Comparing applied general equilibrium and econometric estimates of the effect of an environmental policy shock
Bibliographic record
Abstract
This file explains the programs and datasets used in the following paper: Jared C. Carbone, Nicolas Rivers, Akio Yamazaki, and Hidemichi Yonezawa "Comparing applied general equilibrium and econometric estimates of the effect of an environmental policy shock," forthcoming in Journal of the Association of Environmental and Resource Economists. --------------------------------------------------------------------------------------------------- There are three folders: bc_cge.zip replication_table2_4_5_C1 replication_figures_table3 --------------------------------------------------------------------------------------------------- CGE Approach bc_cge.zip contains the CGE model and its results including the benchmark data in Table 1 and the results of the central scenario and sensitivity analysis in Figure 6, 7 and 8, and the further details about the CGE model's replication can be found in the readme.txt within the zip file. --------------------------------------------------------------------------------------------------- Econometric Approach replication_table2_4_5_C1 contains the program and multiple datasets to generate Table 2, 4, 5, and C.1, explained in detail below. Data Original data is from Yamazaki (2017) -- comes from CANSIM Our data is "Carbone_et_al.dta" Program Run Carbone_et_al_JAERE.do file for replication of Table 2, 4, 5, and C.1. This do file also merge other datasets for more control variables, such as oil price, US unemployment rate, population, and trade index. The data on these variables are obtained from these sources listed below. EIA oil price (crude oil -- WTI) https://www.eia.gov/dnav/pet/pet_pri_spt_s1_a.htm US unemployment from Labor Force Statistics from the Current Population Survey (LNS14000000) https://data.bls.gov/timeseries/LNS14000000 Population data is from Statistics Canada's Table 17-10-0005-01 (formerly CANSIM 051-0001) https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1710000501 World trade index (Export volume relative to 1913) https://ourworldindata.org/trade-and-globalization --------------------------------------------------------------------------------------------------- replication_figures_table3 contains programs and datasets required to generate all the figures and Table 3. R software is used to generate them. TABLE3.R is for Table 3 FIGURE1.R is for Figure 1 FIGURE2.R is for Figure 2 FIGURE3.R is for Figure 3 FIGURE4_5.R is for Figure 4 and 5 FIGURE6_7.R is for Figure 6 and 7 FIGURE8.R is for Figure 8 FIGUREC1.R is for Figure C.1 ---------------------------------------------------------------------------------------------------- Questions regarding this file and any programs and datasets pertains to the econometric approach should be directed to Akio Yamazaki, a-yamazaki@grips.ac.jp.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".