Transcript Determinants of Crime - Economics

Determinants of Crime
Daniel Yu
Pomona College
May 2007
Data
• Data organized by county and year
▫ 2,919/3,077 counties represented
▫ 1990-2002
• FBI Uniform Crime Reports
▫ Crimes reported by county
• County Business Reports (1990, 1997)
▫ Number of sources of social capital by county
• National Cancer Institute’s Population Estimates
▫ Population by race, gender and age group
• Bureau of Economic Analysis County Summary
▫ Income per capita
Model
• Crime Rate Index= α0 +β1Social Capital +β2Population +
β3Dissimilarity +β4ln(Income) +[β5Year + β6State] +ε0
• Crime Rate Index
▫ Principal Component Analysis
• Social Capital Index
•
•
•
•
Total associations/10,000
Total non-profit/10,000
Census response rate
Percent voting for the President
• Population in 100,000
• Index of Dissimilarity
▫ Minorities/Total Population
• ln(Income per capita)
• Year and State Effects
Analysis
Crim e Rate Index Crim e Rate Index
Social Capital Index
-0.935
-0.243
(0.01 69)**
(0.0223)**
ln(Personal Income per capita)
2.451
(0.0544)**
Population in 1 00,000
0.1 466
(0.0035)**
Index of Dissimilaritiy
4.8849
(0.07 98)**
Constant
-0.01 64
-1 8.1 53
-(0.01 1 2)
(0.7 41 9)**
Observ ations
37 947
37 939
R-squared
0.08
0.43
Standard errors in parentheses
* significant at 5%; ** significant at 1 %
Determinants of Child
Labor
Matt Speise
May 2007
 What factors determine whether children work
in Pakistan?
 Using data from about 115,000 households.
 Among those ages 10 to 17, 16.58% worked in
the last month.
 For males this is 22.61%.
Regressions
Orphans in Kagera, Tanzania:
Parental Death and School
Enrollment
Eric Otieno
Professor Andrabi
Econ 190
5/2/07
The Question and the Setting
Does Parental Death Affect Elementary
School Enrollment?
•
•
•
•
•
•
•
Kagera Region in North Western Tanzania
High Prevalence of HIV/AIDS – Survey in 1987 found 10% of adults aged 15-54
infected.
Study uses panel data (1994) from the 1991-994 Kagera Housing and Development
Survey (KHDS)
915 Households with 1376 elementary school age children (7 -14 years old)
Used binomial logit model
Si =f(Orphan Status, Age, Age squared, Gender, Household Characteristics,
Household Head Characteristics)
Si Dependent variable indicating whether child is currently enrolled.
The Data
Table 1: Number and Proportion of Orphans By Type
Number
Non
Paternal
Orphans Orphans
860
161
Proportion 0.63
0.12
Maternal Two Parent Total
Orphans Orphans
238
117
1376
0.17
0.08
Table 2: Enrollment Rates By Orphan Status and Age Group
Age Group
7-10 years 11-14 years
old
old
Non-Orphans
38.32
78.94
Paternal Orphans
36.79
78.95
Maternal Orphans
31.82
76.52
Two-Parent Orphans
37.78
70.83
1.0
Graph 1: Enrollment Rates By Age and Gender
90
80
70
60
50
40
30
20
10
0
Males
Females
7
8
9
10
Age
11
12
13
14
Results and Conclusions
•
•
Dependent variable is whether child is currently enrolled.
Control variables are not reported
•
Hypothesis 1: Orphans have lower enrollment rates than non-orphans
Regression 1
Regression 2
Regression 3
Regression 4
Orphan Type
Paternal Orphan Maternal
Orphan
Two Parent
Orphan`
Any Orphan
Coefficient
-0.277
0.042
-0.418
0.004
Standard Error
0.206
0.198
0.268
0.179
•
No evidence that orphan status affects enrollment rates
•
Hypothesis 2: Among orphans, the relationship of the orphan to the household head affects orphan
enrollment rates
Regression 5: Grandchild is the omitted variable
Relationship of Orphan to Sibling
Household Head
Nephew or Niece
Other Relative
Coefficient
-2.574
-0.603
-1.570
Standard Error
0.876**
0.631
0.567*
**Significant at the 5% level
*Significant at the 10% level
•
Orphans living with grandparents have higher enrollment rates that orphans living with siblings or
orphans living with “other relatives”
The Blue Line
A Hedonic Price Study of
Light Rail in Los Angeles
Carey McDonald, ’07
Pomona College
The Blue Line - Project
Real estate values are determined by
location and transportation costs
If light rail lowers transportation costs,
access to light rail should be capitalized
into property values
Used Census 2000 blockgroup level data,
integrated with GIS
The Blue Line - Project
Rail placement can be endogenous
Blue Line is natural experiment
Tracks were pre-existing
Stations possibly endogenous, but still at
regular intervals
The
Blue Line
GIS Map of
the Blue Line
The Blue Line - Results
Valueir = Geographyir + Neighboorhoodir + Distanceir+ ε
DV = median housing value
IV = distance to nearest station
-3.44 $/ft (2.09)
Median IV = 5165.2 → $17,768.3
Controls for racial demographics, income, distance to
coast & highways, transit usage, housing unit age,
bedrooms per housing unit, blockgroup area & length
The Blue Line - Results
Correct sign, significance at 10% level
Apparent access capitalization
Comparable to Gold Line, Metrolink
Pomona College Economics
Department
Ty Hollingsworth
Econ. 190
Prof. Andrabi
Overall Enrollment Over Time
80
70
60
50
Female
Male
40
30
20
10
0
1994
1996
1998
2000
2002
2004
2006
What Could Cause Differences
by Gender?
Ability
Introductory GPA
Ambition/Organization
Peer Effects
Preferences or Other Unobservables
Professor Fixed Effects
Dependent Variable is whether the Student took Economics above Econ. 52[1]
All Results show Marginal Effects
Male
Eq. 1
Eq. 2
Eq. 3
Eq. 4
Eq. 5
Eq. 6
Eq. 7
.1828902*
(.01745)
.1891123*
(.01765)
.1026882*
(.03523)
.1060828*
(.03435)
.1067014*
(.03434)
.0947297*
(.04261)
.1405753*
(.04427)
-.0104809
(.00628)
-.0200069*
(.00652)
-.0198888
(.00653)
-.0214006*
(.00777)
-.0131988
(.00851)
.0343755*
(.00771)
.03454
(.00773)
.0342656*
(.00937)
.0343386*
(.00982)
-.0626051
(.06722)
-.1010447
(.07208)
-.0708253
(.07967)
-.3062255
(.18661)
-.2815031
(.19214)
Math GPA
Intro GPA
Dual Major
Intro %
Women
Econ
Required
.7097906 *
(.05758)
N
3147
3147
730
671
671
489
489
Pseudo R^2
0.0244
0.0509
0.0283
0.0696
0.0706
0.0848
0.1712
[1]
* Standard Errors are shown in parenthesis below;
** for all dummy variables, marginal effects (change from 0 to 1) are reported
*** asterisk signifies significance at the 5% level or below
****These are the marginal effects with year controls. I didn't’ show the year dummies out of space concerns
Determinants of the
College Decision
Jeff Fortner
Pomona College
May 2007
Hypothesis: Three Broad
Categories of
Determinants

Lack of Academic
Preparation
1,500
1,000
387
0
Inability to Pay for
College
1197
500

Pressure to Seek
Employment
Instead
Number of Respondents

College Track (CTRK)
No College
Going\Went\In College
Dependent Variable:
“College Track”
Independent Variables:
Significant Results
Correlation

Strongly:
–
–
–
–

Male
State performance on standardized tests
Whether high school offers AP courses
Hispanic
(−)
(+)
(+)
(−)
Weakly:
– State median income
– Asians
(+)
(+)
Independent Variables:
Insignificant Results




Importance of religion
Private school
Preparation for job market in high
school (self-reported)
African- or Black-American
Determinants of Contraceptive Use in India
Praween Dayananda (’07)
Econ 190
April 25, 2007
The DHS Dataset
 MEASURE DHS survey data completed in 1992-1993 in India
 A core questionnaire at the household level
 Individual women’s questionnaire
 Selected women: ever-married women (aged13-49)
 Village level questionnaire
 88562 observations
Ever Heard/Use of Contraceptive Methods
Ever Heard/Used any Contraceptive Methods
(all ever married women) (India: 1992-1993)
13
-1
15 4
-1
20 9
-2
25 4
-2
30 9
-3
35 4
-3
40 9
-4
4
4
Li
5
te
ra Illi 49
t
M te era
id - p te
dl r i
e m
H com ary
ig
h ple
sc te
ho
ol
U +
rb
an
R
ur
a
H l
in
M du
u
C slim
hr
ist
ia
n
Si
kh
O
th
er
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Individual Background Characteristics
Ever Heard of a method
Ever Used a method
Theoretical Framework
 Dependent variables:
 Ever used a method
 Currently using a method
 Current use & non-use (four outcome variables)
 Independent variables:
 Motivation to use contraception
 Contraceptive Ability/Cultural Factors
 Supply side factors (Access to Family Planning)
 Hypothesis: Higher standard of living at the community level
can increase usage of contraceptives
Variable
Age: 13-14
Results
Age: 15-19
Age: 20-24
Age: 25-29
Regressions of the determinants
of ever use of contraceptive
methods for ever-married
women in India
Age: 30-34
Age: 35-39
Age: 40-44
Ideal number of children
Education: Literate - primary
Education: Middle complete
Absolute value of t statistics in
parentheses
•*significant at 10%;
•** significant at 5%;
•*** significant at 1%
Education: High school +
1
0.01
0.25
0.15
(4.33)***
0.36
(10.13)***
0.48
(13.73)***
0.52
(14.84)***
0.49
(13.94)***
0.42
(11.87)***
0
(25.67)***
0.15
(28.77)***
0.19
(23.45)***
0.18
(21.81)***
Household is in a rural area
Hindu
Muslim
Christian
Sikh
Has Family Planning Center
Has Primary Health Center
Village is electrified
Constant
Observations
R-squared
-0.04
(-5.32)***
-0.08
(-9.87)***
0.11
(9.76)***
-0.04
(-2.63)***
2
0
0.01
0.15
(4.87)***
0.37
(11.66)***
0.49
(15.44)***
0.53
(16.78)***
0.5
(15.65)***
0.43
(13.51)***
0
(28.43)***
0.15
(35.89)***
0.2
(34.19)***
0.2
(39.85)***
-0.05
(-10.68)***
-0.1
(-14.85)***
0.1
(11.22)***
-0.04
(-3.04)***
-0.05
(-14.97)***
0.02
(4.01)***
0.03
(4.30)***
0.09
(16.14)***
0.02
0.48
55664
0.16
0.14
(4.53)***
85266
0.17
Residential Electricity-Use Trends in the US:
Is Energy Efficiency Legislation Effective?
By Ben Cooper
2001 Residential Energy Consumption Survey


Comprehensive survey conducted by the EIA every four years (2005
data not available yet)
N = 4822 evenly divided between the four regions
US Region

Type of Housing
Northeast
Midwest
South
West
Total
Mobile home
Single-family
detached house
Single-family
attached house
Apt. Building
2-4 Units
Apt. Building
5 or more units
3.68
5.73
8.93
7.67
6.74
50.23
64.66
68.37
57.88
60.87
12.33
10.6
5.98
7.67
8.83
15.55
8.5
6.72
7.17
9.21
18.22
10.51
10.01
19.6
14.35
Other vars used in control: rural/urban, sq. ft., year made, assorted
household appliances
Are Improved Efficiency standards
making a difference?


1979, CA passes SB 331 which significantly increases energy
efficiency standards in CA, nation soon follows
DV = HH dollars per year spent on electricity bill
Year made
Constant (pre-1940)
1940s
1950s
1960s
1970s
1980
1981-89
1990-2001
Simple Reg
Multiple Reg with controls
778.37**
520.21**
(18.13)
(48.32)
37.304
45.84
(34.14)
(40.78)
91.734**
53.00
(27.99)
(33.18)
99.534**
104.12**
(28.37)
(33.60)
223.934**
243.84**
(26.15)
(31.81)
297.568**
297.41**
(51.92)
(57.03)
273.626**
215.20**
(28.24)
(33.49)
283.444**
130.59**
(28.79)
(34.33)
The Secondary Job Market
in Tajikistan
Michael Blackburn
April 2007
Question
• Why do people in Tajikistan choose to work second
jobs?
• I used LSMS Data from 1998 on Tajikistan. I
merged individual-, household-, and communitylevel data to find determinants of the decision to
take on a second job, and tested the data against
three major hypotheses on secondary occupations
in the United States advanced by Paxson and
Sicherman (1996).
Are Work Hours Inflexible?
10
5
0
Percent
15
20
• Not really. There’s no effect of either hours on secondary
wages, nor of working standard 8 hour (presumably
inflexibly scheduled) days on secondary wages.
0
50
100
150
How many hours worked/week
200
Do Secondary Jobs Lower Risk?
• No. They’re more likely to pick a second job in their industry
than random chance would predict. Furthermore, whether
or not someone was paid their full salary was not a
significant indicator of people working a second job.
Are Secondary Jobs Used to Increase Living
Standards?
• Income per household member in the absence of a secondary
job is a statistically significant determinant of secondary
wages, while wealth shocks do not appear to be.
PROGRESA
Targeted School Subsidies in
Rural Mexico
Phil Armour
Structure
1997: Localities randomly designated as
treatment or control
Households designated as poor (eligible)
or not poor (ineligible)
Treatment begins 1998
Control group starts treatment in the
summer of 2000
Results (standard errors)
School Enrollment (N=23,292)
School Inequality (N=31,228)
Pre-Program Difference ’97
Pre-Program Difference ’97
0.006
0.013
(0.005)
(0.008)
Post-Program Difference ’00m
Post-Program Difference ’00m
0.049
-0.034
(0.006)
(0.011)
Difference-in-Differences
Difference-in-Differences
0.043
-0.047
(0.007)
(0.014)