Models, Data, and the Scientific Method in Microeconomics

Models and Data

Introduction to Economic Models

Economic models are essential tools that help economists understand and predict real-world phenomena. They simplify complex realities to focus on key relationships and mechanisms.

• Model: A simplified description of reality, used to analyze economic processes.

• Data: Empirical evidence used to test the accuracy of models and understand how the world works.

• Correlation vs. Causality: Correlation between two variables does not necessarily mean that one causes the other.

• Experiments: Used by economists to measure cause and effect, helping to distinguish between correlation and causality.

Evidence-Based Economics

Is College Worth It? Opportunity Cost and Returns to Education

Economists use data to evaluate the costs and benefits of decisions, such as investing in higher education.

• Tuition and Fees (Toronto Metropolitan University, 2023-2024):

  • Domestic: $7,236–$13,288

  • International: $35,072–$40,485

• Opportunity Cost: The value of the next best alternative forgone. For students, this includes potential earnings lost while attending college.

• Example Calculation: At a minimum wage of $17.60/hour (as of October 1, 2025), working 50 hours/week for 28 weeks/year results in an opportunity cost of $24,640 (before tax).

The Scientific Method in Economics

Steps of the Scientific Method

The scientific method, also known as empiricism, is a systematic approach to understanding economic phenomena.

• Step 1: Develop models that explain some part of the world.

• Step 2: Test those models using data to see how closely the model matches actual observations.

Models as Simplifications

Models are not exact replicas of reality but are designed to capture essential features.

• Example: The Wright brothers used wind tunnel models to test airplane designs before building a real airplane, illustrating the value of models in experimentation.

Evidence-Based Example: Returns to Education

Economists often use models to estimate the returns to education, i.e., how much additional income is generated by an extra year of schooling.

• Assumption: Each additional year of education increases future earnings by 10%.

• Mathematical Model: If the base wage is $15/hour with 13 years of education, then each subsequent year increases earnings by 10%:

• First year: $15 \times 1.10 = $16.50

• Second year: $16.50 \times 1.10 = $18.15$

• Third year: $18.15 \times 1.10 = $19.965$

• Fourth year: $19.965 \times 1.10 = $21.9615$

• n-th year: $15.00 \times (1.1)^n$

Hypothesis: Completing a college degree (years 13–16) increases wages from $15 to $21.9615, or by 46.41%:

• Percentage increase: $(21.9615 - 15)/15 = 0.4641$ or 46.41%

• Alternatively: $(1.1)^4 = 1.4641$

Features of Economic Models

• Models are not exact; not everyone will experience the average predicted increase.

• The average can mask variation among individuals.

• Models generate testable predictions that can be evaluated with data.

Empirical Data: Education and Wages in Canada (2016, ages 30–34)

The following table summarizes average annual wages by education level:

Education Level	Average Wage ($)
No certificate, diploma or degree	31,778
Secondary (high) school diploma or equivalency certificate	39,152
Apprenticeship or trades certificate or diploma	48,785
College, CEGEP and other non-university certificate or diploma	44,535
University certificate or diploma below bachelor level	43,496
University certificate or degree at bachelor level or above	56,182

• Comparison: College graduates ($56,182) earn 43.5% more than high school graduates ($39,152).

• Model Prediction: 46% higher earnings. The model is reasonably close to observed data.

Limitations and Interpretation

• Not all college graduates earn the average wage; there is variation.

• Not every year of education may add exactly 10% to earnings; some years (such as completing a degree) may have a larger effect due to signaling academic ability, work ethic, or perseverance.

Example: Finishing university may increase earnings by more than 10% over the previous year, reflecting both human capital accumulation and signaling effects.