Data

Introduction I have seen more and more papers utilizing satellite imagery data, especially in the environmental economics domain. There are many benefits of using those imagery data and one of such is their very much fine-scaled and consistent spatial and temporal resolutions.

Note that almost all the data can be found at NASA’s Earthdata Search Climate data gridMET (historical) MACA (projected) WorldClim (historical + projected) Historical data Projected data NEX-GDDP from NASA ERA5-Land Hourly Data (historical) Air pollution data Natural disaster data EM-DAT: A complete dataset for disasters NOAA NCEI Storm Events Database International Best Track Archive for Climate Stewardship (IBTrACS) The Tropical Cyclone Extended Best Track Dataset (EBTRK) Fire and Resource Assessment Program (FRAP) in California FireCCILT11 Long-term Burned Area Dataset Hazard Mapping System Fire and Smoke Product (recommended) Natural resource data FAO Land Data - Global Agro-Ecological Zones (GAEZ v4) Vegetation products Landsat Normalized Difference Vegetation Index (NDVI) NOAA Vegetation Products Note that almost all the data can be found at NASA’s Earthdata Search Climate data gridMET (historical) gridMET is a dataset of daily high-spatial resolution (~4-km, 1/24th degree) surface meteorological data covering the contiguous US from 1979-yesterday (updated daily).

I spent quite some time figuring out how to present publications and working papers independently in separate sections. Most importantly, I’d like working papers to be displayed in the same format as a publication.

In many circumstances, we need to create nice tables from regression results and present the table in beamers. As an R user, I primarily use the stargazer package to do the job.

There is an increasing body of literature investigating the environmental problems in China, including air pollution, water pollution, etc. The studies cover a range of topics, such as evaluations of environmental regulations, the health effects of air pollution, and such.

An introduction of clustering in panel data models In my last post, I briefly introduced standard error clustering in panel data settings. In this post, I will continue the topic and present how to do the clustering in R.

Introduction China Health and Nutrition Survey (CHNS) Chinese Longitudinal Healthy Longevity Survey (CLHLS) Chinese General Social Survey (CGSS) Urban Household Survey (UHS) Chinese Family Panel Studies (CFPS) China Health and Retirement Longitudinal Study (CHARLS) China Longitudinal Aging Social Survey (CLASS) The China Urban Labor Survey (CULS) The Chinese Social Survey (CSS) Rural-Urban Migration in China China Education Panel Survey Chinese Household Income Project Chinese Household Income Project Introduction This section is designated to summerize commonly used mirco data for China.