How Wide are European Borders?
New Evidence on the Integration Effects of
Monetary Unions
Guenter W. Beck
Axel A. Weber
Goethe University Frankfurt
Goethe University Frankfurt, CFS
➀ Motivation and Background
➁
➂
➃
➄
Data
Cross-Sectional Evidence for EMU and GEMU
Cross-Sectional/Time-Series Evidence for GEMU
Summary and Conclusions
56th European Meeting of the Econometric Society, August 25-29, 2001
1
M OTIVATION /B ACKGROUND
Empirical literature on goods market integration based on:
➜ trade data
see e.g. McCallum (AER, 1995)
➜ price data
see below
Theoretical background:
➜ PPP/LOOP
Two major strands of literature:
➜ Compare intra-country and inter-country price dispersion
Engel and Rogers (AER (1996), JIE (2000)), ...
➜ Determine half-lives of devations from the PPP/LOOP
Wu (JMCB, (1996), Frankel and Rose (JIE (1996)), ...
M OTIVATION /B ACKGROUND
2
M OTIVATION /B ACKGROUND
Objectives of our paper:
➜ quantify the effects of EMU on intra-European price dispersion
use primarily new data
determine inter-national price dispersion without exchange
rate noise
➜ determine the evolution of inter-German price volatility over
time
➜ quantify the speed of price convergence between East and
West German regions
M OTIVATION /B ACKGROUND
3
DATA
"EMU" data:
➜ CPI data: All items and 7 major groups (not available for all
countries)
➜ 81 locations in 6 European countries
12 German locations, 20 Austrian locations, 4 Swiss locations,
20 Italian locations, Spanish locations, 7 Portugese locations
➜ Data period: January 1995 - June 2000 (updated to June 2001)
"GEMU" data:
➜ CPI data: All items and 7 major groups
➜ 33 locations in Germany, Austria and Switzerland
5 West-German locations, 4 East-German locations, 20
Austrian locations, 4 Swiss locations
➜ Data period: January 1995 - June 2000 (all items and major
groups), all items: January 1991 - June 2000 (updated to June
2001).
DATA
4
DATA
DATA
5
EMU C ROSS -S ECTIONAL E VIDENCE
Objective:
➜ analyze determinants of intra- vs. international regional price
dispersion
Alternative Measures of Price Dispersion
➜ standard deviation of monthly changes in relative prices (base
specification)
➜ standard deviation of 1-month-ahead in-sample forecast error
➜ spread between 10th and 90th percentile
Some characteristics of the data
➜ see tabe1 and figure 1
EMU C ROSS -S ECTIONAL E VIDENCE
6
EMU C ROSS -S ECTIONAL E VIDENCE
Some data properties
Table 1:
Descriptive Statistics, Relative Price Volatility and Distance, Measure 1
Index
All Items,
Mean
Stdv.
Food,
Mean
Stdv.
Clothing,
Mean
Stdv.
Furniture,
Mean
Stdv.
Fuel,
Mean
Stdv.
Health,
Mean
Stdv.
Transport,
Mean
Stdv.
Education,
Mean
Stdv.
Exrate Var.
Distance
Relative Locations, Indicated by Country Names
ge-ge ge-au ge-ch ge-it ge-sp ge-po au-au au-ch au-it au-sp au-po ch-ch ch-it ch-sp ch-po
(6)
(7)
(3)
(4)
(5)
(8)
(9)
(10)
(16)
(17)
(18)
(19)
(20)
(21)
1,66
0,37
4,13
0,38
9,92
0,13
7,35
0,31
8,74
0,18
9,97
0,35
8,78
0,41
9,64 11,77 1,22 13,21 13,66 16,46 2,01
0,32 0,44 0,12 0,26 0,21 0,29 0,34
6,44
0,30
7,56
0,81
1,92
0,59
8,80
0,55
4,17
1,59
2,78
0,38
6,46 11,57 8,65 10,87 12,54 6,08 11,59 9,74 11,54 13,15 4,88 14,37 15,67 17,75 3,75
0,81 0,31 0,39 0,81 1,16 1,02 0,64 0,67 0,80 1,05 0,67 0,57 0,78 0,94 0,65
-
8,60
1,81
9,68
1,50
30,84 25,29
10,98 12,69
-
1,64
0,60
-
-
7,89
1,03
9,04
0,61
11,59
0,66
-
-
-
-
4,63
1,70
-
1,01
1,50
1,01
1,50
9,48
4,92
1,18
-
-
-
-
3.61
0.75
6.39 11.36 8.82 10.00 13.65 4.29 11.25 8.26
0.58 0.28 0.62 0.39 0.94 0.87 0.30 0.60
3.08 20.55
0.66 1.18
-
10,68 9,71 10,23 12,80
0,76 1,41 1,50 1,01 1,50
-
-
20,80 20,63 22,09
6,35 5,67 5,41
-
15.60 16.75 18.47 4.78
1.63 0.73 1.54 4.43
-
0.49
9.08 18.68 8.44
176
309
289
519
10.89
1.010 1.409
EMU C ROSS -S ECTIONAL E VIDENCE
103
-
20,44 20,71 36,57
13,21 11,93 11,57
(15)
8,94 10,23 5,18 11,06 7,99
0,77 1,90 1,75 1,48 3,11
-
-
-
3,87
1,27
6,77 32,52 2,35 29,77 27,26
0,72 12,88 0,99 11,34 13,67
-
-
-
-
4,05
1,83
7,13
1,06
2,66 15,07 14,17 17,02 6,59
0,60 1,14 1,15 0,66 1,76
-
5,87
1,48
9,60 10,49 5,14 10,86 6,32
1,65 1,57 2,61 1,49 2,68
4,29
1,01
9.59 11.85 2.03 12.85 14.36 17.15 3.54
0.49 1.17 0.45 0.48 0.33 0.74 1.30
7.60
0.81
9.09
1.88
2.17 10.16 6.19
1.12 1.12 3.19
-
1.000 1.438
75
-
-
9,04
0,92
9,34
1,49
-
-
3,25
1,14
7,75
1,11
8.98 18.67 8.42 10.88
-
8,38
1,14
4,62
2,02
-
317
(14)
-
23.08 26.50 26.71
2.24 1.16 2.77
271
(13)
it-po sp-sp sp-po po-po
(2)
4,68 17,87
3,05 12,65
(12)
it-sp
(1)
11,32 3,31
0,43 0,74
(11)
it-it
-
22.86 13.56 16.27
308
729
1.150
6.50
3.22
230
9,83
1,15
6,34
1,48
8.50 10.19 4.84 11.15 9.65
1.84 2.07 1.65 1.46 2.05
12.72 10.91
847
1.294
-
7.32
-
321
477
348
7
EMU C ROSS -S ECTIONAL E VIDENCE
Figure 1: The Variances of Average Relative Price Changes and
Nominal Exchange Rate Changes, Overall Period (1995:1-2000:6)
it-ch
24
22
20
18
16
14
12
10
8
6
4
2
0
ge-it au-it
it-sp
it-po
de-at
Intra-national
0
2
4
6
8
10
12
14
16
18
20
22
24
Key to Figure: The variance of nominal exchange rate changes is plotted
on the vertical axis, and the variance of average relative price changes
(across locations) are on the horizontal axis.
EMU C ROSS -S ECTIONAL E VIDENCE
8
EMU C ROSS -S ECTIONAL E VIDENCE
Estimation: Basic Equation:
➜
➜ also included: city dummies and monthly dummies
Total Period: 1995.01 - 2000.06
➜ see table 2b and 2a
Subperiods: 1995.01 - 1998.12 and 1999.01 - 2000.06
➜ see figures 3, 4 and 5 and table 3
Conclusions:
➜ intra-national volatility is initially low and does not decline
significantly
➜ intern-national volatility is initially high and falls drastically for all
EMU cross-border city pairs
➜ however: even in EMU distance and the border continue to
matter!
EMU C ROSS -S ECTIONAL E VIDENCE
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EMU C ROSS -S ECTIONAL E VIDENCE
Is there a border effects? YES!
Table 2b:
Estimation Using Log Distance, Border and Exchange Rate Volatility,
European City Pairs, Overall Period (Jan 1995-June 2000), Measure 1
Variable
Coeff.
Log(Distance)
Border
Nom. Exrate
Volatility
1,33
301,8
R2
R 2 (adj.)
0.871
0.868
1.199
SEE(*1000)
Stde.
0,04
4,99
t-Stat.
29,3
60,5
Coeff.
1,04
1,44
249
Stde.
0,05
0,10
5
t-Stat.
22,5
14,5
45,8
0.880
0.877
1.158
Notes: All regressions contain as explanatory variables a dummy for each of the included
individual in addition to the variables listed in the cells. Heteroscedasticity-consistent
standard errors (White, 1980) are reported. Coefficients and standard errors on log distance
and border are multiplied by 103 . The dependent variable is the standard deviation of the
one-month difference in relative prices. Standard deviations are computed over the sample
period from January 1995 to June 2000 (Exceptions: Italian Data are available from
January 1996 only, Swiss Data end in May 2000). There are 3240 observations in the
overall CPI regression.
EMU C ROSS -S ECTIONAL E VIDENCE
10
EMU C ROSS -S ECTIONAL E VIDENCE
How many borders matter? ALL!
Table 2a: All Items
Estimation Using Log Distance Function, Overall Period (Jan 1995-June 2000), Volatility Measures 1, 2, 3 and 4
Variable
Constant
Log(Distance)
Distance
Distance sqr.
Border ge-au
Border ge-ch
Border ge-it
Border ge-sp
Border ge-por
Border au-ch
Border au-it
Border au-sp
Border au-po
Border ch-it
Border ch-sp
Border ch-po
Border it-sp
Border it-po
Border sp-po
R2
R2 (adj.)
SEE(*1000)
Coeff.
0,065
1,587
8,417
5,452
6,858
8,292
7,637
6,074
6,907
7,902
11,540
11,987
13,639
4,402
4,367
5,730
0.9915
0.9912
0.3087
Stde.
0,022
0,035
0,038
0,032
0,046
0,118
0,060
0,030
0,047
0,121
0,036
0,053
0,135
0,036
0,115
0,110
EMU C ROSS -S ECTIONAL E VIDENCE
Measure 1
t-stat.
Coeff.
Stde.
t-stat.
3,03
44,73
220,10
169,10
148,75
70,25
127,32
204,63
146,78
65,54
319,07
225,01
101,04
122,12
37,95
52,12
0,421
-0,400
1,604
8,438
5,488
7,044
8,718
7,662
6,092
7,096
8,364
11,565
12,082
13,913
4,522
4,727
5,765
0.9920
0.9918
0.2989
0,000
0,000
0,034
0,036
0,032
0,052
0,112
0,060
0,029
0,053
0,116
0,035
0,051
0,125
0,039
0,107
0,091
5,11
-9,95
47,03
232,23
173,24
136,10
77,62
128,31
209,99
134,68
72,32
330,80
238,02
111,75
116,58
44,30
63,67
Measure 2
Coeff.
Stde.
Measure 3
Coeff.
Stde.
0,154
0,056
0,071
0,022
4,031
20,755
11,585
9,353
8,787
17,569
11,179
8,786
8,677
28,856
23,547
23,735
9,739
10,466
6,773
0.9655
0.9644
0.1192
0,088
0,167
0,104
0,124
0,297
0,215
0,095
0,133
0,303
0,143
0,172
0,388
0,112
0,285
0,271
1,436
8,011
5,323
5,071
6,831
7,334
6,145
4,941
6,338
11,878
9,071
11,256
4,483
4,122
4,814
0.9854
0.9849
0.3532
0,035
0,051
0,031
0,047
0,129
0,070
0,031
0,049
0,132
0,052
0,062
0,151
0,038
0,128
0,120
Measure 4
Coeff.
Stde.
0,03
0,11
1,58
0,008
5,415
6,836
8,353
7,632
6,068
6,885
7,965
11,509
11,959
13,698
4,375
4,403
5,876
0,04
0,000
0,042
0,058
0,113
0,061
0,032
0,054
0,113
0,042
0,065
0,132
0,045
0,108
0,094
11
EMU C ROSS -S ECTIONAL E VIDENCE
EMU has reduced volatility!
Figure 3: Volatility of Relative Price Changes in the ERM (1995:1-1998:12)
and EMU (1999:1-2000:6) Periods,
this graph: look at 15 country pairs
20
18
16
14
12
EU-Switzerland
10
8
Intra-national
6
4
2
0
0
5
10
15
20
Key to Figure: EMU Sub-Period (1999:1-2000:6) on the vertical axis,
pre-EMU sub-period on the horizontal axis.
next graph: look at 3240 city pairs
EMU C ROSS -S ECTIONAL E VIDENCE
12
EMU C ROSS -S ECTIONAL E VIDENCE
Figure 4:
(a) Cross-city Relative Price Volatility, Pre-EMU, Jan. 1995- Dec. 1998
Switzerland vs. Portugal
Switzerland vs. Spain/Italy
Within Country
EMU C ROSS -S ECTIONAL E VIDENCE
13
EMU C ROSS -S ECTIONAL E VIDENCE
(b) Cross-city Relative Price Volatility, EMU, Jan. 1999- June 2000
moving closer!
but have borders become irrelevant?
Switzerland vs. EU
Within EMU
EMU C ROSS -S ECTIONAL E VIDENCE
14
EMU C ROSS -S ECTIONAL E VIDENCE
Estimating the border effects of EMU
Table 3:
Estimation Using Log Distance Function, Overall Period (Jan 1995-July 2000) and EMS/ERM-Subperiods
(Jan. 1995-Dec. 1998, Jan. 1999-July 2000), Volatility Measures 1 and 2
Variable
Log(Distance)
Distance sqr.
Border ge-au
Border ge-ch
Border ge-it
Border ge-sp
Border ge-por
Border au-ch
Border au-it
Border au-sp
Border au-po
Border ch-it
Border ch-sp
Border ch-po
Border it-sp
Border it-po
Border sp-po
R2
R2 (adj.)
SEE(*1000)
Overall Period
Coeff.
Stder.
Specification 1
Subperiod 1
Coeff.
Stder.
Subperiod 2
Coeff.
Stder.
0,065
0,022
0,084
0,025
0,029
0,020
1,587
8,417
5,452
6,858
8,292
7,637
6,074
6,907
7,902
11,540
11,987
13,639
4,402
4,367
5,730
0.9915
0.9912
0.3087
0,035
0,038
0,032
0,046
0,118
0,060
0,030
0,047
0,121
0,036
0,053
0,135
0,036
0,115
0,110
1,744
9,128
6,840
8,173
9,972
8,441
7,710
8,224
9,540
13,468
13,607
16,040
5,353
5,504
6,872
0.9913
0.9911
0.3671
0,041
0,045
0,038
0,054
0,123
0,065
0,036
0,055
0,126
0,043
0,062
0,154
0,043
0,118
0,112
1,210
6,391
0,909
1,123
2,000
5,074
0,944
1,682
1,897
5,576
5,751
3,989
1,043
1,338
1,367
0.9918
0.9915
0.3573
0,041
0,074
0,036
0,042
0,139
0,100
0,031
0,042
0,141
0,077
0,076
0,252
0,033
0,137
0,133
EMU C ROSS -S ECTIONAL E VIDENCE
Overall Period
Coeff.
Stder.
0,421
-0,400
1,604
8,438
5,488
7,044
8,718
7,662
6,092
7,096
8,364
11,565
12,082
13,913
4,522
4,727
5,765
0.9920
0.9918
0.2989
0,000
0,000
0,034
0,036
0,032
0,052
0,112
0,060
0,029
0,053
0,116
0,035
0,051
0,125
0,039
0,107
0,091
Specification 2
Subperiod 1
Coeff.
Stder.
0,515
-0,400
1,765
9,154
6,878
8,369
10,426
8,474
7,732
8,423
10,037
13,498
13,709
16,335
5,479
5,889
6,909
0.9501
0.9485
0.3674
0,000
0,000
0,040
0,045
0,037
0,060
0,119
0,065
0,035
0,061
0,124
0,044
0,061
0,146
0,045
0,112
0,093
Subperiod 2
Coeff.
Stder.
0,135
-0,100
1,221
6,404
0,925
1,184
2,129
5,090
0,955
1,745
2,038
5,590
5,788
4,078
1,083
1,448
1,379
0.9502
0.9487
0.3669
0,000
0,000
0,040
0,072
0,038
0,052
0,143
0,098
0,030
0,050
0,144
0,075
0,075
0,251
0,040
0,139
0,129
15
GEMU C ROSS -S ECTIONAL E VIDENCE
Data
➜ as discussed above
Procedure
➜ add a shadow-border dummy for East-Germany
➜ further procedure analogous to EMU analysis
Results
➜ see table 5
GEMU C ROSS -S ECTIONAL E VIDENCE
16
GEMU C ROSS -S ECTIONAL E VIDENCE
How Wide Are European Borders:
Cross-Sectional Results
Table 5:
Estimation Using Log Distance Function, Overall Period (Jan 1991-June 2000)
and EMS/ERM -Subperiods
(Jan. 1991-Dec. 1994, Jan. 1995-Dec. 1998, Jan. 1999-June 2000), Vol. Measure 1
Measure 1
Variable
Log(Distance)
Border we-ea
Border we-au
Border we-ch
Border ea-au
Border ea-ch
Border au-ch
R2
R 2 (adj.)
SEE(*1000)
Overall Sample
Subperiod 1
Subperiod 2
Subperiod 3
Coeff.
Coeff.
Coeff.
Coeff.
0.096
11.567
2.139
8.477
11.412
15.308
8.276
0.9967
0.9965
0.0003
GEMU C ROSS -S ECTIONAL E VIDENCE
Stde.
0.029
0.241
0.043
0.051
0.244
0.247
0.050
0.173
18.407
2.627
8.712
18.476
21.530
9.426
0.9977
0.9975
0.0004
Stde.
0.029
0.366
0.055
0.083
0.371
0.378
0.068
0.088
0.542
2.261
8.721
1.794
8.580
8.258
0.9837
0.9824
0.0004
Stde.
0.044
0.097
0.053
0.070
0.099
0.115
0.082
0.022
0.130
1.037
8.169
0.940
8.040
6.702
Stde.
0.035
0.115
0.065
0.074
0.098
0.107
0.105
0.9690
0.9665
0.0005
17
GEMU PANEL E VIDENCE
Procedure
➜ panel unit root test based on Levin/Lin (1992)
➜ adjust for individual-specific and time-specific means
➜ adjust for serial correlation (ADF)
Estimation equation
➜
Results
➜ see table 6
GEMU PANEL E VIDENCE
18
GEMU PANEL E VIDENCE
How Wide Are European Borders:
Panel Evidence
Table 6:
Time Series (ADF) and Panel (Levin & Lin) Estimates of AR(1) Coefficients from 528 City Pairs,
Overall Sample (1991:1-2000:6)
Location of City Pairs
WE-WE WE-EA WE-AU WE-CH EA-EA EA-AU EA-CH AU-AU AU-CH CH-CH
Mean AR(1) Coef. from ADF tests
0.964
0.923
0.866
0.930
0.901
0.913
0.895
0.896
0.895
0.948
Min. AR(1) Coef. from ADF tests
0.881
0.917
0.469
0.904
0.865
0.900
0.888
0.409
0.860
0.869
Max AR(1) Coef. from ADF tests
1.008
0.934
0.992
0.959
0.957
0.929
0.902
1.003
0.950
0.985
Mean Implied Halflife in Month
19
9
5
9
7
8
6
6
6
13
0.994
0.932
0.970
0.984
0.878
0.918
0.920
0.954
0.959
0.974
-0.743 -10.141
-8.410
-2.923 -8.972 -19.933 -10.653 -15.864 -10.319
-2.952
AR(1) Coef. from Levin & Lin
tests
t-Stat. from Levin & Lin tests
Critical Value of t-Stat,
(Based on Monte Carlo
Simulation)
Implied Halflife in Month
Cross Section Dimension (N)
Time Series Dimension (T)
GEMU PANEL E VIDENCE
124
10
22
42
5
8
8
15
16
26
10
113
20
113
100
113
20
113
6
113
80
113
16
113
190
113
80
113
6
113
19
S UMMARY AND C ONCLUSIONS
➜ EMU has drastically reduced inter-European price dispersion
(only due to elimination of exchange rate volatility?)
➜ however: national borders and distance continue to be
important determinants of relative price volatility
➜ convergence to PPP/LOOP seems to be nonlinear
S UMMARY AND C ONCLUSIONS
20