Here's the context: many countries in the world suffer from loss of tax revenues due to tax avoidance and/or tax evasion. One of the most common methods used by multinational corporations is to set up a subsidiary in the so-called
Having a subsidiary in non-cooperative/low-tax jurisdictions is not necessarily indicative of tax avoidance. Businesses may have legitimate reason such as access to market, diversification, or centralization of one of their business functions. Nevertheless, the concern of tax revenue loss cannot simply be ignored.
But how much is lost?
The study by Crevelli et al. (2015) tries to estimate how much a country loses their tax revenue from tax avoidance. Cobham and Janský (2018) tests and re-estimates this research. I'd like to try estimating the loss of Indonesia.
The Setup
A company avoid taxes in a country by diverting profit to its subsidiaries in other countries. The tax base of the aforementioned country is thus reduced and "spillover" to other countries.
But how much is lost?
The study by Crevelli et al. (2015) tries to estimate how much a country loses their tax revenue from tax avoidance. Cobham and Janský (2018) tests and re-estimates this research. I'd like to try estimating the loss of Indonesia.
The Setup
A company avoid taxes in a country by diverting profit to its subsidiaries in other countries. The tax base of the aforementioned country is thus reduced and "spillover" to other countries.
But how does a company choose the jurisdiction of its subsidiaries?
In a micro-data, such as commercial database (that contains financial reports and jurisdiction of subsidiaries/headquarter), this is much easier and clearer to measure. In fact, many of the measures and monitoring tools for base erosion and profit shifting as outlined in OECD's BEPS Action Plan 11 utilize micro-data from commercial database.
Measuring in a macro (country-level) setting thus requires us to make assumptions. Since a company may create a subsidiary for access to market, we must assume that company does not pick a country purely for its low tax. A company may be hesitant to create a subsidiary in a small or faraway country. Or, given a choice of two countries with similar tax rate, a company will choose the country which is closer or has bigger market than the other one. Thus, we weight the effect of tax rate in every country of the world based on two things: distance and market size (that is represented by GDP).
The Model
We first estimate the φ and λ based on the equation:
, where bit denotes the corporate tax base in country i in time t; τit the domestic tax rate (in this case Indonesia); W-it τit a weighted average of the tax rates in countries other than Indonesia; Xit a vector of controls such as trade openness (import + export divided by GDP), share of agriculture to GDP, and log of GDP per capita in constant 2011 dollar; and μt is time specific effect. I employ LASSO (Least Absolute Shrinkage and Selection Operator) in the estimation to penalize model overfitting, as I don't have much data to begin with.
After we get φ and λ we plug them to equation:
for short term effect (L), and
for long term effect (LL), where Whτ-it denotes the average tax rates of tax haven countries as listed by Gravelle (2013).
The Results
The loss of Indonesia's tax revenue as % of GDP in year 2008-2017 is as follows:
Interestingly, this suggests that if companies actually consider GDP as the main factor to create subsidiary in a country, there is a tax base spillover to Indonesia. Meaning that Indonesia actually gains from tax avoidance.
Measuring in a macro (country-level) setting thus requires us to make assumptions. Since a company may create a subsidiary for access to market, we must assume that company does not pick a country purely for its low tax. A company may be hesitant to create a subsidiary in a small or faraway country. Or, given a choice of two countries with similar tax rate, a company will choose the country which is closer or has bigger market than the other one. Thus, we weight the effect of tax rate in every country of the world based on two things: distance and market size (that is represented by GDP).
The Model
We first estimate the φ and λ based on the equation:
After we get φ and λ we plug them to equation:
for short term effect (L), and
The Results
The loss of Indonesia's tax revenue as % of GDP in year 2008-2017 is as follows:
Interestingly, this suggests that if companies actually consider GDP as the main factor to create subsidiary in a country, there is a tax base spillover to Indonesia. Meaning that Indonesia actually gains from tax avoidance.
After I examine the data, it turns out that Indonesia's tax rate is lower than the rest of the world if weighted by GDP. This is understandable as small GDP countries with zero tax rates such as British Virgin Island matters less in this scenario than, for instance, Germany (whose tax rate approximately 30% is higher than Indonesia and whose GDP is much bigger than Indonesia as well.)
On the other hand, if a company seeks to establish a subsidiary in low tax jurisdictions near Indonesia (i.e. assuming it also wants to exploit agglomeration effect or to shorten supply chains), then Indonesia loses tax base approximately 3-5% of GDP.
Translating the above result into Rupiah, this is how much Indonesia loses (gains) the tax revenue:
This is a very crude estimation due to severe data limitation. Maybe I will update this if I have the time to collect more and better data. Maybe.
References:
Crivelli E, De Mooij R, Keen M. 2016. Base erosion, profit shifting and developing countries. FinanzArchiv: Public Finance Analysis 72(3): 268–301. https://doi.org/10.1628/001522116X14646834385460
Cobham A, Janský P. 2018. Global Distribution of Revenue Loss from Corporate Tax Avoidance: Re-estimation and Country Results. Journal of International Development. UNU-WIDER.
Gravelle J G. 2013. Tax havens: international tax avoidance and evasion. Washington, DC. http://fas.org/sgp/crs/misc/R40623.pdf. Accessed 25 June 2018
OECD. 2015. Measuring and Monitoring BEPS, Action 11 - 2015 Final Report. Paris: Organisation for Economic Co-operation and Development.
Data is from World Development Indicator of World Bank; World Economics Outlook and International Finance Statistics of IMF; tax rate data is from KPMG Tax Rate Table, with some additional research for small jurisdictions; Badan Pusat Statistik; and APBN of Indonesia.
Translating the above result into Rupiah, this is how much Indonesia loses (gains) the tax revenue:
This is a very crude estimation due to severe data limitation. Maybe I will update this if I have the time to collect more and better data. Maybe.
References:
Crivelli E, De Mooij R, Keen M. 2016. Base erosion, profit shifting and developing countries. FinanzArchiv: Public Finance Analysis 72(3): 268–301. https://doi.org/10.1628/001522116X14646834385460
Cobham A, Janský P. 2018. Global Distribution of Revenue Loss from Corporate Tax Avoidance: Re-estimation and Country Results. Journal of International Development. UNU-WIDER.
Gravelle J G. 2013. Tax havens: international tax avoidance and evasion. Washington, DC. http://fas.org/sgp/crs/misc/R40623.pdf. Accessed 25 June 2018
OECD. 2015. Measuring and Monitoring BEPS, Action 11 - 2015 Final Report. Paris: Organisation for Economic Co-operation and Development.
Data is from World Development Indicator of World Bank; World Economics Outlook and International Finance Statistics of IMF; tax rate data is from KPMG Tax Rate Table, with some additional research for small jurisdictions; Badan Pusat Statistik; and APBN of Indonesia.
