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Quizzes > Social & Behavioral Sciences

Impact Evaluation Quiz

Free Practice Quiz & Exam Preparation

Difficulty: Moderate
Questions: 15
Study OutcomesAdditional Reading
3D voxel art illustrating the concept and process of Impact Evaluation course

Get ready to sharpen your skills with our engaging Impact Evaluation practice quiz, designed for students eager to master the art of causal inference. This quiz covers essential techniques such as randomized field experiments, propensity score matching, differences in differences, instrumental variables, and regression discontinuity, while also incorporating practical exercises using STATA for hands-on learning.

What is the primary objective of impact evaluation?
To determine the causal effect of interventions on outcomes
To generate descriptive statistics
To analyze observational associations
To monitor policy trends
Impact evaluation aims to quantify the causal effect of an intervention by comparing outcomes between treated and control groups. The focus is on establishing a causal link rather than merely describing associations.
Which method utilizes random assignment to ensure unbiased estimates of causal effects?
Regression discontinuity
Instrumental variables
Randomized field experiments
Propensity score matching
Randomized field experiments involve randomly assigning subjects to treatment and control groups, which helps ensure that observed differences in outcomes can be attributed to the intervention. This randomization minimizes selection bias, making the causal estimates more reliable.
What is a common challenge in establishing causal relationships in impact evaluation studies?
Overfitting
Heteroscedasticity
Selection bias
Random error
Selection bias occurs when there are systematic differences between treatment and control groups that affect the outcome. Addressing selection bias is critical for isolating the true causal effect of an intervention.
Which software is specifically mentioned for econometric exercises in impact evaluation studies?
Excel
STATA
R
SPSS
STATA is widely used in applied econometrics and is specifically mentioned for exercises in impact evaluation studies. Its robust statistical tools make it a popular choice for analyzing causal relationships.
In observational studies, how does propensity score matching primarily address selection bias?
By improving data visualization
By matching units based on their probability of receiving treatment
By increasing sample size
By simplifying variable selection
Propensity score matching calculates the likelihood that a unit receives treatment given observed characteristics and matches treated and untreated units based on this score. This process helps reduce selection bias, mimicking the conditions of a randomized experiment.
Differences-in-differences analysis relies on which crucial assumption for causal identification?
Parallel trends
No spillover effects
Selection on observables
Constant treatment effects
Differences-in-differences analysis assumes that, in the absence of treatment, the trajectories of the treated and control groups would have followed parallel paths over time. This parallel trends assumption is essential for attributing post-intervention differences to the treatment effect.
What is the main advantage of randomized field experiments in causal analysis compared to observational approaches?
They simplify data analysis
They are less resource-intensive
They eliminate selection bias through random assignment
They reduce the need for control groups
Randomized field experiments use random assignment, which minimizes pre-existing differences between treatment and control groups, effectively eliminating selection bias. This advantage allows for a more straightforward interpretation of causal effects.
In an instrumental variables approach, what are the two key conditions that a valid instrument must satisfy?
Relevance and exogeneity
Random assignment and independence
Non-collinearity and homoscedasticity
Sample adequacy and linearity
A valid instrument must be correlated with the endogenous explanatory variable (relevance) and must not be correlated with the error term in the outcome equation (exogeneity). These conditions ensure that the instrument can properly isolate exogenous variation for causal inference.
In regression discontinuity designs, what provides the primary source of identification?
Random assignment regardless of the cutoff
Instrumental variables
The arbitrary cutoff threshold for treatment assignment
Time variation in the treatment effect
Regression discontinuity designs exploit a cutoff or threshold in an assignment variable, providing a quasi-experimental framework. Units just above and below this cutoff are assumed to be comparable, allowing the discontinuity to serve as the source of causal identification.
Which issue in instrumental variables analysis can lead to biased estimates if not properly addressed?
Overfitting
High multicollinearity
Autocorrelation
Omitted variable bias
Omitted variable bias occurs when a relevant variable that influences both the instrument and the outcome is excluded from the model. This violation of the exogeneity requirement undermines the validity of the instrumental variables approach, leading to biased estimates.
How does propensity score matching simulate aspects of a randomized experiment?
By increasing the treatment effect estimate
By randomly assigning treatment within matched pairs
By matching units based on the probability of receiving treatment
By ensuring all potential confounders are eliminated
Propensity score matching calculates the probability of receiving treatment based on observed characteristics and pairs units with similar scores. This matching process mimics random assignment, reducing selection bias in observational studies.
Which practice enhances the credibility of the differences-in-differences design by testing the parallel trends assumption?
Excluding all control variables
Including multiple pre-treatment periods
Using cross-sectional data only
Ignoring time trends
Including multiple pre-treatment periods allows researchers to verify that the treatment and control groups were trending similarly before the intervention. This check reinforces the validity of the parallel trends assumption, which is critical for causal interpretation in differences-in-differences designs.
What is a common limitation of regression discontinuity designs that researchers must consider carefully?
Overuse of instrumental variables
Excessive reliance on random assignment
Limited external validity
Inability to address selection bias
Regression discontinuity designs typically provide strong internal validity close to the cutoff, but the estimated effects may not generalize beyond that specific context. This limited external validity is an important consideration when interpreting and applying the results.
Which of the following best defines the identification problem in econometric analysis?
Challenges in establishing causality due to simultaneous confounding factors
Problems related to overfitting in models
Issues with computational software
Difficulties in collecting reliable data
The identification problem refers to the challenges of isolating the causal effect of a variable when confounding factors are present. Proper econometric techniques are required to overcome this issue and establish credible causal relationships.
In STATA, which command is most commonly used to implement a differences-in-differences analysis using interaction terms?
regress
graph
logit
merge
The 'regress' command in STATA is often used to estimate models that include interaction terms for differences-in-differences analysis. By specifying the appropriate interaction between treatment and time indicators, it facilitates the estimation of causal effects.
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Study Outcomes

  1. Understand identification challenges in causal inference.
  2. Apply randomized field experiments and propensity score matching to evaluate impacts.
  3. Analyze differences-in-differences, instrumental variables, and regression discontinuity designs.
  4. Implement econometric techniques using STATA for practical evaluation exercises.
  5. Interpret and critically assess empirical evidence from impact evaluations.

Impact Evaluation Additional Reading

Here are some top-notch resources to supercharge your understanding of impact evaluation methods and STATA exercises:

  1. Impact Evaluation Using Stata This interactive training package offers a comprehensive guide to conducting rigorous experimental impact evaluations, complete with a PDF manual, audio podcasts, STATA command files, and datasets.
  2. Causal Inference Using Stata: Estimating Average Treatment Effects This course delves into using STATA's treatment-effects estimators to estimate causal effects in observational data, covering both theoretical concepts and practical examples.
  3. The State of Applied Econometrics - Causality and Policy Evaluation This paper discusses recent developments in econometrics, focusing on identification strategies, supplementary analyses, and machine learning methods for causal effects.
  4. World Bank's ietoolkit for Stata This toolkit provides a set of STATA commands designed for impact evaluations and general data work, including commands for balance tables, difference-in-differences regressions, and project folder setup.
  5. Empirical Exercises in Stata These exercises, designed for an undergraduate course on program evaluation, cover topics like selection bias, difference-in-differences, and regression discontinuity, complete with STATA code and data.
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