arXiv subjects

Guy Tchuente

Publications and source records attributed to Guy Tchuente.

18 recordsLinked to original sources

arXiv2026

Monitoring Limits in DAO Governance: Capacity Breakpoints and Endogenous Concentration

Decentralized autonomous organizations (DAOs) are designed to disperse control, yet recent evidence shows that effective governance is often concentrated in a small number of participants. This note studies one simple mechanism behind that pattern. Because decentralized governance is monitor-intensive, rising proposal flow may eventually outpace the capacity of broad-based participation. Using a DAO--quarter panel, I estimate a fixed-effects kink model with DAO and quarter fixed effects and find a statistically significant decline in the marginal responsiveness of active voters once proposal activity crosses an interior threshold. I then study realized voting concentration using kink specifications with data-driven cutoffs. Across specifications, decentralization gains do not persist indefinitely once governance workload becomes sufficiently high, and load-based measures show especially clear evidence of a transition toward more concentrated realized control. The results provide reduced-form evidence consistent with a ``too big to monitor'' mechanism in DAO governance: when proposal flow grows faster than broad participation can keep up, effective control may drift toward a smaller set of highly active participants.

econ.GN↗

arXiv2026

Who Matters to Whom? Identifying Peer Effects with Propagation Geometry

This paper develops a unifying theory of peer effects that treats the peer aggregator (the social norm mapping peers' actions into a scalar exposure) as the central behavioral primitive. We formulate peer influence as a norm game in which payoffs depend on own action and an exposure index, and we provide equilibrium existence and uniqueness for a broad class of aggregators. Using economically interpretable axioms, we organize commonly used exposure maps into a small taxonomy that nests linear-in-means, CES (peer-preference) norms, and smooth ``attention-to-salient-peers'' aggregators; rank-based quantile norms are treated as a complementary class. Building on this unification, we show that each aggregator induces an operator that governs how exogenous variation propagates through the network. Linear-in-means corresponds to constant transport (adjacency matrix), recovering the classic (friends-of-friends) instrument families. For nonlinear norms, operator becomes state- and preference-dependent and is characterized by the Jacobian of the exposure map evaluated at an exogenous predictor. This perspective yields geometry-induced instrument that exploit heterogeneity in marginal influence and nonredundant paths, and can remain informative when one-step moments or adjacency-power instruments become weak. Monte Carlo evidence and an application to NetHealth illustrate the practical implications across alternative aggregators and outcomes.

econ.EM↗

arXiv2026

Scale and Capacity Limits in Decentralized FDA Food-Safety Enforcement

This paper asks whether regulatory monitoring exhibits nonlinear capacity limits as the scale and complexity of the regulated environment increase. Using a county--year panel of U.S. Food and Drug Administration (FDA) inspections merged with local establishment counts, we identify a sharp breakpoint: beyond a threshold scale, severe inspection findings rise while inspection effort per establishment flattens or declines. The threshold and the post-break deterioration vary across food-related industry groups and shift with proxies for local density and connectedness, consistent with monitoring becoming ``too big to monitor" in more interconnected production environments rather than driven by simple reallocation or delay. Methodologically, we provide a portable breakpoint selection and piecewise-estimation framework that can be applied to other enforcement settings.

econ.GN↗

arXiv2026

Distributional Instruments: Identification and Estimation with Quantile Least Squares

We study instrumental-variable designs where policy reforms strongly shift the distribution of an endogenous variable but only weakly move its mean. We formalize this by introducing distributional relevance: instruments may be purely distributional. Within a triangular model, distributional relevance suffices for nonparametric identification of average structural effects via a control function. We then propose Quantile Least Squares (Q-LS), which aggregates conditional quantiles of X given Z into an optimal mean-square predictor and uses this projection as an instrument in a linear IV estimator. We establish consistency, asymptotic normality, and the validity of standard 2SLS variance formulas, and we discuss regularization across quantiles. Monte Carlo designs show that Q-LS delivers well-centered estimates and near-correct size when mean-based 2SLS suffers from weak instruments. In Health and Retirement Study data, Q-LS exploits Medicare Part D-induced distributional shifts in out-of-pocket risk to sharpen estimates of its effects on depression.

econ.EM↗

arXiv2025

Too Big to Monitor? Network Scale and the Breakdown of Decentralized Monitoring

Many public services are produced in networked systems where quality depends on local effort and on how higher-level authorities monitor providers. We develop a simple model in which monitoring is a public good on a network with strategic complementarities. A regulator chooses between decentralized monitoring (cheaper, local oversight) and centralized monitoring (more costly, but internalizing spillovers). The model delivers an endogenous centralization threshold: for a given spillover strength, there exists a network size $n^\ast(λ)$ above which centralized monitoring strictly dominates; equivalently, for a given network size $n$, there is a critical complementarity $λ^\ast(n)$ beyond which decentralized oversight becomes fragile. A stochastic extension suggests that, above this region, idiosyncratic shocks are amplified, producing stronger peer correlations, higher variance, and more frequent deterioration in quality. We test these predictions in the U.S. nursing home sector, where facilities belong to overlapping organizational (chain) and geographic (county) networks. Using CMS facility data, We document strong within-chain and within-county peer effects and estimate network-size thresholds for severe regulatory failure (Special Focus Facility designations). We find sharp breakpoints at roughly 7 homes per county and 34 homes per chain, above which spillovers intensify and deficiency outcomes become more dispersed and prone to deterioration, especially in large counties.

econ.GN↗

arXiv2025

Religious Competition, Cultural Change, and Domestic Violence: Evidence from Colombia

We study how religious competition-defined as the entry of a religious organization with innovative worship practices into a predominantly Catholic municipality-affects domestic violence. Using municipality-level data from Colombia and a two-way fixed effects design, we find that the arrival of the first non-Catholic church leads to a significant reduction in reported cases of domestic violence. We argue that religious competition incentivizes churches to adopt and diffuse norms and practices that more effectively discourage such violence. Effects are largest in municipalities with smaller, younger, and more homogeneous populations-contexts that facilitate both intense competition and norm diffusion. Consistent with this mechanism, areas with more new non-Catholic churches exhibit greater rejection of domestic violence-particularly among the religiously observant-and higher female labor force participation. These findings contribute to the literature on the cultural determinants of domestic violence by identifying religious competition as a catalyst for cultural change.

econ.GN↗

arXiv2024

Monetary Incentives, Landowner Preferences: Estimating Cross-Elasticities in Farmland Conversion to Renewable Energy

This study examines the impact of monetary factors on the conversion of farmland to renewable energy generation, specifically solar and wind, in the context of expanding U.S. energy production. We propose a new econometric method that accounts for the diverse circumstances of landowners, including their unordered alternative land use options, non-monetary benefits from farming, and the influence of local regulations. We demonstrate that identifying the cross elasticity of landowners' farming income in relation to the conversion of farmland to renewable energy requires an understanding of their preferences. By utilizing county legislation that we assume to be shaped by land-use preferences, we estimate the cross-elasticities of farming income. Our findings indicate that monetary incentives may only influence landowners' decisions in areas with potential for future residential development, underscoring the importance of considering both preferences and regulatory contexts.

econ.EM↗

arXiv2024

Moran's I 2-Stage Lasso: for Models with Spatial Correlation and Endogenous Variables

We propose a novel estimation procedure for models with endogenous variables in the presence of spatial correlation based on Eigenvector Spatial Filtering. The procedure, called Moran's $I$ 2-Stage Lasso (Mi-2SL), uses a two-stage Lasso estimator where the Standardised Moran's I is used to set the Lasso tuning parameter. Unlike existing spatial econometric methods, this has the key benefit of not requiring the researcher to explicitly model the spatial correlation process, which is of interest in cases where they are only interested in removing the resulting bias when estimating the direct effect of covariates. We show the conditions necessary for consistent and asymptotically normal parameter estimation assuming the support (relevant) set of eigenvectors is known. Our Monte Carlo simulation results also show that Mi-2SL performs well against common alternatives in the presence of spatial correlation. Our empirical application replicates Cadena and Kovak (2016) instrumental variables estimates using Mi-2SL and shows that in that case, Mi-2SL can boost the performance of the first stage.

econ.EM↗

arXiv2023

Moran's I Lasso for models with spatially correlated data

This paper proposes a Lasso-based estimator which uses information embedded in the Moran statistic to develop a selection procedure called Moran's I Lasso (Mi-Lasso) to solve the Eigenvector Spatial Filtering (ESF) eigenvector selection problem. ESF uses a subset of eigenvectors from a spatial weights matrix to efficiently account for any omitted cross-sectional correlation terms in a classical linear regression framework, thus does not require the researcher to explicitly specify the spatial part of the underlying structural model. We derive performance bounds and show the necessary conditions for consistent eigenvector selection. The key advantages of the proposed estimator are that it is intuitive, theoretically grounded, and substantially faster than Lasso based on cross-validation or any proposed forward stepwise procedure. Our main simulation results show the proposed selection procedure performs well in finite samples. Compared to existing selection procedures, we find Mi-Lasso has one of the smallest biases and mean squared errors across a range of sample sizes and levels of spatial correlation. An application on house prices further demonstrates Mi-Lasso performs well compared to existing procedures.

econ.EM↗

arXiv2023

Armed Conflict and Early Human Capital Accumulation: Evidence from Cameroon's Anglophone Conflict

This paper examines the impact of the Anglophone Conflict in Cameroon on human capital accumulation. Using high-quality individual-level data on test scores and information on conflict-related violent events, a difference-in-differences design is employed to estimate the conflict's causal effects. The results show that an increase in violent events and conflict-related deaths causes a significant decline in test scores in reading and mathematics. The conflict also leads to higher rates of teacher absenteeism and reduced access to electricity in schools. These findings highlight the adverse consequences of conflict-related violence on human capital accumulation, particularly within the Anglophone subsystem. The study emphasizes the disproportionate burden faced by Anglophone pupils due to language-rooted tensions and segregated educational systems.

econ.GN↗

arXiv2021

Optimally Targeting Interventions in Networks during a Pandemic: Theory and Evidence from the Networks of Nursing Homes in the United States

This study develops an economic model for a social planner who prioritizes health over short-term wealth accumulation during a pandemic. Agents are connected through a weighted undirected network of contacts, and the planner's objective is to determine the policy that contains the spread of infection below a tolerable incidence level, and that maximizes the present discounted value of real income, in that order of priority. The optimal unique policy depends both on the configuration of the contact network and the tolerable infection incidence. Comparative statics analyses are conducted: (i) they reveal the tradeoff between the economic cost of the pandemic and the infection incidence allowed; and (ii) they suggest a correlation between different measures of network centrality and individual lockdown probability with the correlation increasing with the tolerable infection incidence level. Using unique data on the networks of nursing and long-term homes in the U.S., we calibrate our model at the state level and estimate the tolerable COVID-19 infection incidence level. We find that laissez-faire (more tolerance to the virus spread) pandemic policy is associated with an increased number of deaths in nursing homes and higher state GDP growth. In terms of the death count, laissez-faire is more harmful to nursing homes than more peripheral in the networks, those located in deprived counties, and those who work for a profit. We also find that U.S. states with a Republican governor have a higher level of tolerable incidence, but policies tend to converge with high death count.

econ.TH↗

arXiv2021

The Forest Behind the Tree: Heterogeneity in How US Governor's Party Affects Black Workers

Income inequality is a distributional phenomenon. This paper examines the impact of U.S governor's party allegiance (Republican vs Democrat) on ethnic wage gap. A descriptive analysis of the distribution of yearly earnings of Whites and Blacks reveals a divergence in their respective shapes over time suggesting that aggregate analysis may mask important heterogeneous effects. This motivates a granular estimation of the comparative causal effect of governors' party affiliation on labor market outcomes. We use a regression discontinuity design (RDD) based on marginal electoral victories and samples of quantiles groups by wage and hours worked. Overall, the distributional causal estimations show that the vast majority of subgroups of black workers earnings are not affected by democrat governors' policies, suggesting the possible existence of structural factors in the labor markets that contribute to create and keep a wage trap and/or hour worked trap for most of the subgroups of black workers. Democrat governors increase the number of hours worked of black workers at the highest quartiles of earnings. A bivariate quantiles groups analysis shows that democrats decrease the total hours worked for black workers who have the largest number of hours worked and earn the least. Black workers earning more and working fewer hours than half of the sample see their number of hours worked increase under a democrat governor.

econ.GN↗

arXiv2021

Fighting for Not-So-Religious Souls: The Role of Religious Competition in Secular Conflicts

Many countries embroiled in non-religious civil conflicts have experienced a dramatic increase in religious competition in recent years. This study examines whether increasing competition between religions affects violence in non-religious or secular conflicts. The study focuses on Colombia, a deeply Catholic country that has suffered one of the world's longest-running internal conflicts and, in the last few decades, has witnessed an intense increase in religious competition between the Catholic Church and new non-Catholic churches. The estimation of a dynamic treatment effect model shows that establishing the first non-Catholic church in a municipality substantially increases the probability of conflict-related violence. The effect is larger for violence by guerrilla groups, and is concentrated on municipalities where the establishment of the first non-Catholic church leads to more intense religious competition. Further analysis suggests that the increase in guerrilla violence is associated with an expectation among guerrilla groups that their membership will decline as a consequence of more intense competition with religious groups for followers.

econ.GN↗

arXiv2021

A Note on the Topology of the First Stage of 2SLS with Many Instruments

The finite sample properties of estimators are usually understood or approximated using asymptotic theories. Two main asymptotic constructions have been used to characterize the presence of many instruments. The first assumes that the number of instruments increases with the sample size. I demonstrate that in this case, one of the key assumptions used in the asymptotic construction may imply that the number of ``effective" instruments should be finite, resulting in an internal contradiction. The second asymptotic representation considers that the number of instrumental variables (IVs) may be finite, infinite, or even a continuum. The number does not change with the sample size. In this scenario, the regularized estimator obtained depends on the topology imposed on the set of instruments as well as on a regularization parameter. These restrictions may induce a bias or restrict the set of admissible instruments. However, the assumptions are internally coherent. The limitations of many IVs asymptotic assumptions provide support for finite sample distributional studies to better understand the behavior of many IV estimators.

econ.EM↗

arXiv2021

Early Human Capital Accumulation and Decentralization

Decentralization is a centerpiece in Cameroonian's government institutions' design. This chapter elaborates a simple hierarchy model for the analysis of the effects of power devolution. The model predicts overall positive effects of decentralization with larger effects when the local authority processes useful information on how to better allocate the resources. The estimation of the effects of the 2010's power devolution to municipalities in Cameroon suggests a positive impact of decentralization on early human capital accumulation. The value added by decentralization is the same for Anglophone and Francophone municipalities; the effects of decentralization are larger for advanced levels of primary school.

econ.GN↗

arXiv2021

Fuzzy Difference-in-Discontinuities: Identification Theory and Application to the Affordable Care Act

This paper explores the use of a fuzzy regression discontinuity design where multiple treatments are applied at the threshold. The identification results show that, under the very strong assumption that the change in the probability of treatment at the cutoff is equal across treatments, a difference-in-discontinuities estimator identifies the treatment effect of interest. The point estimates of the treatment effect using a simple fuzzy difference-in-discontinuities design are biased if the change in the probability of a treatment applying at the cutoff differs across treatments. Modifications of the fuzzy difference-in-discontinuities approach that rely on milder assumptions are also proposed. Our results suggest caution is needed when applying before-and-after methods in the presence of fuzzy discontinuities. Using data from the National Health Interview Survey, we apply this new identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on older Americans' health care access and utilization.

econ.EM↗

arXiv2020

Spatial Differencing for Sample Selection Models with Unobserved Heterogeneity

This paper derives identification, estimation, and inference results using spatial differencing in sample selection models with unobserved heterogeneity. We show that under the assumption of smooth changes across space of the unobserved sub-location specific heterogeneities and inverse Mills ratio, key parameters of a sample selection model are identified. The smoothness of the sub-location specific heterogeneities implies a correlation in the outcomes. We assume that the correlation is restricted within a location or cluster and derive asymptotic results showing that as the number of independent clusters increases, the estimators are consistent and asymptotically normal. We also propose a formula for standard error estimation. A Monte-Carlo experiment illustrates the small sample properties of our estimator. The application of our procedure to estimate the determinants of the municipality tax rate in Finland shows the importance of accounting for unobserved heterogeneity.

econ.EM↗

arXiv2019

Weak Identification and Estimation of Social Interaction Models

The identification of the network effect is based on either group size variation, the structure of the network or the relative position in the network. I provide easy-to-verify necessary conditions for identification of undirected network models based on the number of distinct eigenvalues of the adjacency matrix. Identification of network effects is possible; although in many empirical situations existing identification strategies may require the use of many instruments or instruments that could be strongly correlated with each other. The use of highly correlated instruments or many instruments may lead to weak identification or many instruments bias. This paper proposes regularized versions of the two-stage least squares (2SLS) estimators as a solution to these problems. The proposed estimators are consistent and asymptotically normal. A Monte Carlo study illustrates the properties of the regularized estimators. An empirical application, assessing a local government tax competition model, shows the empirical relevance of using regularization methods.

econ.EM↗