Advanced Tools and Techniques of Program Evaluation

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Presentation transcript:

Advanced Tools and Techniques of Program Evaluation James Fodor, July 2018 Effective Altruism Melbourne

Simple Linear Regression

Simple Linear Regression

Least Squares Method The “right” parameters are the ones which minimise the sum of squared residuals of the model. This is called Ordinary Least Squares (OLS). If our model is correctly specified and we have no endogeneity, OLS gives unbiased estimates for the population parameters.

Assumptions of OLS

Assumptions of OLS

1: Omitted Variables But our estimates will be biased if we have omitted variables.

2: Model Misspecification But our estimates will be biased if we have specified the wrong interactions and functional form.

So What is the Right Model?

3: Simultaneity But our estimates will be biased if we have not incorporated bidirectional causation through simultaneous equations.

4: Measurement Error But our estimates will be biased if we have measurement error in our independent variable.

Program Evaluation Approach Instead of trying to control for all confounding variables explicitly, we can just let random variation do the job for us. If something is decided by chance or by some exogenous factor, it should not be correlated with any unobserved variables!

Instrumental Variables Instrument must be correlated with independent variable (can test) Instrument must not be correlated with errors (can’t test) What does the result actually mean? (Local Average Treatment Effect)

Randomised Controlled Trial Can be hard/expensive to conduct, but if done properly there cannot be hidden confounds!

Randomised Controlled Trial ?

Clash of Econometricians

Limitations of RCTs Expensive and time consuming, can’t conduct everywhere Do not factor in general equilibrium effects Do not incorporate heterogeneity of parameters Trials differ from full-scale programs Do not tell us why anything works or doesn’t work

Hard to Implement

General Equilibrium

Heterogeneity Eva Vivalt meta-analysis of RCT results.

What Do RCTs Tell Us?

Structural Models These make assumptions about the causal processes that generate results Typical approach: Define utility function Define production function or resource constaints Define timespan and information available Define institutional setup Maximise utility subject to constraints over timespan given information Derive equation to estimate Use data to determine structural parameters

Structural Models Actually tells you about how the system works But requires lots of assumptions about functional forms Also often hard to identify all parameters (not enough data)

Structural Models

Summary

Book Recommendations

My Blog