Presentation on theme: "Econometric analysis informing policies UNICEF workshop, 13 May 2008 Christian Stoff Statistics Division, UNESCAP,"— Presentation transcript:
Econometric analysis informing policies UNICEF workshop, 13 May 2008 Christian Stoff Statistics Division, UNESCAP, firstname.lastname@example.org
Outline Causality in experiments Confounding factors Quasi experiments: Difference estimators Difference-in-difference estimators Possible questions
A quick intro: The ideal situation Causality means a specific action leads to a specific, measurable consequence Ideal: Randomized controlled experiment Subjects are randomly allocated to either control or treatment group ONLY difference between two groups is treatment
The reality In practice experiments are rare Subjects are NOT randomly assigned so that sorting out of other relevant factors is difficult Econometrics provides the tools for controlling these other factors
Challenge: Confounding factors Regress test score on student-teacher ratio But what about number of students in class still learning English? – Omitted variable? Omitted variable correlated with explanatory and dependent variable (low student-teacher ratios -> high % English learners -> bad scores) Thus a policy of increasing no. teachers may not increase test scores because high English learners (%) are the real problem Solution: Control for differences in English learners (%), i.e. regress test score on student-teacher ratio AND English learners (%)
Limits to controlling these factors Many years of cross-country research However, countries often have such different settings and the causal relationships are only specific to the country and the time period Therefore in search of quasi or natural experiments between units that are not too different Danger lies in… –Possible correlation between error term and explanatory variable (i.e. treatment not assigned at random) –Teachers try especially hard in areas with programs –General equilibrium effects: when program is enlarged additional factors may arise (external validity)
Difference estimators: Using MICS3 Define unit of analysis (households, districts, provinces, countries) Selected units gone through policy program (i.e. treatment) AND assignment was as if random If is binary, then no functional form assumption needed; it is simply the difference in the conditional expectations If can take on multiple values, then the above regression assumes linearity But often there are pre-treatment differences between control and treatment group…
Difference-in-difference estimators: Using MICS2 and MICS3 Types of datasets: Cross-section, panel and time-series Includes observations on same units before and after experiment OLS estimator is the difference in the group means of Control for district-level context constant over time through fixed or random effects or adjust standard errors for clustering Advantage over difference estimator: 1. More efficient; 2. Eliminates pre-treatment differences
Some possible questions Education research: –Study drop-out rates and relate it to child labour questions –Study effect of different child disciplining strategies (punishment, praise, etc.) on a childs success in school –Combine MICS data with GIS disaster data and study effect of disasters on school attendance –Combine with policy data between 2000-2005 and evaluate the effectiveness of policies aimed at promoting higher school attendance Child health: –Effect of different fuel types for cooking on child-health indicators? –Effects of different types of access to water and sanitation on a childs probability of having diarrhoea or succeeding in school? –How does Vitamin A affect a childs health? –Impact of different health service facilities Adults knowledge and attitude towards violence: –What is the effect of having information access (TV or radio) on knowledge about HIV or contraception? –What is the effect of having information access (TV or radio) on education methods or attitudes towards domestic violence?
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