from terrorism has
important consequences for political and social behaviors, thereby adding signiﬁcance to
understanding the sources of these perceptions.

Though others have conducted a limited analysis of perceived threat from
terrorism in the U.S., this study provides a theoretical framework for the analysis and
conducts empirical tests with a multiple-country sample. The present study reafﬁrms
previous ﬁndings about vulnerable populations and increased threat perception in some
ways. Sex and education do not produce consistent results, but lower-income individuals
and those who feel more concerned about their own personal future welfare exhibit
higher perceived threat from terrorism.

I also ﬁnd that increased perceived threat is associated with worry, generalized
pessimism, xenophobic attitudes, and positive views of the U.S. As was the case with

corruption, negativity, blame, and affect are key predictors of terror threat perceptions.

127

These attitudes reafﬁrm and go beyond previous ﬁndings concerning authoritarianism
and threat perception, as well as the assertions about cultural worldviews contained in
Terror Management Theory. Informational effects also appear in these results. Lesser
access to information and lesser accessibility of information both contribute to higher
perceived threat, as does a lack of contact with the world beyond one’s own country.
Finally, living in a powerful country increases perceived threat, though stability and
greater comfort with ambiguity tend to counter such apprehension. The result based on
country population is perhaps open to dispute. Certainly, some of the effect for larger
countries is due to the fact that some very populous countries are frequently embroiled in
conﬂict and/or are subject to “internal” terrorism from separatist movements (e.g., India,
Pakistan, Nigeria, Russia). The more correct assertion may be that less populous
countries typically have less reason to worry about terror attacks.

This study has used the approach of incorporating the “objective” risk from
terrorism via the power of a respondent’s country. However, controlling for actual
terrorism events may also be useful. LaFree et al. (2006) and Clauset, Young, and
Gleditsch (2007) discuss some of the data sources available. The U.S. Department of
State’s Country Reports on Terrorism and Patterns of Global Terrorism
(http://wwwstategov/s/CU) constitute one source of information. The ITERATE data set
(Mickolus et al. 2004) and the RAND-MIPT Terrorism Incident Database Project are two
other commonly used sources that catalogue international terrorist events. The latter
database, however, is in the process of being fused with the Global Terrorism Database
(http://www.start.umd.edu/data/gtd/) run by the National Consortium for the Study of

Terrorism and Responses to Terrorism at the University of Maryland. The Global

128

Terrorism Database, in turn, appears to be built upon the original database assembled by
the PGIS Corporation’s Global Intelligence Service. Yet another potential source of
information is the Political Terror Scale (http://www.politicalterrorscale.org/abouthtml),
though this resource is focused on terror produced by governments for the people of their
own societies.

As with corruption, the policy implications of these ﬁndings are in some ways
normatively troubling. Governments wishing to craft messages that will increase
perceived threat should focus on appealing to worry, pessimism, xenophobia, and on
increasing positive affect for the targets of terrorism. Less normatively troubling are the
implications for governments wishing to dampen concern about terrorism, in which case

appeals should attempt to reduce factors like anxiety, pessimism, and xenophobia.

129

CHAPTER 5

CONCLUSIONS

130

The Microfoundations of Security

This project has proposed an examination of the microfoundations of security as a
means of better understanding “human security” and its implications for governance. The
theoretical framework proposed here proceeds based on three assumptions. First, a non-
trivial proportion of important political attitudes and behaviors are predicated on self-
interested security considerations, including in some cases what would appear to be
other-focused attitudes and behaviors. Second, the domain of security microfoundations
is one of bounded rationality, meaning that cognitive and informational limitations have
substantive impacts. Third, empirical research can usefully contribute to understanding
the microfoundations of security.

Additionally, the framework consists of four categories of factors thought to drive
individual security considerations. The ﬁrst category includes the elements of individual
risk assessments. Essentially, an individual assesses threats using her baseline security,
which is a function of other perceived threats, capabilities for warding off those threats,
and uncertainty. The vulnerability hypothesis proposes that individuals in more
vulnerable situations tend to estimate greater threat from a wide range of sources. The
second category includes what I have called approaches and orientations toward
insecurity. The ‘yvorldviews” of the psychological risk perception literature and the
“cultural worldviews” of Terror Management Theory overlap to a degree with this second
category. The third category includes informational characteristics of an individual, since
these characteristics inﬂuence the inputs and processing for individual threat assessments.
The fourth category includes environmental patterns and processes that may affect

individual evaluations of threat.

131

Though each of the empirical chapters contains a relatively thorough treatment of
the success of these categories in explaining threat assessments, a recapitulation here will
aid in overall consideration of the framework. The vulnerability hypothesis fares
relatively well, though it is at times subject to alternative interpretations. With regard to
attitudes toward globalization, the effects for ten different variables operationalizing
actual or perceived vulnerability all conform to the vulnerability hypothesis. In the case
of corruption, ﬁndings regarding various forms of sociotropic assessments correspond to
the vulnerability hypothesis. For terrorism, the individual’s income and assessment of her
personal future are meaningful indicators of vulnerability. The results for approaches and
orientations toward security are perhaps even more impressive, and they work in very
similar ways across the three issue areas. In particular, approaches focused on blame,
pessimism, solidarity against outgroups, and tradition tend to increase perceived threat.
General worry or anxiety is a powerful predictor for corruption and terrorism, as well.

Informational characteristics also perform as expected, but the directionality of
effects is not constant due to dependence on the informational environment. While
greater information decreases threat assessments for globalization and terrorism, it
increases threat assessments in the case of corruption. The a priori reasoning for the
corruption ﬁnding is that people with greater access to information should receive more
of the messages about the negative consequences of corruption. On the other hand, the
majority of globalization messages have been positive. The relative infrequency of terror
attacks is an accessible message for more educated individuals, as well. Worth noting is

that the results for globalization and terrorism may also be indicative of the vulnerability

132

effect at work (i.e., more educated individuals are less vulnerable or perceive themselves
to be less vulnerable).

Environmental patterns and processes generally work in ways consistent with a
security microfoundations approach, as well. Here the importance of issue context is
paramount, however. With globalization, people seem to use sociotropic clues (either
directly perceived or obtained via elite messages) as assessment tools. The monetary
beneﬁts provided to a society by globalization appear to be particularly important gauge
of security. With corruption, informational patterns and transparency drive threat
assessments. With terrorism, the status of the individual’s environment as a target of
terrorists, political stability, and the level of comfort with ambiguity are important
factors.

Overall, the security microfoundations framework appears to provide signiﬁcant
theoretical and analytical leverage, though the framework’s breadth of relevance is a
subject for ﬁirther work. The framework is relatively comprehensive, but this
comprehensiveness does not mean that “anything goes.” The system of relationships
within the ﬁ'amework must be theoretically consistent and must bear ﬁuit empirically.
The boundedly rational approach also seems productive — a feature most clearly on
display in comparing the rational choice approach to the boundedly rational approach
with regard to attitudes toward globalization. Previously identiﬁed cognitive heuristics
and biases largely frmction as expected across issue areas, as well.

The “punch lines” for the three empirical studies are supportive of the security
microfoundations framework. The framework provides a more plausible and

comprehensive explanation for attitudes toward globalization than the one provided by

133

the standard rational choice approach (though the framework is not necessarily at odds
with very relaxed forms of rational choice theory). Applying the framework in the area of
corruption provides leverage over a rather unstudied question. The framework
consistently demonstrates that corruption perceptions are the result of much more than
mere observation. The framework’s explanation of perceived threat from terrorism
expands previous U.S.-based research and again shows that “objective” risk is only one
of multiple components used in individual evaluations of threat.

While the three studies demonstrate that certain individual-level aspects of
security are rather constant, the results also suggest that an individual’s political, social,
and economic contexts are important. The variance in random country intercepts and
variables (where used) is one way in which context manifests its signiﬁcance. The impact
of country-level variables is another manifestation, since these variables typically

indicate differences in informational, social, and institutional patterns.

Understanding Risk Perception

Another goal of this research was to expand existing knowledge concerning risk
perception. While largely consistent with previous ﬁndings in the risk perception
literature, the present studies also incorporate new and more complex issues, new sources
of vulnerability, and new “worldviews.” Perhaps the two largest contributions of the
present research are the demonstrations of contextual impacts and the demonstration of
the relative importance of approaches and orientations toward insecurity.

Topical context is one consequential contextual differentiator. While certain

facets of individual security are relatively universal (e. g., the role of vulnerability), how

134

the individual assesses these facets changes from one issue area to another. The
complexity of the potential threat also plays a key role. As opposed to health or
environmental threats, which in general have clear downsides, a phenomenon like
globalization also may enhance security. The studies have also demonstrated that the
geopolitical context matters. Some effects match the risk perception literature on average
but differ in their impacts across countries. The signiﬁcant results for system-level
variables further reinforce the importance of an individual’s location. Topical and
geopolitical context both matter in that they are indicators of different informational
environments, as well. The content and volume of messages are not constant across
issues, times, and places. Consequently, we should expect threat perceptions to be
somewhat variable, as well.

The full list of “worldviews” considered in the present research expands the
previous list substantially. Multiple forms of pessimism, xenophobia, blame, and
conservatism are relevant. Rough estimates of effect sizes additionally indicate that these
approaches and orientations toward insecurity have substantively large impacts —

typically even larger than common heuristics and biases.

Governance and Human Security

Each empirical chapter has also brieﬂy considered the implications of the ﬁndings
for governance, but closer consideration of this issue is an important exercise. The
bottom line is that human security, as considered from the bottom up, is based on many
factors beyond objectively measured threats. The results of all three chapters reveal

reinforcing patterns of negativity and positivity. Feelings of threat and insecurity generate

135

attitudes and orientations that ﬁrrther inﬂate the sense of threat from many directions. For
example, increased threat can bolster xenophobia and reliance on the ingroup, and these
types of approaches to the world will increase future threat assessments. The results also
provide evidence for a “security-development nexus” operating at the individual level.
Feelings of insecurity, vulnerability, isolation, and pessimism feed off one another. Those
individuals who feel more vulnerable due to lesser capacity and power also see greater
threats in the world. Such individuals are also less willing to take risks that could help the
situation.

This project has made the case that dealing with individual insecurity is an
obligation of governance. What, then, are the policy prescriptions for getting perceptions
of threat to better align with more objective measures and for making people feel more
secure so as to limit the negative ramiﬁcations of feeling insecure? While the
implications mentioned in each individual chapter have been grim in some ways,
stepping back provides a useful perspective.

One conclusion we can draw is that ﬁnding ways to instill hope, optimism, and
feelings of security can have a multiplier effect across many policy areas. Building trust
and conﬁdence in government is one potential avenue for achieving such a result, while
programs that provide targeted help for traditionally vulnerable populations may be
another. Fostering tolerance of diversity and understanding of the “outside” are other
factors that will feed into positive reinforcing loops.

Another conclusion is that creating a more open information environment full of
citizens equipped to take in and use that information can help to align mass threat

perceptions with more objective standards. Though we cannot be absolutely sure without

136

data explicitly linking elite messages to individual perceptions, the results obtained here
suggest that more educated individuals with access to more outside information suffer
from smaller bias in assessing risk. Such individuals are more responsive to elite
messages, which can be craﬁed to emphasize such things as sociotropic successes and the
human side of outsiders. Another factor working in favor of governments is that
perceptions are often more important than actual conditions. The former can sometimes
be easier to change.

The normatively troubling area is the one of generating assessments of greater
threat to prevent unnecessary loss. Closing informational ﬂows and engendering
negativity and blame are strategies with undesirable secondary and long-term effects.
However, the results also imply that solid informational networks and a well-educated
citizenry are other methods to bolster threat perceptions when necessary. Additionally,
emphasizing negative sociotropic indicators in messages may be another method with
more palatable consequences.

Human security is neither an entirely objective phenomenon nor one built within
individual minds in such a way that knowing about one person is uninfonnative with
regard to others. Yes, elements of security are subjectively determined, but patterns are
evident in how individuals go about assessing their security. Though policymakers must
keep context dependence in mind, the broad messages and implications of this research

are rather general in their applicability.

137

APPENDICES

138

APPENDIX A

SUhthARY OF VARIABLES USED IN MAIN GLOBALIZATION ANALYSIS

 

Variable Description Source Level
Globalization Index of attitudes toward interstate trade and Pew GAP 2002 Individual
index business ties; interstate communication and (q24-q28)
travel; interstate availability of movies, television,
and music; interstate availability of products; and
interstate connectedness
Unemployed Self-identiﬁcation as unemployed Pew GAP 2002 Individual
status (q6c)
Food lacking Not enough money to buy food for the family at Pew GAP 2002 Individual
some point during last year (q87a)
High income Higher income than median observed category Pew GAP 2002 Individual
for country (i.e., category including median (q88 variants)
individual)
Female Female sex as identiﬁed by interviewer Pew GAP 2002 Individual
(c173)
Dissatisfaction Level of satisfaction / dissatisfaction with Pew GAP 2002 Individual
with income household income (q6a)
Job availability Trend in availability of good jobs in country over Pew GAP 2002 Individual
worse last ﬁve years (q31a)
Macroeconomic Evaluation of aggregate economic conditions in Pew GAP 2002 Individual
situation individual’s country (q12)
Personal future Future assessment minus present assessment of Pew GAP 2002 (q2, Individual
trend personal situation 4)
Macroeconomic Evaluation of future economic conditions in Pew GAP 2002 Individual
future individual’s country over the next year (q13)
Children’s future Assessment of future conditions for children Pew GAP 2002 Individual
when grown as compared to present conditions (q14)
Worry index Assessment of whether the following are Pew GAP 2002 Individual
problems: crime, poor drinking water, spread of ((1153: 1‘56: 158:
HIV/ AIDS, poor quality schools, and people 15h, 15])
leaving for jobs
International Inﬂuence of foreign corporations on conditions Pew GAP 2002 Individual
corporations in individual’s country (q351)
International Inﬂuence of international organizations on Pew GAP 2002 Individual
organizations conditions in individual’s country (q351)

 

139

 

 

Variable Description Source Level
Immigrants Inﬂuence of immigrants on conditions in Pew GAP 2002 Individual
individual’s country (q35g)
Protect culture Need to protect our way of life from foreign Pew GAP 2002 Individual
inﬂuence (C1378)
Own country Our culture is superior to others Pew GAP 2002 Individual
superior ((1370
Efﬁcacy Success not determined by forces outside our Pew GAP 2002 Individual
control ((1171))
Modern pace Dislike modern pace of life Pew GAP 2002 ((119) Individual
Prefer traditional Marriage with traditional roles is better Pew GAP 2002 (q38) Individual
marriage
Age Self—reported respondent age Pew GAP 2002 (974) Individual
No international Do not watch an international news channel Pew GAP 2002 Individual
news (q60c)
No computer Do not use a computer at least occasionally Pew GAP 2002 (958) Individual
News inﬂuence Inﬂuence of news media on conditions in Pew GAP 2002 Individual
bad individual’s country (q35d)
Education level Highest level of education completed, Pew GAP 2002 (‘184 Individual
standardized by categories across countries V3333“)
GDP per capita Gross domestic product per capita adjusted for United Nations Country
PPP in $US purchasing power parity in SUS for 2002 Human Development
Report
Net trade in SUS Total exports minus imports in SUS billions for CIA World Factbook Country
billions 2002
Net migration Net migration rate (inﬂow — outﬂow) per 1,000 CIA \X’orld Factbook Country
rate people for 2002
Life expectancy Life Expectancy Index score for 2002 United Nations Country
index Human Development
Report
Uncertainty Uncertainty Avoidance Index value Hofstede 8c Hofstede Country
avoidance (2005)

NOTES: Pew GAP 2002 indicates the Pew Global Attitudes Project surveys conducted in 2002. The
information in parentheses in the source column indicates the original Pew question number(s) prior to any
recoding. In an effort to avoid losing cases, “don’t know” responses at the individual level have been
included in the analysis either as a middle category for multiple-category variables or in the zero category of
dichotomous variables.

140

APPENDIX B

MULTILATERAL ANTICORRUPTION AGREEMENTS AND STANDARDS

 

 

Adoption Name Organization(s) or Event
1996 March Inter-American Convention against Corruption Organization of American
States

1996 December International Code of Conduct for Public Ofﬁcials United Nations

1997 November Twenty Guiding Principles for the Fight against Council of Europe
Corruption

1997 November Convention on Combating Bribery of Foreign Organization for Economic
Public Ofﬁcials in International Business Cooperation and Development
Transactions

1999 january Criminal Law Convention on Corruption Council of Europe

1999 February Guiding Principles for Fighting Corruption and Global Forum on Fighting
Safeguarding Integrity amongjustice and Security Corruption I
Ofﬁcials

1999 June SCSP Constituent Document Stability Pact for South Eastern

Europe
1999 November Civil Law Convention on Corruption Council of Europe
2000 May Model Code of Conduct for Public Ofﬁcials Council of Europe

2000 November Convention against Transnational Organized Crime United Naﬁons

2000 December Anti-Corruption Initiative for Asia-Paciﬁc Asian Development Bank &
OECD
2003 June The Forty Recommendations Financial Action Task Force on

Money Laundering

2003 July Convention on Preventing and Combating African Union
Corruption

2003 October Convention against Corruption United Nations

2004 November Course of Action on Fighting Corruption and Asia-Paciﬁc Economic
Ensuring Transparency Cooperation

141

APPENDIX C

SUMMARY OF VARIABLES USED IN MAIN CORRUPTION ANALYSIS

 

Variable Description Source Level
Female Female sex as identiﬁed by interviewer Pew GAP 2002 Individual
((173)
High income Higher income than median observed category Pew GAP 2002 (q88 Individual
for country (i.e., category including median variants)
individual)
Unemployed Self-identiﬁcation as unemployed Pew GAP 2002 Individual
status (96C)
own situation Evaluation of present personal status Pew GAP 2002 (q2) Individual
Macroeconomic Evaluation of aggregate economic conditions in Pew GAP 2002 Individual
Situation individual’s country (q12)
own future Evaluation of future personal status Pew GAP 2002 (q4) Individual
situation
Macroeconomic Evaluation of future economic conditions in Pew GAP 2002 Individual
future individual’s country over the next year (q13)
Income gap Income inequality worse than ﬁve years ago Pew GAP 2002 Individual
(C1310
Worry index Assessment of whether the following are Pew GAP 2002 Individual
problems: crime, moral decline, poor drinking ((1153, 15d, 156’ 15f,
water, terrorism, spread of HIV/ AIDS, and 158, 151‘)
poor quality schools
Politics a major Politics among the top three problems facing Pew GAP 2002 Individual
problem the country (q8.1, q8.2, q8.3)
National Evaluation of national government’s inﬂuence Pew GAP 2002 Individual
government on conditions in individual’s country (q35a)
National. Evaluation of national executive’s inﬂuence on Pew GAP 2002 Individual
executive conditions in individual’s country (q35b)
Dissatisfaction Level of satisfaction with the way things are Pew GAP 2002 (q9) Individual
With world going in the world
Immigration Assessment of extent to which immigration is a Pew GAP 2002 Individual
problem in the individual’s country (qISi)

 

142

Variable

 

   

Level

 

Description Source
Protect culture Need to protect our way of life from foreign Pew GAP 2002 Individual
inﬂuence ((1378)
Efﬁcacy Success not determined by forces outside our Pew GAP 2002 Individual
control ((1171))
Losing tradition Traditional ways being lost Pew GAP 2002 Individual
(q20)
Education Age of full education, truncated at 22 Pew GAP 2002 Individual
((185)
Bribery . Frequency of public bribes required in last year Pew GAP 2002 Individual
experience to receive government services (asked in less (q48)
developed countries only)
Regime durability Regime age as of 2002 Polity Country
Intercommunal Annual index of intercommunal conﬂict 1998- Minorities at RiSk Country
conﬂict 2002
Press freedom Freedom of the press 2002 Freedom House Country
National debt per External public debt per capita (in billions 08$) CIA World Factbook Country
capita 2002
Corruption Control of corruption 2002, rescaled to 0—1 and World Bank Country
inverted lnstitute’s
Governance
Indicators
GDP per capita Gross domestic product per capita adjusted for United Nations Country
PPP in $US purchasing power parity in $US for 2002 Human
Development Report
Literacy Percentage of population literate 2002 CIA World Factbook Country

NOTES: Pew GAP 2002 indicates the Pew Global Attitudes Project surveys conducted in 2002. The
information in parentheses in the source column indicates the original Pew question number(s) prior to any
recoding. In an effort to avoid losing cases, “don’t know” responses at the individual level have been
included in the analysis either as a middle category for multiple-category variables or in the zero category of
dichotomous variables.

143

APPENDIX D

CORRELATIONS AMONG COUNTRY CORRUPTION INDICATORS

ﬂ

 

 

Political Control of Freedom from BEEPS Bribery
Corruption Corruption Corruption Additional Experience
(Pew) (WB) (HF) Payments (WB) (Pew)
Political 1.000
Corruption
(Pew)
Control of 0.557 1.000
Corruption (n = 41)
(WE)
Freedom from 0.598 0.928 1.000
Corruption (n = 40) (n = 43)
(HF)
BEEPS 0.518 0.694 0.677 1.000
Additional (n =35) (n = 36) (n = 36)
Payments (\WB)
Bribery ' 0.209 0.538 0.473 0.639 1.000
Experience (n = 34) (n = 34) (n = 33) (n = 29)
(Mb

NOTES: These aggregate correlations are for countries within the Pew Global Attitudes Project (GAP)
sample of 44 countries. The correlations would differ for the three innermost cells if applied to all common
countries for a pair of indicators. Political Corruption is the unweighted percentage of respondents saying political
corruption is a “very big” problem in the 2002 Pew GAP. Control of Corruption is an inverted and rescaled
(within the 0-1 interval) version of the 2002 measure from the World Bank’s “Governance Indicators.” Freedom
from Corruption is an inverted component of the Heritage Foundation’s 2002 “Index of Economic Freedom.”
BEEPS Additional quent: is the country mean from the World Bank’s 1999 “Business Environment and
Enterprise Performance Survey” for a question asking whether additional payments are necessary to get things
done. Bribery Experience is the unweighted country mean from the Pew 2002 GAP for a question asking how
frequently a respondent had to give bribes to receive government services to which she was otherwise entitled.

144

APPENDIX E

SUMMARY OF VARIABLES USED IN MAIN TERRORISM ANALYSIS

 

Variable Description Source Level
Children Whether respondent has children Pew GAP 2002 (q92) Individual
Female Female sex as identiﬁed by interviewer Pew GAP 2002 (q73) Individual
High income Higher income than median observed Pew GAP 2002 (q88 Individual
category for country (i.e., category including variants)
median individual)
OW“ 5101360“ Evaluation of present personal status Pew GAP 2002 (q2) Individual
own future Evaluation of future personal status Pew GAP 2002 (q4) Individual
Macroeconomic Evaluation of aggregate economic conditions Pew GAP 2002 (q12) Individual
Situation in individual’s country
Worry index Assessment of whether the following are Pew GAP 2002 Individual
problems: crime, poor drinking water, spread ((1153: 156. 158: 15h,
of HIV/ AIDS, poor quality schools, and 151)
people leaving for jobs
Dissatisfaction With Level of satisfaction with the way things are Pew GAP 2002 (q9) Individual
world going in the world
Restrict entry Need to better restrict entry into country Pew GAP 2002 Individual
(q37i)
Chauvinism Superiority of own country’s culture Pew GAP 2002 Individual
(9370
U5 ideas and Spread of U.S. ideas and customs Pew GAP 2002 (q67) Individual
customs
U-S- ﬁght against U.S. ﬁght against terror Pew GAP 2002 (q72) Individual
terror
No computer Does not own a computer Pew GAP 2002 Individual
(q60a)
Educadon level Highest level of education completed, Pew GAP 2002 (C184 Inleidual
standardized by categories across countries variants)

 

145

   

Variable Description Source Level

 

Country population Country population (in millions) 2002 CLA World Factbook Country
(in millions)
Govemment Total govemment expenditures per capita CIA World FaCIbOOk Country
expenditures per 2002
capita
Political stability Political stability 2002 World Bank Institute Country
Governance
Indicators
Uncertainty Uncertainty Avoidance Index value, with Hofstede 8‘ Country
avordance some irnputations based on region Hofstede (2005)

   

NOTES: Pew GAP 2002 indicates the Pew Global Attitudes Project surveys conducted in 2002. The
information in parentheses in the source column indicates the original Pew question number(s) prior to any
recoding. In an effort to avoid losing cases, “don’t know” responses at the individual level have been
included in the analysis either as a middle category for multiple-category variables or in the zero category of
dichotomous variables.

146

REFERENCES

147

REFERENCES

Acemoglu, Daron, and James A. Robinson. 2002. “The Political Economy of the Kuznets
Curve.” Review of Development Economics 6 (2): 183-203.

Adam, Barbara, and J oost van Loon. 2000. “Introduction: Repositioning Risk; the
Challenge for Social Theory.” In The Risk Society and Beyond: Critical Issues for
Social Theory, eds. Barbara Adam, Ulrich Beck, and Joost van Loon. Thousand Oaks,
CA: SAGE Publications, 1-31.

Adomo, T. W., Else Frenkel-Brunswik, Daniel J. Levinson, and R. Nevitt Sanford. 1950.
The Authoritarian Personality. New York: Harper Brothers.

Adsera, Alicia, Carles Boix, and Mark Payne. 2003. “Are You Being Served? Political
Accountability and Quality of Government.” Journal of Law, Economics, &
Organization 19 (2): 445-90.

Alkire, Sabina. 2004. “A Vital Core That Must Be Treated with the Same Gravitas as
Traditional Security Threats.” Security Dialogue 35 (3): 359-60.

Alt, James E., and David Dreyer Lassen. 2003. “The Political Economy of Institutions
and Corruption in the American States.” Journal of Theoretical Politics 15 (3): 341-
65.

Anderson, Christopher, and Yuliya Tverdova. 2003. “Corruption, Political Allegiances,
and Attitudes toward Government in Contemporary Democracies.” American Journal
of Political Science 47 (l )1 91-109.

Arman, Koﬁ. 2001. “Nobel Lecture, Oslo, December 10, 2001.” Available online at
http://nobelprizeorg/nobel prizes/peace/laureates/ZOOl/man-lecturehtml. (March
21, 2008).

 

Auvinen, Juha, and E. Wayne Nafziger. 1999. “The Sources of Humanitarian
Emergencies.” Journal of Conﬂict Resolution 43 (3): 267-90.

Axelrod, Robert. 1984. The Evolution of Cooperation. New York: Basic Books.

Axworthy, Lloyd. 1997. “Canada and Human Security: The Need for Leadership.”
International Journal 52: 183-96.

Axworthy, Lloyd. 2001. “Human Security and Global Governance: Putting Pe0ple First.”
Global Governance 7: 19-23.

Bajpai, Kanti. 2003. “The Idea of Human Security.” International Studies 40 (3): 195-
228.

148

Bajpai, Kanti. 2004. “An Expression of Threats versus Capabilities across Time and
Space.” Security Dialogue 35 (3): 360-61.

Baldwin, David A. 1997. “The Concept of Security.” Review of International Studies 23:
5-26.

Bardhan, Pranab. 1997. “Corruption and Development: A Review of Issues.” Journal of
Economic Literature 35 (3): 1320-46.

Baumgartner, Frank, and Bryan Jones. 1993. Agendas and Instability in American
Politics. Chicago: University of Chicago Press.

Beck, Paul J ., and Michael W. Maher. 1986. “A Comparison of Bribery and Bidding in
Thin Markets.” Economic Letters 20: 1-5.

Beck, Ulrich. 1992. Risk Society: Towards a New Modernity. London: SAGE
Publications.

Beck, Ulrich. 1999. World Risk Society. Malden, MA: Blackwell Publishers Inc.

Becker, Gary S., and George J. Stigler. 1974. “Law Enforcement, Malfeasance, and the
Compensation of Enforcers.” Journal of Legal Studies 3: 1-19.

Bellamy, Alex J ., and Matt McDonald. 2002. “The Utility of Human Security: Which
Humans? What Security? A Reply to Thomas & Tow.” Security Dialogue 33 (3):
373-77.

Belongia, Edward A., Burney Kieke, Ruth Lynﬁeld, Jeffrey P. Davis, and Richard E.
Besser. 2005. “Demand for Prophylaxis after Bioterrorism-related Anthrax Cases,

2001.” Emerging Infectious Diseases (serial on the Internet) (January). Available
online at http://www.cdc.gov/ncidod/EID/vol11noOl/04-0272.htm. (March 21, 2008).

Berger, Charles R., and Richard J. Calabrese. 1975. “Some Explorations in Initial
Interaction and Beyond: toward a Developmental Theory of Interpersonal
Communication.” Human Communication Research 1 (2): 99-112.

Berry, William D., and Jacqueline H. Rubin. 2007. “How to Test for Interaction in
Models with Binary Dependent Variables.” Manuscript. Tallahassee, FL: Florida
State University.

Bickel, Robert. 2007. Multilevel Analysis for Applied Research. New York: The Guilford
Press.

Bilal, Sanoussi, J can-Marie Grether, and Jaime de Melo. 2003. “Attitudes toward

Immigration: a Trade-Theoretic Approach.” Review of International Economics 11
(2): 253-67.

149

Blum, Alain. 2002. “Resistance to Identity Categorization in France.” In Census and
Identity: The Politics of Race, Ethnicity, and Language in National Censuses, eds.
David I. Kertzer and Dominique Arel. New York: Cambridge University Press, 121-
47.

Boyd, R., and P. J. Richerson. 1985. Culture and the Evolutionary Process. Chicago:
Chicago University Press.

Brambor, Thomas, William Roberts Clark, and Matt Golder. 2006. “Understanding
Interaction Models: Improving Empirical Analyses.” Political Analysis 14: 63-82.

Brown, A. J. 2006. “What Are We Trying to Measure? Reviewing the Basics of
Corruption Deﬁnition.” In Measuring Corruption, eds. Charles Sampford, Arthur
Shacklock, Carmel Connors, and Fredrik Galtung. Burlington, VT: Ashgate
Publishing Company, 57-79.

Brunetti, Ayrno, and Beatrice Weder. 2003. “A Free Press is Bad News for Corruption.”
Journal of Public Economics 87: 1801-24.

Buzan, Barry, Ole erver, and J aap de Wilde. 1997. Security: A New Framework for
Analysis. Boulder, CO: Lynne Rienner Publishers.

Ciofﬁ-Revilla, Claudio. 1998. Politics and Uncertainty: Theory, Models, and
Applications. New York: Cambridge University Press.

Citrin, Jack, Donald P. Green, Christopher Muste, and Cara Wong. 1997. “Public
Opinion Toward Immigration Reform: The Role of Economic Motivations.” Journal
of Politics 59 (3): 858-81.

Clauset, Aaron, Maxwell Young, and Kristian Skrede Gleditsch. 2007. “On the
Frequency of Severe Terrorist Events.” Journal of Conﬂict Resolution 51 (1): 58-87.

CNN. 2002. “A Sequel to “Summer of the Shark”? Researchers Downplay Reported
Wave of Attacks as Hype.” Available online at

ht_tp://archives.cnn.com/2002/US/05/21/shark.attacks/. (March 21, 2008).

Cohen, Florette, Daniel M. Ogilvie, Sheldon Solomon, Jeff Greenberg, and Tom
Pyszczynski. 2005. “American Roulette: The Effect of Reminders of Death on
Support for George W. Bush in the 2004 Presidential Election.” Analyses of Social
Issues and Public Policy 5 (1): 177-87.

Commission on Human Security. 2003. Human Security Now. New York: Commission
on Human Security.

Conlisk, John. 1996. “Why Bounded Rationality.” Journal of Economic Literature 34
(2): 669-700.

150

Cosmides, Leda, and John Tooby. 1987. “From Evolution to Behavior: Evolutionary
Psychology as the Missing Link.” In The Latest on the Best: Essays on Evolution and
Optimality, ed. John Dupré. Cambridge, MA: MIT Press.

Cosnrides, Leda, and John Tooby. 1992. “Cognitive Adaptations for Social Exchange.” In
The Adapted Mind: Evolutionary Psychology and the Generation of Culture, eds. J.
H. Barkow, Leda Cosmides, and John Tooby. New York: Oxford University Press,
163—228.

Davidson, Carl, Steve Matusz, and Doug Nelson. 2006. “Fairness and the Political
Economy of Trade.” World Economy: 989-1004.

Davis, Darren W., and Brian D. Silver. 2004. “Civil Liberties vs. Security: Public
Opinion in the Context of the Terrorist Attacks on America.” American Journal of
Political Science 48 (1): 28-46.

Deutsch, J. A., and D. Deutsch. 1963. “Attention: Some Theoretical Considerations.”
Psychological Review 70 (1): 80-90.

Dominitz, Jeff, and Charles F. Manski. 1997. “Perceptions of Economic Insecurity.”
Public Opinion Quarterly 61 (2): 261-87.

Douglas, Mary. 1992. Risk and Blame: Essays in Cultural Theory. New York: Routledge.

Downs, Anthony. 1972. “Up and Down with Ecology: The Issue-Attention Cycle.”
Public Interest 28: 38-50.

Duncan, Nick. 2006. “The Non-Perception Based Measurement of Corruption: A Review
of Issues and Methods from a Policy Perspective.” In Measuring Corruption, eds.
Charles Sampford, Arthur Shacklock, Carmel Connors, and F redrik Galtung.
Burlington, VT: Ashgate Publishing Company, 131-61.

Edelrnan, Murray. 1960. “Symbols and Political Quiescence.” American Political Science
Review 54 (3): 695-704.

Edelrnan, Murray. 1967. The Symbolic Uses of Politics. Urbana, IL: University of Illinois
Press.

Entrnan, Robert M. 1989. “How the Media Affect What People Think: An Information
Processing Approach.” Journal of Politics 51 (2): 347-70.

Erikson, Robert 8., Norman R. Luttbeg, and Kent L. Tedin. 1991. American Public
Opinion: Its Origins, Content, and Impact (4“ ed. ). New York: Macmillan.

Ferraro, Kenneth F. 1995. Fear of Crime: Interpreting Victimization Risk. Albany, NY:
State University of New York Press.

151

Festinger, Leon. 1957. A Theory of Cognitive Dissonance. Stanford, CA: Stanford
University Press.

Fetzer, Joel S. 2000. Public Attitudes toward Immigration in the United States, France,
and Germany. Cambridge: Cambridge University Press.

F inucane, Melissa L., Ali Alhakami, Paul Slovic, and Stephen M. Johnson. 2000a. “The
Affect Heuristic in J udgrnents of Risks and Beneﬁts.” In The Perception of Risk, ed.
Paul Slovic. Sterling, VA: Earthscan, 413-29.

Finucane, Melissa L., Paul Slovic, C. K. Mertz, James Flynn, and Theresa A. Satterﬁeld.
2000b. “Gender, Race, and Perceived Risk: The ‘White Male’ Effect.” Health, Risk &
Society 2 (2): 159-72.

Fischhoff, Baruch, Paul Slovic, Sarah Lichtenstein, Stephen Read, and Barbara Combs.
2000. “How Safe Is Safe Enough? A Psychometric Study of Attitudes toward
Technology Risks and Beneﬁts.” In The Perception of Risk, ed. Paul Slovic. Sterling,
VA: Earthscan, 80—103.

Franzese, Robert J ., Jr., and Cindy D. Kain. 2007. Modeling and Interpreting Interactive
Hypotheses in Regression Analysis. Ann Arbor, MI: University of Michigan Press.

Galtung, Fredrik. 2006. “Measuring the Irnmeasurable: Boundaries and Functions of
(Macro) Corruption Indices.” In Measuring Corruption, eds. Charles Sampford,
Arthur Shacklock, Carmel Connors, and F redrik Galtung. Burlington, VT: Ashgate
Publishing Company, 101-130.

Gardiner, John. 2002. “Deﬁning Corruption.” In Political Corruption: Concepts &
Contexts, eds. Arnold J. Heidenheimer and Michael Johnston. New Brunswick, NJ:
Transaction Publishers, 25-40.

Gehnan, Andrew, and Jennifer Hill. 2007. Data Analysis Using Regression and
Multilevel/Hierarchical Models. New York: Cambridge University Press.

Gilpin, Robert. 1987. The Political Economy of International Relations. Princeton:
Princeton University Press.

Glassner, Barry. 1999. The Culture of F ear: Why Americans Are Afraid of the Wrong
Things. New York: Basic Books.

Graf Lambsdorff, Johan. 2003. “How Corruption Affects Persistent Economic Flows.”
Economic Governance 4 (3): 229-43.

Grayson, Kyle. 2003. “Securitization and the Boomerang Debate: A Rejoinder to Liotta
and Smith-Windsor.” Security Dialogue 34 (3): 337-43.

Gupta, Sanjeev, Hamid Davoodi, and Rosa Alonso-Tenne. 2002. “Does Corruption
Affect Income Inequality and Poverty?” Economics of Governance 3: 23—45.

152

Gupta, Sanjeev, Luiz de Mello, and Raju Sharan. 2001. “Corruption and Military
Spending.” European Journal of Political Economy 17: 749-77.

Gurr, Ted Robert. 1970. Why Men Rebel. Princeton, NJ: Princeton University Press.

Haﬁendom, Helga. 1991. “The Security Puzzle: Theory-Building and Discipline-
Building in International Security.” International Studies Quarterly 35 (1): 3-17.

Halek, Martin, and Joseph G. Eisenhauer. 2001. “Demography of Risk Aversion.”
Journal of Risk and Insurance 68 (1): 1-24.

Hamilton, William D. 1964. “The Genetical Evolution of Social Behaviour I.” Journal of
Theoretical Biology 7: 1-16; 17-52.

Hanson, Gordon H., Kenneth Scheve, and Matthew J. Slaughter. 2007. “Public Finance
and Individual Preferences over Globalization Strategies.” Economics & Politics 19
(1): 1-33.

Heidenheimer, Arnold J. 2002. “Perspectives in the Perception of Corruption.” In
Political Corruption: Concepts & Contexts, eds. Amold J. Heidenheimer and Michael
Johnston. New Brunswick, NJ: Transaction Publishers, 141-54.

Hellman, Joel S., Geraint Jones, and Daniel Kaufrnann. 2000. “Seize the State, Seize the
Day: State Capture, Corruption and Inﬂuence in Transition.” World Bank Policy
Research Working Paper 2444. Washington, DC: World Bank Institute.

Hellwig, Timothy. 2007. “Globalization and Perceptions of Policy Maker Competence.”
Political Research Quarterly 60 (1): 146-58.

Henk, Dan. 2005. “Human Security: Relevance and Implications.” Parameters
(Summer): 91-106.

Hemnann, Richard K., Philip E. Tetlock, and Matthew N. Diascro. 2001. “How
Americans Think about Trade: Reconciling Conﬂicts among Money, Power, and
Principles.” International Studies Quarterly 45 (2): 191-218.

Hoffer, Eric. 1951. The True Believer: Thoughts on the Nature of Mass Movements. New
York: Harper and Row.

Hoffer, Eric. 1963. The Ordeal of Change. New York: Harper & Row.

Hofstede, Geert, and Gert Jan Hofstede. 2005. Cultures and Organizations: Software of
the Mind (Second Edition). New York: McGraw-Hill.

Hoogensen, Gunhild, and Kirsti Stuvoy. 2006. “Gender, Resistance and Human
Security.” Security Dialogue 37 (2): 207-28.

153

Huddy, Leonie, Stanley F eldman, Theresa Capelos, and Colin Provost. 2002. “The
Consequences of Terrorism: Disentangling the Effects of Personal and National
Threat.” Political Psychology 23 (3): 485-509.

Huddy, Leonie, Stanley Feldman, Charles Taber, and Gallya Lahav. 2005. “Threat,
Anxiety, and Support of Antiterrorism Policies.” American Journal of Political
Science 49 (3): 593-608.

Huntington, Samuel P. 1957. “Conservatism as an Ideology.” American Political Science
Review 51 (2): 454-73.

Huntington, Samuel P. 1968. Political Order in Changing Societies. New Haven, CT:
Yale University Press.

Hutchcroﬁ, Paul. 1997. “The Politics of Privilege: Assessing the Impact of Rents,
Corruption, and Clientelism on Third World Development.” Political Studies 45: 639-
58.

Inglehart, Ronald. 1997. Modernization and Postmodernization: Cultural, Economic, and
Political Change in 43 Societies. Princeton, NJ: Princeton University Press.

Inglehart, Ronald, and Christian Welzel. 2005. Modernization, Cultural Change, and
Democracy: The Human Development Sequence. New York: Cambridge University
Press.

J aeger, Carlo C., Ortwin Renn, Eugene A. Rosa, and Thomas Webler. 2001. Risk,
Uncertainty, and Rational Action. Sterling, VA: Earthscan Publications Ltd.

Jain, Arvind K. 2001. “Corruption: A Review.” Journal of Economic Surveys 15 (1): 71-
121.

J anssen, Maarten. 2006. “Microfoundations.” T inbergen Institute Discussion Paper T I
2006-041/1. Amsterdam, The Netherlands.

Johnson, Simon, Daniel Kaufrnann, and Pablo Zoido-Lobaton. 1998. “Regulatory
Discretion and the Unofﬁcial Economy.” American Economic Review 88 (2): 387-92.

Johnston, Michael. 2001. “The Deﬁnitions Debate: Old Conﬂicts in New Guises.” In The
Political Economy of Corruption, ed. Arvind K. Jain. New York: Routledge, 11-31.

Jones, Bryan D. 1999. “Bounded Rationality.” Annual Review of Political Science 2: 297-
321.

Kahneman, Daniel. 2003. “Maps of Bounded Rationality: Psychology for Behavioral
Economics.” American Economic Review 93 (5): 1449-75.

154

Karneda, Tatsuya, Masanori Takezawa, and Reid Hastie. 2003. “The Logic of Social
Sharing: An Evolutionary Game Analysis of Adaptive Norm Development.”
Personality and Social Psychology Review 7 (1): 2-19.

Kasperson, Jeanne X., Roger B. Kasperson, Nick Pidgeon, and Paul Slovic. 2003. “The
Social Ampliﬁcation of Risk: Assessing Fifteen Years of Research and Theory.” In
The Social Amplification of Risk, eds. Nick Pidgeon, Roger B. Kasperson, and Paul
Slovic. New York: Cambridge University Press, 13-46.

Kaufrnann, Daniel, and Aart Kraay. 2002. “Growth without Governance.” Economia
(Fall): 169-229.

Kaufmann, Daniel, and Aart Kraay. 2003. “Governance and Growth: Causality Which
Way?” World Bank Working Paper. Washington, DC: World Bank Institute.

Kaufrnann, Daniel, Aart Kraay, and Massimo Mastruzzi. 2005. “Governance Matters IV:
Governance Indicators for 1996-2004.” World Bank Policy Research Working Paper
No. 3630.

Khan, Mushtaq H. 1996. “The Efﬁciency Implications of Corruption.” Journal of
International Development 8 (5): 683-96.

Kinder, Donald R., and D. Roderick Kiewiet. 1979. “Economic Discontent and Political
Behavior: The Role of Personal Grievances and Collective Economic Judgments in
Congressional Voting.” American Journal of Political Science 23 (3): 495-527.

Kinder, Donald R., and D. Roderick Kiewiet. 1981. “Sociotropic Politics: The American
Case.” British Journal of Political Science 11 (2): 129-61.

King, Gary, and Christopher J. L. Murray. 2001. “Rethinking Human Security.” Political
Science Quarterly 116 (4): 585-610.

Kingdon, John W. 2003. Agendas, Alternatives, and Public Policies (2” edition). New
York: Addison-Wesley Educational Publishers, Inc.

Krueger, Anne 0. 1974. “The Political Economy of the Rent-Seeking Society.” American
Economic Review 64 (3): 291-303.

Kurer, Oskar. 2001. “Why Do Voters Support Corrupt Politicians?” In The Political
Economy of Corruption, ed. Arvind K. Jain. New York: Routledge, 63-86.

Kuznets, Simon. 1955. “Economic Growth and Income Inequality.” American Economic
Review 45 (1): 1-28.

LaFree, Gary, Laura Dugan, Heather V. F ogg, and Jeffrey Scott. 2006. “Building a
Global Terror Database.” Manuscript. Available online at:
http://www.ncirs.gov/pdfﬁles1/nij/grants/214260.2df. (April 9, 2008).

155

Laland, Kevin N., and Gillian R. Brown. 2002. Sense and Nonsense: Evolutionary
Perspectives on Human Behavior. New York: Oxford University Press.

Lancaster, Thomas, and Gabriella Montinola. 2001. “Comparative Political Corruption:
Issues of Operationalization and Measurement.” Studies in Comparative International
Development 36 (3): 3-28.

Langseth, Petter. 2006. “Measuring Corruption.” In Measuring Corruption, eds. Charles
Sampford, Arthur Shacklock, Carmel Connors, and Fredrik Galtung. Burlington, VT:
Ashgate Publishing Company, 7-44.

Learner, Edward E. 1995. The Heckscher-Ohlin Model in Theory and Practice. Princeton,
NJ: Princeton University.

Lederman, Daniel, Norman V. Loayza, and Rodrigo R. Scares. 2005. “Accountability
and Corruption: Political Institutions Matter.” Economics & Politics 17 ( 1): 1-35.

Leff, Nathaniel H. 1964. “Economic Development through Bureaucratic Corruption.”
American Behavioral Scientist 8: 291-303.

Lewis-Beck, Michael S., and Mary Stegrnaier. 2000. “Economic Determinants of
Electoral Outcomes.” Annual Review of Political Science 3: 183-219.

Lien, Da Hsiang Donald. 1986. “A Note on Competitive Bribery Games.” Economic
Letters 22 (4): 337-41.

Liotta, P. H. 2005. “Through the Looking Glass: Creeping Vulnerabilities and the
Reordering of Security.” Security Dialogue 36 (1): 49-70.

Lui, F. T. 1985. “An Equilibrium Queuing Model of Bribery.” Journal of Political
Economy 93: 760-81.

Lupia, Arthur, and Jesse Menning. 2005. “When Can Politicians Scare Citizens Into
Supporting Bad Policies? A Theory of Incentives with Fear-Based Content.”
Manuscript.

Lupton, Deborah. 1999. Risk. New York: Routledge.

Manzetti, Luigi, and Carole J. Wilson. 2007 . “Why Do Corrupt Governments Maintain
Public Support?” Comparative Political Studies 40 (8): 949-70.

Marshall, Grant N., Camille B. Wortman, Jeff W. Kusulas, Linda K. Hervi g, and Ross R.
Vickers, Jr. 1992. “Distinguishing Optimism from Pessimism: Relations to

Fundamental Dimensions of Mood and Personality.” Journal of Personality and
Social Psychology 62 (6): 1067-74.

Maslow, Abraham H. 1943. “A Theory of Human Motivation.” Psychological Review 50
(4): 370-96.

156

Maslow, Abraham H. 1970. Motivation and Personality (Second Edition). New York:
Harper & Row, Publishers.

Mathews, Jessica Tuchrnan. 1989. “Redeﬁning Security.” Foreign Affairs 68: 162-77.

Mauro, Paolo. 1995. “Corruption and Growth.” Quarterly Journal of Economics 110 (3):
68 l -7 13.

Mauro, Paolo. 1998. “Corruption and the Composition of Government Expenditure.”
Journal of Public Economics 69: 263—79.

Mayda, Anna Marie. 2005. “Who Is Against Immigration? A Cross-Country
Investigation of Individual Attitudes toward Immigrants.” Review of Economics and
Statistics 88 (3): 510-30.

Mayda, Anna Marie, and Dani Rodrik. 2005. “Why Are Some People (and Countries)
More Protectionist than Others?” European Economic Review 49 (6): 1393-1430.

Maynard Smith, John. 1964. “Group Selection and Kin Selection.” Nature 201: 1145—47.

McClosky, Herbert. 1958. “Conservatism and Personality.” American Political Science
Review 52 (1): 27-45.

Meehl, Paul E. 1977. “The Selﬁsh Voter Paradox and the Thrown-Away Vote
Argument.” American Political Science Review 71 (1): 11-30.

Mickolus, Edward F ., Todd Sandler, Jean M. Murdock, and Peter A. Flemming. 2004.
International Terrorism: Attributes of Terrorist Events, 1968-2003 (I T ERA T E 3-4).
Dunn Loring, VA: Vinyard Software.

Miller, William L. 2006. “Perceptions, Experience and Lies: What Measures Corruption
and What Do Corruption Measures Measure?” In Measuring Corruption, eds. Charles
Sampford, Arthur Shacklock, Carmel Connors, and Fredrik Galtung. Burlington, VT:
Ashgate Publishing Company, 163-85.

Mohamed Salih, Mohamed A. 1999. “The Horn of Aﬁica: Security in the New World
Order.” In Globalization, Human Security, and the African Experience, eds. Caroline
Thomas and Peter Wilkin. Boulder, CO: Lynne Rienner Publishers, 127-43.

Montinola, Gabriella R., and Robert W. Jackman. 2002. “Sources of Corruption: A
Cross-Country Study.” British Journal of Political Science 32: 147-70.

Murphy, Kevin, Andrei Shleifer, and Robert Vishny. 1991. “Allocation of Talent:
Implications for Growth.” Quarterly Journal of Economics 106 (2): 503-30.

National Commission on Terrorist Attacks upon the United States. 2004. The 9/11
Commission Report: Final Report of the National Commission on Terrorist Attacks
upon the United States. New York: W. W. Norton & Company, Inc.

157

National Weather Service. 2005. “Urgent — Weather Message, New Orleans, LA, 10:11
am. CDT, Sun., Aug. 28, 2005.” Available online at
mpz//www.srh.noa_a.gov/data/wam archive/LIX/NPW/0828 155101 .txt. (March 21,
2008)

Nef, Jorge. 1999. Human Security and Mutual Vulnerability: The Global Political
Economy of Development and Underdevelopment (2”! Edition). Ottawa: International
Development Research Centre.

Noland, Marcus. 2005. “Popular Attitudes, Globalization, and Risk.” International
Finance 8 (2): 199-229.

Norman, Donald A. 1968. “Toward a Theory of Memory and Attention.” Psychological
Review 75 (6): 522-36.

Norris, Pippa, Montague Kern, and Marion Just. 2003. “Framing Terrorism.” In Framing
Terrorism, eds. Pippa Norris, Montague Kern, and Marion Just. New York:
Routledge, 3-23.

Norton, Edward C., Hua Wang, and Chunrong Ai. 2004. “Computing Interaction Effects
and Standard Errors in Logit and Probit Models.” Stata Journal 4 (2): 154-67.

Nye, Joseph S. 1989. “Corruption and Political Development: A Cost-Beneﬁt Analysis.”
In Political Corruption: A Handbook, eds. A. J. Heidenheimer, M. Johnston, and V.
T. LeVine. New Brunswick, NJ: Transaction, 963-84.

Ogata, Sadako, and Johan Cels. 2003. “Human Security—Protecting and Empowering
the People.” Global Governance 9: 273-82.

Ohlin, Berti] Gotthard. 1933. Interregional and International Trade. Cambridge: Harvard
University Press.

O’Rourke, Kevin, and Richard Sinnott. 2001. “The Determinants of Individual Trade
Policy Preferences: International Survey Evidence.” In Brookings Trade Forum 2001,
eds. Susan Margaret Collins and Dani Rodrik. Washington, DC: Brookings
Institution Press.

O’Rourke, Kevin H., and Richard Sinnott. 2006. “The Determinants of Individual
Attitudes towards Immigration.” European Journal of Political Economy 22: 83 8-61.

Owen, Taylor. 2004. “Human Security -- Conﬂict, Critique, and Consensus: Colloquium
Remarks and a Proposal for a Threshold-Based Deﬁnition.” Security Dialogue 35 (3):
373-87.

Paris, Roland. 2001. “Human Security: Paradigm Shift or Hot Air?” International
Security 26 (2): 87-102.

158

Paris, Roland. 2004. “Still an Inscrutable Concept.” Security Dialogue 35 (3): 370-72.

Persson, Torsten, Gerard Roland, and Guido Tabellini. 1997. “Separation of Powers and
Political Accountability.” Quarterly Journal of Economics 1 12: 1163-1202.

PEW Research Center. 2002. “Sniper Attacks Draw Most Public Interest in 2002.”
Available online at http:ﬂpeople-press.org/reports/pdf/168.ndf. (March 21, 2008).

Philip, Mark. 2006. “Corruption Deﬁnition and Measurement.” In Measuring Corruption,
eds. Charles Sampford, Arthur Shacklock, Carmel Connors, and F redrik Galtung.
Burlington, VT: Ashgate Publishing Company, 45-56.

Rabe-Hesketh, Sophia, and Anders Skrondal. 2005. Multilevel and Longitudinal
Modeling Using STA TA. College Station, TX: STATA Press.

Raudenbush, Stephen W., and Anthony S. Bryk. 2002. Hierarchical Linear Models:
Applications and Data Analysis Methods. Thousand Oaks, CA: Sage Publications.

Renn, Ortwin, and Bemd Rohrmann. 2000. “Cross-Cultural Risk Perception: State and
Challenges.” In Cross-Cultural Risk Perception: A Survey of Empirical Studies, eds.
Ortwin Renn and Bemd Rohrmann. London: Kluwer Academic Publishers, 211-33.

Richerson, P. J ., and R. Boyd. 1998. “The Evolution of Human Ultra-Sociality.” In
Indoctrinability, Warfare and Ideology: Evolutionary Perspectives, eds. I. Eibl-
Eibesfeldt and F. K. Salter. Oxford: Berghahn Books.

Roberts, David. 2006. “Review Essay: Human Security or Human Insecurity? Moving
the Debate Forward.” Security Dialogue 37 (2): 249-61.

Rohrmann, Bemd, and Ortwin Renn. 2000. “Risk Perception Research: An Introduction.”
In Cross-Cultural Risk Perception: A Survey of Empirical Studies, eds. Ortwin Renn
and Bemd Rohrmann. London: Kluwer Academic Publishers, 13-53.

Rose-Ackerman, Susan. 1999. Corruption and Government: Causes, Consequences, and
Reform. New York: Cambridge University Press.

Rose-Ackerman, Susan. 2002. “When Is Corruption Harmful?” In Political Corruption:
Concepts & Contexts, eds. Arnold J. Heidenheimer and Michael Johnston. New
Brunswick, NJ: Transaction Publishers, 353-71.

Rose-Ackerman, Susan. 2006. “Corruption and Development.” In International
Development Governance, eds. Ahmed Shaﬁqul Huque and Habib Zafarullah. Boca
Raton, FL: Taylor & Francis Group, 289-304.

Ross, John F. 1999. Living Dangerously: Navigating the Risks of Everyday Life.
Cambridge, MA: Perseus Publishing.

159

Samuelson, William, and Richard Zeckhauser. 1988. “Status Quo Bias in Decision
Making.” Journal of Risk and Uncertainty 1 (1): 7-59.

Scheve, Kenneth, and Matthew J. Slaughter. 2001. “What Determines Trade-Policy
Preferences?” Journal of International Economics 54: 267-92.

Scheve, Kenneth, and Matthew J. Slaughter. 2004. “Economic Insecurity and the
Globalization of Production.” American Journal of Political Science 48 (4): 662-74.

Scheve, Kenneth, and Matthew J. Slaughter. 2006. “Public Opinion, International
Economic Integration, and the Welfare State.” In Globalization and Egalitarian
Redistribution, eds. Samuel Bowles, Pranab Bardhan, and Michael Wallerstein.
Princeton, NJ: Princeton University Press, 217-60.

Schulden, J efﬁey, J ieru Chen, Marcie-jo Kresnow, Ileana Arias, Alexander Crosby,
James Mercy, Thomas Simon, Peter Thomas, John Davies-Cole, and David Blythe.
2006. “Psychological Responses to the Sniper Attacks: Washington DC Area,
October 2002.” American Journal of Preventive Medicine 31 (4): 324-27.

Scott, James C. 1972. Comparative Political Corruption. Englewood Cliffs, NJ: Prentice-
Hall.

Sears, David O., and Carolyn L. Funk. 1990. “Self-Interest in Americans’ Political
Opinions.” In Beyond Self-Interest, ed. Jane J. Mansbridge. Chicago: University of
Chicago Press.

Sears, David 0., Richard R. Lau, Tom R. Tyler, and Hanis M. Allen, Jr. 1980. “Self-
Interest vs. Symbolic Politics in Policy Attitudes and Presidential Voting.” American
Political Science Review 74 (3): 670-84.

Seligson, Mitchell. 2002a. “The Impact of Corruption on Regime Legitimacy: A
Comparative Study of Four Latin American Countries.” Journal of Politics 64 (2):
408-33.

Seligson, Mitchell. 2002b. “On the Measurement of Corruption.” APSA—CP Newsletter
13 (2): 5-6.

Shleifer, Andrei, and Robert W. Vishny. 1993. “Corruption.” Quarterly Journal of
Economics 108 (3): 599-617.

Shleifer, Andrei, and Robert W. Vishny. 1994. “Politicians and Firms.” Quarterly
Journal of Economics 109: 995-1026.

Sik, Endre. 2002. “The Bad, the Worse and the Worst: Guesstimating the Level of
Corruption.” In Political Corruption in Transition: A Skeptic ’s Handbook, eds.

Stephen Kotkin and Andras Sajo. Budapest: Central European University Press, 91 -
1 13.

160

Silver, Brian D., and Paul Burton. 2006. “The Credibility of Disaster Evacuation
Warnings: The Effects of Issue Framing and Trust in Government.” Paper prepared
for presentation at the Annual Meeting of the American Political Science Association,
Philadelphia, PA.

Simon, Rita J. 1987. “Immigration and American Attitudes.” Public Opinion 10 (2): 47-
50.

Sjoberg, Lennart, Daniela Kolarova, Alina-Anca Rucai, and Marie-Louise Bemstrom.
2000. “Risk Perception in Bulgaria and Romania.” Cross-Cultural Risk Perception: A
Survey of Empirical Studies, eds. Ortwin Renn and Bemd Rohrmann. Boston: Kluwer
Academic Publishers, 145-83.

Skitka, Linda J ., Christopher W. Bauman, and Elizabeth Mullen. 2004. “Political
Tolerance and Coming to Psychological Closure Following the September 11, 2001,

Terrorist Attacks: An Integrative Approach.” Personality and Social Psychology
Bulletin 30(6): 743-56.

Slaughter, Ann-Marie. 2005. “Security, Solidarity, and Sovereignty: The Grand Themes
of UN Reform.” American Journal of International Law 99 (3): 619-31.

Slovic, Paul. 2000a. “Introduction and Overview.” In The Perception of Risk, ed. Paul
Slovic. Sterling, VA: Earthscan, xxi-xxxvii.

Slovic, Paul. 2000b. “Trust, Emotion, Sex, Politics and Science: Surveying the Risk-
assessment Battleﬁeld.” In The Perception of Risk, ed. Paul Slovic. Sterling, VA:
Earthscan, 390-412.

Slovic, Paul, Baruch Fischhoff, and Sarah Lichtenstein. 1979. “Rating the Risks.”
Environment 2 (3): 14-20, 36-39.

Slovic, Paul, James Flynn, C. K. Mertz, Marc Poumadere, and Claire Mays. 2000.
“Nuclear Power and the Public: A Comparative Study of Risk Perception in France
and the United States.” In Cross-Cultural Risk Perception: A Survey of Empirical
Studies, eds. Ortwin Renn and Bemd Rohrmann. London: Kluwer Academic
Publishers, 55-102.

Smith, Craig S. 2005. “10 Ofﬁcers Shot as Riots Worsen in French Cities.” New York
Times, November 7.

Steenbergen, Marco R., and Bradford S. Jones. 2002. “Modeling Multilevel Data
Structures.” American Journal of Political Science 46 (1): 218-37.

Stone, Deborah. 2002. Policy Paradox: The Art of Political Decision Making (Revised
Edition). New York: W. W. Norton & Company.

161

Suhrke, Astri. 1999. “Human Security and the Interests of States.” Security Dialogue 30
(3): 265-76.

Suhrke, Astri. 2004. “A Stalled Initiative.” Security Dialogue 35 (3): 365.

Sung, Hung-en. 2004. “Democracy and Political Corruption: A Cross-National
Comparison.” Crime, Law & Social Change 41: 179-94.

Tanzi, Vito. 1998. “Corruption around the World: Causes, Consequences, Scope, and
Cures.” IMF Staﬂ Papers 45 (4): 559-94.

Tanzi, Vito, and Hamid Davoodi. 1997. “Corruption, Public Investment, and Growth.”
IMF Working Paper WP/9 7/139. Washington, DC: International Monetary Fund.

Theobald, Robin. 1990. Corruption, Development and Underdevelopment. Durham, NC:
Duke University Press.

Thomas, Caroline. 1999. “Introduction.” In Globalization, Human Security, and the
African Experience, eds. Caroline Thomas and Peter Wilkin. Boulder, CO: Lynne
Rienner Publishers, 1-19.

Thomas, Nicholas, and William T. Tow. 2002. “The Utility of Human Security:
Sovereignty and Humanitarian Intervention.” Security Dialogue 33 (2): 177-92.

Torgler, Benno. 2005. “Tax Morale in Latin America.” Public Choice 122: 133-57.

Treisman, Daniel. 2000. “The Causes of Corruption: A Cross-National Study.” Journal of
Public Economics 76: 399-457.

Tversky, Amos, and Daniel Kahneman. 1974. “Judgment under Uncertainty: Heuristics
and Biases.” Science 185 (4157): 1124-31.

Ullman, Richard H. 1983. “Redeﬁning Security.” International Security 8: 129-53.

United Nations. 1999. “Secretary-General Presents His Annual Report to General
Assembly.” Press Release SG/SM/1736 GA/95696 (1999). New York.

United Nations. 2000. “Secretary-General Statement to General Assembly.” Available
online at hm;://www.un.orgimillennium/sg/report/state.htrn. (March 21, 2008).

United Nations. 2004. A More Secure World: Our Shared Responsibility. Report of the
High-Level Panel on Threats, Challenges, and Change. New York: United Nations.

 

United Nations Development Program (UNDP). 1994. Human Development Report I 994.
New York: Oxford University Press.

Waterbury, John. 1973. “Endemic and Planned Corruption in a Monarchial Regime.”
World Politics 25 (4): 533-55.

162

Wei, Shang-J in. 2000. “How Taxing Is Corruption on International Investors?” Review of
Economics and Statistics 82 (1): 1-11.

Wellisch, Dietmar, and Uwe Walz. 1999. “Why Do Rich Countries Prefer Free Trade
over Free Migration? The Role of the Modern Welfare State.” European Economic
Review 42: 1595-1612.

Wilkin, Peter. 1999. “Human Security and Class in a Global Economy.” In Globalization,
Human Security, and the African Experience, eds. Caroline Thomas and Peter Wilkin.
Boulder, CO: Lynne Rienner Publishers, 23-40.

Wilson, Barbara J ., Nicole Martins, and Amy L. Marske. 2005. “Children’s and Parents’
Fright Reactions to Kidnapping Stories in the News.” Communication Monographs
72 (1): 46-70.

Wolfe, Robert, and Matthew Mendelsohn. 2005. “Values and Interests in Attitudes
toward Trade and Globalization.” Canadian Journal of Political Science 38 (l): 45-
68.

Wolfers, Arnold. 1952. “‘National Security’ as an Ambiguous Symbol.” Political Science
Quarterly 67 (4): 481-502.

Zaller, John R. 1992. The Nature and Origins of Mass Opinion. New York: Cambridge
University Press.

163

s
m
R
A
R
m
L
V-
H
s
R
E
.v.
N
U
E
T
A
T
S
N
A
m
H
m
M

336

llllllllllllll

8

303

31

 

.
.
..
. . .
n
.
.
. .
r
. .
. ..
. .
. . .
. .
. .. .-
.
.I I q
.-
.c .I
. ...-
. .
. n .
.
. . . .
.
. .
. - Q
~ Q.
n O
. .
. .
. . .. .
a o
.. . . .
. . . . n
. . . .
. .
. .. .
. I
. ... ..
.- I a .
. C
I . . ‘
. . .
. u I |
v. D . .
. .
. ,.
‘ o .l. a
. .
\ . c .
. .
.
.
. .
..
. .