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Breaking down and cutting across silos Robert M. Goerge, Ph.D. FCSM Statistical Policy Seminar Data Communities Coming Together to Support the Enhanced Use of Administrative Records Tuesday, December 4, 2012, 3:40 – 5:30 pm, Washington Convention Center

Breaking down and cutting across silos Robert M. Goerge, Ph.D

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Breaking down and cutting across silos Robert M. Goerge, Ph.D. FCSM Statistical Policy Seminar Data Communities Coming Together to Support the Enhanced Use of Administrative Records Tuesday, December 4, 2012, 3:40 – 5:30 pm, Washington Convention Center. My charge for this session. - PowerPoint PPT Presentation

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Page 1: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Breaking down and cutting across silosRobert M. Goerge, Ph.D.

FCSM Statistical Policy SeminarData Communities Coming Together to Support the Enhanced Use of Administrative

RecordsTuesday, December 4, 2012, 3:40 – 5:30 pm,

Washington Convention Center

Page 2: Breaking down and cutting across silos Robert M. Goerge, Ph.D

My charge for this session

• Compare the challenges facing the different data communities.

• Good news• What we really want• The nature of the challenges• The communities

Page 3: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Good news first

• States and cities are developing their administrative data sources faster than ever

• They are even using the data• And they are making the data public, so that data

entrepreneurs are creating apps that inform the public and policymakers

• There are a number of federal initiatives that are promoting the development (not necessarily the use) of administrative data

Page 4: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Examples

• Given the national effort to improve our competitiveness, a focus of the federal government has been in education and workforce development.

• In June 2012, the U.S. Department of Education (ED) awarded new Statewide Longitudinal Data Systems (SLDS) grants (started in 2005) and the U.S. Department of Labor (DOL) awarded new Workforce Data Quality Initiatives (WDQI) grants (started in 2011).

• Eight states received their first SLDS grants (Delaware, Oklahoma, New Jersey, South Dakota, Vermont, West Virginia, Puerto Rico, and the U.S. Virgin Islands).

• Three states (Hawaii, New Jersey, and Rhode Island) have new SLDS grants focused on workforce linkages and WDQI grants.

• Of course, the Longitudinal Employer-Household Dynamics (LEHD) program is the premier example of linking data to provide greater intelligence around employment.

http://www.dataqualitycampaign.org/files/2012%20SLDS%20and%20WDQI%20grants.pdf

Page 5: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Employment in 2010 was Highest for CPS Graduates Who Enrolled in College

CPS Cohort47,006

Graduated high school:57% of cohort

26,696

Enrolled Post-Secondary

70%

Employed any quarter in 2010: 73%For those employed: Avg # quarters employed : 3.4 Avg quarterly earnings: $4,832

No Post-Secondary30%

Employed any quarter in 2010: 65%For those employed:

Avg # quarters employed : 3.3Avg quarterly earnings: $4,721

Dropped out of high school:37% of cohorts

17,281

Enrolled Post-Secondary

28%

Employed any quarter in 2010: 55%For those employed:

Avg # quarters employed : 3.1Avg quarterly earnings: $3,887.76

No Post-Secondary72%

Employed any quarter in 2010: 45%For those employed:

Avg # quarters employed : 3.0Avg quarterly earnings: $,3826.76

Left/transferred out of CPS:6% of cohorts

2,973

Employed any quarter in 2010: 56%For those employed:

Avg # quarters employed : 3.1 Avg quarterly earnings: $4,392.18

Page 6: Breaking down and cutting across silos Robert M. Goerge, Ph.D

However …

• It’s happening to different degrees in different states and there is a wide variation in who has access to the data that is being created and the quality of the data that is being built.

• It’s also taking many years to develop these efforts in states• Best practices have not been disseminated• States often rely on large corporate vendors, who will only

go so far, and government agencies don’t have the skilled staff necessary to take full advantage of the efforts

• Much of this exists because of …

Page 7: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Silos of all kinds

• Across levels of gov’t – fed, state, county, city• Within levels of government – agency silos• Within agencies and across agencies – program silos• Across domains – health, education, workforce/employment,

law enforcement, anti-poverty• Academic/professional silos – disciplines have their own

interests• Advocacy silos• All work to the detriment of comprehensive data made

available in a efficient format conducive to policy research and analysis

Page 8: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Characteristics of silos

• They are someone’s “turf”• They are someone’s special interest• They have their own set of laws, rules and

regulations• They have their own data• They have their own specific reason for having

data or not having data that does not cross into other silos

Page 9: Breaking down and cutting across silos Robert M. Goerge, Ph.D

“Good luck getting the data sharing agreement through our lawyers….”

Page 10: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Everything is related

• Special interests want us to believe that problems can be addressed one-by-one

• But everyone knows that:– Early nutrition and good parenting is related to learning– Learning is related to getting a job– A parent having a job is related to child well-being– Lack of school success is related to criminal behavior

• This is why we believe that “integration” or breaking down the silos is necessary in order to make progress—however you define that.

Page 11: Breaking down and cutting across silos Robert M. Goerge, Ph.D

For example

• Of all the poor people that the state serves, 23 percent of them use about 86 percent of the dollars in Medicaid, the correctional system and the child welfare system.

• We also know that the greatest school failure happens in the areas where these 23 percent live. We also know that there are high levels of family violence within these households.

• What we don’t know, given current siloed policy and practice regimes is what to do about it at scale. (We have some evidence-based practices that have shown to work at small scales in rather controlled environments.)

• This work, which cuts across all silos, allows us, at least, to know where we need to target our social programs.

Page 12: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Data collection -- For each area, different flow

Frontline collection

Data stays local

City/county State Federal Researchers

Page 13: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Medicaid

Claims made by providers for health care

Data stays with provider

State MMIS

Analysts

State/City/County policymakers

Federal CMS (MSIS)

Policymakers Researchers

Federal Policymakers

Page 14: Breaking down and cutting across silos Robert M. Goerge, Ph.D

UI wage data

Employer to State Agency

Employer gets back?

LEHD

Researchers through Census

State

Revenue

Federal?

Page 15: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Elementary school data

Teacher to District MIS

Teacher gets back?

State SIS (SLDS)

Researchers

Federal?

Page 16: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Interaction with local public sector

• 30 years ago, when there was less data, most public sector agencies had handfuls of analysts

• Now the Research Director, if there is one, has few, if any analysts

• More of a focus on Quality Assurance/Compliance• However, the federal government is requiring

evidence-based practice in many areas of human services, which is a major challenge, given the last of research expertise in these agencies

Page 17: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Interaction with public sector (cont’d)

• Data sharing agreements– More complicated as identity theft became more prevalent– More complicated as FERPA, HIPPA, CFR 42 …– More complicated as leaders and their lawyers viewed

information as power and potential negative media• Contracts

– Certainly the easiest way to work with government, even though Universities concerned with academic freedom

• Evaluations– Done more and more by private, non-university based

organizations (MDRC, Mathematica, SRI …)

Page 18: Breaking down and cutting across silos Robert M. Goerge, Ph.D

New world order

• Data sits in governmental (or private*) databases either static (Census) or continuously being updated by the transactions completed by the government agency or private entity.

• When needed or periodically, data is transferred to an analytic engine that conducts a specified analysis – descriptive, multivariate, mapped …

• OR, it is posted on a data portal with API capability for anyone (?) to access and distribute the analysis

* Google, Twitter, Facebook, Utility company databases . . .

Page 19: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Alignment in Chicago (and a few other cities)

• New Mayor, Cook County Board President who believe in information and hired in a way that reflects that

• Human capital (researchers and programmers) who can make good use of …

• Data – “opening” it up and combining it across domains• Public sector budget crises which leads to the need for

more information• But, perhaps private resources that can make up for the

public sector problems- Philanthropy and Corporate Sector

Page 20: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Questions for government to address (not us, maybe)

• What data is going to be open and what isn’t?• How do you make data available to

administrators, policymakers, and researchers who need to combine data across agencies?

Page 21: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Skepticism about government

• Politics matter the most—policy and facts come second

• There is not enough human capital in government to link to the researchers who can help– Can they provide enough data?– Can they deal with the legal problems in order to

share the data?

Page 22: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Skepticism about the data

• Most social scientists would rightly recommend the city make decisions based on evidence developed from high quality research. To them, that usually means data that they themselves collected or at least had a big hand in collecting OR is blessed by the discipline AND a research design that fits the research question at hand.

Page 23: Breaking down and cutting across silos Robert M. Goerge, Ph.D

The end• There are real barriers that lead to data not flowing to those

that need it• The nature of these barriers vary from sector to sector and

place to place, but there are common themes• These barriers can be addressed and the federal government

has to learn how to learn from those places that have had success

• Incentives have to be put into place for all jurisdictions to use their data to get smarter about what they are doing –

• Reviewing all federal research projects so that they are effectively using administrative data before placing burdens on respondents

Page 24: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Extra slides (if needed)

Page 25: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Balance between surveys and administrative data

• Surveys– Special case of census data

• Administrative data– Register-based data

• Combinations– Depends on overlap of the two– Importance of LEHD

Page 26: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Criteria

• Geographic coverage• Topical coverage– Some things you can’t ask in surveys

• Policy purpose• Data quality

Page 27: Breaking down and cutting across silos Robert M. Goerge, Ph.D

Survey vs. Administrative dataAdapted from Wallgren and Wallgren

Advantages Disadvantages

Surveysbased ondatacollection:samplesurveysandcensuses

Can choose which questions toaskCan be up-to-date

Some respondents ..... do not understand the question... have forgotten how it was... do not respond (nonresponse)... respond carelesslyBurden on respondents can be highExpensiveLow quality for estimates for small studydomains (for sample surveys)

Register-basedsurveys

No further burden on the respondentfor the statisticsLow costsAlmost complete coverage ofpopulationComplete coverage of timeRespondents answer carefully toimportant administrative questionsGood possibilities for reporting forsmall areas, regional statisticsand longitudinal studies

Cannot ask questionsDependent on the administrative system’spopulation, object and variable definitionsThe reporting of administrative data canbe slow; the time between the referenceperiod and when data are available forstatistical purposes can be longChanges in the administrative systemsmake comparisons difficultVariables that are less important foradministrative work can be of lowerquality