Wisconsin National Data Consortium

The Core WiNDC Buildstream: Core Database and Template Models

If you are interested only in the core WiNDC database, you can download it in GDX format (45.4 MB). If you are interested in details of the build and the template models, please follow the steps outlined below.

1. Download the WiNDC buildstream package: windc_build_3_0.zip (13.8 MB).
2. Unzipping the file will create a directory called windc_build_3_0 with the subdirectories core, household and bluenote. Go to the subdirectory core. This directory contains the input data in GDX format, all GAMS routines needed to generate the core WiNDC database and the GAMS code for the template models. If you are interested in details of the input data, see Getting Started: WiNDC Data System.
3. If you have a local version of GAMS and have access to the relevant licenses, navigate in your command line or terminal to the directory windc_build_3_0, subdirectory core and run the GAMS file build.gms by simply typing the following command:
   gams build.gms
   Note that this build will work in both, Windows and UNIX/LINUX environments. See the WiNDC paper for a description of all subroutines within the buildstream.
4. Two versions of the core WiNDC database will be generated and saved in the directory core: WiNDCdatabase.gdx and WiNDCdatabase_huber. The first version is based on the least squares matrix balancing routine and the second version is based on the Huber method. Both databases contain data for all US states and 69 (summary) sectors from 1997 to 2017.
5. If you don't have access to a GAMS license including needed solver licenses, you can generate the databases locally using NEOS. To run the routines on NEOS, type the following command:
   gams build.gms --neos=yes
6. Once the core WiNDC database is generated, it can be loaded into a general equilibrium model in GAMS. The file windc_coredata.gms demonstrates how to read data from the database and extract data for a specific year. The file replicate.gms includes a simple general equilibrium model in MCP and MPSGE format, verifies benchmark consistency, solves a counterfactual (tariff shock) and verifies consistency at that point as well.

WiNDC Household Buildstream: Household Datasets

As noted above, the WiNDC household buildstream is an extension to the core WiNDC buildstream. The household buildstream generates disaggregated consumer accounts based on the core WiNDC database, CPS and SOI data. The key challenges were denominating reasonable transfer income and understanding income tax liabilities, savings, capital ownership versus demands, salaries and wages. We provide a static and a dynamic calibration and use income elasticities to separate household level commodity expenditures. The presentation on the household build at the WiNDC Annual Meeting 2021 offers a detailed description of the calibration routine.

If you haven't alreday done so, download the WiNDC buildstream package windc_build_3_0.zip (13.8 MB). Unzipping the file will create a directory called windc_build_3_0 with the subdirectories core, household and bluenote. Go to the subdirectory household. This directory contains the necessary input data (subdirectory data_sources) and all GAMS code needed to generate the WiNDC household datasets for the years 2015 to 2017. In addition to the data in the subdirectory data_sources, the WiNDC household buildstream takes as input the core WiNDC database, so make sure that the GDX file WiNDCdatabase.gdx is in the directory core.

If you have a local version of GAMS and access to the relevant licenses, navigate in your command line to the directory household and run the GAMS file build.gms by typing the following command:

The code creates the directory datasets, generates the household datasets and saves all household datasets in this directory. The code in the file build.gms runs the following routines for the years 2015, 2016 and 2017.

1. cps_data.gms
   Reads Current Population Survey (CPS) data from the directory data_sources, subdirectory cps, processes it and saves the processed data in a GDX file in the directory gdx.
2. soi_data.gms
   Reads Statistics of Income (SOI) data from the directory data_sources, subdirectory soi, processes it and saves the processed data in a GDX file in the directory gdx. The routine also computes and saves data on capital gains that will be used to construct the CPS dataset.
3. hhcalib.gms
   Reads a household dataset (CPS or SOI) and recalibrates it to match the core WiNDC database. There are two calibration options: static and dynamic. The dynamic option forces investment to line up with reported capital demands for a balanced growth path. This impacts the calibration of the household accounts since total savings need to equal total investment. The calibration routine uses income elasticities (estimated on the national level) based on the Consumer Expenditure Survey (CEX) from the Bureau of Labor Statistic (BLS). The resulting parameters are saved in a GDX file in the directory gdx. The capital tax rate is saved in its own file and is used in the next routine.
4. dynamic_calib.gms
   Reads the dynamic datasets and recalibrates the rest of the commodity accounts to satisfy balanced growth requirements; saves the recalibrated parameters in a GDX file in the directory gdx.
5. consolidate.gms
   Merges the household data of each type to one GDX file and saves the resulting files to the directory datasets. There are four files:

   	static		dynamic
   CPS	WiNDC_cps_static.gdx		WiNDC_cps_dynamic.gdx
   SOI	WiNDC_soi_static.gdx		WiNDC_soi_dynamic.gdx

   Each dataset has household data for the years 2015 to 2017 on the US state level with WiNDC economic sectors.
6. aggr.gms
   This aggregation routine is optional.
   Aggregates the four household datasets to given economic sectors and regions. The environment variable smap denotes the sectoral aggregation and rmap denotes the regional aggregation. The options for smap are windc (the 69 WiNDC sectors), bluenote (the sectors of the WiNDC energy-environment module), gtap (GTAP sectors) and macro (6 sectors). The options for rmap are state (US states) and census ( 9 Census regions). The routine reads the relevant mapping from the subdirectory maps; users can easily create their own maps for customized sectoral and regional aggregations. The aggregated datasets are saved in the directory datasets.

Bluenote Buildstream: Energy-Environment Datasets and Template Models

The Bluenote buildstream incorporates energy-environment satellite data from the State Energy Data System (SEDS) to the WiNDC household datasets.

If you haven't alreday done so, download the WiNDC buildstream package windc_build_3_0.zip (13.8 MB). Unzipping the file will create a directory called windc_build_3_0 with the subdirectories core, household and bluenote. Go to the subdirectory bluenote. This directory contains the GAMS code needed to generate datasets with disaggreagted consumer accounts that include energy-environment satellite data. The buildstream is based on the source data for the core WiNDC buildstream (the GDX file windc_base.gdx in the directory core) that includes data from SEDS and the WiNDC household datasets WiNDC_cps_static.gdx and WiNDC_soi_static.gdx in the directory household, subdirectory datasets. Please make sure that you run the WiNDC household build first in order to generate the necessary household datasets.

If you have a local version of GAMS and access to the relevant licenses, navigate in your command line to the directory bluenote and run the GAMS file build.gms by typing the following command:

The code creates the directory datasets, generates the Bluenote datasets for the years 2015 to 2017 and saves the datasets in this directory. The GAMS routines that generate the Bluenote datasets are described in the WiNDC paper. The following datasets are generated:

	state		census
CPS	WiNDC_bluenote_cps_state_%year%.gdx		WiNDC_bluenote_cps_census_%year%.gdx
SOI	WiNDC_bluenote_cps_state_%year%.gdx		WiNDC_bluenote_cps_census_%year%.gdx

The values for the variable %year% are 2015, 2016 and 2017. The datasets in the first column are on the US state level and the datasets in the secod column are aggreggated to the 9 Census regions. The datasets in the first row are based on household data from the Current Population Survey (CPS) and the datasets in the second row are based on household data from the Statistics of Income (SOI).

In addition to the routines to generate the datasets, the directory bluenote has two models that demonstrate how the Bluenote datasets may be used in a modeling application. The energy tax model in the file bluenote_model.gms includes disaggregated households, while the energy tax model in the file bluenote_model_v2_1.gm demonstrates how the Bluenote datasets on the US state level may be read and used in a model with just a single representative household per region. This model mimics the Bluenote model from WINDC version 2.1.