Safe havens in Europe Switzerland and the ten dwarfs.

AuthorPaldam, Martin
  1. Introduction

    This essay explains the many safe havens in Europe. No commonly accepted definition exists of an SH, safe haven, but the actual cases are well known. Table 1 (overleaf) lists Switzerland and 10 much smaller countries as the ESHs, the European Safe Havens. (2) The various definitions may be summarized as: An SH is a country that makes substantial money by exporting a problematic SH-good to neighboring countries, where it is restricted or illegal. The SH-export takes place by keeping a restriction, r, lower than in the neighbors. (3) The largest SH-product is offshore financial services, which protect funds from taxes levied in the land where the money are earned, but gambling and the sale of cheap booze may also be mentioned.

    1.1 A preview of the content: Causality and offshore finance

    The essay deals with two main points about the safe havens: (i) Why do they come about? This deals with causality. (ii) How do they fit into the financial structure of the continent? This contains a size-puzzle: The pool of offshore finance is far in excess of the absorptive capacity of the SHs. Two sections deals with each point:

    The first two section deals with (i) and show that it is a tempting possibility for a small country to be an SH. Section 2 takes off from a survey of a few basic data for all European countries. The analysis proves the causal chain empirically: From small to safe haven to rich. Section 3 presents a theory to explain this causal chain. Equilibriums exist for the SH-regulation, where the small country keeps it 'too' low, while it's large neighbors do not to react even if they resent the SH-policy. It is also explained why dependencies are often 'allowed' to be safe havens.

    The following two sections consider the size-puzzle: Section 4 looks at the size of the pool of funds seeking shelter by means of offshore finance. The uncertain estimates cited are all very large. The pool is a stock, but we also bring estimates of the flow of funds and on the annual accumulation of funds in the ESHs. It is much smaller than the inflow.

    Section 5 deals with the effects of the SH-policies on the EHS economies. It shows that the inflow is far in excess of the absorptive capacity for finance in the ESHs. Thus, the microstates mainly provide short-run veils of anonymity for funds passing through, while Switzerland also provides long-run storage of funds either in the country itself or as a guarantor for funds invested abroad. It stresses the difference between the exchange rate regimes of the 10 dwarfs and Switzerland, which is about 4 times larger than the 10 small countries added together.

    Section 6 summarizes and considers the pressures on the safe havens to make them comply with the regulations of other countries. A background paper (Paldam 2013b) documents the data and reports a number of additional calculations.

    1.2 The literature: Where does the present essay fit in?

    The essay draws upon several literatures that are not normally referred to in the same paper: One literature deals with the relation between the size of nations and their income; see e.g. Easterly and Kraay (2000). This literature is surveyed in Alesina et al. (2005). The main conclusion is that small nations do as well as large ones if they are open. In political science the main work on small states is still Kazenstein (1985) that disregards microstates and concludes that the small European states do better than the larger states due to corporatist institutions. (4)

    A separate literature looks at microstates and concludes that they do well, especially if they are dependent; see Armstrong and Read (1998, 2000) and Baldacchino (2004). The literature on microstates overlooks (deliberately?) that a great many are safe havens. Little has been written on the individual European microstates, except in reports by their own economic authorities, which certainly downplays the safe haven aspects of their success. The literature on Switzerland is surveyed in Christoffersen (2013). I have not seen a causality analysis as the one given in section 2, while various versions of the model in section 3 have appeared; see e.g. Slemrod and Wilson (2006).

    The literature on offshore finance are either (positive) practical guides or (negative) descriptions of the way money moves, how it can be regulated, and why it should be regulated. A recent (negative) book surveying the older literature and discussing the data is Palan et al. (2010), see also the references to Schneider and Henry in section 4. It appears that at the level of precision attempted in section 4 most studies reach numbers much like the ones I present.

    The analysis of the absorptive capacity and the transfer problem is once again covered by separate literatures; see Paldam (2013a) for a recent survey. (5) The literature on offshore finance does not discuss the transfer problem.

  2. The causal chain: From small to safe haven to rich

    Table 1 is the first link in the chain: Safe havens come about in old and small countries. Section 2.1 looks at some correlations, while Section 2.2 shows that it pays to be a safe haven.

    2.1 The main pattern of correlation in the data

    Europe has 54 fully and partly independent countries with enough policy autonomy to develop into safe havens. All sources I have found classify at least 11 of these countries as the ESHs, European Safe Havens (see Paldam 2013b). They are listed in Table 1. They are all small, eight are even microstates, and all are DCs, developed countries.

    The table gives the population size and the year of international recognition. However, the countries have older roots. By and large the ESHs are as old as other European countries, and the ESHs came into independence long before they became safe havens. None of the countries came about as a result of their safe haven policies.

    All, except Cyprus, (7) were established well before the Napoleonic wars and modern economic growth. As regards Cyprus, it is worth noting that Greek debt crisis (since 2009) did spill over to Cyprus, though only in 2011/12. The Euro Countries did provide a loan package, but they took the opportunity to impose restrictions on the banks of Cyprus that seem likely to end the SH-policies. It illustrates the resentment of other countries towards the ESHs.

    Data for most microstates are scarce and they are routinely excluded in international statistics, but we always know the population, area and national status. Also, the CIA World Factbook (URL ref.) reports the income rank for 2010 for all 11 safe havens.

    The six series used for Table 2 have non-normal distributions, so I use a rank correlation technique to calculate the table. It has 15 different entries of which 11 are significant. The two measures of size: Area and Population are so correlated (0.86) that they tell the same story. Safe havens are small, often dependent, not post-communist and have high income (8) Post-com countries are poor and not dependent, so the variables have some collinearity.

    2.2 Causality: Safe havens come about in small states and they become wealthy

    From Table 1 we know that SHs are old and small. Table 2 confirms that they are small. Thus, causality must start at being a small country. Tables 3 and 4 are causality tests that assume that in the absence of the safe haven policies the income of the ESH-countries would be the average of its surrounding neighbors. (9) In some cases it is difficult to choose the neighbors as explained in the note to the table.

    The tables compare a small SH-country and its neighbor(s) that are always larger. (10) Consequently, the modeling in section 3 considers a (S, N)-pair of countries, where S

    The test in Table 3 shows that the safe havens are richer than their neighbors by 20.1 places in income rank. This is substantial and statistically significant. It is also robust to the possible changes in the comparison.

    Table 4 is an alternative causality test. It is done on the income data in the WDI (URL ref.). The latest data for the microstates reported in the WDI are for 2007 and they only cover 8 of the 10 microstates. The ranking implied by Table 4 is somewhat different from the one in Table 3. In most cases it may be explained by the difference in the year considered, but in three cases--Monaco, Andorra and Cyprus--the data are inconsistent. (11)

    In spite of the data consistency problems, Table 4 confirms the result from Table 3: The income level in safe havens is higher than the one of their neighbors. The result is statistically significant in both tables. Thus, we know that it pays to be a safe haven.

    This completes the tests of the causality sequence: From small to SH to rich. If you are a small country, it is tempting to become a safe haven as it is likely to make you rich.

    Finally, it should be noted that safe havens tend to have a somewhat faster population growth than other countries due to immigration. Andorra has the fastest growing population in Europe. Rich people want to live there for tax reasons, and they generate local production and employment, and thus more immigration.

  3. The safe haven model for the (S, N) country pair (12)

    The purpose of this section is to explain why the causal links found in section 2 are theoretically plausible. The analysis considers two countries: the small safe haven S and its big neighbor N. It is a partial analysis as it only considers one good, A, the SH-good, (13) and one policy variable. A is a 'problematic' good that is regulated by the rate, r, which has a perceived optimum [r.sup.*] in a closed economy. It is scaled so that r is reduced when it causes an export, A, of the SH-good.

    Section 3.1 formulates the policy problem of choosing r by means of the marginal costs, MC, and marginal benefits, MB, per capita. Section 3.2 looks at the outcome when the MC and MB curves have the most likely form and show that the policy choice may be different in S and N so that [r.sup.S]

    3.1 The marginal cost and benefits...

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