This paper investigates the role of social learning in the diffusion of a new agri cultural technology in Ghana. We use unique data on farmers' communication patterns to define each individual's information neighborhood. Conditional on many potentially confounding variables, we find evidence that farmers adjust their inputs to align with those of their information neighbors who were surpris ingly successful in previous periods. The relationship of these input adjustments to experience further indicates the presence of social learning. In addition, applying the same method to input choices for another crop, of known technol ogy, correctly indicates an absence of social learning effects. (JEL D83, 013, 033, Q16)

/pdf/learning-about-a-new-technology-pineapple-in-ghana-3vxvkgahoh.pdf

Learning About a New Technology: Pineapple in Ghana

We use a novel dataset and research design to empirically detect the effect of social interactions among neighbors on labor market outcomes. Specifically, using Census data that characterize residential and employment locations down to the city block, we examine whether individuals residing in the same block are more likely to work together than those in nearby blocks. We find evidence of significant social interactions operating at the block level: residing on the same versus nearby blocks increases the probability of working together by over 33 percent. The results also indicate that this referral effect is stronger when individuals are similar in sociodemographic characteristics (e.g., both have children of similar ages) and when at least one individual is well attached to the labor market. These findings are robust across various specifications intended to address concerns related to sorting and reverse causation. Further, having determined the characteristics of a pair of individuals that lead to an especially strong referral effect, we provide evidence that the increased availability of neighborhood referrals has a significant impact on a wide range of labor market outcomes including employment and wages.

Place of Work and Place of Residence: Informal Hiring Networks and Labor Market Outcomes

This article proposes an evolutionary model of risky behavior in adolescence and contrasts it with the prevailing developmental psychopathology model. The evolutionary model contends that understanding the evolutionary functions of adolescence is critical to explaining why adolescents engage in risky behavior and that successful intervention depends on working with, instead of against, adolescent goals and motivations. The current article articulates 5 key evolutionary insights into risky adolescent behavior: (a) The adolescent transition is an inflection point in development of social status and reproductive trajectories; (b) interventions need to address the adaptive functions of risky and aggressive behaviors like bullying; (c) risky adolescent behavior adaptively calibrates over development to match both harsh and unpredictable environmental conditions; (d) understanding evolved sex differences is critical for understanding the psychology of risky behavior; and (e) mismatches between current and past environments can dysregulate adolescent behavior, as demonstrated by age-segregated social groupings. The evolutionary model has broad implications for designing interventions for high-risk youth and suggests new directions for research that have not been forthcoming from other perspectives.

https://evolution-institute.org/wp-content/uploads/2014/11/DP-Ellis-et-al-2012-risky-adolescent-behavior.pdf

The evolutionary basis of risky adolescent behavior: Implications for science, policy, and practice

We use a novel research design to empirically detect the effect of social interactions on labor market outcomes. Using Census data on residential and employment locations, we examine whether individuals residing in the same city block are more likely to work together than those in nearby blocks. We find evidence of significant social interactions. The estimated referral effect is stronger when individuals are similar in sociodemographic characteristics. These findings are robust across specifications intended to address sorting and reverse causation. Further, the increased availability of neighborhood referrals has a significant impact on a wide range of labor market outcomes.

/pdf/place-of-work-and-place-of-residence-informal-hiring-3ix6sjhhqr.pdf

This paper develops a model of crime in which human capital increases the opportunity cost of crime from foregone work and expected costs associated with incarceration. Older, more intelligent, and more educated adults should commit fewer street (unskilled) crimes. White collar crimes decline less (or increase) with age and education. Predictions for age-crime and education-crime relationships receive broad empirical support in self-report data from the National Longitudinal Survey of Youth and arrest data from the Uniform Crime Reports. The effects of education, training, and wage subsidies, as well as enforcement policies on criminal behavior are discussed.

Education, Work, and Crime: A Human Capital Approach

This paper analyzes the influence that juvenile offenders serving time in the same correctional facility have on each other’s subsequent criminal behavior. The analysis is based on data on over 8,000 individuals serving time in 169 juvenile correctional facilities during a two-year period in Florida. These data provide a complete record of past crimes, facility assignments, and arrests and adjudications in the year following release for each individual. To control for the non-random assignment to facilities, we include facility fixed effects, thereby estimating peer effects using only within-facility variation over time. We find strong evidence of peer effects for various categories of theft, burglary, and felony drug and weapon crimes; the influence of peers primarily affects individuals who already have some experience in a particular crime category. We also find evidence that peer effects are stronger in smaller facilities and that the predominant types of peer effects differ in residential versus non-residential facilities. Effects in the latter are consistent with network formation among youth serving time close to home.

/pdf/building-criminal-capital-behind-bars-social-learning-in-3zxxrexpaq.pdf

Building Criminal Capital Behind Bars: Social Learning in Juvenile Corrections

This paper analyzes the influence that juvenile offenders serving time in the same correctional facility have on each other's subsequent criminal behavior. The analysis is based on data on over 8,000 individuals serving time in 169 juvenile correctional facilities during a two-year period in Florida. These data provide a complete record of past crimes, facility assignments, and arrests and adjudications in the year following release for each individual. To control for the non-random assignment to facilities, we include facility and facility-by-prior offense fixed effects, thereby estimating peer effects using only within-facility variation over time. We find strong evidence of peer effects for burglary, petty larceny, felony and misdemeanor drug offenses, aggravated assault, and felony sex offenses; the influence of peers primarily affects individuals who already have some experience in a particular crime category. We also find evidence that the predominant types of peer effects differ in residential versus non-residential facilities; effects in the latter are consistent with network formation among youth serving time close to home.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID441882_code346717.pdf?abstractid=441882&mirid=1

Randi Pintoff

Papers

Building Criminal Capital Behind Bars: Social Learning in Juvenile Corrections

Building Criminal Capital Behind Bars: Peer Effects in Juvenile Corrections

Do Arrest and Incarceration Affect High School Graduation