| Project/Area Number |
22K13415
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 07050:Public economics and labor economics-related
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| Research Institution | Hitotsubashi University |
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Principal Investigator |
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| Project Period (FY) |
2022-04-01 – 2024-03-31
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| Project Status |
Granted (Fiscal Year 2022)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2023: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2022: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | tax evasion / firm network / Tax evasion / Tax capacity / Firm network / Mongolia |
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| Outline of Research at the Start |
Many developing countries struggle to raise an adequate amount of tax revenue to fund policies that are important for economic growth. This project aims to explore one of the potential explanations - corporate tax evasion. I aim to study the relationship between a firm’s tax evasion behaviour and their business network using highly detailed, confidential administrative tax data from Mongolia.
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| Outline of Annual Research Achievements |
I extended the coverage period of the tax return datasets until 2020, and currently they cover the period between 2014 and 2020. Additionally, I obtained and updated firms’ ownership information for 2020, as well as their GIS location for 2016. I cleaned the datasets, merged them using the masked firm tax IDs, and checked the consistency of the datasets.
Furthermore, I identified the firms in the same trade network with tax evading firms using the operational audit and VAT invoice datasets. I started the analysis of identifying the firm clusters using machine learning algorithms based on the past relation between tax audit results and information on firms’ various tax returns such as corporate income tax and VAT returns.
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| Current Status of Research Progress |
Current Status of Research Progress
3: Progress in research has been slightly delayed.
Reason
Initially, I encountered some logical inconsistencies in the ownership and geographic location information of the firms. For instance, the masked IDs of the most firms did not match correctly. As a result, I could not merge them into the main tax return datasets. I had to physically travel to Mongolia twice to consult with tax officers and resolve the issues with the datasets. Despite this delay, I was eventually able to link the updated datasets consistently and begin my analysis.
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| Strategy for Future Research Activity |
In 2023, my focus will be on analyzing the identification of firm clusters based on trade, ownership, and geographical networks. My plan is to determine if tax-evading firms form clusters and study the dynamics of these clusters. I will also investigate what motivates firms to join or leave evasion-prone networks using data on firm entry and exit, as well as the impact of a 5-fold increase in the value-added tax threshold in 2016.
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