1. Microdata, evidence and causal questions
Econometric analysis turns observations about people, households, firms and other individual units into evidence about economic relationships. Its central challenge is not merely to calculate a correlation or fit a line. It is to establish what a comparison means, which explanation it supports and which alternative explanations remain possible. A large dataset can describe the world in great detail while still providing a misleading answer to a causal question.
What microdata reveal
Microdata record information at the level of individual observational units. A household survey may describe income, education, employment and insurance coverage; a firm dataset may report output, investment and employment; a school dataset may link student performance to resources and local characteristics. The prefix “micro” identifies the level of observation, not the size of the dataset. Millions of individual records are still microdata, whereas a short series of national output figures describes an aggregate.
The unit of observation must match the question. A relationship between average income and health across regions does not necessarily describe the relationship between individual income and individual health within each region. Aggregation changes both the comparison and the information available. Likewise, a school-district average cannot reveal every difference between students within a district. Before estimating a model, identify what each row represents, how observations enter the dataset and which population the observations are intended to represent.
Governments use these data to assess policies, businesses to understand demand, and non-profit organisations to evaluate interventions. Each application requires a decision about the relevant outcome. A programme may increase participation without improving final outcomes, or improve an average while leaving important groups worse off. Carefully defined variables and comparisons are therefore part of the substance of an analysis.
Data availability and data quality
Access to data does not guarantee reliable evidence. Errors may arise because a variable is recorded incorrectly, the sample excludes relevant people, definitions change over time or records are joined using imperfect identifiers. Missing income, for example, is not equivalent to zero income. If people with unusually high incomes are more likely to leave that question unanswered, deleting incomplete observations can change the population represented by the analysis.
Measurement deserves particular attention because many concepts are represented by imperfect proxies. A test score measures performance on a particular assessment, not every dimension of learning. An insurance indicator records coverage, not the quality or accessibility of care. A useful analysis explains the relationship between the concept of interest and the available measure, checks the coding and units, and considers how measurement limitations might affect the conclusions.
Data quality also includes the history of collection. Administrative records, surveys and experimental data are produced for different purposes and have different strengths. Administrative data may offer broad coverage but omit variables required for a causal comparison. A detailed survey may measure those variables but suffer from nonresponse. More observations improve precision under suitable conditions; they do not automatically correct an unrepresentative sample or a misleading definition.
Correlation and causation
Two variables are correlated when they vary together statistically. Causation asks whether changing one variable would change the other. The distinction is essential because a correlation can arise through several mechanisms: one variable may affect the other, the direction of influence may run in reverse, a third factor may affect both, or the relationship may occur by chance.
A deliberately striking example pairs US spending on science, space and technology with deaths by hanging, strangulation and suffocation over 1999–2009. The two series move together in the displayed period, but this does not establish that changing science spending changes those deaths. Trending series can appear closely related even when no credible causal mechanism connects them. Selecting an interesting pair after searching many series makes such coincidences especially easy to find.
More plausible relationships need the same scrutiny. Insured people may be healthier than uninsured people because insurance improves access to care. They may also differ in income, education, employment and initial health. A comparison of observed health combines these possibilities. To isolate an insurance effect, the analysis needs a reason to believe that the comparison represents what would happen to otherwise comparable people under different insurance conditions.
A causal claim therefore contains a counterfactual: what would have happened without the change being studied? The counterfactual is usually unobserved. Research design specifies how other observations can provide a credible substitute for it.
Description, prediction and intervention
Descriptive analysis characterises observed data: means, distributions, differences between groups and changes over time. Predictive analysis asks how well available information forecasts an outcome in observations that were not used to fit the model. Causal analysis asks how an intervention changes an outcome. These are different objectives, even when they use similar statistical tools.
A historical example illustrates the distinction. A 2012 report described the travel platform Orbitz as presenting more expensive hotel options to Mac users after finding that they tended to spend more on accommodation than Windows users. Device type could be useful for predicting spending because it is associated with customer characteristics. That association does not show that buying a Mac would itself increase a person's willingness to pay for a hotel. Nor does the reported ranking practice, by itself, demonstrate that identical rooms were sold at different prices.
Prediction can be valuable without a causal interpretation. Conversely, a model with excellent predictive accuracy may be unsuitable for evaluating a policy that changes the relationships on which the predictions depend. Econometric work should state its objective explicitly and judge the model against that objective. Machine-learning methods can support measurement, prediction and parts of causal estimation, but a flexible algorithm does not supply the missing identification argument.
Research design and the credibility of evidence
The emphasis on credible research design arose partly from concern that empirical conclusions could change with small alterations to specifications, assumptions or functional forms. Better computing and richer data made more elaborate analyses possible, but also made it easier to search across many models. The important response was to focus on why the variation used in an analysis can answer the causal question.
Random assignment provides a particularly clear benchmark because assignment is independent of potential outcomes. When experiments are infeasible, observational methods seek comparisons whose identifying assumptions can be stated and examined. Regression compares observations after accounting for measured characteristics. Panel methods use repeated observations of the same units. Difference-in-differences compares changes across groups under an assumption about their counterfactual trends. Each method succeeds only when its assumptions are appropriate to the setting.
Randomised evaluations of antipoverty programmes illustrate the connection between evidence and policy. The J-PAL research network uses such evaluations to study interventions and help decision-makers interpret the resulting evidence. The substantive lesson is that a programme's appeal or popularity is different from evidence about its effects. Evaluation asks what changes because of the programme and how that conclusion depends on the population, implementation and comparison being studied.
This shift is often called the credibility revolution in empirical economics. It does not imply that every recent empirical result is robust or causal. A persuasive analysis still requires transparent definitions, a defensible comparison, an appropriate treatment of uncertainty and checks targeted at plausible alternative explanations. A sophisticated estimator cannot compensate for a comparison that does not address the question.
From a question to a reproducible analysis
A sound empirical workflow begins with a precisely stated question and a definition of the population, treatment, outcome and period. The next step is to inspect the data before fitting a model. Check whether identifiers are unique, whether numerical values have plausible units and ranges, and whether missing values or sample restrictions change the composition of the data. Describe the outcome distribution and inspect the main relationships graphically.
Model selection then follows the research question and design. An estimate should be reported with its units, the relevant comparison and an assessment of uncertainty. For instance, a coefficient measured in test-score points per additional student per teacher answers a different question from a percentage change in test scores. Interpretation also requires distinguishing statistical precision from practical importance: a precisely estimated change can be small in substantive terms.
Reproducibility means keeping the transformations from raw observations to final results explicit. Stata, R and Python can all implement this workflow. The essential features are documented cleaning decisions, code that can be rerun, clear sample definitions and tables or figures that agree with the underlying calculations. Sensitivity checks should address specific concerns, such as omitted characteristics, unusual observations or alternative measurement choices.
Economic and political applications
Microeconometric methods can address questions far beyond a narrow definition of market behaviour. Examples include whether insurance improves health, whether smaller classes improve learning, whether legalising undocumented migrants affects mobility or political representation, and whether terrorist attacks alter electoral support. These questions combine individual or local data with mechanisms that are economically and socially important.
The same discipline applies across applications: define the change of interest, identify a comparison that can approximate its counterfactual, examine threats to that comparison and communicate the limits of the evidence. An estimated average effect describes a particular population and setting. Applying it elsewhere requires considering differences in institutions, behaviour and implementation. The following chapters develop the statistical tools needed to make these arguments explicit, beginning with randomisation and linear regression.