Measurement
Following the spirit of the late Sir Tony Atkinson, researchers around the world have been collaborating to leverage administrative registers to document trends and patterns in inequality. This tremendous effort at improving the measurement of inequality, led by prominent public finance economists like Thomas Piketty and Emmanuel Saez, has been central to putting back inequality at the centre of the academic, public and policy debates.
While most progress has been made on income inequality (e.g., Atkinson & Piketty 2007, 2010), the rigorous analysis of wealth inequality has followed suit (e.g., Saez & Zucman 2014). However, income and wealth separately provide only a partial picture of the disparity that exists between households.
Our research uses original data and methods to go beyond the measurement of income and wealth and provide a more comprehensive account of the distribution of welfare. To evaluate how differences in income and wealth come together and translate into consumption opportunities, we can shift attention to consumption expenditures and prices individuals face using new data resources. Standard measures of income and wealth inequality do not account for evasive income and wealth, which can be overcome by data from audits and tax enforcement programs. Going beyond the narrow economic measures, administrative registers allow us to further improve our distributional welfare analysis by accounting for inequality in health, education and the provision of local public goods and infrastructure.
In the coming cycle, we would focus attention on:
1. Consumption measures: (i) the use of differences in consumption across retired workers to evaluate the social costs and benefits of pension reforms, (ii) the use of marginal propensities to consume to understand the social value of transfers, and in particular, the optimal design of stimulus policies following the Covid crisis.
2. Inflation inequality: (i) develop new price indices when consumer preferences change with income; (ii) leverage “big data” to provide policymakers with a comprehensive picture of inflation inequality by (a) gathering an international database of credit card data allowing to compute inflation inequality in twenty countries, (b) collecting micro price and expenditure datasets covering all important sectors in the United States, (c) using machine learning techniques to run new hedonic regressions for services, and (d) estimating a new housing inflation index to address existing biases. These new datasets and techniques will be shared with the scientific community via a project website to maximize impact.
3. Tax avoidance and tax evasion: (i) better understand the macro extent of tax avoidance and tax evasion at the top of the income and wealth distributions, (ii) use this understanding to construct improved measures of inequality that account more comprehensively for tax avoidance and tax evasion (iii) estimate the distribution of tax gaps by position in the income distribution in a manner compatible with Distributional National Accounts.
4. Health Inequality: to broaden the evidence base to revisit the socio-economic inequality in health outcomes by constructing the most comprehensive data sets to date in the Netherlands and Sweden, linking long panels of individual health and income records with detailed information from other administrative registers and surveys
Mechanisms
A large literature has documented patterns and trends in income and wealth inequality. But where is this inequality coming from? From heterogeneity in endowments and earning abilities, in preference and tastes, or in cognitive abilities? From the heterogeneous and diverse constraints that individuals face in their environment or in the family? Or is the heterogeneity due to the diverse shocks in income, capital returns and expenditures that individuals experience over their lifetime? We will also examine the role of general equilibrium effects in generating inequality. The research conducted in our programme focuses on uncovering these mechanisms.
In the coming cycle, we would focus attention on:
1. The drivers of productivity and innovation, and the two-way relationship between inequality and growth. In particular, we will assess whether promoting social mobility among innovation leaders (inventors, entrepreneurs, venture capitalists, etc.) could serve as a new engine of inclusive growth. This project will use micro data to examine whether innovators’ social backgrounds and peer groups have a causal impact on the direction of innovation and on the distribution of the gains from innovation across consumer groups.
2. The effect of trade on inequality, by structurally estimating the distributional effects of trade in a general equilibrium trade model accounting for the fact that workers of different skill levels work and consume in different industries. The approach will highlight the rich interactions between demand patterns and their impacts on labor markets.
3. The effects of monetary policy on inequality, in particular studying how heterogeneity in price rigidities translates into unequal effects from monetary policy (through general equilibrium effects affecting both employment and consumer prices).
4. The drivers of gender inequality, and in particular the role played by gender norms related to child care. For this, we will develop new historical measures of gender norms using recent advances in natural language processing methods applied to rich textual data from newspapers archives.
5. To uncover different mechanisms and mediators underlying health inequality, using frontier empirical methods to separate the differential impact and incidence of health shocks across socio-economic groups. We will also explore the role of barriers to health choices to explain inequality in health outcomes.
6. The role of family structure in generating inequality, and in particular the role played by family size and assortative mating in the short term and in long-run inequality. This would be compared to the short- and long-run effect of historical changes in inheritance rules. The analysis would be applied in the first instance to China with a focus on the impact of changes in the one-child policy.
Policy Design
Better measurement of inequality, and a better understanding of the mechanisms driving it, are vital to design policies to allow for a fairer allocation of welfare and opportunities.
These policies include for instance taxes and transfers to redistribute income and wealth, social insurance to insure against risk or adversity such as unemployment, disability or old-age, family policies, monetary policies, etc.
The design of these policies relies on balancing complex trade-offs. Redistribution and insurance for instance tends to attenuate incentives, introducing an equity-efficiency trade-off that limits the ability to provide social protection. This research programme builds on a rich tradition in public economics to develop general frameworks and common methodologies, tightly integrating theory and empirics, intended to inform and improve policy design.
In the coming cycle, we would focus attention on:
1. Developing new generations of social insurance models to guide the design of retirement pension policies, or labor market policies to insure workers against various shocks in the labor market.
2. Developing a new generation of optimal tax models, accounting for general equilibrium effects of taxes on the rate and direction of innovation, as well as on a countries’ competitiveness in internationally traded markets.
3. Extending theories of optimal redistributive taxation to study optimal tax enforcement on wealthy households.
The hallmark of our approach is to: (i) express the trade-offs in terms of simple statistics that can be identified empirically and (ii) carefully estimate these statistics with high-quality data to (iii) provide robust, evidence-led policy recommendations. In the empirical implementation, we bring several innovations: First, we provide new evidence on behavioral distortions from policies along dimensions that have been under-explored (migration, savings and capital accumulation, innovation, evasion, etc). Second, we provide direct empirical evidence on the benefit side of the trade-offs, such as the value of providing insurance or redistribution, using new methodologies that account for all the resources that households have available.