In this paper we discuss first the measurement of financial inequalities, focusing on top income and wealth shares. We then consider the scope for policy reforms to tackle some of the issues raised.

Using administrative data on the globally connected super-rich in the UK, we study the effect of a large tax reform on migration behaviour. Prior to 2017, offshore investment returns for ‘non-doms’ – individuals tax resident in the UK but with connections to other countries – were untaxed. Average offshore investment returns for these individuals exceeded £420,000; even without considering other types of income, this puts them in the top 0.2% of the population. A reform in 2017 brought long-stayers and UK-born non-doms into the standard tax system, reducing their effective net of average tax rate by between 8.8% and 13.0%. We find that migration responses were limited: our central estimate of the migration elasticity is 0.02, and across a range of specifications we can rule out elasticities larger than 0.5. Using reforms for the UK-born super-rich who were living abroad, we find that migration elasticities are limited even for recent arrivals, for whom our central estimate is 0.18. Assuming similar elasticities for all non-doms, abolition of the preferential regime would increase tax revenue collected from non-doms by £3.2bn (84%).

In this paper we show the importance of international ties amongst the UK’s global economic elite, by exploiting administrative data derived from tax records. We show how this data can be used to shed light on the kind of transnational dynamics which have long been hypothesised to be of major significance in the UK, but which have previously proved intractable to systematic study. Our work reveals the enduring and distinctive influence of long-term imperial forces, especially to the former ‘white settler’ ex-dominions which have been called the ‘anglosphere’. These are allied to more recent currents associated with European integration and the rise of Asian economic power. Here there are especially strong ties to the ‘old EU-6’ nations of France, Germany, Netherlands, Belgium, Luxembourg, and Italy. The incredible detail and universal coverage of our data means that we can study those at the very top with a level of granularity that would be impossible using traditional survey sources. We find compelling support for the public perception that non-doms are disproportionately highly affluent individuals who can be viewed as a part of a global elite. However, whilst there is some evidence for the stereotype of the global wealthy parking themselves in the UK, this underplays the significance of the working rich. Our analysis also reveals the remarkable concentration of non-doms in central areas of London.

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 business 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.

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.

Using administrative data on the universe of UK taxpayers, we study the contribution of migrants to the rise in UK top incomes. We show migrants are over-represented at the top of the income distribution, with migrants twice as prevalent in the top 0.01% as anywhere in the bottom 97%. These high incomes are predominantly from labour, rather than capital, and migrants are concentrated in only a handful of industries, predominantly finance. Almost all (90%) of the observed growth in the UK top 1% income share over the past 20 years has accrued to migrants.

We study the effects of audits on long run compliance behaviour, using a random audit program covering more than 53,000 tax returns. We find that audits raise reported tax liabilities for five years after audit, effects are longer lasting for more stable sources of income, and only individuals found to have made errors respond to audit. 60-65% of revenue from audit comes from the change in reporting behaviour. Extending the standard model of rational tax evasion, we show these results are best explained by information revealed by audits constraining future misreporting. Together these imply that more resources should be devoted to audits, audit targeting should account for reporting responses, and audit threat letters miss a key benefit of audit

We compare two approaches to measuring UK top income shares—the share of income going to particular subgroups, such as the top 1%. We set out four criteria that an ideal top share series should satisfy: (i) comparability between numerator and denominator; (ii) comparability over time; (iii) international comparability; and (iv) practical sustainability. Our preferred approach meets three of these; by contrast the approach currently used to produce UK fiscal income series meets none of them. Changing to our preferred approach matters quantitatively: the share of income going to the top 1% is 2 percentage points higher, but rising more slowly, than under the alternative.

How does economics compare to other social sciences in its study of issues related to race and ethnicity? We assess this using a corpus of 500,000 academic publications in economics, political science, and sociology. Using an algorithmic approach to classify race-related publications, we document that economics lags far behind the other disciplines in the volume and share of race-related research, despite having higher absolute volumes of research output. Since 1960, there have been 13,000 race-related publications in sociology, 4,000 in political science, and 3,000 in economics. Since around 1970, the share of economics publications that are race-related has hovered just below 2% (although the share is higher in top-5 journals); in political science the share has been around 4% since the mid-1990s, while in sociology it has been above 6% since the 1960s and risen to over 12% in the last decade. Finally, using survey data collected from the Social Science Prediction Platform, we find economists tend to overestimate the amount of race-related research in all disciplines, but especially so in economics.

Policymakers tend to ‘treasure what is measured’ and overlook phenomena that are not. In an era of increased reliance on administrative data, existing policies also often determine what is measured in the first place. We analyse this two-way interaction between measurement and policy in the context of the investment incomes and capital gains that are missing from the UK’s official income statistics. We show that these ‘missing incomes’ change the picture of economic inequality over the past decade, revealing rising top income shares during the period of austerity. The underestimation of these forms of income in official statistics has diverted attention from tax policies that disproportionately benefit the wealthiest. We urge a renewed focus on how policy affects and is affected by measurement.

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.

Capital gains are particularly complex to tax given their infrequency, the different ways in which they are generated, and worries about harming productivity. There are theoretical arguments in support of everything from zero rates to high rates of tax on capital. In this paper, I first discuss the impact of capital gains on inequality, which often motivates discussions about how gains should be taxed. I then set out the principles that determine how gains should be taxed, in particular how the tax rate should relate to income tax rates. I propose that capital gains tax rates be equalized with income tax rates, subject to provisions to allow gains to be ‘smoothed’ over time and to remove inflation from the tax base. I highlight key transitional issues in moving to such a tax structure. Finally, I discuss the specific lessons for Canada.

This paper introduces a special issue on a Wealth Tax, which draws together the latest thinking on wealth taxes with the aim of filling this gap. It draws heavily on international experience and evidence, applying these insights to the UK context. The papers build on work undertaken for the Wealth Tax Commission, which delivered its final report in December 2020. The contributors to this special issue include tax practitioners and academic lawyers as well as economists, reflecting our view that this range of expertise is essential to evaluating the practice, as well as principles, of a wealth tax. In this paper we touch upon some common themes arising across the papers. We also highlight some important remaining gaps in the evidence base on wealth taxes, particularly on the measurement of wealth and behavioural responses at the very top of the wealth distribution.

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.

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%.

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.

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.

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.

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.

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.

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.