contrast to cross-sectional studies, fixed-effect modeling i.e., the inclusion of state dummy variables allows us to consider different shares of unreported crime at the state level.
4. Data
For our empirical investigations on the supply-of-offenses functions, we use two different panel data sets. The first one—the long panel—is an exclusively West German panel containing
Table 4 Estimates of the supply-of-offenses functions: West Germany 1975–1996
a
Independent variables lnO
lnO1 lnO2
lnO3 lnO4
Constant 21.93
20.66 1.75
1.18 212.96
23.74 20.46
2.03 1.68
25.58 lnp, lnp1–lnp4
20.20 20.74
20.43 20.08
1.12 23.43
27.72 212.43
20.77 4.07
lnY
a
0.89 0.40
0.34 0.41
1.23 19.56
3.33 4.24
4.80 7.91
Y
r
21.12 20.73
20.90 20.39
21.02 29.68
22.35 2-4.71
22.12 22.68
lnFOREIGN 0.35
0.99 0.42
0.49 0.26
9.80 10.77
7.42 9.53
2.16 lnUNEMPL
0.04 0.01
0.08 20.14
0.37 2.95
0.25 3.51
25.43 7.62
lnM15-24 0.54
0.38 1.01
0.66 20.05
14.38 4.12
17.08 12.41
20.37 Fixed effects
Baden-Wu¨rttemberg 20.07
20.16 0.11
20.18 0.17
22.77 22.78
2.84 25.51
{2.26 Berlin
0.88 1.02
1.25 0.77
0.53 27.25
13.31 26.61
18.72 5.64
Bremen 0.90
1.30 1.46
0.88 0.56
22.12 15.81
27.17 19.97
5.49 Hamburg
0.93 1.31
1.48 0.81
0.93 16.90
9.77 18.63
11.64 5.90
Hesse 0.23
0.25 0.70
0.10 0.22
7.62 3.95
18.74 2.81
3.02 Lower Saxony
0.40 0.86
0.89 0.57
0.15 11.81
11.52 19.14
12.11 1.46
North Rhine-Westphalia 0.20
0.33 0.76
0.19 0.02
8.33 7.78
29.46 7.24
0.39 Rhineland-Palatinate
0.14 0.42
0.46 0.25
0.19 5.74
7.01 12.47
6.11 2.41
Saarland 0.16
0.65 0.39
0.46 20.28
5.44 9.63
9.43 10.23
23.05 Schleswig-Holstein
0.66 1.02
1.10 0.89
0.27 17.14
11.94 20.84
15.76 2.29
Adjusted R
2
0.986 0.965
0.982 0.959
0.903 Sum of squared residuals
0.049 0.123
0.076 0.070
0.158 BFN-DW-statistic
1.12 0.82
1.04 0.76
0.83 Wald test on fixed effects
3117.26 847.26
3201.38 1887.29
266.39 92
H. Entorf, H. Spengler International Review of Law and Economics 20 2000 75–106
annual data from all 11 Laender that formed the Federal Republic of Germany before the German unification in 1990. This panel is unbalanced because reliable data for the former West Berlin are
only available until 1989. All other states are considered from 1975–1996. The second data set—the short panel— contains annual data from all 16 Laender that
constitute the Federal Republic of Germany now. In the years following the unification, there were difficulties in the registration of crimes and clear-ups in the five new Laender Bran-
denburg, Mecklenburg-Vorpommern, Saxony, Saxony-Anhalt, and Thuringia. For that
Table 4 Continued
Independent variables LnO
lnO5 lnO6
lnO7 lnO8
Constant 21.93
9.87 11.75
28.72 23.41
23.74 4.37
8.86 24.92
22.22 lnp, lnp5–lnp8
20.20 20.64
20.41 0.86
20.27 23.43
21.99 23.43
3.28 23.36
lnY
a
0.89 20.64
20.95 0.74
0.88 19.56
24.35 28.61
7.57 6.78
Y
r
21.12 0.08
0.42 21.23
21.19 29.68
0.22 1.48
24.78 24.62
lnFOREIGN 0.35
0.23 0.42
0.44 0.20
9.80 1.90
4.86 5.37
2.55 lnUNEMPL
0.04 0.08
20.18 20.05
0.00 2.95
1.60 25.00
21.52 20.04
lnM15–24 0.54
0.25 0.72
0.38 0.39
14.38 2.06
7.98 4.74
3.99 Fixed Effects
Baden-Wu¨rttemberg 20.07
0.08 20.05
20.20 20.10
22.77 1.00
20.91 23.98
22.00 Berlin
0.88 0.53
0.91 1.32
0.87 27.25
5.64 11.74
17.64 14.58
Bremen 0.90
0.90 1.20
1.09 0.74
22.12 8.84
15.07 13.73
11.92 Hamburg
0.93 0.46
1.16 0.74
0.73 16.90
2.89 9.75
6.55 7.65
Hesse 0.23
0.34 0.19
20.01 0.20
7.62 4.62
3.25 20.25
4.04 Lower Saxony
0.40 0.19
0.44 0.27
0.32 11.81
1.90 6.12
4.27 5.28
North Rhine-Westphalia 0.20
20.14 0.11
0.23 0.14
8.33 22.60
2.82 6.36
3.95 Rhineland-Palatinate
0.14 0.29
0.21 0.08
0.06 5.74
3.66 3.72
1.55 1.17
Saarland 0.16
0.26 0.18
0.36 0.23
5.44 2.93
2.74 6.32
4.07 Schleswig-Holstein
0.66 20.05
0.50 0.53
0.71 17.14
20.41 6.14
6.85 10.29
Adjusted R
2
0.986 0.732
0.921 0.933
0.925 Sum of squared residuals
0.049 0.161
0.112 0.103
0.090 BFN-DW-statistic
1.12 1.06
1.04 0.49
0.65 Wald test on fixed effects
3117.26 362.33
558.46 972.79
940.40
a
Number of observations is 235 202 for vandalism. “Bavaria” represents the reference state dummy variable. Represents t-values .2.
93 H. Entorf, H. Spengler International Review of Law and Economics 20 2000 75–106
Table 5 ECM estimates of the supply-of-offenses functions: West Germany 1975–1996
a
Independent variables DlnO
DlnO1 DlnO2
DlnO3 DlnO4
Adjustment parameters g lnO
21
, lnO1
21
-lnO4
21
20.68 20.34
20.54 20.47
20.45 211.56
26.30 29.19
28.82 27.82
Long-run coefficients b, g
1
, g
2
, d Constant
21.49 211.65
2.03 2.62
212.29 21.57
22.50 1.04
1.49 22.00
lnp
21
, lnp1
21
-lnp4
21
20.28 21.52
20.42 20.08
0.95 23.67
25.94 27.32
20.40 1.54
lnY
a 21
0.93 1.93
0.30 0.38
1.26 10.09
4.08 1.59
2.09 2.65
Y
r 21
21.24 22.59
21.15 20.50
21.22 27.77
23.27 23.50
21.58 21.50
lnFOREIGN
21
0.21 0.35
0.32 0.28
0.27 3.82
1.39 3.01
2.89 0.96
lnUNEMPL
21
0.05 20.08
0.00 20.13
0.25 1.81
20.67 20.02
22.57 2.06
lnM15–24
21
0.43 0.16
1.20 0.31
20.06 7.85
0.66 10.64
2.99 20.20
Fixed effects Baden-Wu¨rttemberg
20.01 0.02
0.15 20.08
0.14 20.20
0.17 2.22
21.34 0.81
Berlin 0.94
1.23 1.39
0.85 0.66
21.57 6.55
17.00 11.80
3.32 Bremen
0.90 1.40
1.63 0.91
0.64 16.41
6.82 17.82
12.09 3.05
Hamburg 0.97
1.26 1.73
0.89 1.02
13.42 3.90
12.99 7.74
3.17 Hesse
0.27 0.33
0.81 0.18
0.25 6.74
2.29 12.69
2.84 1.70
Lower Saxony 0.31
0.50 0.85
0.44 0.21
6.60 2.73
10.52 5.41
0.97 North Rhine-Westphalia
0.21 0.44
0.83 0.23
0.10 6.76
4.58 19.49
4.97 0.93
Rhineland-Palatinate 0.09
0.17 0.42
0.16 0.24
2.65 1.23
6.90 2.31
1.47 Saarland
0.09 0.39
0.37 0.37
20.23 2.22
2.45 5.04
4.67 21.17
Schleswig-Holstein 0.54
0.48 1.04
0.72 0.31
9.90 2.20
11.11 7.17
1.15 Short-run coefficients b, g
1
, g
2
, d Dlnp, Dlnp1–Dlnp4
20.12 20.22
20.16 0.16
0.63 21.81
22.19 23.58
1.78 2.37
DlnY
a
20.09 0.23
20.35 20.28
0.08 20.67
0.76 21.59
21.56 0.17
DY
r
0.00 0.00
0.00 0.00
20.02 0.56
20.23 0.41
0.20 20.72
DlnFOREIGN 0.18
0.11 0.38
0.23 20.21
2.41 0.63
3.22 2.35
20.84 DlnUNEMPL
0.07 0.06
0.06 20.08
0.31 2.80
1.10 1.51
22.48 3.68
DlnM15–24 0.53
1.52 0.31
0.68 20.07
3.07 3.64
1.11 2.76
20.12 Adjusted R
2
0.512 0.244
0.498 0.419
0.249 Sum of squared residuals
0.038 0.083
0.060 0.049
0.127 BFN-DW-statistic
1.89 1.93
1.78 1.86
2.13 Wald test on fixed effects
1818.87 135.10
1244.77 593.62
83.16
94 H. Entorf, H. Spengler International Review of Law and Economics 20 2000 75–106
Table 5 Continued
Independent variables DlnO
DlnO5 DlnO6
DlnO7 DlnO8
Adjustment parameters g lnO
21
, lnO5
21
-lnO8
21
20.68 20.54
20.51 20.25
20.27 211.56
28.42 27.78
25.87 24.68
Long-run coefficients b, g
1
, g
2
, d Constant
21.49 1.60
9.71 216.50
5.34 21.57
0.27 2.74
23.27 0.66
lnp
21
, lnp5
21
-lnp8
21
20.28 20.30
20.43 20.09
0.03 23.67
20.38 21.51
20.12 0.11
lnY
a 21
0.93 20.08
20.64 2.09
0.06 10.09
20.18 21.89
4.73 0.08
Y
r 21
21.24 20.97
0.23 22.30
20.49 27.77
21.26 0.38
23.32 20.51
lnFOREIGN
21
0.21 0.38
0.17 20.04
0.15 3.82
1.43 0.84
20.14 0.55
lnUNEMPL
21
0.05 20.07
20.18 20.09
0.02 1.81
20.56 22.01
20.89 0.18
lnM15–24
21
0.43 0.65
0.42 20.15
20.10 7.85
2.47 2.03
20.64 20.29
Fixed effects Baden-Wu¨rttemberg
20.01 0.00
0.03 0.00
0.02 20.20
0.02 0.27
20.04 0.15
Berlin 0.94
0.60 0.97
1.31 0.94
21.57 3.34
6.01 6.84
4.91 Bremen
0.90 1.09
1.20 0.90
0.81 16.41
5.52 7.18
4.37 4.26
Hamburg 0.97
0.66 1.15
0.46 0.87
13.42 2.23
4.82 1.55
3.09 Hesse
0.27 0.36
0.23 0.04
0.23 6.74
2.71 2.07
0.33 1.54
Lower Saxony 0.31
0.24 0.33
0.16 0.28
6.60 1.25
2.19 0.95
1.48 North Rhine-Westphalia
0.21 20.13
0.16 0.33
0.17 6.76
21.29 2.12
3.87 1.64
Rhineland-Palatinate 0.09
0.30 0.14
0.04 0.08
2.65 2.11
1.27 0.32
0.52 Saarland
0.09 0.29
0.08 0.38
0.20 2.22
1.69 0.58
2.47 1.13
Schleswig-Holstein 0.54
20.01 0.35
0.22 0.65
9.90 20.06
1.98 1.01
2.94 Short-run coefficients b, g
1
, g
2
, d Dlnp, Dlnp5–Dlnp8
20.12 20.32
20.33 20.35
0.02 21.81
21.07 22.71
21.50 0.33
DlnY
a
20.09 20.19
20.36 0.16
20.12 20.67
20.35 20.95
0.77 20.47
DY
r
0.00 20.01
0.01 0.00
0.02 0.56
20.45 0.31
0.36 1.80
DlnFOREIGN 0.18
0.11 0.06
0.03 0.25
2.41 0.38
0.28 0.22
1.78 DlnUNEMPL
0.07 0.07
20.02 20.04
20.09 2.80
0.74 20.35
21.03 21.93
DlnM15–24 0.53
0.40 0.82
1.34 0.17
3.07 0.59
1.70 5.07
0.42 Adjusted R
2
0.512 0.238
0.222 0.300
0.216 Sum of squared residuals
0.038 0.143
0.105 0.057
0.061 BFN-DW-Statistic
1.89 2.08
2.03 1.98
2.01 Wald test on fixed effects
1818.87 130.42
122.92 172.45
113.49
a
Numbers of observations is 224 191 for vandalism. “Bavaria” represents the reference state dummy
variable Represents t-values .2.
95 H. Entorf, H. Spengler International Review of Law and Economics 20 2000 75–106
Table 6 Estimates of the supply-of-offenses functions: Germany 1993–1996
a
Independent variables lnO
lnO1 lnO2
lnO3 lnO4
lnO5 lnO6
lnO7 lnO8
Constant 6.65
22.98 24.76
0.33 20.37
5.43 29.47
0.58 3.86
2.35 20.86
21.09 0.16
20.12 1.84
23.16 0.14
1.11 EAST
0.44 1.28
0.95 0.33
0.54 0.53
0.57 20.72
0.31 2.05
4.93 2.73
2.02 1.87
2.17 2.53
23.12 1.08
CITY 0.60
0.77 0.44
0.54 0.69
0.45 0.94
0.64 0.58
6.26 5.95
2.53 6.94
5.77 3.30
8.49 3.93
4.46 lnp, lnp1–lnp8
20.65 21.20
20.60 20.91
20.02 21.94
0.18 0.87
0.23 23.73
25.72 23.83
25.34 20.08
27.08 0.66
1.30 1.16
lnY
a
0.03 0.43
0.18 0.47
0.36 20.10
0.21 20.50
20.34 0.15
2.00 0.62
3.25 1.65
20.43 1.12
22.55 21.44
lnFOREIGN 0.22
0.44 0.36
0.07 0.21
0.33 0.23
0.23 0.20
2.61 4.16
2.54 0.97
1.91 3.25
2.51 2.35
1.60 lnUNEMPL
0.41 1.13
1.25 0.67
0.30 0.45
0.31 0.99
0.67 2.33
5.57 4.50
5.12 1.45
2.29 1.73
5.34 2.95
lnUNEMPL24 0.67
1.29 1.95
0.72 0.78
1.63 1.95
0.23 0.92
1.38 2.25
2.45 1.94
1.35 2.98
3.92 0.45
1.46 lnM15–24
0.75 0.12
1.98 1.29
0.02 20.10
1.18 1.30
0.67 1.59
0.21 2.57
3.39 0.04
20.17 2.32
2.45 1.03
Adjusted R
2
0.824 0.921
0.790 0.867
0.776 0.796
0.838 0.798
0.668 Sum of squared residuals
0.152 0.188
0.251 0.122
0.190 0.182
0.167 0.172
0.211 BFN-DW-statistic
0.62 0.95
0.85 1.11
0.64 0.93
1.24 0.65
0.49
a
Note: number of observations is 64. Represents t-values .2. 96
H. Entorf,
H. Spengler
International
Review of
Law and
Economics
20 2000
75–106
reason, only a period of 4 years 1993–1996 can be considered in a crime-related data set containing all 16 Laender of the unified Germany.
10
Furthermore, it should be mentioned that Berlin, which contained West German and East German parts, is treated as a West German
state in our empirical analysis.
11
Table 3 describes all variables that are used in our estimations. All crime and clear-up rates are taken from the German Federal Criminal Police Office Bundeskriminalamt. The
choice of crime categories is limited by the availability of clear-up rates on the state level. The variables FOREIGN percentage of foreigners in the population, Y
a
real GDP per capita
in constant prices, M15-24 percentage of males aged 15–24 years in the population, and Y
r
relative distance between states’ GDP and federal GDP result from our own calculations on the basis of Statistical Yearbooks from the Federal Statistical Office of
Germany Statistisches Bundesamt. The variable UNEMPL unemployment rate was taken from annual reports of the Federal Employment Service Bundesanstalt fu¨r Arbeit, and the
variable UNEMPL24 share of unemployed persons under 25 years of age out of all unemployed persons is our own calculation on the basis of the periodical “Strukturanalyse”
of the same office. Because data on the number of unemployed persons under 25 years of age are not available for the years before 1991 at the state level, the variable UNEMPL24 can
only be used for estimations based on the short panel. Because we run exclusively fixed- effects regressions in the long panel and because the latter only consists of West German
states, the variables EAST indicator variable for East Germany and CITY indicator variable for the city-states can only be used in the short panel. Other variables are exclusively used
in the long panel. The use of Y
r
in the short panel is not reasonable because the relative income measure does not exhibit enough variation over time.
5. Results
