Determinants of Crime Daniel Yu Pomona College May 2007
Slide 2
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 Institutes Population
Estimates Population by race, gender and age group Bureau of
Economic Analysis County Summary Income per capita
Slide 3
Model Crime Rate Index= 0 + 1 Social Capital + 2 Population + 3
Dissimilarity + 4 ln(Income) +[ 5 Year + 6 State] + 0 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
Slide 4
Analysis
Slide 5
Determinants of Child Labor Matt Speise May 2007
Slide 6
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%.
Slide 7
Slide 8
Regressions
Slide 9
Orphans in Kagera, Tanzania: Parental Death and School
Enrollment Eric Otieno Professor Andrabi Econ 190 5/2/07
Slide 10
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 S i =f(Orphan Status, Age, Age
squared, Gender, Household Characteristics, Household Head
Characteristics) S i Dependent variable indicating whether child is
currently enrolled.
Slide 11
The Data Table 1: Number and Proportion of Orphans By Type Non
Orphans Paternal Orphans Maternal Orphans Two Parent Orphans Total
Number8601612381171376 Proportion0.630.120.170.08 1.0 Table 2:
Enrollment Rates By Orphan Status and Age Group Age Group 7-10
years old 11-14 years old Non-Orphans38.3278.94 Paternal
Orphans36.7978.95 Maternal Orphans31.8276.52 Two-Parent
Orphans37.7870.83 Graph 1: Enrollment Rates By Age and Gender
Age
Slide 12
Dependent variable is whether child is currently enrolled.
Control variables are not reported Hypothesis 1: Orphans have lower
enrollment rates than non-orphans 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 Orphans living with grandparents have higher
enrollment rates that orphans living with siblings or orphans
living with other relatives Regression 1Regression 2Regression
3Regression 4 Orphan TypePaternal OrphanMaternal Orphan Two Parent
Orphan` Any Orphan Coefficient-0.2770.042-0.4180.004 Standard
Error0.2060.1980.2680.179 Regression 5: Grandchild is the omitted
variable Relationship of Orphan to Household Head SiblingNephew or
NieceOther Relative Coefficient-2.574-0.603-1.570 Standard
Error0.876**0.6310.567* **Significant at the 5% level *Significant
at the 10% level Results and Conclusions
Slide 13
The Blue Line A Hedonic Price Study of Light Rail in Los
Angeles Carey McDonald, 07 Pomona College
Slide 14
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
Slide 15
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
Slide 16
The Blue Line GIS Map of the Blue Line
Slide 17
The Blue Line - Results V alue ir = G eography ir + N
eighboorhood ir + D istance ir + 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
Slide 18
The Blue Line - Results Correct sign, significance at 10% level
Apparent access capitalization Comparable to Gold Line,
Metrolink
Slide 19
Female Enrollment in the Pomona College Economics Department Ty
Hollingsworth Econ. 190 Prof. Andrabi
Slide 20
Overall Enrollment Over Time
Slide 21
What Could Cause Differences by Gender? Ability Introductory
GPA Ambition/Organization Peer Effects Preferences or Other
Unobservables Professor Fixed Effects
Slide 22
Dependent Variable is whether the Student took Economics above
Econ. 52 [1] [1] All Results show Marginal Effects Eq. 1Eq. 2Eq.
3Eq. 4Eq. 5Eq. 6Eq. 7 Male.1828902* (.01745).1891123*
(.01765).1026882* (.03523).1060828* (.03435).1067014*
(.03434).0947297* (.04261).1405753* (.04427) Math GPA-.0104809
(.00628) -.0200069* (.00652) -.0198888 (.00653) -.0214006* (.00777)
-.0131988 (.00851) Intro GPA.0343755* (.00771).03454
(.00773).0342656* (.00937).0343386* (.00982) Dual Major-.0626051
(.06722) -.1010447 (.07208) -.0708253 (.07967) Intro % Women
-.3062255 (.18661) -.2815031 (.19214) Econ Required.7097906 *
(.05758) N3147 730671 489 Pseudo
R^20.02440.05090.02830.06960.07060.08480.1712 [1] [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
Slide 23
Determinants of the College Decision Jeff Fortner Pomona
College May 2007
Slide 24
Hypothesis: Three Broad Categories of Determinants Pressure to
Seek Employment Instead Pressure to Seek Employment Instead
Inability to Pay for College Inability to Pay for College Lack of
Academic Preparation Lack of Academic Preparation Dependent
Variable: College Track
Slide 25
Independent Variables: Significant Results Correlation
Strongly: Strongly: Male() State performance on standardized
tests(+) Whether high school offers AP courses(+) Hispanic()
Weakly: Weakly: State median income(+) Asians (+)
Slide 26
Independent Variables: Insignificant Results Importance of
religion Importance of religion Private school Private school
Preparation for job market in high school (self-reported)
Preparation for job market in high school (self-reported) African-
or Black-American African- or Black-American
Slide 27
Determinants of Contraceptive Use in India Praween Dayananda
(07) Econ 190 April 25, 2007
Slide 28
The DHS Dataset MEASURE DHS survey data completed in 1992-1993
in India A core questionnaire at the household level Individual
womens questionnaire Selected women: ever-married women (aged13-49)
Village level questionnaire 88562 observations
Slide 29
Ever Heard/Use of Contraceptive Methods
Slide 30
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
Slide 31
Results Regressions of the determinants of ever use of
contraceptive methods for ever-married women in India Absolute
value of t statistics in parentheses *significant at 10%; **
significant at 5%; *** significant at 1%
Slide 32
Residential Electricity-Use Trends in the US: Is Energy
Efficiency Legislation Effective? By Ben Cooper
Slide 33
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 Other vars used in
control: rural/urban, sq. ft., year made, assorted household
appliances
Slide 34
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
Slide 35
The Secondary Job Market in Tajikistan Michael Blackburn April
2007
Slide 36
Question Why do people in Tajikistan choose to work second
jobs? I used LSMS Data from 1998 on Tajikistan. I merged
individual-, household-, and community- level 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).
Slide 37
Are Work Hours Inflexible? Not really. Theres no effect of
either hours on secondary wages, nor of working standard 8 hour
(presumably inflexibly scheduled) days on secondary wages.
Slide 38
Do Secondary Jobs Lower Risk? No. Theyre 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.
Slide 39
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.
Slide 40
PROGRESA Targeted School Subsidies in Rural Mexico Phil
Armour
Slide 41
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