arXiv subjects

Paolo Verme

Publications and source records attributed to Paolo Verme.

7 recordsLinked to original sources

arXiv2026

Female Nomination and Party Vote Share in US Gubernatorial Elections

What happens to a party's vote share when it nominates a woman for executive office? The evidence on female candidates is dominated by legislative races and by designs that pool parties and periods into a single average effect, which is typically null. This paper builds a new county-level panel of US gubernatorial elections - 578 races in 49 states from 1980 to 2024, reconstructed and audited from primary sources - and estimates party-specific, time-varying nomination effects with a stacked difference-in-differences design around within-state transitions from male to female nominees. The average null conceals a sharp asymmetry. Republican transitions to a female nominee since 2016 are followed by a county-level vote-share decline of 6.5 percentage points, robust across cohort, weighting and inference checks. No comparable average differential is detected for Democratic nominees. Survey-ballot diagnostics are compatible with demand-side discrimination but cannot identify that channel.

econ.GN↗

arXiv2026

A World of Ginis

The Gini index remains the most important measure of economic inequality worldwide, and accurate estimates of this index are essential for effective public policies. Yet, Gini estimates for the same country and year vary considerably across data sources, a problem that remains largely unresolved. The paper reviews the largest global and regional databases providing Gini estimates, surveys the related literature, and constructs a unified dataset of 122,351 Gini observations spanning 222 countries and territories and 158 years, from 1867 to 2024. The analysis of this new dataset shows that income-based Ginis exceed consumption-based ones by 4.7 points on average globally, and by as much as 10 points in some regions, with these gaps widening over time. The gross--net income distinction and the use of alternative equivalence scales together with several other measurement choices add further systematic differences. Based on these findings, the paper provides correction factors that can be used to harmonise Ginis built on different welfare concepts. We further show that overall divergence across databases has grown only modestly since 1960, and mainly through the proliferation of databases rather than through genuine divergence among long-standing sources. Thus, improving on the existing discrepancies across Ginis globally is possible, but ultimately depends on database administrators disclosing full details of Gini construction and on users selecting Ginis built on comparable measures.

econ.GN↗

arXiv2026

A Toolkit for the Study of Treatment-Effect Discontinuities

This paper provides a toolkit for the study of distributional treatment effects (DTEs) focused on treatment-effect discontinuities defined as points where marginal distributional effects change sign. Building on the Treatment Effects Curve (TEC, Verme, 2010), the paper makes three contributions. First, we propose a methodological framework comprising a Horizontal Discontinuity Analysis (HDA) comparing groups in regions of opposite-signed effects using causal forests, and a Vertical Discontinuity Analysis (VDA) examining sign-switch points. Second, we adapt crossing-point asymptotics to locate where a TEC crosses zero and to test the non-tangentiality of its local slope with a bias-corrected Wald statistic. Third, we illustrate the full workflow on synthetic data and add a diagnostic application to Mexico's PROGRESA data. The paper shows how these contributions complement and expand existing instruments for DTE analyses.

econ.EM↗

arXiv2026

The Role of Data and Metrics in Measuring Inequality Worldwide. A Tribute to Giovanni Andrea Cornia's Lifelong Work on the World Ginis

This paper pays tribute to Professor Giovanni Andrea Cornia's lifelong contributions to the measurement of global inequality. We review twelve world and regional databases of the Gini coefficient, illustrate their coverage, overlapping, and data gaps, and analyse the major sources of discrepancy among published Ginis. Merging all databases into a unified collection of over 122,000 observations spanning 222 countries from 1867 to 2024, we document how differences in welfare metrics, reference units, sub-metric definitions, post-survey adjustments, and survey design produce Gini estimates that diverge considerably -- sometimes by as much as 50 percentage points -- for the same country and year. We quantify pairwise cross-database discordance, document the income-consumption Gini gap by region and income group, and discuss the contributions of welfare metric and equivalence scale choices to cross-database dispersion. We extend the analysis with a dedicated discussion of comparability across time and across measurement dimensions, showing how multiple layers of methodological choice interact to make any single Gini figure a product of a complex chain of decisions that are rarely fully disclosed. Our analysis confirms that the choice of welfare metric remains the single most important source of cross-country non-comparability, while sub-metric definitions and equivalence scales introduce further systematic differences that are routinely overlooked in comparative work.

econ.GN↗

arXiv2025

Predicting Poverty

Poverty prediction models are used to address missing data issues in a variety of contexts such as poverty profiling, targeting with proxy-means tests, cross-survey imputations such as poverty mapping, top and bottom incomes studies, or vulnerability analyses. Based on the models used by this literature, this paper conducts a study by artificially corrupting data clear of missing incomes with different patterns and shares of missing incomes. It then compares the capacity of classic econometric and machine learning models to predict poverty under different scenarios with full information on observed and unobserved incomes, and the true counterfactual poverty rate. Random forest provides more consistent and accurate predictions under most but not all scenarios.

econ.GN↗

arXiv2025

Who Flees Conflict?

Despite the growing numbers of forcibly displaced persons worldwide, many people living under conflict choose not to flee. Individuals face two lotteries - staying or leaving - characterized by two distributions of potential outcomes. This paper proposes to model the choice between these two lotteries using quantile maximization as opposed to expected utility theory. The paper posits that risk-averse individuals aim at minimizing losses by choosing the lottery with the best outcome at the lower end of the distribution, whereas risk-tolerant individuals aim at maximizing gains by choosing the lottery with the best outcome at the higher end of the distribution. Using a rich set of household and conflict panel data from Nigeria, the paper finds that risk-tolerant individuals have a significant preference for staying and risk-averse individuals have a significant preference for fleeing, in line with the predictions of the quantile maximization model. These findings are in contrast to findings on economic migrants, and call for separate policies toward economic and forced migrants.

econ.GN↗

arXiv2025

Optimizing Data-driven Weights In Multidimensional Indexes

Multidimensional indexes are ubiquitous, and popular, but present non-negligible normative choices when it comes to attributing weights to their dimensions. This paper provides a more rigorous approach to the choice of weights by defining a set of desirable properties that weighting models should meet. It shows that Bayesian Networks is the only model across statistical, econometric, and machine learning computational models that meets these properties. An example with EU-SILC data illustrates this new approach highlighting its potential for policies.

econ.EM↗