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Weather Panel Tutorial


Introduction to the Tutorial

Welcome to the Weather Panel Tutorial!

The use of econometrics to study how social, economic, and biophysical systems respond to weather has started a torrent of new research. It is allowing us to better understand the impacts of climate change, disaster risk and responses, resource management, human behavior, and sustainable development. Here are some of the relationships that have been uncovered in recent years:

Examples from Carleton and Hsiang (2016) Source: Carleton and Hsiang (2016).

This tutorial will walk you through the steps necessary to relate socioeconomic outcomes to weather data at high resolution. We will cover:

  1. How to find and use weather data, and what you should be aware of when using it.
  2. How to relate your socioeconomic outcomes to weather variables, and develop your regression specification.
  3. How to work with shapefiles, and use them to generate your predictor variables.

This tutorial will assume a knowledge of econometrics and basic experience with one scientific programming language (Stata, R, Matlab, Julia, python). We try to provide examples in more than one language, so you can get started.

At the same time, this tutorial asks you to perform every step yourself. In particular, we do not provide prepared weather data or a ready-made script to prepare it. Each particular project is too specific, so you, the researcher, need to think through everything. This tutorial is aimed at helping you do that.

We will also assume that you already have a research question. There are plenty of ways to find important questions, and maybe we will try to offer a tutorial on that in the future.

A useful resource to better understand the basics of weather, climate, and the physical changes occuring in the climate system is An Economist’s Guide to Climate Change Science. If you have not had experience with climate (or meterological) science, that is a great place to start. For a more general introduction to the sciences of climate and climate change, check out the Encyclopedia of Earth and the IPCC WGI report.

For a theoretical foundation for the work of estimating weather and climate responses, read Climate Econometrics by Solomon Hsiang. This tutorial complements this kind of theoretical foundation with more practical advice.

Definitions and conventions

We will use the following terms throughout this tutorial.

Point data, region data, and gridded data

The data being related in climate econometric studies comes in three forms:

  1. Point data describes the conditions at a particular geographic point in space. For weather data, this is typically the location of a weather station or gauge. For socioeconomic data, it may be a field, factory, or household.
  2. Region data describes an aggregate over an irregular space. Typical natural science regions include basins and water/land bodies. But economic region data is much more common, where quantities are totalled across an entire political unit before they are reported. The region over which a data point is provided is the geographic unit.
  3. Gridded data provides information on a regular grid, almost always either across latitude and longitude, or distance north and east. Gridded data can come from remote sensing products or other models or analyses. In the latter case, it often is not clear exactly what is being measured (e.g., the point data at the centroid, or the average over a rectangular region). Keeping information at a high resolution is important to avoid misusing such data.

It is always appropriate to analyze data in the spatial structure it is offered, even if translating it to another structure would be easier. We will discuss this more later.

Mathematical notation

In many cases, it will be useful to describe how to work with weather variables irrespective of the specific data being represented. For this, we introduce the following notation:

  • $T_{it}$: Any weather variable for geographic unit $i$ in reporting period $t$.
  • $T_{ps}$: Point or grid-level weather data for location/grid cell $p$, at a native temporal resolution indexed by $s$.