Replication Data for: The Implications of Pre-Existing Market Power Distortions for Electric Vehicle Tax Policies: Evidence from Colombia (doi:10.7910/DVN/NWIGJK)

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Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
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Replication Data for: The Implications of Pre-Existing Market Power Distortions for Electric Vehicle Tax Policies: Evidence from Colombia

doi:10.7910/DVN/NWIGJK

Harvard Dataverse

2024-08-08

1

Callejas, Jeronimo; Joshua Linn; Jevgenijs Steinbuks, 2024, "Replication Data for: The Implications of Pre-Existing Market Power Distortions for Electric Vehicle Tax Policies: Evidence from Colombia", https://doi.org/10.7910/DVN/NWIGJK, Harvard Dataverse, V1, UNF:6:mtzHx0jbA91Z+yfEmAtSkw== [fileUNF]

Replication Data for: The Implications of Pre-Existing Market Power Distortions for Electric Vehicle Tax Policies: Evidence from Colombia

doi:10.7910/DVN/NWIGJK

Callejas, Jeronimo (Amazon)

Joshua Linn (University of Maryland)

Jevgenijs Steinbuks (World Bank - Development Research Group (DECRG))

Harvard Dataverse

Callejas, Jeronimo

Callejas, Jeronimo

2024-06-13

https://doi.org/10.7910/DVN/NWIGJK

Social Sciences, Colombia, electric vehicles, import tariffs, market power, vehicle taxes

This repository comprises a set of functions intended to facilitate the replication of the economic model employed in the paper titled "The Implications of Pre-Existing Market Power Distortions for Electric Vehicle Tax Policies: Evidence from Colombia." Note: Certain data crucial for replication has not been disclosed due to constraints imposed by a Non-Disclosure Agreement (NDA).

Callejas, Jerónimo, Joshua Linn, and Jevgenijs Steinbuks. "Welfare and Environmental Benefits of Electric Vehicle Tax Policies in Developing Countries." (2022).

Callejas, Jerónimo, Joshua Linn, and Jevgenijs Steinbuks. "Welfare and Environmental Benefits of Electric Vehicle Tax Policies in Developing Countries." (2022).

• Number of cases: 74

• No. of variables per record: 2

• Type of File: text/tab-separated-values

UNF:6:Q2KHlBDgLmM/TtNeZtrzEw==

• Number of cases: 72

• No. of variables per record: 2

• Type of File: text/tab-separated-values

UNF:6:KyPBDPPV96f3ddOz/SDAoA==

• Number of cases: 74

• No. of variables per record: 2

• Type of File: text/tab-separated-values

UNF:6:EPh70H4jP6Osi0gcK6WPCg==

• Number of cases: 74

• No. of variables per record: 2

• Type of File: text/tab-separated-values

UNF:6:TSPyzUJO3FdJ/l6ahFD36Q==

• Number of cases: 12056

• No. of variables per record: 1

• Type of File: text/tab-separated-values

UNF:6:wW0cdr0Ra+T+TVCJUs9gLw==

• Year-Month - Year-Month
• CE - CE
• Year-Month - Year-Month
• ELEC - ELEC
• Year-Month - Year-Month
• ER - ER
• Year-Month - Year-Month
• GAS - GAS
• 1347.905282 - 1347.905282

f10426624 Location:

Variable Format: character

Notes: UNF:6:TFi4FQsNeRYc3+BeJd2Jlg==

f10426624 Location:

Summary Statistics: Min. 0.0; Valid 74.0; Max. 103.0; StDev 35.99222426206274; Mean 24.891891891891902

Variable Format: numeric

Notes: UNF:6:jsQRnxchW6seaG/QYQ/pEg==

f10426626 Location:

Variable Format: character

Notes: UNF:6:a/RPUblyEW0v++KVjPF2vQ==

f10426626 Location:

Summary Statistics: StDev 32.636279518742775; Mean 326.53263888888887; Min. 253.84; Max. 396.85; Valid 72.0

Variable Format: numeric

Notes: UNF:6:M8+PdjpfnSjCDkcJu/ut3A==

f10426617 Location:

Variable Format: character

Notes: UNF:6:TFi4FQsNeRYc3+BeJd2Jlg==

f10426617 Location:

Summary Statistics: StDev 358.1592282625871; Min. 2381.0; Mean 3138.92527027027; Max. 4059.0; Valid 74.0;

Variable Format: numeric

Notes: UNF:6:EjULmbDN+4JKtkHZ3PWlSw==

f10426634 Location:

Variable Format: character

Notes: UNF:6:jSwy5GrDRJjxAlpCgy1jcw==

f10426634 Location:

Summary Statistics: Valid 74.0; StDev 630.9146737860349; Max. 9412.222222; Min. 7388.698235; Mean 8402.34156872973

Variable Format: numeric

Notes: UNF:6:Hc+1tBxB4n6b3I4IFjwCSQ==

f10426640 Location:

Summary Statistics: Mean 8641.958341456922; StDev 42320.43402848337; Valid 12056.0; Max. 3573187.614; Min. 0.35701275

Variable Format: numeric

Notes: UNF:6:wW0cdr0Ra+T+TVCJUs9gLw==

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Algorithm to match car datasets.pptx

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emissions.zip

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Fill_Nan.py

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Read_me.pdf

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mktshares.m

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contraction.m

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elast_blp.m

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Elast_Gas_type.txt

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Elast_Mark_up.txt

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expmu.m

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getranddraw.m

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Random_Coef_demand.txt

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Random_Coef_supply.txt

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Table_1_Descriptive_stats.txt

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CS.m

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Figure 1 Market share evolution by vehicle type.png

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Figure 2 Average vehicle prices by fuel type.png

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getranddraw.m

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ind_shares.m

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Table_10_13_year_2019.txt

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Table_10_13_year_2020.txt

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Table_2.txt

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Table_8_11_year_2019.txt

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Table_8_11_year_2020.txt

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Table_9_14_year_2019.txt

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Table_9_14_year_2020.txt

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CS.m

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Table_12_year_2019.txt

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Table_12_year_2020.txt

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.gitignore

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carspecs_eff.py

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settings.cpython-37.pyc

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