Dwight Steward, Ph.D. – Page 3 – Economic Thinking In Action

Recent economic studies on recidivism

The list is not exhaustive but does provide a start for those looking into the issue of recidivism.(parts excerpted from Bad Ink, by Hartger)

Anderson, D. B., Schumacker, R. E. & Anderson, S. L. 1991. Release Characteristics and Parole Success. Journal of Offender Rehabilitation 17:133-145.

Bales, W. D. & Mears, D.P. 2008. Inmate Social Ties and the Transition to Society: Does Visitation Reduce Recidivism? Journal of Research in Crime and Delinquency 20:1 35.

Baumer, E. 1997. Levels and Predictors of Recidivism: The Malta Experience. Criminology 35(4), 601-628.

Beck, A.J., and Shipley, B.E. 1989. Recidivism of Prisoners Released in 1983. Bureau of Justice Statistics—Special Report. Washington, DC: U.S. Department of Justice.

Beck, Allen J. & Bernard E. Shipley. 1987. Recidivism of Young Parolees. Bureau of Justice Statistics—Special Report. Washington, DC: U.S. Department of Justice.

Beck, Allen J. & Bernard E. Shipley. 1997. Recidivism of Prisoners Released in 1983. Bureau of Justice Statistics—Special Report. Washington, DC: U.S. Department of Justice.

Benda, B. B. & Toombs, N. J. 2002. Two Preeminent Theoretical Models: A Proportional Hazard Rate Analysis of Recidivism. Journal of Criminal Justice 30, 217-228.

Benedict, R. W., Huff-Corzine, L., & Corzine, J. 1998. Clean Up and Go Straight: Effects of Drug Treatment on Recidivism among Felony Probationers. American Journal of Criminal Justice, 22, 169-187.

Blumstein, A., Barnett, A., & Farrington, D. 1987. Probabilistic Models of Youthful Criminal Careers. Criminology, 25, 83-107.

Carson, E.A., Sabol, W.J. 2011. Prisoners in 2011. Bureau of Justice Statistics. Washington, D.C.: U.S. Department of Justice.

Chen, K. & Shapiro, J. 2007. Do Harsher Prison Conditions Reduce Recidivism? A Discontinuity-based Approach. American Law and Economics Review, 9(1), 1-29.

Crowe, M. 2012, September 20. Are Tattoos in the Workplace Still Taboo?. USA Today.

Dooley, B., Seals, A., Skarbek, D. 2013. The Effect of Prison Gang Membership on Recidivism. Journal of Criminal Justice, 42(3), 267-275..

Drago, F., Galbiati, R. & Vertova, P. 2011. Prison Conditions and Recidivism. American Law and Economics Review, 13(1), 103-130.

Duwe, G. & Donnay, W. 2008. The Impact of Megan’s Law on Sex Offender Recidivism: The Minnesota Experience. Criminology, 46(2), 411-446.

Florida Department of Corrections (FDOC). 2013. Florida Prison Recidivism Report: Releases from 2004 to 2011. FDOC Publications.

Freeman, R. 2003. Can We Close the Revolving Door?: Recidivism vs. Employment of Ex- Offenders in the U.S. Urban Institute Reentry Roundtable.

Gainey, R.R., Payne, B.K., & O’Toole, M. 2000. The relationships between time in jail, time on electronic monitoring, and recidivism: An event history analysis of a jail based program. Justice Quarterly, 17(4), 733-752.

Gambetta, D. 2009. Codes of the Underworld: How Criminals Communicate. Princeton University Press.

Gendreau, P., Little, T., & Claire Goggin. 1996. A Meta-Analysis of the Predictors of Adult Offender Recidivism: What Works? Criminology 34: 575-607.

Gruenewald, P.J. & West, B.R. 1989. Survival Models of Recidivism Among Juvenile Delinquents. Journal of Quantitative Criminology, 5(3), 215- 229.

Hanley, D.E. & Latessa, E.J. 1997. Correlates of Recidivism: The Gender Division. Academy of Criminal Justice Sciences.

Hennessey, R. 2013, February 27. Tattoos No Longer A Kiss of Death in the Workplace. Forbes.

Hepburn, J. R. & Albonetti, C.A. 1994. Recidivism among Drug Offenders: A Survival Analysis of the Effects of Offender Characteristics, Type of Offense, and Two Types of Intervention. Journal of Quantitative Criminology 10:159-179.

Husock, H. 2012, August 3. From Prison to a Paycheck. The Wall Street Journal.

Jurik, N. C. 1983. The Economics of Female Recidivism. Criminology, 21, 603-622.

Kaufman, J. 2013, April 17. Keeping Their Art to Themselves. The New York Times.

Kilgannon, C. 2009, April 1. When Tattoos Hurt Job Prospects. The New York Times.

Kohl, R., Hoover, H.M., McDonald, S.M. & Solomon, A.L. 2008. Massachusetts Recidivism Study: A Closer Look at Releases and Returns to Prison. Urban Institute-Justice Policy Center: Washington D.C.

Kruttschnitt, C., Uggen, C., & Shelton, K. 2000. Predictors of Desistance Among Sex Offenders: The Interaction of Formal and Informal Social Controls. Justice Quarterly, 17, 61-87.

Kubrin, C.E. & Stewart, E.A. 2006. Predicting Who Reoffends: The Neglected Role of Neighborhood Context in Recidivism Studies. Criminology 44:165-197.

Langan, P.A., and Levin, D.J. 2002. Recidivism of Prisoners Released in 1994. Bureau of Justice Statistics. Washington, D.C.: Bureau of Justice Statistics.

Langan, P.A., Schmitt, E.L. & Durose, M.R. 2003. Recidivism of Sex Offenders Released from Prison in 1994. Bureau of Justice Statistics. Washington, DC: U.S. Department of Justice.

Lozano, A.T.R., Morgan, R.D., Murray, D.D., & Verghese, F. 2010. Prison Tattoos as a reflection of the Criminal Lifestyle. International Journal of Offender Therapy and Comparative Criminology 55 (4), 509-529.

Pew Center on the States. 2010. Millennials: Confident. Connected. Open to Change. Pew Center on the States.

Pia Negro, M. 2012, October 16. Baltimore Program Provides Job Support for Ex-Prisoners Coming Home. Baltimore News.

Putnins, A. 2002. Young Offenders, Tattoos and Recidivism. Psychiatry, Psychology and Law, 9(1), 62-68.

Rosenberg, T. 2012, March 28. Out of Jail, and Into a Job. The New York Times.

Spohn, C. & Holleran, D. 2002. The Effect of Imprisonment on Recidivism Rates of Felony Offenders: A Focus on Drug Offenders. Criminology 40:329-358.

Steward, Dwight, Estimating Recidivism Risk in Earnings Loss Calculations for Persons Recently Released from Incarceration (October 11, 2010). Available at SSRN: http://ssrn.com/abstract=1753285 orhttp://dx.doi.org/10.2139/ssrn.1753285

Tahmincioglu, E. 2010, February 17. Unable to get Jobs, Freed Inmates Return to Jail. NBC News.

Uggen, C. 2000. Work as a Turning Point in the Life Course of Criminals: A Duration Model of Age, Employment, and Recidivism. American Sociological Review 67, 529-546.

Visher, C.A. & Linster, R.L. 1990. A Survival Model of Pretrail Failure. Journal of Quantitative Criminology, 6(2), 153-184.

Visher, C.A., Lattimore, P.K., & Linster, R.L. 1991. Predicting the Recidivism of Serious Youthful Offenders Using Survival Models. Criminology, 29(3), 329-366.

Waters, K. 2012. The Tattooed Inmate and Recidivism. Electronic Theses, Treatises, and Dissertations. Paper 5262.

Windzio, M. 2006. Is There a Deterrent Effect of Pains of Imprisonment? The Impact of ‘Social Costs’ of First Incarceration on the Hazard Rate of Recidivism. Punishment and Society, 8(3), 341-364.

Measuring systematic risk in the international banking system

It is easier than ever to invest across international borders. Measuring the risk associated with a country’s banking system is often a key decision international investment decisions. Banking system z-scores are one way of measuring overall financial risk within a country’s banking system. The banking z-score captures the probability of default of a country’s banking system.

In general, the Z-score compares a bank’s buffers (capitalization and returns) with the volatility of those returns. The banking z-score for Honduras, Brazil, and Mexico is shown below.

Here is some recent research on measuring banking system risk.

http://www.sciencedirect.com/science/article/pii/S104244311300005X

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1753735

http://www.hnb.hr/dub-konf/14-konferencija/ivicic-kunovac-ljubaj-2.pdf

Business closure rates study

SBA study on business closure rates (as opposed to failure) provides some insights on business survival rates. In a 2003 study, the SBA found 50 % survived, 17% were successful but closed and 33% closed and unsuccessful after 4-5 years.

The study found 39.5% survived after 6 years.

Source:
Headd, Brian, “Redefining Business Success: Distinguishing Between Closure and Failure,” Small Business Economics, 21, 2003, pp. 51-61, reprinted by the Office of Advocacy, U.S. Small Business Administration, 2003.

Using U.S. BLS location quotient to study job concentration within a given labor market

Job location quotients are ratios that compare the concentration of a particular job in a defined area to that of a larger area. For example, job location quotients compare MSA employment to that of the nation.

For instance, the BLS shows that employment per thousand jobs for JOB 1 is 0.41 in AREA 1. In the nation as a whole, the employment per thousand for JOB 1 is 0.13. The job title location quotient in this example is 3.09 = (0.41)/(0.13) .

The location quotient in this example is the ratio of the area concentration of occupational employment to the national average concentration. A location quotient greater than one indicates that the occupation has a higher share of employment than average, and a location quotient less than one indicates the occupation is less prevalent in the area than average.

Statistical analysis of VA wait times on the way

The number of military Veterans requesting care from the VA is at a historic high.

In the coming weeks, we will be publishing a series of post describing the work we have performed on VA wait times data obtained from the LA Times.

The underlying VA data was obtained from the June 2014 government audit of the VA. In our study we look at:

– The different types of wait times experienced by military veterans

– Socio-economic factors related to VA times such as percentage of vets and poverty rates in the surrounding area

– Detailed look at vets receiving mental health care and the wait times that they experience

Significant new government compensation data reporting requirements on the way from the OFCCP

Seyfarth Shaw LLP and a number of other OFCCP watchers reported recently on Office of Federal Contract Compliance Programs (“OFCCP”) notice of proposed rulemaking (“NPRM”) that would require most Federal contractors and first-tier subcontractors to submit compensation data to the OFCCP through a web-based filing system on an annual basis. The stated goal of the new data is to provide the OFCCP better data to monitor and analyze the pay of men and women in the workforce.

According to Federal Register, Vol. 79, No. 153, August 8, 2014, the OFCCP’s new data requirement will require contractors and subcontractors to provide:

The total number of workers within a specific EEO-1 job category by race, ethnicity and sex;
Total W-2 wages for all workers in the EEO-1 job category by race, ethnicity and sex; and
Total hours worked by all employees in the EEO-1 job category by race, ethnicity and sex.

The Department of Labor estimates that the the total cost of the rule change is $50,009,885, $46,250,189 will be first year cost.

What’s the latest on the economic impact of outsourcing?

Outsourcing

According to Moretti, economic studies have generally found that outsourcing is a net good for the U.S. economy.

According to Moretti, many of the outsourced services are complements and not substitutes for the services that we use locally in the U.S.

In addition to citing several economic studies on the topic, Moretti uses the case study of Oracle. According to Moretti, In 2000 Oracle had 22,008 U.S. workers and 20,919 workers abroad. In 2014, as the company grew the number of jobs outsourced also grew.. By 2014, Oracle had 40,000 in the U.S. and 60,000 abroad.

According to Moretti, this was a net gain for the U.S. even though clearly outsourcing has increased for Oracle. Many of the U.S. jobs created by Oracle jobs are high paying jobs. Also, he further shoes that many of the jobs, like routine computer software programmers, were jobs that helped U.S. workers do their jobs better as opposed to replacing the work of native U.S. workers. Further the multiplier effect resulted in many support jobs being created in the U.S.

Why is the innovation job sector so important? Because 5 > 1.6 (Moretti)

Why it is import?

Moretti argues that innovation jobs, like software jobs, make intensive use of human capital and human ingenuity are big economic drivers.

Based on his calculations from 320 Metro areas and approximately 11 million employee and previous studies, Moretti estimates that one innovation job creates five supporting jobs.

In contrast, Moretti estimates that traditional jobs create about 1.6 for every job. This is based on 320 metro areas and 11 million employees.

Useful data in analyzing FLSA and wage and hour cases involving truck drivers

Useful data for analyzing FLSA and wage and hour allegations in cases involving truck drivers. (Continued from previous post)

1. Waiting Time. The Bill of Lading (BOL) for a particular load. The BOL typically shows the time the driver arrived at the site and what time he/she started loading or unloading.

2. Pre- and Post-Trip Work. Driver’s Logs typically show the time of arrival at a truck yard and the time the driver left the yard to head to the first job site as well as the time left and returning from subsequent jobs.

3. Pay records

Allegations in a truck driver FLSA and wage and hour case

(Continued from previous blog post)

1. Waiting Time. The drivers are seeking to be paid for all time spent waiting at a load or delivery site.

2. Pre- and Post-Trip Work. Time spent time at the beginning and end of each shift doing pre- and post-trip inspections and other duties in the yard was allegedly not paid.

3. Driving Empty. The drivers were allegedly not paid hourly for driving empty trucks to and from job sites.