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. People making use of that tax status are strongly concentrated at the top of the income distribution: 86% are in the UK top 1% and 29% in the top 0.1% once overseas investment income is taken into account. A reform in 2017 brought long-stayers, who had been in the UK for at least 15 of the last 20 years, into the standard tax system, reducing their effective net-of-average-tax rate by 18%. We find that emigration responses were modest: our central estimate is that the emigration rate increases by 0.26 percentage points for a 1% decline in the net-of-tax rate, and we can rule out increases larger than 0.4 percentage points. Dispelling fears that the targeted taxpayers were able to circumvent the tax hike, we find large average increases in income reported and tax paid in the UK of more than 150%.

In this paper we study the contribution of migrants to the rise in UK top incomes. Using administrative data on the universe of UK taxpayers we show migrants are over-represented at the top of the income distribution, with migrants twice as prevalent in the top 0.1% 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 (85%) of the growth in the UK top 1% income share over the past 20 years can be attributed to migration.

Top incomes have grown rapidly in recent decades and this growth has sparked a debate about rising inequality in Western societies. This column combines data from UK tax records with new information on migrant status to show that that migrants are highly represented at the top of the UK’s income distribution. Indeed, migration can account for the majority of top-income growth in the past two decades and can help explain why the UK has experienced an outsized increase in top incomes.

An established literature has studied potential biases in the economics publication process based on traits of authors. We complement such work by studying whether the subject matter of study relates to publication outcomes. We do so in the context of race-related research: work that studies economic well-being across racial/ethnic groups. We investigate the implicit career incentives economists have to work on such topics by examining paths to publication for a corpus of 22,056 NBER working papers (WPs) posted from 1974 to 2015. We use an algorithm to classify whether a given WP studies race-related issues. We then construct paths to publication from WPs to data on published articles, and compare paths for race-related WPs to various counterfactual sets of WPs. We document that unconditionally, race-related NBER WPs are less likely to be published in any journal, in an economics journal, and more likely to publish in lower tier economics journals. Once we condition on observable characteristics including field and author affiliations, differences in paths to publication largely disappear, and such work is actually slightly more likely to publish in top-tier economics journals. Consistent with unconditional differences in paths to publication being salient to researchers, we find evidence of ex ante selection into WPs studying race-related issues in that they are of higher readability than other WPs. To understand the interplay with selection of researchers, we compare results to paths to publications for 10,306 CEPR WPs posted from 1984 to 2015. We conclude by discussing implications for economists' incentives to contribute to debates on race and ethnicity in the economy.

Issues of racial justice and economic inequalities between racial and ethnic groups have risen to the top of public debate. Economists’ ability to contribute to these debates is based on the body of race-related research. We study the volume and content of race-related research in economics. We base our analysis on a corpus of 225,000 economics publications from 1960 to 2020 to which we apply an algorithmic approach to classify race-related work. We present three new facts. First, since 1960 less than 2% of economics publications have been race-related. There is an uptick in such work in the mid 1990s. Among the top-5 journals this is driven by the American Economic Review, Quarterly Journal of Economics and the Journal of Political Economy. Econometrica and the Review of Economic Studies have each cumulatively published fewer than 15 race-related articles since 1960. Second, on content, while over 50% of race-related publications in the 1970s focused on Black individuals, by the 2010s this had fallen to 20%. There has been a steady decline in the share of race-related research on discrimination since the 1980s, with a rise in the share of studies on identity. Finally, we apply our algorithm to NBER and CEPR working papers posted over the last four decades, to study an earlier stage of the research process. We document a balkanization of race-related research into a few fields, and its continued absence from many others – a result that holds even within the subset of research examining issues of inequality or diversity. We discuss implications of our findings for economists’ ability to contribute to debates on race and ethnicity in the economy.

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.

In the wake of last summer’s Black Lives Matter protests, many have asked themselves what they are doing to tackle racial injustice. For economists, one central question is the extent to which the profession has examined the causes and consequences of racial inequality. This column reports evidence that race-related research in economic journals constitutes a far lower share than in comparable publications in sociology and political science. What’s more, economists over-estimate the extent of race-related research done by the profession. Understanding why economists produce so little race-related research is essential if the discipline is going to be able to reform.

Economists are central to policymaking in the UK, and to providing the research that underpins that policymaking. Despite having this important role in society, economists are not very representative of society, with a well-documented under-representation of women in the profession. In this briefing note, we examine the ethnic diversity of academic economists who provide much of the research that ultimately feeds into policymaking. We use data from the Higher Education Statistics Agency (HESA) to look at which groups are more or less well represented as academic economic researchers. We then examine economics students, to understand both the source of current under-representation and the prospects for change. Finally, we study some of the barriers faced by economics students. We are not able to examine diversity among the large number of economists who work outside academia, due to a lack of data.

The future of UK economics is looking predominantly male and disproportionately privately educated. This column introduces #DiscoverEconomics – a campaign to increase diversity in economics led by the Royal Economic Society and with the support of a wide range of institutions involved in economic research, communication and policymaking, including the Bank of England, the Government Economic Service, the Society of Professional Economists and many leading research institutions. The campaign aims to attract more women, ethnic minority students, and students from state schools and colleges to study the subject at university.

The gender pay gap opens up immediately after graduation, with male graduates earning 5% more than female graduates on average at age 25. Ten years after graduation – before most graduates start having children – the gender pay gap stands at 25%. Most of the initial gap can be explained by university subject choices, with women less likely to study subjects that lead to high-paying jobs: women make up just a third of graduates in economics, the subject with the highest financial returns, and two thirds of graduates in creative arts, the subject with the lowest returns. Subject choice continues contribute to the gender pay gap over time, but its relative importance fades as other factors (like having children and working part-time) come into play.

The economics profession – and the current student population studying economics – is not representative of society, with women, some ethnic minorities, and state school students underrepresented. While more than 7% of private school boys doing an undergraduate degree were studying economics in 2018/19, less than 1% of state school girls were. We highlight that interventions aimed at changing this picture need to consider the choices students make early on in their educational career. A Level Economics is a key gateway to further study in the subject, but access to and take-up of this qualification varies substantially according to a student’s background. As a result, improving representation within the economics profession in the long-term must include steps to ensure young students understand what the subject involves and the opportunities it provides, and have the chance to study it before university.

Carbon pricing will be one of the most talked about policy options at COP26. The idea of carbon pricing is that putting a price on the emission of greenhouse gases (GHGs) to reflect the social costs of climate change should provide producers and consumers with strong incentives to reduce such emissions. In this observation, we consider the opportunities and risks from introducing carbon taxes in developing countries, with reference to Ethiopia and Ghana as case studies.

This CAGE Policy Report chapter describes how policy can do more to protect the environment without having to hurt the least well-off.

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.

The report analyses and assesses: the rationale and objectives of energy policy; the current policy landscape faced by UK energy users; how current and future policy has led to inconsistencies in the implicit carbon prices faced by different users; and potential ways in which to improve policy affecting domestic and business energy users.

Government wants both to reduce carbon emissions and to reduce ‘fuel poverty’. Energy prices have risen in part because of a multitude of policies aimed at reducing emissions. There are also multiple policies aimed at ameliorating these effects. Altogether, this leads to a complex policy landscape, inefficient pricing and opaque distributional effects.
In this report, we show the effects of energy price rises over the recent past, look at what current policies mean for effective carbon prices and their impact on bills, and consider the distributional consequences of a more consistent approach to carbon pricing, alongside possible changes to the tax and benefit system that could mitigate these effects.

An IFS Observation on the current state of carbon prices and environmental policy in the UK.

An IFS Observation on the costs of hypothecating green taxes.

Carbon pricing will be one of the most talked about policy options at COP26. The idea of carbon pricing is that putting a price on the emission of greenhouse gases (GHGs) to reflect the social costs of climate change should provide producers and consumers with strong incentives to reduce such emissions. In this observation, we consider the opportunities and risks from introducing carbon taxes in developing countries, with reference to Ethiopia and Ghana as case studies.

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.

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.

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.