I was inspired by I Wayan Agus Eka’s presentation at Seminar Perpajakan Nasional 2019 organized by DJP. Basically, he tested whether illicit financial flow to a country is affected by its financial secrecy, which is proxied by Financial Secrecy Index (FSI).
I was intrigued to know whether a disincentive towards financial secrecy affects illicit financial flow. For example, an expose that consequently put a pressure to secrecy jurisdiction, a la SwissLeaks, LuxLeaks, Offshore Leaks, Panama Papers, Bahamas Leaks, and Paradise Papers.
My theory, if leaks were to happen, countries affected will put some efforts in preventing money to flow from them to secrecy jurisdiction, or otherwise, pressure the secrecy jurisdiction to be more open. As an illustration, SwissLeaks implicated Swiss as destination of shady financial transactions. By making this information public, either government of other country enacts a measure to stop money to flow to Swiss, or people would stop transferring money to Swiss out of a reputational cost/fear from investigation, or that Swiss will make their country more “open” by changing its regulation.
To sum up in hypotheses, I want to know whether leaks correlate to downward pressure to countries that are implicated by the leaks. Such downward pressure may be proxied with lower illicit financial flow.
When I gather the data, however, I ran into some problems.
The latest data of illicit financial flow is 2015. I tried move to FSI as a proxy. Unfortunately, FSI data is also published bienally and the latest data is only available for 2015 as well. More unfortunately, in 2015, only SwissLeaks, LuxLeaks, Offshore Leaks can be taken into account (since they all happened in 2013-2014, unlike Panama Papers which was announced by ICIJ in 2016). Out of these three, only Offshore Leaks can be further used as variable since SwissLeaks and LuxLeaks are tied to Swiss and Luxembourg and neither of them are in Global Financial Integrity data of illicit financial flow. In the end, I get only 45 jurisdictions, year 2013 and 2015 (n = 90).
The model
I first tried to employ difference-in-differences (DID) estimation. But the pre-estimation test suggests violation of parallel trend assumption. Therefore DID cannot be used and I had to do regular regression. Consequently, the result here should be taken as correlational, not causative.
The model below is taken from my own estimation. Basically the first (to my horribly limited knowledge) that estimate such thing. (But I could indeed be mistaken). Anyway, the model:
, where i denotes country, t denotes time, log(IFFIN) is logged illicit financial inflow to country i in time t, OFFLEAKSIN is dichotomous variable equal to 0 if that country is not implicated as destination jurisdiction in the leaks, 1 if it is implicated; TAXRATE denotes statutory/headline corporate tax rate; GDPGROW is GDP growth; log(GDPPCAP) is logged GDP per capita; θ is country fixed effect; and ε is error term.
The predicted sign of OFFLEAKSIN is negative, since we hypothesize that if leaks = 1 it correlates to lower inflow of illicit money. TAXRATE is a control variable whose sign should be negative as well, as there can be theorized that there are tax-motivated illicit financial flow to jurisdiction with low tax rate (Hearson, 2014). GDPGROW and logGDPCAP are control variables that denote inflows that are affected by domestic economy (GFI, 2015). Reuter (2012) also observe that 5 out of 7 countries with high illicit financial flows have high GDP per capita. So I assume the signs must be positive.
As robustness check, I switch illicit financial inflow with Financial Secrecy Index Score. If Financial Secrecy Index Score is lower for countries implicated in the leaks, it could signify a downward pressure. See the above reasoning. The model for robustness check:
, where FSI is Financial Secrecy Index Score and the rests are similar to the previous.
The result
In our model, OFFLEAKSIN is statistically significant with correctly predicted sign. Meaning, there may be a correlation between leaks and downward pressure to countries implicated by them. In this case, illicit money inflow to a country is negatively correlated with whether that country is mentioned in the leaks as destination of illicit flow.
If the result is hard to read, let me put in a graph:
As you can see, pre-leaks illicit financial flow is quite similar amongst all country. However, after the leaks, flow to countries implicated in the leaks is significantly reduced (the blue curve shift to the left).
Our robustness check result is as follows:
In the second model, OFFLEAKSIN is statistically significant with correctly predicted sign. Financial Secrecy Index of a country is negatively correlated with whether that country is mentioned in the leaks as destination of illicit flow. It could be surmised that some form of pressure to be more “open” did indeed exist.
Put in a graph for easier read:
There is a clear shift to the left, but not quite as pronounced as previous graph. (This is to be expected as the second model statistically significant only at 3%, compared to the first model.)
Conclusion
Leaks are good to put pressure to secrecy jurisdiction. Thank God for the whistleblowers and ICIJ.
Reference
Eka, I. W. A. (2019). Does Financial Secrecy Affect Profit Shifting? Presented on Seminar Perpajakan Nasional 2019 (forthcoming)
Global Financial Integrity (GFI). (2015). Illicit Financial Flows: The Most Damaging Economic Condition Facing the Developing World. Global Financial Integrity
Hearson, M. (2014). "Tax-motivated illicit financial flows: A guide for development practitioners." U4 Issue January 2014 no. 2. U4 Anti-Corruption Resource Centre
Reuter. P. (2012). Policy and Research Implications of Illicit Flows, in "Draining development? : Controlling Flows of Illicit Funds from Developing Countries" by P. Reuter (ed.) The World Bank: Washington DC
Data Source:
Illicit Financial Flow is from Global Financial Integrity
Countries implicated in leaked is manually gathered from International Consortium of Investigative Journalists database
Statutory Tax Rate is from KPMG
GDP growth and per capita are from IMF WEO
Financial Secrecy Index Score is from Tax Justice Network
