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Coonoor Behal

Involvement of TANF Applicants with Child Protective Services (July 2001) - 0 views

www.irp.wisc.edu/...dp122901.pdf

behaviors policy TANF children overlap data

shared by Coonoor Behal on 15 Feb 13 - No Cached
  • Coonoor Behal on 15 Feb 13
     
    This paper presents findings from an exploratory study of Temporary Assistance for Needy Families (TANF) applicants in Milwaukee County, Wisconsin. We examine the level of involvement of TANF applicants with the child welfare system both before and after their application for TANF assistance and inclusion in our study. We also present preliminary multivariate models of the hazard of our sample's CPS involvement with child protective services subsequent to their application for TANF. We find a high level of overlap between TANF and child welfare populations. We also find a set of correlates of CPS involvement after TANF application that are robust to a variety of model specifications. Although our findings are preliminary and further analyses based on longer-term follow-up of our sample will no doubt provide greater clarity, we believe that our findings to date provide food for thought for the designers and administrators of both TANF and child welfare programs.
Vetan Kapoor

Notes from "Poverty in America" by John Iceland (2012) - 0 views

Poverty in America: A Handbook (John Iceland, 2012) Chapter 4: Characteristics of the Poverty Population * 22.4% of Americans were poor in 1959, 11.1% in 1973, and 12.5% in 2003 * 70% of impoveri...

notes povertytraits books stats

started by Vetan Kapoor on 22 Mar 13 no follow-up yet
Coonoor Behal

The Death of Consumer Segmentation? | CMO Strategy - Advertising Age - 0 views

adage.com/...135961

customersegmentation productaffinity Apple Amazon consumer marketing

shared by Coonoor Behal on 08 Jan 13 - No Cached
  • the rather static definition of consumer segments is becoming less reliable in our extremely volatile society, especially in today's economic climate. A consumer's lifetime value may have decreased significantly in the past six months, a fact not reflected by any segmentation method. A person might be out of a purchase cycle for a particular product because of a significant household change
  • These life-changing events are becoming more difficult to predict because consumers live their lives on a much less traditional path than they did 10 or 20 years ago.
  • consumers are never just part of one segment. Rather, they feel, rightfully, that they belong to a multitude of segments.
  • ...7 more annotations...
  • This individual belongs to three segments with different behavior patterns, product affinities and interests -- depending on the time of day or the day of the week. This is particularly true for the growing multicultural groups in the U.S. who are moving through several segment identities every single day.
  • consumers are gaining more control of any marketing activity. And they like it.
  • t's easier to let them choose and decide what is relevant for them than to predict relevance based on any expensively calculated segment identity. This is a plea to marketers for a stronger focus on enabling the consumer to self-segment.
  • following in the footsteps of Amazon in recommending segment identities by correlating the interest in one product to another. An investment in a smart product-affinity recommendation engine could be more worthwhile than spending huge dollars against micro-segmenting the consumer base.
  • wo of the most successful product and retail companies, Apple and Amazon, are not masters of consumer segmentation but experts in building relevant products that consumers choose.
  • They are far more focused on building and communicating relevance relationships than in micro-segmenting consumers by any kind of attributes.
  • consumer segmentation and self-segmentation have now entered the stage of becoming equal forces in today's marketing discipline.
Coonoor Behal

What You'll Do Next - NYTimes.com - 0 views

www.nytimes.com/...brooks-what-youll-do-next.html

bigdata behavior correlation causation intervention

shared by Coonoor Behal on 16 Apr 13 - No Cached
  • The theory of big data is to have no theory, at least about human nature. You just gather huge amounts of information, observe the patterns and estimate probabilities about how people will act in the future.
  • Thus, the passing of time can produce gigantic and unpredictable changes in taste and behavior, changes that are poorly anticipated by looking at patterns of data on what just happened.
  • If you are relying just on data, you will have a tendency to trust preferences and anticipate a continuation of what is happening right now.
  • ...1 more annotation...
  • One of my take-aways is that big data is really good at telling you what to pay attention to. It can tell you what sort of student is likely to fall behind. But then to actually intervene to help that student, you have to get back in the world of causality, back into the world of responsibility, back in the world of advising someone to do x because it will cause y.
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