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R OCKWOOL F ONDENS F ORSKNINGSENHED Torben Tranæs R OCKWOOL F ONDENS F ORSKNINGSENHED Copenhagen, August 12, 2010 The need for breadth and depth Compatibility.

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Presentation on theme: "R OCKWOOL F ONDENS F ORSKNINGSENHED Torben Tranæs R OCKWOOL F ONDENS F ORSKNINGSENHED Copenhagen, August 12, 2010 The need for breadth and depth Compatibility."— Presentation transcript:

1 R OCKWOOL F ONDENS F ORSKNINGSENHED Torben Tranæs R OCKWOOL F ONDENS F ORSKNINGSENHED Copenhagen, August 12, 2010 The need for breadth and depth Compatibility between Demand and Supply for research data

2 R OCKWOOL F ONDENS F ORSKNINGSENHED Introduction n What is the demand for data when you do research; broad applied as well as academic? n And, how do these demands match up with the way national statistical agencies are operating? n Two main issues/problems: 1. the ways of data access. 2. the existing variables

3 R OCKWOOL F ONDENS F ORSKNINGSENHED Preconditions n The main operation run by a national statistical agency is not about research – it is about description and documentation n And also, the way data access has been considered – at least in Denmark – has mainly accommodated other users than academic researchers n My main focus will be the independent academic research

4 R OCKWOOL F ONDENS F ORSKNINGSENHED My talk: n The research process and data access n The need (demand) for and supply of data, and mismatch problem n Case: crime and the labor market n Conclusion

5 R OCKWOOL F ONDENS F ORSKNINGSENHED A generic research process: 1. The researcher formulates a hypothesis 2. A data set with the relevant variables is acquired 3. Hypothesis is tested u … and e.g., rejected! 4. Inspired by data, the theories are being revised/new or revised hypotheses are being formulated/the controlling environment is reconsidered 5. Data is arranged with (some) new variables 6. The new/revised hypothesis is being tested, etc.

6 R OCKWOOL F ONDENS F ORSKNINGSENHED n This way of working would be very cumbersome with the way DsT use to think of data access n According to that model a data set was made available for a given project only, and for a fixed period – all of it being decided up front n Fortunately, the practice was different

7 R OCKWOOL F ONDENS F ORSKNINGSENHED Demand and Supply for Data; and possible mismatch n The demand (from the research community): u Variables defined based on social science theory u High precision, conceptually u Panel data with: 1) a long time dimension, and 2) a big big number of relevant and irrelevant variables, to get exogenous variation and instruments, e.g., by constructing discontinuities (if they don’t exist naturally) n The data supply u Administratively defined variables u High precision in terms of low measurement errors u Somewhat short but ever increasing time dimension u An extremely rich set of information (many variables)

8 R OCKWOOL F ONDENS F ORSKNINGSENHED Mismatch problems: n Long run: The set of existing variables does not co-inside with the set of warranted variables n Short run: The set of existing relevant data does not co-inside with the set of accessible data

9 R OCKWOOL F ONDENS F ORSKNINGSENHED Case: Does unemployment increase after a prison sentence? n What is the key variable? n Is it u ‘unemployment’, or u the fraction of people without a job? n In Denmark we have full-population information on the latter, not the former. We know who receive unemployment benefits, but that’s not necessarily the same as being ‘unemployed’ u Being ‘unemployed’ means that you are employable, available and active searching for jobs

10 R OCKWOOL F ONDENS F ORSKNINGSENHED n Thus, three different measures of difficulties at the labor market: 1. People without job 2. Registered unemployment 3. (Employable) Jobless individuals who search and are available for employment u There exists information on 1. and 2., but on 3. only for small sub-samples of the population – and that is not enough when studying crime and former inmates

11 R OCKWOOL F ONDENS F ORSKNINGSENHED n As we shall see below; it makes a big difference what definition is used

12 R OCKWOOL F ONDENS F ORSKNINGSENHED Unemployment before and after prison

13 R OCKWOOL F ONDENS F ORSKNINGSENHED Unemployment before and after prison

14 R OCKWOOL F ONDENS F ORSKNINGSENHED Unemployment before and after prison

15 R OCKWOOL F ONDENS F ORSKNINGSENHED Unemployment before and after prison

16 R OCKWOOL F ONDENS F ORSKNINGSENHED Unemployment before and after prison

17 R OCKWOOL F ONDENS F ORSKNINGSENHED Fraction of ex-convicted that are on public support after prison relative to fraction in population age 15-59

18 R OCKWOOL F ONDENS F ORSKNINGSENHED Wage earnings before and after having served a long prison sentence Earnings relative to unskilled, same age, same year

19 R OCKWOOL F ONDENS F ORSKNINGSENHED Summary of the case: n Dramatic different conclusions depending on which measure of unemployment is used n The richness of data reveals new stylized facts, e.g.: u the deroute begins before crime n That implies extra research questions in order to answer the question: u What is it that triggers the deroute? u School/labor market problems or family problems, substance abuse, beginning mental illness, etc. n But these questions cannot be pursued right away given the (existing) DSt policy.

20 R OCKWOOL F ONDENS F ORSKNINGSENHED Conclusion Two main problems/challenges: n A policy for data access that is not very compatible with the research process n Mismatch between the needed variables and the existing variables

21 R OCKWOOL F ONDENS F ORSKNINGSENHED n What has been fixed lately or is being fixed as we speak? u In the near future it will be possible to operate big multi-project data sets with practical no ending date n What is not being resolved - remaining problems from the researcher’s point of view u In some major areas: Either the variables are somewhat wrong compared to the hypothesis in question, or u they are very expensive and still only possible to get in small samples


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