Microeconometrics. Bernd Süssmuth. IEW Institute for Empirical Research in Economics. University of Leipzig. April 4, 2011

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1 Microeconometrics Bernd Süssmuth IEW Institute for Empirical Research in Economics University of Leipzig April 4, 2011 Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

2 Organizational stu Our team for this class I I Bernd Süssmuth (lecture); o ce hour: pls make an appointment Marco Sunder (lab work) Central texts I I I Greene, W.H. (2008): Econometric Analysis, Prentice Hall, 6th ed. Cameron C. A. and P.K. Trivedi (2005): Microceconometrics. Methods and Applications, Cambridge University Press Further literature will be given in class Assessment and credits I I 90 min. written exam 20 min. presentation based on term paper (4 weeks) = 10 LP Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

3 What is microeconometrics? Econometrics Econometric analysis is the eld of economics that concerns itself with the application of mathematical statistics and the tools of statistical inference to the empirical measurement of relationships postulated by economic theory." Greene (2008, p. 1) Microeconometrics... microeconometric analysis, the analysis of individual-level data on the economic behavior of individuals or rms." Cameron and Trivedi (2005, p. 3) Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

4 Concern and contribution of microeconometrics Contents I 1 Introduction Concern and contribution of microeconometrics Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

5 Concern and contribution of microeconometrics Concern and contribution of microeconometrics Concern Microeconometrics is concerned with the following situations (i) Non-continuous dependent variables: discrete, qualitative, censored, survival, count data; (ii) Missing randomness of sample: requires correction for selection bias; (iii) Non-measurable variables: in particular, unobserved heterogeneity, i.e. y i is not only determined by observed (X i ) and observable characteristics but also by unobserved characteristics of individuals. As long as this is not controlled for, the estimator is potentially biased. Panel data may help you out. Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

6 Concern and contribution of microeconometrics Concern and contribution of microeconometrics Contribution Microeconometrics... adds to scienti c progress by testing hypotheses on the behavior of rms, households, and individuals allows to measure the existence and size of conjectured e ects (beware of sign econometrics": McCloskey, AER, 1985; McCloskey and Ziliak, JEL, 1996; EJW, 2004) makes you sensible for not randomly drawn samples helps to correctly evaluate policy measures can be used diversely in market and rm-level research improves your understanding of analytical conduct (survey design etc.) Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

7 Contents I 1 Introduction Concern and contribution of microeconometrics Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

8 (Cameron & Trivedi, Ch. 3) Observations nature of data By source type of survey, we discriminate (a) Cross sectional data describe all units of observation at the same point in time. They are adequate if the phenomena at stake are constant over time and static in nature. They are inadequate if intertemporal dependencies play a role. (b) Repeated cross sections in this case, cross sectional data are drawn at di erent points in time without relying on identical units of observation. They are also inadequate if intertemporal dependencies play a role. (c) Panel or longitudinal data describe given units over time. (d) Event data describe given units for a pre-de ned period (window). Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

9 Observations nature of data Example: Labor supply decision - implications of data structure (a) Using cross sectional data, we may analyze which variables determine the fact that persons work at a given point in time. (b) Using repeated cross sections, we are able to model time trends in the overall participation decision. However, we are not able to model changes of participation at the individual level. (c) Panel data allow us to model the individual dynamics of labor market participation. It can be observed whether persons who worked in a particular year supplied labor or exited from the labor force in the following year. (d) Event data would allow us to measure how long labor is actually supplied (without interruption). We are able to study the determinants of the duration of unemployment. Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

10 Observations sampling For random samples the probability that unit i is drawn from a population consisting of N observations is for all i exactly given by 1/N. Usually a survey based sample is drawn in distinct steps. In a two-stage random draw, geographical regions are, in a rst step, pre-de ned as primary sampling units. In a second step, the sample is randomly drawn. If there are obvious deviations from a random sample, these will usually be controlled for using sample weights. Individual weights are inversely proportional to the probability of being an element of the sample. Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

11 Observations sampling Deviations from random samples (a) Response-biased sampling considered are only units that ful ll a certain condition (maximal income, participation in a lottery, high speed internet access, etc.): problematic identi cation scheme. (b) Length-biased sampling to measure the mean duration of a certain state we may consider ows and stocks. The mean duration of those 2 stock (e.g. job less) is usually > than for those 2 ows (e.g. re-hired sta ). (c) Sample selection bias is given in the case of the probability of being part of the sample is correlated with the analyzed item (success of a program evaluated by a sample of participants in the program). Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

12 Observations potential de ciencies (a) Survey-Nonresponse can be interpreted as a special case of sample selection and may produce biased results (one possible solution: control experiments). (b) Measurement error in variables (c) Item-Nonresponse, i.e. missing responses for speci c survey questions (possible solutions: deletion, imputation, consider results with and without item and compare, i.e., assess the missingness mechanism"). (d) Panel attrition may lead to biased samples for longitudinal data. Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

13 Contents I 1 Introduction Concern and contribution of microeconometrics Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

14 Social Experiments In social experiments the economic framework for a treatment group is intentionally modi ed to compare the resultant behavior with a control group. The intention is to gauge the causal e ect of the treatment. It is decisive that the two groups di er by nothing else than the treatment; Advantages: - random selection given through experiment - endogeneity and sample selection problems are overcome Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

15 Social Experiments Problems: - expensive - voluntary participation is problematic: sample selection - substitution bias (persons who know they /2 treatment group) - attrition (sample drop-outs) of participants - Hawthorne e ect: change of behavior due to experiment - hard (impossible) to generalize results Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

16 Natural Experiments For natural experiments: There is an exogenous change of the economic framework for a sub-group of the population only. The behavior of the groups before and after the change can be compared The causal e ect of the change in conditions becomes measurable. We may have to discuss issues of internal validity (can we draw any inference from our analytical framework about the e ectiveness of the policy measure?) and external validity (can we really generalize our ndings?) on a case-by-case basis. Problems of omitted variable bias, selectivity, and attrition can play a role independent of the type of analysis (natural/social experiment). Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

17 DiD estimator for experimental data Classical approach: di erence-in-di erences (DiD) estimation Step 1. Comparison: D it = 0 obs i before; D it = 1 obs i after change y it = α + βd it + ε it, where i = 1,..., N; t = 1,..., T ; β is the average e ect of the change under the condition that the group remains comparable over time. Step 2: Comparison: treatment T it = 1 vs. control group T it = 0 y it = α 0 + α 1 D it + α 2 T it + β (D it T it ) + ε it. Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

18 DiD estimator for experimental data Treatment group before Treatment group after y T i0 = α 0 + α 2 + ε T i0 y T i1 = α 0 + α 1 + α 2 + β + ε T i1 Di erence y T i1 y T i0 = α 1 + β + ε T i1 ε T i0 Control group before Control group after y C i0 = α 0 + ε C i0 y C i1 = α 0 + α 1 + ε C i1 Di erence y C i1 y C i0 = α 1 + ε C i1 ε C i0 Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

19 DiD estimator for experimental data We can eliminate the temporal e ect α 1 by simply taking the di erence of di erences: yi1 T yi0 T yi1 C yi0 C = β + ε T i1 ε T i0 ε C i1 ε C i0 Under the assumption h E ε T i1 ε T i0 ε C i1 ε C i0 i = 0, the causal e ect of a change of the setting β equals simply the double di erentiation of the means of the two groups T and C. Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

20 DiD estimator for experimental data Example: In 1987, the US government allowed the speed limit on highways in rural areas to be raised from 55 Mph to 65 Mph (from about 88 km/h to 105 km/h). In 21 federal states the limit was raised subsequently. Ashenfelter and Greenstone (JPE, 2004) analyze the e ect of this change on fatal accidents. Basic DiD analysis: Source of table following: Ashenfelter and Greenstone (JPE, 2004), Table 2A Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

21 DiD estimator for experimental data States that raised limit [T] did not raise [C] 1 2 (1) (2) = (3) Panel A, period: [0] [0] Rate of deadly accidents Speed (mph) Panel B, period: [1] [1] Rate of deadly accidents Speed (mph) Panel B Panel A [1] [0] [1] [0] Rate of deadly accidents Speed (mph) Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

22 DiD estimator for experimental data A DiD estimation for i = 1,..., 52 states in the years t = 1982,..., 1993 would be given by acc.rate it = α 0 + α 1 D it + α 2 T it + β (D it T it ) + ε it. The analysis could be done just as well resorting to average speed as dependent. Bernd Süssmuth (University of Leipzig) Microeconometrics April 4, / 22

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