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Determinants of Crime Daniel Yu Pomona College May 2007

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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

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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

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Analysis

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Determinants of Child Labor Matt Speise May 2007

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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%.

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Regressions

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Orphans in Kagera, Tanzania: Parental Death and School Enrollment Eric Otieno Professor Andrabi Econ 190 5/2/07

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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.

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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

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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

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The Blue Line A Hedonic Price Study of Light Rail in Los Angeles Carey McDonald, 07 Pomona College

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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

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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

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The Blue Line GIS Map of the Blue Line

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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

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The Blue Line - Results Correct sign, significance at 10% level Apparent access capitalization Comparable to Gold Line, Metrolink

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Female Enrollment in the Pomona College Economics Department Ty Hollingsworth Econ. 190 Prof. Andrabi

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Overall Enrollment Over Time

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What Could Cause Differences by Gender? Ability Introductory GPA Ambition/Organization Peer Effects Preferences or Other Unobservables Professor Fixed Effects

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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

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Determinants of the College Decision Jeff Fortner Pomona College May 2007

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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

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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 (+)

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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

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Determinants of Contraceptive Use in India Praween Dayananda (07) Econ 190 April 25, 2007

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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

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Ever Heard/Use of Contraceptive Methods

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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

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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%

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Residential Electricity-Use Trends in the US: Is Energy Efficiency Legislation Effective? By Ben Cooper

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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

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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

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The Secondary Job Market in Tajikistan Michael Blackburn April 2007

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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).

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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.

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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.

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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.

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PROGRESA Targeted School Subsidies in Rural Mexico Phil Armour

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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

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Results (standard errors) School Enrollment (N=23,292) School Inequality (N=31,228) Pre-Program Difference 97 0.0060.013 (0.005)(0.008) Post-Program Difference 00m 0.049-0.034 (0.006)(0.011) Difference-in-DifferencesDifference-in-Differences 0.043-0.047 (0.007)(0.014)