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Foundations of Applied Econometrics and Impact Evaluation

Course Orientation: Policy Questions and Causal Evidence

Correlation, Causation, and Counterfactuals

Potential Outcomes and Treatment Effects

Threats to Valid Causal Inference

Data, Variables, and Descriptive Analysis

Data Sources for Development and Environmental Evaluation

Variables, Measurement, and Data Structure

Data Preparation and Quality Checks

Descriptive Statistics and Visual Evidence

Statistical Foundations for Policy Evaluation

Sampling, Uncertainty, and Statistical Inference

The Difference in Means Estimator

Introduction to Regression for Policy Analysis

Interpreting Regression Results Responsibly

Experimental and Quasi-Experimental Methods

Randomized Controlled Trials

Matching and Selection on Observables

Difference-in-Differences

Regression Discontinuity Designs

Instrumental Variables and Natural Experiments

Applications in Development and Environmental Policy

Evaluating Development Interventions

Evaluating Environmental Interventions

Equity, Heterogeneous Effects, and Policy Targeting

Communicating Evidence for Decision-Making

Integrated Applied Evaluation Exercise

Developing an Evaluation Plan

Interpreting a Policy Evaluation Case

Quiz

Question 1: Correlation and Causation

Question 2: Identifying Evaluation Variables

Question 3: Difference in Means

Question 4: Difference-in-Differences Assumption

Question 5: Interpreting Policy Evidence

Survey and Feedback

Course Feedback Survey

Are you ready to make data-driven decisions that tackle some of the world's most pressing development and environmental challenges. Welcome to 'Applied Econometrics for Development and Environmental Policy Evaluation.

Are you ready to make data-driven decisions that tackle some of the world's most pressing development and environmental challenges? Welcome to 'Applied Econometrics for Development and Environmental Policy Evaluation.' In today's complex policy landscape, distinguishing between simple correlation and true causation is critical. This comprehensive course is designed to equip you with the robust econometric tools and impact evaluation methodologies needed to assess the true effectiveness of policy interventions.

Whether you are analyzing the impact of cash transfers on poverty reduction, evaluating clean-energy subsidies, or measuring the success of protected areas, this course bridges the gap between statistical theory and real-world application. You will journey through the foundations of causal inference, data preparation, and descriptive analysis before diving deep into experimental and quasi-experimental methods like Randomized Controlled Trials (RCTs), Difference-in-Differences, Regression Discontinuity, and Instrumental Variables.

By the end of this course, you won't just understand econometric models—you will be able to design evaluation plans, interpret complex regression results responsibly, and communicate compelling, evidence-based recommendations to policymakers and stakeholders. Join us to transform raw data into credible causal evidence and drive equitable, sustainable policy outcomes.

Applied Econometrics for Development and Environmental Policy Evaluation

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            <p>Rigorous policy evaluation is essential for addressing complex global challenges in economic development and environmental sustainability. The course Applied Econometrics for Development and Environmental Policy Evaluation provides a comprehensive framework for understanding and applying quantitative methods to assess the causal impacts of policy interventions. Decision-makers increasingly rely on empirical evidence to determine the efficacy of initiatives such as cash transfers, clean-energy subsidies, protected areas, and water-quality regulations. This curriculum bridges the gap between theoretical econometrics and practical policy analysis, equipping learners with the analytical tools required to distinguish true causal effects from mere correlations. By establishing a strong foundation in counterfactual reasoning, potential outcomes, and the identification of threats to valid causal inference, the course prepares participants to critically evaluate the validity of causal claims in real-world policy contexts.</p>

<p>The curriculum is systematically structured to guide learners through the entire lifecycle of policy evaluation, from data preparation to the communication of findings. It begins with the fundamentals of data management, descriptive analysis, and statistical inference, ensuring a robust understanding of sampling, uncertainty, and regression models. Participants will then explore advanced experimental and quasi-experimental methodologies, including Randomized Controlled Trials (RCTs), Difference-in-Differences, Regression Discontinuity Designs, and Instrumental Variables. Beyond methodological mechanics, the course emphasizes practical applications in both the development and environmental sectors, addressing critical issues such as spatial spillovers, heterogeneous effects, and equitable policy targeting. Through an integrated applied evaluation exercise, learners will synthesize these concepts to develop rigorous evaluation plans, interpret complex datasets, and effectively communicate evidence-based recommendations to stakeholders and policymakers.</p>
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