Microeconometric

Microeconometrics is the branch of econometrics that applies statistical methods to micro-level data—observations on individuals, households, firms, and other small units—to analyze economic behavior and outcomes. It focuses on causal inference and parameter estimation in contexts where decisions occur at the micro level, such as labor supply, consumer demand, investment, or health choices. Microeconometric work complements macroeconometrics by exploiting rich datasets that track units over time or across settings.

Data types include cross-sectional, panel, and field experiments, including randomized controlled trials. Researchers use a mix of reduced-form and structural models. Reduced-form approaches estimate associations or treatment effects under identification assumptions (for example, exogeneity or instrument relevance). Structural microeconometrics specifies a model of decision making and recovers structural parameters to enable counterfactual predictions.

Common methods include ordinary least squares and generalized linear models for continuous and discrete outcomes; instrumental variables and two-stage least squares for addressing endogeneity; probit, logit, and tobit models for limited-dependent variables; fixed-effects and random-effects models for panel data; regression discontinuity, difference-in-differences, and propensity score matching as quasi-experimental strategies; and dynamic panel methods for longitudinal data.

Applications span labor economics, education, health, development, consumer demand, and industrial organization. Data sources include household surveys, administrative records, and firm-level datasets; researchers address issues such as measurement error, sample selection, attrition, and external validity. Software commonly used includes Stata, R, and Python.