In this paper we model the revenue that could be raised from an annual and a one-off wealth tax of the design recommended by Advani, Chamberlain and Summers (2020)
. We examine the distributional effects of the tax, both in terms of wealth and other characteristics. We also estimate the share of taxpayers who would face liquidity constraints in meeting their tax liability. We find that an annual wealth tax charging 0.18% on wealth above £500,000 could generate £10 billion in revenue, before admin costs. Alternatively, a one-off tax charging 4.8% (effectively 0.96% per year, paid over a 5-year period) on wealth above the same threshold, would generate £250 billion in revenue. To put our revenue estimates into context, we present revenue estimates and costings for some commonly-proposed reforms to the existing set of taxes on capital.
A. Advani and H. Tarrant (2020), Wealth and Policy Working Paper 105
In this paper, we review the existing empirical evidence on how individuals respond to the incentives created by a net wealth tax. Variation in the overall magnitude of behavioural responses is substantial: estimates of the elasticity of taxable wealth vary by a factor of 800. We explore three key reasons for this variation: tax design, context, and methodology. We then discuss what is known about the importance of individual margins of response and how these interact with policy choices. Finally, we use our analysis to systematically narrow down and reconcile the range of elasticity estimates. We argue that a well-designed wealth tax would reduce the tax base (of reported wealth) by 7-17% if levied at a tax rate of 1%.
A. Advani, G. Bangham and J. Leslie (2020), Wealth and Policy Working Paper 101
Household wealth is profoundly important for living standards. We show that wealth inequality in the UK is high and has increased slightly over the past decade as financial asset prices increased in the wake of the financial crisis. But data deficiencies are a major barrier in understanding the true distribution, composition and size of household wealth. We find that the most comprehensive survey of household wealth in the UK does a good job of capturing the vast majority of the wealth distribution, but that nearly £800 billion of wealth held by the very wealthiest UK households is missing. We also find tentative evidence to suggest that survey measures of high-wealth families undervalue their assets – our central estimate of the true value of wealth held by households in the UK is 5% higher than the survey data suggests.
A. Advani and A. Summers (2020), CAGE Working Paper 465
Aggregate taxable capital gains in UK have tripled in past decade. Using confidential administrative data on the universe of UK taxpayers, we show that including gains changes the picture of UK inequality over the past two decades. These taxable gains are largely repackaged income, so their exclusion biases the picture of inequality. Including them changes who is at the top of the distribution, adding more buiness owners and older people. The share of income plus gains (both pre- and post-tax) going to the top 1% is 3pp higher than for income only, and this gap has been steadily rising.
Poor households regularly borrow and lend to smooth consumption, yet we see much less borrowing for investment. This cannot be explained by a lack of investment opportunities, nor by a lack of resources available for investment. This paper provides a novel explanation for this puzzle: informal risk sharing can crowd out investment. I extend the canonical model of limited commitment in risk-sharing networks to allow for lumpy investment. The key insight is that the cost of losing insurance is lower for a household that has invested, since it has an additional stream of income. This limits its ability to credibly promise future transfers, and so limits its ability to borrow from other households. The key prediction of the model is a non-linear relationship between total income and investment at the network level – namely there is a network level poverty trap. I test this prediction using a randomised control trial in Bangladesh, that provided capital transfers to the poorest households. The data covers 27,000 households from 1,400 villages, and contain information on risk-sharing networks, income and investment. I exploit variation in the number of program recipients in a network to identify the threshold level of capital provision needed at the network level for the program to move the network out of a poverty trap and generate further investment. I also verify additional predictions of the model and rule out alternative explanations. My results highlight how capital transfer programs can be made more cost-effective by targeting communities at the threshold of the aggregate poverty trap.
A. Advani and B. Reich (2015), IFS Working Paper W15/30
Relatively little is known about what determines whether a heterogenous population ends up in a cooperative or divisive situation. This paper proposes a theoretical model to understand what social structures arise in heterogeneous populations. Individuals face a trade-off between cultural and economic incentives: an individual prefers to maintain his cultural practices, but doing so can inhibit interaction and economic exchange with those who adopt different practices. We find that a small minority group will adopt majority cultural practices and integrate. In contrast, minority groups above a certain critical mass, may retain diverse practices and may also segregate from the majority. The size of this critical mass depends on the cultural distance between groups, the importance of culture in day to day life, and the costs of forming a social tie. We test these predictions using data on migrants to the United States in the era of mass migration, and find support for the existence of a critical mass of migrants above which social structure in heterogeneous populations changes discretely towards cultural distinction and segregation.
Published and Forthcoming Papers
A. Advani (2021), Fiscal Studies
We use administrative tax data from audits of self-assessment tax returns to understand what types individuals are most likely to be non-compliant. Non-compliance is common, with one-third of taxpayers underpaying by some amount, although half of aggregate under-reporting is done by just 2% of taxpayers. Third party reporting reduces non-compliance, while working in a cash-prevalent industry increases it. However, compliance also varies significantly with individual characteristics: non-compliance is higher for men and younger people. These results matter for measuring inequality, for understanding taxpayer behaviour, and for targeting audit resources.
A. Advani, T. Kitagawa and T. Słoczyński (2019), Journal of Applied Econometrics
We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to help select a treatment effect estimator under unconfoundedness. We show theoretically that neither is likely to be informative except under restrictive conditions that are unlikely to be satisfied in many contexts. To test empirical relevance, we also apply the approaches to a real-world setting where estimator performance is known. Both approaches are worse than random at selecting estimators which minimise absolute bias. They are better when selecting estimators that minimise mean squared error. However, using a simple bootstrap is at least as good and often better. For now researchers would be best advised to use a range of estimators and compare estimates for robustness.
A. Advani and B. Malde (2018), Journal of Economic Surveys
Understanding whether and how connections between agents (networks) such as declared friendships in classrooms, transactions between firms, and extended family connections, influence their socio-economic outcomes has been a growing area of research within economics. Early methods developed to identify these social effects assumed that networks had formed exogenously, and were perfectly observed, both of which are unlikely to hold in practice. A more recent literature, both within economics and in other disciplines, develops methods that relax these assumptions. This paper reviews that literature. It starts by providing a general econometric framework for linear models of social effects, and illustrates how network endogeneity and missing data on the network complicate identification of social effects. Thereafter, it discusses methods for overcoming the problems caused by endogenous formation of networks. Finally, it outlines the stark consequences of missing data on measures of the network, and regression parameters, before describing potential solutions.
A. Advani and G. Stoye (2017), Fiscal Studies
Current UK energy use policies, which primarily aim to reduce carbon emissions, provide abatement incentives which vary by user and fuel, creating inefficiency. Distributional concerns are often given as a justification for the lower carbon price faced by households, but there is little rationale for carbon prices associated with the use of gas to be lower than those for electricity. We consider reforms that raise carbon prices faced by households, and reduce the variation in carbon prices across gas and electricity use, improving the efficiency of emissions reduction. We show that the revenue raised from this can be recycled in a way that ameliorates some of the distributional concerns. Whilst such recycling is not able to protect all poorer households, existing policy also makes distributional trade-offs, but does this in an opaque and inefficient way.
A. Advani and B. Malde (2018), Swiss Journal of Economics and Statistics (solicited)
In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper we review the literature studying econometric methods for the analysis of linear models of social effects, a class that includes the `linear-in-means' local average model, the local aggregate model, and models where network statistics affect outcomes. We provide an overview of the underlying theoretical models, before discussing conditions for identification using observational and experimental/quasi-experimental data.
A. Advani and B. Malde (2014), IFS Working Paper W14/34
In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper we review the literature studying econometric methods for the analyis of social networks. We begin by providing a common framework for models of social effects, a class that includes the ‘linear-in-means’ local average model, the local aggregate model, and models where network statistics affect outcomes. We discuss identification of these models using both observational and experimental/quasi-experimental data. We then discuss models of network formation, drawing on a range of literatures to cover purely predictive models, reduced form models, and structural models, including those with a strategic element. Finally we discuss how one might collect data on networks, and the measurement error issues caused by sampling of networks, as well as measurement error more broadly.