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Semi-Structural Econometric Methods for the Analysis of Inequality

Project description

New statistical methods for the analysis of inequality

Income inequality and socioeconomic disparities remain pressing global challenges. Research often relies on statistical tools to document trends, understand determinants, and evaluate policy responses. The ERC-funded SEMANI project aims to expose the limitations of these common approaches, showing that their structural economic interpretations are only valid under strong (and often unrealistic) assumptions. SEMANI will develop statistical methods that preserve the advantages of existing techniques but rely on weaker assumptions. This will provide more accurate insights into the causes of inequality and the effectiveness of different policy interventions. The project also aims to reinterpret key empirical studies that have shaped public and academic thinking on inequality.

Objective

Rising income inequality and related disparities, accompanied by high persistence of socioeconomic outcomes across generations, have become a major public policy concern around the world. Documenting inequality, understanding its determinants, and evaluating policies with the goal of reducing inequality is an important part of empirical research in economics.
Widely used statistical tools for quantifying the importance of various drivers of inequality and intergenerational mobility have produced influential findings that are directly, and often informally, interpreted in public and political debates. In these debates, causal and economic meaning tends to be attached to the stylized statistical findings without the use of empirically tested structural models.

The first objective of the proposed project is to show theoretically and empirically that these widely used statistical tools generate findings that permit a structural economic interpretation only under very strong assumptions. If these assumptions fail, then the structural interpretation may be misleading, and the findings may not in fact measure what they purport to measure.
The second objective is to develop new, robust statistical tools that are guaranteed to produce findings with a structural interpretation while retaining the attractive features of popular existing approaches, e.g. weak assumptions and data requirements.

Through substantive economic applications of the new statistical methods the project is expected to discover powerful new insights about what drives inequality and what are effective policies for reducing it. In addition, it is expected to provide a new interpretation of a vast and hugely influential body of empirical research, which has shaped our understanding of inequality and has informed policy decisions around the world.

Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2024-COG

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Host institution

LUDWIG-MAXIMILIANS-UNIVERSITAET MUENCHEN
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 580 832,00
Address
GESCHWISTER SCHOLL PLATZ 1
80539 MUNCHEN
Germany

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Region
Bayern Oberbayern München, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 580 832,00

Beneficiaries (1)

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