Models, Data, and the Scientific Method in Microeconomics

Models and Data

Introduction to Economic Models

Economic models are simplified representations of reality that help economists analyze complex phenomena. By focusing on essential elements and ignoring less relevant details, models allow for clearer understanding and prediction of economic behavior.

• Model: A simplified description of reality used to explain and predict economic outcomes.

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

• Correlation vs. Causality: Correlation refers to a statistical relationship between two variables, but does not necessarily mean that one causes the other.

• Experiments: Controlled or natural experiments help economists measure cause and effect.

Example: Economists may use models to predict the impact of minimum wage changes on employment, then use data to test these predictions.

Evidence-Based Economics

Opportunity Cost of College

Evidence-based economics uses real-world data to evaluate economic decisions, such as the value of a college education. Opportunity cost is a key concept in microeconomics, representing the value of the next best alternative forgone.

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

  • Domestic: $7,236—$13,288

  • International: $35,072—$40,485

• Opportunity Cost: The minimum wage ($17.60/hour as of October 1, 2025), working 50 hours/week for 28 weeks/year, results in a foregone income of $24,640 (before tax).

Example: The decision to attend college involves weighing tuition costs and lost income against future earning potential.

The Scientific Method in Economics

Steps of the Scientific Method

The scientific method, also known as empiricism, is fundamental to economic analysis. It involves developing models and testing them against data.

• 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.

Example: The Wright brothers used models and wind tunnel experiments to design successful airplanes, illustrating the value of model testing before real-world application.

Returns to Education: An Evidence-Based Example

Modeling the Impact of Education on Earnings

Economists often model the returns to education by assuming that each additional year of schooling increases future earnings by a fixed percentage.

• Assumption: Each additional year of education results in a 10% increase in future earnings.

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

• Second Year: $16.50 \times 1.10 = $15.00 \times (1.1)^2 = $18.15$

• Third Year: $18.15 \times 1.10 = $15.00 \times (1.1)^3 = $19.965$

• Fourth Year: $19.965 \times 1.10 = $15.00 \times (1.1)^4 = $21.9615$

• General Formula: $15.00 \times (1.1)^n$

Example: Completing four years of college increases hourly wages from $15 to $21.9615, a 46.41% increase.

Testing the Model: Real-World Data

Comparing Model Predictions to Actual Earnings

Models generate predictions that can be tested with data. The average wage for college graduates can be compared to that of high school graduates to assess the accuracy of the model.

• College Graduate Average Wage: $56,182

• High School Graduate Average Wage: $39,152

• Percentage Difference: $\frac{56,182 - 39,152}{39,152} \approx 43.5\%$

• Model Prediction: 46% higher

Example: The model's prediction is close to the observed data, but not exact, highlighting the importance of testing and refining models.

Variation and Signaling

Not all individuals experience the same returns to education. The average can mask variation, and completing a degree may also signal qualities like ability or perseverance, not just increase productivity.

• Variation: Some individuals earn more or less than the average predicted by the model.

• Signaling: Degrees may act as signals of academic ability, work ethic, or perseverance, in addition to increasing productivity.

Additional info: The signaling effect is a key concept in labor economics, distinguishing between human capital and signaling theories of education.

Canadian Earnings by Education Level (2016)

Tabular Comparison

The following table compares average earnings by education level for Canadians aged 30-34 in 2016.

Education Level	Average Earnings ($)
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

Additional info: This table illustrates the positive correlation between education level and average earnings, supporting the model's predictions.

Key Takeaways

• Economic models simplify reality to make analysis possible, but must be tested against data.

• Correlation does not imply causality; experiments and careful analysis are needed to establish cause and effect.

• Returns to education can be modeled and tested using real-world data, but individual outcomes may vary.

• Opportunity cost is a crucial concept in evaluating economic decisions, such as attending college.