VoxEU Column Financial Markets Taxation

A rationale for the Tobin tax

Tobin taxes on financial markets, such as the EU Financial Transactions Tax, are regularly under consideration. This column argues that a rationale for a Tobin tax exists even in competitive and informationally efficient markets when traders have private information and they condition on prices. In this situation traders overreact to private information, and a transactions tax may offset this externality. 

When James Tobin (1978) proposed a tax on financial transactions, he used the celebrated description that it was “to throw sand in the wheels of the excessively efficient international money markets”. It has always been controversial. Tobin's focus was on foreign exchange markets and how to preserve sound macroeconomic policies. Now, any tax on financial transactions is labelled a 'Tobin tax'. A tax on short-term speculation was proposed also by Keynes (1936) and advocated by, among others, Stiglitz (1989) and Summers and Summers (1989), who argue for its benefits even if it reduces market liquidity. Ross (1989) refuted the arguments in favour of the tax.

Taxes of this sort have been used in countries such as the UK and Sweden and more recently, after the Global Crisis, a Financial Transactions Tax (FTT) was on the agenda in 2011, and relaunched in a 2013 proposal by the European Commission. Eleven European countries, under an 'enhanced cooperation' procedure including major European countries (but not the UK), committed to introduce it, but there have been delays, and the self-imposed deadlines have not been met (Estonia pulled out, Belgium and Slovenia have doubts, while France is pushing for the agreement). Brexit has provided another shock. It may cause continental European countries to wonder whether it would be a good idea to introduce this tax when they are trying to attract financial business from the City of London.

The de facto aim of the proposed tax is more about raising revenue (to recover public money spend during the Global Crisis by collecting annually about 0.4% of GDP of member states) than correcting a malfunctioning market. The question is whether there is an efficiency justification, à la Pigou, to impose such a tax.

Tobin (1978) thought that financial markets were efficient “only in a mechanical sense: transaction costs are low, communications are speedy, prices are instantaneously kept in line all over the world, credit enables participants to take large long or short positions at will or whim”. He stated all this in the 1970s, well before algorithmic trading! Tobin was sceptical about the informational efficiency of market prices, seeing markets dominated “by traders in the game of guessing what other traders are going to think”, much as in Keynes’s beauty contest. Tobin was also aware of the difficulty of enforcing and administering such a tax.

In favour of a Tobin tax

In a new paper, I argue that a rationale for a Tobin tax exists, even if markets are competitive and informationally efficient, and traders do not suffer from behavioural biases and extract information from prices in the rational expectations tradition (Vives 2016). The key conditions for this result are that traders have private information, and conditioning on prices – two standard features of financial markets.

Imagine a competitive financial market where there are only two frictions: a transaction cost or trader risk aversion, and private information. Each trader, perhaps out of own research or personal reading of public information, receives a private signal about the fundamental value of an asset. Investors condition on market prices when formulating their trade (more precisely, they submit demand schedules to the market mechanism) and markets clear.

In this context, we may suspect that traders will rely too much on public information. The reason is that traders do not consider that their reaction to private information affects how informative public statistics (prices) and general welfare would be. In other words, traders do not internalise an information externality. This type of externality will make agents insufficiently responsive to their private information (Vives 1997, Amador and Weill 2012) and, in the limit, to disregard it (Banerjee 1992, Bikhchandani et al. 1992). As an example, Morris and Shin (2005) point to the paradox that, by publishing aggregate statistics, a central bank makes them less reliable because it induces agents in the economy to rely less on their private signals.

The previous reasoning, however, disregards one important fact. When traders condition on prices there will be a pecuniary externality in the use of private information. This is that, when traders react to their private information, they do not consider that they are influencing the price, which in turn influences the actions of other traders who are also conditioning on the price. This pecuniary externality may counteract the learning from the price externality, and lead agents to put too much weight on private information. In Vives (2016) I show that, in normal circumstances, where the demand schedules of the traders are downward-sloping, the pecuniary externality is stronger than the learning externality, and so traders overreact to private information. 'Overreaction' is understood with respect to a (second-best) welfare benchmark in which traders internalise collective welfare but respect the decentralised information structure of the economy - that is, a structure in which each trader can act only on his or her information.

The somewhat surprising possibility of prices that are ‘too informative’ may arise because even though more informative prices are good for aggregate efficiency, in a second-best world, this comes at the cost of increasing the dispersion of trades. Indeed, for prices to be more informative, traders have to respond more to their private signals, and this magnifies the noise contained in the signals. The problem for welfare is that when prices are very sensitive to a surprise in fundamentals, this induces traders to trade too much when fundamentals are low and too little when they are high – this is the outcome of overreaction to private information.

The market inefficiency can be corrected by inducing traders to moderate their response to their private signals, and by making the price less sensitive to surprises in fundamentals. This is where a Tobin-style tax may play a role. If properly designed and calibrated, a transaction tax makes informed traders internalise the pecuniary externality in the use of private information. The end result is a price that contains less information, and possibly even a deeper market. A potential problem is that the regulator typically cannot distinguish between informed and uninformed trade. Fortunately, the tax can be calibrated to apply to all traders to induce an efficient result.

Alternative rationales for financial transaction taxes have been provided by Dow and Rahi (2000), who found conditions in which a transaction tax would be Pareto-improving, even if the tax revenue were wasted. Subrahmanyam (1998) also considered a transaction tax, but found that such a tax would reduce market liquidity. More recently, Davila (2016) found that a positive transaction tax would be optimal if trade were driven by heterogeneous beliefs across investors.

So, are we home and dry? No, because as Tobin pointed out, there are other potential costs associated with an FTT, not least the implementation costs. Regarding the latter, the decision over the range of securities affected is crucial (in principle, the tax has to be levied on shares, debt, and derivative instruments in both organised and OTC markets, though with some exceptions). At the same time, there is the question of how to avoid trade diversion towards jurisdictions with more favourable tax treatment, and what effect the tax may have on the Capital Markets Union. More generally, the impact on the cost of capital of issuers, market liquidity, and volatility have to be examined.

In this column, I offered a potential benefit of an FTT that is based on the tendency of traders to overreact to private information in competitive markets when they condition on prices. Nevertheless, before take any decision on an FTT, the benefits and costs of the tax would have to be calibrated carefully – even if it would promote efficiency in the economic system.


Amador, M and P O Weill (2012), “Learning from Private and Public Observations of Others' Actions”, Journal of Economic Theory, 147, 3, 910-940.

Banerjee, A (1992), “A Simple Model of Herd Behavior”, Quarterly Journal of Economics, 107, 3, 797-817.

Bikhchandani, S, D Hirshleifer and I Welch (1992), “A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades”, Journal of Political Economy, 100, 5, 992-1026.

Davila, E (2016), “Optimal Financial Transaction Taxes”, Working paper.

Dow, J and R Rahi (2000), “Should Speculators Be Taxed?”, Journal of Business 73, 89-107.

Keynes, J M (1936), The General Theory of Employment Interest and Money, London: Macmillan.

Morris, S and H S Shin (2005), “Central Bank Transparency and the Signal Value of Prices”, Brookings Papers on Economic Activity, 2, 43-85.

Ross, S A (1989), “Commentary: Using Tax Policy to Curb Speculative Short-term Trading”, Journal of Financial Services Research, 3: 117–120.

Stiglitz, J (1989), “Using Tax Policy to Curb Speculative Short-term Trading", Journal of Financial Services Research, 3, 101-115.

Subrahmanyam, A (1998), “Transaction Taxes and Financial Market Equilibrium”, The Journal of Business, 71, 1, 81–118.

Summers, L and V Summers (1989), “When Financial Markets Work Too Well: A Cautious Case for a Securities Transactions Tax”, Journal of Financial Services Research, 3, 261-286.

Tobin, J (1978), “A Proposal for International Monetary Reform”, Eastern Economic Journal, 4, 153-159.

Vives, X (1997), “Learning from Others: A Welfare Analysis”, Games and Economic Behavior, 20, 2, 177-200.

Vives, X (2016), “Endogenous Public Information and Welfare”, Review of Economic Studies, forthcoming.

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