Online Appendix Communication Infrastructure and Stabilizing Food Prices:Evidence from the Telegraph Network in China Pei Gao Yu-Hsiang Lei Figure A 1. Original Telegram Transmitting Commercial Information Notes: This figure presents the original telegram transmitting commercial information on grain trade (Tsu and Elman, 2014). The right panel shows a series of four-digit codes used to transmit the telegram message, and the left panel depicts the message deciphered from the codes. The fact that code for rice existed even in the earliest version of the telegraph codebook suggests that there might have been a high volume of telegrams exchanged about rice, including those between businessmen. The telegram was sent from Mr. Li in Shanghai to Mr. Zhang, who was handling a business called Tiansheng Hao in Suzhou, which was at the time the most important grain market in southern China. The message was, “instead of cotton cloth, purchase 3200 shi rice and ship quickly.” It is possible that there was a sudden surge in rice prices in Shanghai, and Mr. Li responded by immediately sending a telegram to his supplier in Suzhou to secure a bulk order for rice. 1
16
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Online Appendix
Communication Infrastructure and Stabilizing Food Prices:Evidence from theTelegraph Network in China
Pei Gao Yu-Hsiang Lei
Figure A 1. Original Telegram Transmitting Commercial InformationNotes: This figure presents the original telegram transmitting commercial information on grain trade (Tsu
and Elman, 2014). The right panel shows a series of four-digit codes used to transmit the telegram message,
and the left panel depicts the message deciphered from the codes. The fact that code for rice existed even in
the earliest version of the telegraph codebook suggests that there might have been a high volume of telegrams
exchanged about rice, including those between businessmen. The telegram was sent from Mr. Li in Shanghai
to Mr. Zhang, who was handling a business called Tiansheng Hao in Suzhou, which was at the time the
most important grain market in southern China. The message was, “instead of cotton cloth, purchase 3200
shi rice and ship quickly.” It is possible that there was a sudden surge in rice prices in Shanghai, and Mr.
Li responded by immediately sending a telegram to his supplier in Suzhou to secure a bulk order for rice.
1
Figure A 2. The Variation of Price across Prefectures between 1890 and 1911Notes: This figure presents the standard deviation of the maximum price for rice of all three grades from
1890 to 1904. The volatility of price for R1 and R2 (high and medium-quality rice), which are considered
the commonly traded grains, increased substantially after 1904. Such structural change in rice prices could
be caused by political chaos at that time. Qing government also started to employ the wireless telegraph
system. Therefore, we restrict our sample between 1870 and 1904, which begins ten years before the first
domestic telegraph line was introduced in China and ends before the adoption of the wireless telegraph
system.
2
Table A 1: Telegraph-Connection Year and Prefecture Characteristics(1) (2) (3) (4) (5)
Notes: This table shows the associations between a list of prefecture-specific features and the year inwhich telegraph connection starts. Those prefectures without telegraph access during the sample periodare assumed to access to the telegraph in 1905. Robust standard errors are in the parentheses.
3
Tab
leA
2:R
obust
nes
s–
Ass
um
pti
ons
onT
eleg
raph
Con
nec
tion
Tim
eA
ssum
pti
on:
(1)
(2)
(3)
(4)
(5)
(6)
The
mon
thof
the
tele
grap
h’s
arri
via
lM
arch
Sep
tem
ber
Dec
emb
er
Hig
h-q
ual
ity
Med
ium
-qual
ity
Hig
h-q
ual
ity
Med
ium
-qual
ity
Hig
h-q
ual
ity
Med
ium
-qual
ity
Ric
e(R
1)R
ice
(R2)
Ric
e(R
1)R
ice
(R2)
Ric
e(R
1)R
ice
(R2)
Pan
elA
:O
utc
ome
vari
able
-M
axim
um
Pri
ce
Tel
egra
ph
acce
ss-1
1.69
0-8
.727
-11.
720
-8.9
23-1
1.45
0-8
.704
(3.6
19)
(3.6
74)
(3.6
49)
(3.7
04)
(3.6
58)
(3.7
11)
Pre
fect
ure
FE
YY
YY
YY
Tim
eF
EY
YY
YY
YP
rovin
ce×
Tim
eY
YY
YY
YT
ime-
inva
rian
tco
nt.
×T
ime
YY
YY
YY
Tim
e-va
ryin
gco
ntr
ols
YY
YY
YY
Obse
rvat
ions
47,4
3647
,436
47,4
3647
,436
47,4
3647
,436
R-s
quar
ed0.
815
0.80
20.
815
0.80
20.
815
0.80
2
Pan
elB
:O
utc
ome
vari
able
-Spik
esof
Max
imum
Pri
ce
Tel
egra
ph
acce
ss-0
.007
0-0
.008
2-0
.005
4-0
.006
6-0
.004
8-0
.006
3(0
.002
2)(0
.002
4)(0
.002
1)(0
.002
4)(0
.002
1)(0
.002
4)
Pre
fect
ure
FE
YY
YY
YY
Tim
eF
EY
YY
YY
YP
rovin
ce×
Tim
eY
YY
YY
YT
ime-
inva
rian
tco
nt.
×T
ime
YY
YY
YY
Tim
e-va
ryin
gco
ntr
ols
YY
YY
YY
Obse
rvat
ions
47,4
3647
,436
47,4
3647
,436
47,4
3647
,436
R-s
quar
ed0.
054
0.05
50.
054
0.05
50.
054
0.05
5
Not
es:
Th
ista
ble
rep
lica
tes
the
bas
elin
ere
sult
ssh
own
inT
ab
le3
but
ass
um
esth
at
the
tele
gra
ph
conn
ecti
on
month
tob
eM
arc
h,
Sep
tem
ber
,and
Dec
emb
erin
stea
d.
Th
ed
epen
den
tva
riab
lein
Panel
Ais
the
maxim
um
pri
ce;
an
dth
ed
epen
den
tva
riab
lein
Pan
elB
isth
ein
cid
ence
of
pri
cesp
ikes
.Telegra
phaccess i
tis
ab
inar
yva
riab
leth
at
take
sth
eva
lue
of
on
efr
om
the
month
of
the
arr
ival
of
the
tele
gra
ph
onw
ard
s.T
he
contr
ols
are
the
sam
eas
inco
lum
n(3
)in
Tab
le3.
Sta
nd
ard
erro
rsin
pare
nth
eses
are
clu
ster
edat
the
pre
fect
ura
lle
vel.
4
Tab
leA
3:R
obust
nes
s–
Diff
eren
tC
ut-
offs
toD
efine
Pri
ceSpik
es
Dep
.V
ar.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Pri
ceS
pik
esof
Hig
h-q
ual
ity
Ric
e(R
1)P
rice
Sp
ikes
ofM
ediu
m-q
ual
ity
Ric
e(R
2)
2S
D2.
25S
D2.
75S
D3
SD
2S
D2.
25S
D2.
75S
D3
SD
abov
eth
em
ean
abov
eth
em
ean
abov
eth
em
ean
abov
eth
em
ean
abov
eth
em
ean
abov
eth
em
ean
abov
eth
em
ean
abov
eth
em
ean
Tel
egra
ph
acce
ss-0
.005
5-0
.005
8-0
.006
5-0
.006
3-0
.005
9-0
.007
1-0
.008
3-0
.007
1(0
.002
7)(0
.002
4)(0
.002
0)(0
.002
0)(0
.003
0)(0
.002
6)(0
.002
1)(0
.002
1)
Pre
fect
ure
FE
YY
YY
YY
YY
Tim
eF
EY
YY
YY
YY
YP
rovin
ce×
Tim
eY
YY
YY
YY
YT
ime-
inva
rian
tco
nt.
×T
ime
YY
YY
YY
YY
Tim
e-va
ryin
gco
ntr
ols
YY
YY
YY
YY
Ob
serv
atio
ns
47,4
3647
,436
47,4
3647
,436
47,4
3647
,436
47,4
3647
,436
R-s
qu
ared
0.06
190.
0584
0.05
120.
0489
0.06
280.
0583
0.05
120.
0492
Not
es:
Th
ista
ble
show
sth
eeff
ect
ofte
legr
aph
acc
ess
inm
itig
ati
ng
the
inci
den
ceof
pri
cesp
ikes
defi
ned
wit
hd
iffer
ent
cuto
ff.
InT
ab
le3,
we
defi
ne
pri
cesp
ikes
asth
ose
mon
th-o
ver-
mon
thgr
owth
rate
sm
ore
than
2.5
stan
dard
dev
iati
on
sth
an
the
mea
n,
an
din
this
tab
le,
we
chan
ge
the
cuto
ffto
be
2,2.
25,
2.75
,an
d3
stan
dar
dd
evia
tion
sh
igh
erth
an
the
mea
nre
spec
tive
ly.
Colu
mn
s(1
)-
(4)
rep
ort
the
resu
lts
on
hig
h-q
uali
tyR
1ri
ce,
an
dC
olu
mn
s(5
)-
(8)
rep
ort
the
resu
lts
for
med
ium
-qu
ali
tyR
2ri
ce.
Th
ere
gre
ssorTelegra
phaccess i
tis
ab
inary
vari
ab
leth
at
take
sth
eva
lue
of
one
from
the
mon
thof
the
arri
val
ofth
ete
legr
ap
honw
ard
s.T
he
contr
ols
are
the
sam
eas
inco
lum
n(3
)in
Tab
le3.
Sta
nd
ard
erro
rsin
pare
nth
eses
are
clu
ster
edat
the
pre
fect
ura
lle
vel.
5
Table A 4: Robustness – Spatial Clustered Standard Errors(1) (2) (3) (4)
Dep. Var. Panel A: Maximum price Panel B: Spikes of max price
Prefecture FE Y Y Y YTime FE Y Y Y YProvince×Time Y Y Y YTime-invariant cont. × Time Y Y Y YTime-varying controls Y Y Y YObservations 47,436 47,436 47,436 47,436R-squared 0.273 0.275 0.00220 0.00244
Notes: This table replicates the baseline results shown in Table 3 but adjusts the standard errors toreflect spatial dependence as modeled in Conley (1999) and Conley (2008). Spatial autocorrelation isassumed to linearly decrease with distance up to a cut-off of 500 km. Distances are computed fromprefecture centroids. The regressor Telegraph accessit is a binary variable that takes the value of onefrom the month of the arrival of the telegraph onwards. Spatial HAC errors in parentheses are clusteredat the prefectural level.
6
Table A 5: The Effect of Telegraph Access on Extreme Price of Soya Bean(1) (2) (3) (4) (5) (6)
Dep. Var. Panel A: Maximum price Panel B: Price Spikes
Prefecture FE Y Y Y Y Y YTime FE Y Y Y Y Y YProvince × Time Y Y Y Y Y YTime-invariant cont. × Time Y Y Y YTime-varying controls Y YNo. of Obs. 20,014 20,014 20,014 20,014 20,014 20,014R-squared 0.679 0.682 0.683 0.048 0.048 0.049
Notes: This table shows the effect of telegraph access on attenuating extreme price of soya bean. Thedependent variable in Panel A is the maximum price; and the dependent variable in panel B is theincidence of price spikes. The regressor Telegraph accessit is a binary variable that takes the value ofone from the month of the arrival of the telegraph onwards. The basic specification includes prefectureFE, time FE and provincial time trend in columns (1) and (4). In columns (2) and (5) we allow thetime-invariant prefectural characteristics Xi (i.e. longitude, latitude, river density, ruggedness, rice yieldpotential index and coastal access) to vary over time by interacting them with the time trend; and incolumns (3) and (6) we add a vector of the time-varying prefecture characteristics, Zijt (i.e. yearlyextreme weather index, railway access dummy and treaty port status). Standard errors in parenthesesare clustered at the prefectural level.
7
Table A 6: The Effect of Telegraph Access on Extreme Price of Low-quality Rice
(1) (2) (3) (4) (5) (6)
Panel A: Panel B:Dep. Var. Maximum price Spikes of maximum price
Prefecture FE Y Y Y Y Y YTime FE Y Y Y Y Y YProvince × Time Y Y Y Y Y YTime-invariant cont. × Time Y Y Y YTime-varying controls Y YNo. of Obs. 41,682 41,682 41,682 41,682 41,682 41,682R-squared 0.848 0.850 0.851 0.0634 0.0636 0.0638
Notes: This table shows the effect of telegraph access on the extreme price of low-quality rice (R3). Thedependent variable in columns (1)-(3) is the maximum price; and the dependent variable in columns(4)-(6) is the incidence of price spikes. The regressor Telegraph accessit is a binary variable that takesthe value of one from the month of the arrival of the telegraph onwards. The basic specification includesprefecture FE, time FE, and provincial time trend in columns (1) and (4). In columns (2) and (5) weallow the time-invariant prefectural characteristics Xi (i.e. longitude, latitude, river density, ruggedness,rice yield potential index and coastal access) to vary over time by interacting them with the time trend;and in columns (3) and (6) we add a vector of the time-varying prefecture characteristics, Zijt (i.e. yearlyextreme weather index, railway access dummy, and treaty port status). Standard errors in parenthesesare clustered at the prefectural level.
8
Table A 7: The Spillover Effect of Adopting the Telegraph
Share of neighbors with telegraph -0.0003 -0.0063(0.0045) (0.0042)
Indicator for any neighbor with telegraph -0.0001 -0.0017(0.0031) (0.0031)
All baseline controls Y Y Y YObservations 47,436 47,436 47,436 47,436R-squared 0.054 0.054 0.055 0.055
Notes: This table addresses the concern of spillover effect from neighboring prefectures that are connectedwith the telegraph. In Panel A the dependent variable is the maximum price; and in Panel B thedependent variable is the incidence of price spikes. Telegraph accessit is a binary variable that takes thevalue of one from the month of the arrival of the telegraph onwards. Share of neighbors with telegraph isdefined as the share of neighboring prefectures that adopted the telegraph in a given year. Any neighborwith telegraph is an indicator that a prefecture has a neighboring prefecture with access to the telegraph.The controls are the same as in column (3) in Table 3. Standard errors in parentheses are clustered atthe prefectural level.
9
Tab
leA
8:T
he
Eff
ect
ofT
eleg
raph
Con
nec
tion
and
the
Sca
leof
Net
wor
k
Dep
.V
ar.
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Max
imum
pri
ceSpik
esof
Max
imum
Pri
ce
Hig
h-q
ual
ity
Ric
e(R
1)M
ediu
m-q
ual
ity
Ric
e(R
2)H
igh-q
ual
ity
Ric
e(R
1)M
ediu
m-q
ual
ity
Ric
e(R
2)
Pan
elA
:Sca
leof
Net
wor
kI
-th
eN
um
ber
ofO
ther
Pre
fect
ure
sw
ith
Tel
egra
ph
Acc
ess
Tel
egra
ph
acce
ss-1
1.65
-18.
91-1
1.01
-20.
10-0
.013
0-0
.001
4-0
.013
9-0
.005
0(5
.295
)(8
.282
)(5
.178
)(7
.920
)(0
.007
8)(0
.007
8)(0
.007
7)(0
.007
4)T
ele×
Sca
leof
net
wor
kI
0.07
860.
0983
-0.0
0012
5-0
.000
096
(0.0
880)
(0.0
847)
(0.0
0005
2)(0
.000
056)
Sca
leof
net
wor
kI
0.31
40.
475
-2.0
56-1
.854
-0.0
067
-0.0
070
-0.0
062
-0.0
064
(4.9
43)
(5.0
17)
(4.8
92)
(4.9
73)
(0.0
082)
(0.0
081)
(0.0
080)
(0.0
080)
All
bas
elin
eco
ntr
ols
YY
YY
YY
YY
Obse
rvat
ions
47,4
3647
,436
47,4
3647
,436
47,4
3647
,436
47,4
3647
,436
R-s
quar
ed0.
815
0.81
50.
802
0.80
20.
0541
0.05
420.
0550
0.05
51
Pan
elB
:Sca
leof
Net
wor
kII
-th
eN
um
ber
ofO
ther
Pre
fect
ure
sw
ith
Tel
egra
ph
Acc
ess
(Rel
atic
epri
cead
just
ed)
Tel
egra
ph
acce
ss-1
2.05
-14.
59-9
.106
-11.
29-0
.006
590.
0084
-0.0
080
0.00
45(3
.625
)(4
.034
)(3
.689
)(4
.233
)(0
.002
2)(0
.005
5)(0
.002
5)(0
.006
6)T
ele×
Sca
leof
net
wor
kII
-0.0
922
-0.0
754
-0.0
057
-0.0
048
(0.0
397)
(0.0
371)
(0.0
020)
(0.0
022)
Sca
leof
net
wor
kII
0.03
110.
0630
0.02
590.
0507
-0.0
348
-0.0
438
-0.0
433
-0.0
508
(0.0
373)
(0.0
413)
(0.0
425)
(0.0
489)
(0.0
224)
(0.0
223)
(0.0
196)
(0.0
200)
All
bas
elin
eco
ntr
ols
YY
YY
YY
YY
Obse
rvat
ions
47,4
3647
,436
47,4
3647
,436
47,4
3647
,436
47,4
3647
,436
R-s
quar
ed0.
815
0.81
50.
802
0.80
30.
0542
0.05
430.
0552
0.05
53
Not
es:
This
tab
lesh
ows
wh
eth
erth
ete
legr
aph
’seff
ect
dep
end
son
the
scale
of
the
tele
gra
ph
net
work
.In
Pan
elA
,th
esi
zeof
the
net
work
issi
mp
lym
easu
red
by
the
nu
mb
erof
oth
erp
refe
ctu
res
wit
hte
legra
ph
conn
ecti
on
,an
din
Panel
Bth
em
easu
rem
ent
isad
just
edby
agiv
enm
ark
et’s
pri
cep
osit
ion
rela
tive
toot
her
con
nec
ted
regi
ons
inth
en
etw
ork
.Telegra
phaccess i
tis
abin
ary
vari
ab
leth
at
takes
the
valu
eof
on
efr
om
the
month
of
the
arri
val
ofth
ete
legr
aph
onw
ard
s.T
he
contr
ols
are
the
sam
eas
inco
lum
n(3
)in
Tab
le3.
Sta
nd
ard
erro
rsin
pare
nth
eses
are
clu
ster
edat
the
pre
fect
ura
lle
vel.
10
Tab
leA
9:R
obust
nes
sfo
rShock
sN
ear
and
Far
–A
ssum
eD
ista
nce
Ela
stic
ity
as-1
.5
Gra
des
ofR
ices
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Pan
elA
:O
utc
ome
vari
able
-M
axim
um
pri
ceP
anel
B:
Outc
ome
vari
able
-Spik
esof
Max
Pri
ces
Hig
h-q
ual
ity
Ric
e(R
1)M
ediu
m-q
ual
ity
Ric
e(R
2)H
igh-q
ual
ity
Ric
e(R
1)M
ediu
m-q
ual
ity
Ric
e(R
2)
Tel
egra
ph
acce
ss-1
1.94
-23.
25-9
.040
-20.
96-0
.006
4-0
.010
6-0
.007
8-0
.013
2(3
.618
)(5
.876
)(3
.677
)(5
.655
)(0
.002
1)(0
.005
1)(0
.002
4)(0
.005
3)L
oca
lflood
2.90
24.
668
2.75
64.
426
0.00
390.
0061
0.00
460.
0068
(1.5
52)
(1.8
05)
(1.4
94)
(1.7
39)
(0.0
021)
(0.0
023)
(0.0
019)
(0.0
021)
Loca
ldro
ugh
t2.
203
3.46
82.
589
3.81
00.
0067
0.00
880.
0059
0.00
69(1
.356
)(1
.545
)(1
.318
)(1
.508
)(0
.002
4)(0
.002
5)(0
.002
4)(0
.002
7)T
ele×
Loca
lflood
-6.4
68-5
.959
-0.0
092
-0.0
093
(3.1
61)
(3.1
05)
(0.0
041)
(0.0
044)
Tel
e×
Loca
ldro
ugh
t-7
.054
-6.9
50-0
.009
7-0
.005
2(3
.169
)(3
.104
)(0
.005
2)(0
.005
0)F
loods
inot
her
connec
ted
regi
ons
21.3
012
.76
21.4
113
.19
0.02
450.
0174
0.01
610.
0095
(7.8
06)
(8.3
41)
(8.1
24)
(8.8
89)
(0.0
073)
(0.0
082)
(0.0
070)
(0.0
080)
Dro
ugh
tsin
other
connec
ted
regi
ons
2.80
8-3
.284
2.39
0-4
.473
0.01
470.
0136
0.01
440.
0124
(6.9
55)
(7.2
83)
(7.4
28)
(7.5
68)
(0.0
088)
(0.0
090)
(0.0
084)
(0.0
085)
Tel
e×
Flo
ods
inot
her
connec
ted
regi
ons
31.6
531
.00
0.02
540.
0256
(10.
67)
(10.
70)
(0.0
129)
(0.0
123)
Tel
e×
Dro
ugh
tsin
other
connec
ted
regi
ons
28.5
230
.96
0.01
280.
0127
(12.
14)
(11.
83)
(0.0
122)
(0.0
115)
All
bas
elin
eco
ntr
ols
YY
YY
YY
YY
Obse
rvat
ions
47,4
3647
,436
47,4
3647
,436
47,4
3647
,436
47,4
3647
,436
R-s
quar
ed0.
815
0.81
60.
803
0.80
40.
054
0.05
50.
055
0.05
5
Not
e:T
his
tab
lep
rese
nts
aro
bu
stn
ess
chec
ksh
owin
gth
at
wit
hd
iffer
ent
dis
tan
ceel
ast
icit
yass
um
ed,
the
resu
lts
of
Tab
le5
rem
ain
con
sist
ent.
We
rep
lica
teth
esp
ecifi
cati
onin
Tab
le5
bu
tse
tth
ed
ista
nce
elast
icit
yof
trad
eto
-1.5
.In
Pan
elA
the
dep
end
ent
vari
ab
leis
the
maxim
um
pri
ce;
and
inP
anel
Bth
ed
epen
den
tva
riab
leis
the
inci
den
ceof
pri
cesp
ikes
.Telegra
phaccess i
tis
ab
inary
vari
ab
leth
at
take
sth
eva
lue
of
on
efr
om
the
mon
thof
the
arri
val
ofth
ete
legr
aph
onw
ard
s.Localflood
/drought i,t
isa
du
mm
yva
riab
lein
dic
ati
ng
wh
eth
erth
eex
trem
efl
ood
/d
rou
ght
occ
urr
edin
agi
ven
pre
fect
ure
.Flood/droughtin
other
telegraph-connectedregions
are
con
stru
cted
by
takin
ga
sum
of
the
ind
icato
rfo
rw
eath
ersh
ock
sin
oth
erte
legr
aph
-con
nec
ted
pre
fect
ure
s,w
eighte
dby
the
(inve
rse)
bil
ate
ral
dis
tan
ce.
We
set
the
dis
tan
ceel
ast
icit
yof
trad
eto
-1.5
.T
he
contr
ols
are
the
sam
eas
inco
lum
n(3
)in
Tab
le3.
Sta
nd
ard
erro
rsin
pare
nth
eses
are
clu
ster
edat
the
pre
fect
ura
lle
vel.
11
Tab
leA
10:
Pla
ceb
o–
Shock
sIn
Reg
ions
wit
hN
oT
eleg
raph
Acc
ess
Gra
des
ofR
ices
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Pan
elA
:O
utc
ome
vari
able
-M
axim
um
pri
ceP
anel
B:
Ou
tcom
eva
riab
le-
Pri
ceS
pik
esof
Max
Pri
ces
Hig
h-q
ual
ity
Ric
e(R
1)M
ediu
m-q
ual
ity
Ric
e(R
2)H
igh
-qu
alit
yR
ice
(R1)
Med
ium
-qu
alit
yR
ice
(R2)
Tel
egra
ph
acce
ss-1
1.88
-17.
27-8
.999
-13.
91-0
.006
3-0
.012
5-0
.007
8-0
.016
3(3
.607
)(6
.162
)(3
.666
)(5
.903
)(0
.002
2)(0
.004
9)(0
.002
4)(0
.004
6)F
lood
sin
oth
erre
gion
sw
ith
out
tele
grap
h1.
537
1.26
61.
034
1.04
7-0
.001
1-0
.003
0-0
.002
8-0
.004
5(4
.139
)(4
.118
)(4
.094
)(4
.091
)(0
.004
3)(0
.004
5)(0
.003
7)(0
.003
7)D
rou
ghts
inot
her
regi
ons
wit
hou
tte
legr
aph
5.46
04.
503
4.39
83.
629
-0.0
041
-0.0
073
-0.0
057
-0.0
081
(3.3
00)
(3.3
84)
(3.0
58)
(3.1
71)
(0.0
045)
(0.0
047)
(0.0
045)
(0.0
047)
Tel
e×
Flo
od
sin
oth
erre
gion
sw
ith
out
tele
grap
h-1
6.26
-24.
150.
0269
0.02
01(1
6.34
)(1
5.49
)(0
.020
2)(0
.021
2)T
ele×
Dro
ugh
tsin
oth
erre
gion
sw
ith
out
tele
grap
h-2
4.46
-30.
130.
0141
0.03
80(2
6.21
)(2
5.78
)(0
.021
3)(0
.023
5)
Loca
ld
rou
ghts
/fl
ood
sY
YY
YY
YY
YL
oca
ld
rou
ghts
/fl
ood
s×
Tel
egra
ph
YY
YY
Dro
ugh
ts/F
lood
sin
oth
erco
nn
ecte
dre
gion
sY
YY
YY
YY
YD
rou
ghts
/Flo
od
sin
oth
erco
nn
ecte
dre
gion
s×
Tel
egra
ph
YY
YY
All
bas
elin
eco
ntr
ols
YY
YY
YY
YY
Ob
serv
atio
ns
47,4
3647
,436
47,4
3647
,436
47,4
3647
,436
47,4
3647
,436
R-s
qu
ared
0.81
50.
816
0.80
30.
804
0.05
40.
055
0.05
50.
056
Not
es:
Th
ista
ble
per
form
sa
pla
ceb
ote
stto
see
wh
eth
erth
ete
legra
ph
’sarr
ival
make
slo
cal
pri
cere
spon
dto
extr
eme
wea
ther
shock
sin
dis
tant
regi
ons
that
had
no
tele
grap
hac
cess
.In
Pan
elA
the
dep
end
ent
vari
ab
leis
the
maxim
um
pri
ce;
an
din
Pan
elB
the
dep
end
ent
vari
ab
leis
the
inci
den
ceof
pri
cesp
ikes
.Telegra
phaccess i
tis
ab
inary
vari
ab
leth
at
take
sth
eva
lue
of
on
efr
om
the
month
ofth
earr
ivalof
the
tele
gra
ph
onw
ard
s.Localflood
/drought i,t
isa
du
mm
yva
riab
lein
dic
ati
ng
wh
eth
erth
eex
trem
efl
ood
/d
rou
ght
occ
urr
edin
agiv
enp
refe
ctu
re.Flood/droughtin
other
telegraph-connectedregions
are
con
stru
cted
by
takin
ga
sum
of
the
ind
icato
rfo
rw
eath
ersh
ock
sin
oth
erte
legr
ap
h-c
on
nec
ted
pre
fect
ure
s,w
eighte
dby
the
(inve
rse)
bil
ater
ald
ista
nce
.Flood/droughtin
other
regionswithoutthetelegraph
mea
sure
wea
ther
shock
sin
pre
fect
ure
sw
ith
ou
tte
legra
ph
acce
ss.
Th
eco
ntr
ols
are
the
sam
eas
inco
lum
n(3
)in
Tab
le3.
Sta
nd
ard
erro
rsin
pare
nth
eses
are
clu
ster
edat
the
pre
fect
ura
lle
vel.
12
Tab
leA
11:
Rob
ust
nes
sfo
rShock
sN
ear
and
Far
–E
xcl
udin
gP
refe
cture
sw
ith
Dis
aste
rR
elie
f
Gra
des
ofR
ices
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Pan
elA
:O
utc
ome
vari
able
-M
axim
um
pri
ceP
anel
B:
Ou
tcom
eva
riab
le-
Sp
ikes
ofM
axP
rice
s
Hig
h-q
ual
ity
Ric
e(R
1)M
ediu
m-q
ual
ity
Ric
e(R
2)H
igh
-qu
alit
yR
ice
(R1)
Med
ium
-qual
ity
Ric
e(R
2)
Tel
egra
ph
acce
ss-1
2.19
-18.
42-9
.304
-16.
13-0
.005
6-0
.007
7-0
.006
2-0
.010
2(3
.565
)(5
.240
)(3
.604
)(5
.107
)(0
.002
2)(0
.004
3)(0
.002
4)(0
.004
5)L
oca
lfl
ood
2.52
24.
230
2.33
53.
971
0.00
386
0.00
580.
0042
0.00
62(1
.618
)(1
.855
)(1
.572
)(1
.798
)(0
.002
2)(0
.002
4)(0
.002
1)(0
.002
2)L
oca
ld
rou
ght
1.83
53.
244
2.23
53.
620
0.00
660.
0094
0.00
560.
0072
(1.3
20)
(1.5
30)
(1.2
89)
(1.4
91)
(0.0
024)
(0.0
026)
(0.0
0243
)(0
.002
7)T
ele×
Loca
lfl
ood
-6.8
24-6
.382
-0.0
0807
-0.0
081
(3.2
57)
(3.2
18)
(0.0
042)
(0.0
045)
Tel
e×
Loca
ld
rou
ght
-7.7
58-7
.894
-0.0
133
-0.0
084
(3.4
04)
(3.3
30)
(0.0
056)
(0.0
052)
Flo
od
sin
oth
erco
nn
ecte
dre
gion
s27
.50
13.3
428
.59
15.1
50.
0277
0.01
610.
0172
0.00
50(1
2.30
)(1
2.68
)(1
2.68
)(1
3.31
)(0
.011
9)(0
.012
8)(0
.011
5)(0
.012
4)D
rou
ghts
inot
her
con
nec
ted
regi
ons
-10.
32-1
9.61
-8.8
35-1
9.69
0.01
870.
0129
0.02
000.
0131
(9.7
94)
(10.
48)
(10.
40)
(10.
36)
(0.0
152)
(0.0
146)
(0.0
146)
(0.0
141)
Tel
e×
Flo
od
sin
oth
erco
nn
ecte
dre
gion
s54
.92
53.4
70.
0435
0.04
83(2
3.36
)(2
3.63
)(0
.023
9)(0
.023
8)T
ele×
Dro
ugh
tsin
oth
erco
nn
ecte
dre
gion
s50
.23
57.0
20.
0409
0.04
03(2
4.44
)(2
4.03
)(0
.027
0)(0
.025
3)
All
bas
elin
eco
ntr
ols
YY
YY
YY
YY
Ob
serv
atio
ns
45,5
0945
,509
45,5
0945
,509
45,5
0945
,509
45,5
0945
,509
R-s
qu
ared
0.81
90.
819
0.80
60.
807
0.05
50.
056
0.05
60.
057
Not
es:
Th
ista
ble
show
sth
atm
ore
effec
tive
gov
ern
men
tin
terv
enti
on
sd
idn
ot
dri
veth
ete
legra
ph
’sm
itig
ati
ng
effec
t.T
od
oso
,w
ere
pli
cate
the
spec
ifica
tion
inT
able
5b
ut
excl
ud
esp
refe
ctu
res
wit
hst
ate
-op
erate
dd
isast
erre
lief
.In
pan
elA
the
dep
end
ent
vari
ab
leis
the
maxim
um
pri
ce;
and
inP
anel
Bth
ed
epen
den
tva
riab
leis
the
inci
den
ceof
pri
cesp
ikes
.Telegra
phaccess i
tis
ab
inary
vari
ab
leth
at
take
sth
eva
lue
of
on
efr
om
the
mon
thof
the
arri
val
ofth
ete
legr
aph
onw
ard
s.Localflood
/drought i,t
isa
du
mm
yva
riab
lein
dic
ati
ng
wh
eth
erth
eex
trem
efl
ood
/d
rou
ght
occ
urr
edin
agi
ven
pre
fect
ure
.Flood/droughtin
other
telegraph-connectedregions
are
con
stru
cted
by
takin
ga
sum
of
the
ind
icato
rfo
rw
eath
ersh
ock
sin
oth
erte
legr
aph
-con
nec
ted
pre
fect
ure
s,w
eighte
dby
the
(inve
rse)
bil
ate
ral
dis
tan
ce.
Th
eco
ntr
ols
are
the
sam
eas
inco
lum
n(3
)in
Tab
le3.
Sta
nd
ard
erro
rsin
par
enth
eses
are
clu
ster
edat
the
pre
fect
ura
lle
vel
.
13
Table A 12: Robustness – The Effect of Telegraph on Price Volatility(1) (2) (3) (4)
Grades of Rice: High-quality Rice (R1) Medium-quality Rice (R2)
Panel A: Subsample-excluding prefectures without telegraph before 1904
Tele × Past volatility -0.0893 -0.146(0.130) (0.133)
Tele × Past weather-induced volatility 0.214 -0.534(0.340) (0.663)
All baseline controls Y Y Y YObservations 2,062 2,062 2,062 2,062R-squared 0.405 0.405 0.400 0.400
Panel D: Change RHS to state-owned telegraph
Public Telegraph 0.0116 0.0274 0.0123 0.0242(0.00323) (0.00932) (0.00341) (0.0129)
Tele × Past volatility -0.419 -0.414(0.0943) (0.0943)
Tele × Past weather-induced volatility -0.889 -0.829(0.304) (0.434)
All baseline controls Y Y Y YObservations 3,529 3,529 3,529 3,529R-squared 0.362 0.359 0.353 0.350
Notes: This table presents four robustness checks to address the potential selection bias of regions withthe telegraph connection. The dependent variable is the volatility of the monthly maximum price. PanelA repeats the same exercise as the baseline in Table 3 in a subsample that only includes prefecturesthat had adopted the telegraph before 1904. Panel B excludes both prefectures that never adopted thetelegraph and provincial capitals from our sample. Panel C performs another sub-sample analysis byexcluding the treaty ports along with their bordered prefectures from our sample. Panel D changes thetreatment variable to a dummy variable indicating whether a prefecture had state-owned telegraph lines.In columns (1)-(2), the dependent variable is the maximum price, and in columns (3)-(4), the dependentvariable is price spikes. The controls are the same as in column (3), as in Table 3. Standard errors inparentheses are clustered at the prefectural level.14
Table A 13: Robustness – The Spillover Effect of Telegraph on Price Volatility(1) (2) (3) (4) (5) (6) (7) (8)
Share of neighbors with telegraph 0.00304 0.00214 0.0012 0.0003(0.0038) (0.0040) (0.0037) (0.0038)
Indicator for any neighbor with telegraph 0.0009 0.0007 0.0019 0.0015(0.0022) (0.0022) (0.0023) (0.0023)
All baseline controls Y Y Y Y Y Y Y YObservations 3,529 3,529 3,529 3,529 3,529 3,529 3,529 3,529R-squared 0.365 0.359 0.364 0.359 0.355 0.350 0.355 0.350
Notes: This table addresses the concern of spillover effect from neighboring prefectures that are con-nected with the telegraph. The dependent variable is the volatility of the monthly maximum price.Telegraph accessit is a binary variable that takes the value of one from the month of the arrival of thetelegraph onwards. Share of neighbors with telegraph is defined as the share of neighboring prefecturesthat adopted the telegraph in a given year. Any neighbor with telegraph is an indicator that a prefecturehas a neighboring prefecture with access to the telegraph. The controls are the same as in column (3)in Table 3. Standard errors in parentheses are clustered at the prefectural level.
All baseline controls Y Y Y YObservations 47,436 47,436 47,436 47,436R-squared 0.815 0.802 0.0541 0.0550
Notes: This table shows whether the effect of telegraph access depends on the railroad connection.In Panel A the dependent variable is the maximum price; and in Panel B the dependent variable isthe incidence of price spikes. Telegraph accessit is a binary variable that takes the value of one fromthe month of the arrival of the telegraph onwards. Railway is a dummy variable indicating whether aprefecture has access to the railway in a given year. The controls are the same as in column (3) in Table3. Standard errors in parentheses are clustered at the prefectural level.