Brice Romuald Gueyap Kounga

This paper studies the identification and estimation of a semiparametric dynamic logit model with nonparametric link formation, in which a time-varying index of unobserved characteristics affects outcomes and links. The outcome depends on covariates, the past outcome, a fixed effect, and an unknown function of that index. Conditioning on a switch by the same agent across waves removes the fixed effect but not the change in the unknown function. When this change is common within network types and followed by a stable period, comparing two agents of the same network type, meaning they have the same probability of linking with each of the other agents, removes it and identifies covariate effects and state dependence. The pairwise conditional likelihood estimator is consistent, including when the index is continuous, and asymptotically normal when the index takes finitely many values and matches are exact. In simulations, bias falls with sample size, and coverage is near nominal among successful fits. The fixed-effects dynamic logit and pooled logits with peer-average controls remain biased when the index moves. A second estimator, conditioning on recovered network classes, is more precise. A feasibility exercise on the Glasgow Teenage Friends and Lifestyle Study examines how past use and pocket money affect adolescents' recorded tobacco or cannabis use and documents the data requirements.

This paper studies identification and estimation in semiparametric logit models when social networks are endogenous. In many applications, unobserved individual traits shape both the outcome of interest and the formation of social ties, so standard logit specifications, including those augmented with common network controls, can be biased. I show how network data can be used to address this endogeneity without imposing a parametric structure on the link formation process. Although the outcome equation is semiparametric in this social component and the network formation process is left unspecified, the logistic distribution assumption is crucial for identification. I show that slope parameters are point identified by pairwise comparisons of agents who share identical network formation behavior. I propose feasible estimators based on matching agents using network similarity measures and establish their consistency and asymptotic normality. Monte Carlo simulations demonstrate good finite-sample performance, and an empirical application to microfinance adoption demonstrates that accounting for endogenous network formation materially affects estimated covariate effects.

This paper studies a nonseparable model for dyadic outcomes, such as bilateral trade flows, in which the outcome depends on both agents' characteristics and on a scalar unobservable through an unknown function increasing in that unobservable. I establish identification of a normalized structural function and the error distribution, propose kernel plug-in estimators, and derive a two-regime central limit theory under dyadic dependence in which a shared-agent variance component generically dominates. An agent-level bootstrap is proved consistent in that regime. Simulations show independence-based intervals undercover severely while the bootstrap substantially improves coverage. In bilateral trade data, conditional dispersion falls by more than half between small and large exporters.

African governments are widely believed to favour the ethnic group of the leader, and two in five respondents in the pooled sample say their own group is treated unfairly by the state. We ask what this belief responds to: what the state gives, or who holds it. Following a single question asked of 255,659 Afrobarometer respondents in 42 countries over two decades, and identifying from the 30 ethnic-group-by-country cells in eleven countries whose access to the presidency changes, we find that gaining the presidency lowers the share of a group’s members reporting frequent unfair treatment by 3.8 percentage points relative to other groups interviewed in the same country and round, a fifth of the mean. Inference is at the country level, where the estimate survives a wild cluster bootstrap. Over the same transitions, the confidence intervals rule out a favourable lived-poverty response larger than half of the belief response and a favourable local-public-goods response larger than a third of it. In a stacked design the response appears by the first survey round in which the group holds the presidency, and it is precisely estimated only for competitive electoral turnovers; successions that change the president’s ethnicity without an electoral victory are not detectably negative. We therefore cannot separate ethnic representation from partisan victory, nor a belief about state treatment from a broader change in perceived group standing. A co-ethnic group’s acquisition of the presidency, particularly through a competitive electoral turnover, is associated with a substantial relative change in reported group treatment, while the two contemporaneous material outcomes we observe respond much less.

How does public trust change across institutions during armed conflict? I argue that trust trajectories depend on institutions’ roles in the conflict and on the regional position of the citizens evaluating them. Using repeated cross-sectional Afrobarometer surveys conducted in Cameroon in 2015, 2018, 2021, and 2022, I compare the Northwest and Southwest, the core regions of the Anglophone crisis, with the adjacent West and Littoral regions. Three findings stand out. Police trust fell from about 31% to 10% in the conflict regions while rising from 39% to 47% in the comparison regions, producing a 29-percentage-point divergence; the ruling party and army display the next-largest divergences. Trust in parliament declined in both regional groups, whereas the opposition-party divergence was driven mainly by rising trust outside the conflict regions. Finally, somewhat-or-a-lot trust in traditional leaders partially rebounded by 2022, while the share reporting a lot of trust remained sharply lower. Armed conflict therefore does not produce a uniform loss of institutional confidence. By comparing eleven institutions and making each region’s trajectory visible, the paper shows how institutional roles and citizens’ regional positions together structure changes in trust.

We propose a generalized method of moments (GMM)-type procedure to estimate parametric binary choice models when the researcher only observes degenerate pure choice-based or presence-only data and has some information about the distribution of the covariates. This auxiliary information comes in the form of moments. We establish conditions for global identification and derive the asymptotic distribution of the estimator. Monte Carlo simulations demonstrate that the proposed estimator has good finite-sample properties, with bias that shrinks with the sample size and coverage rates close to nominal levels. When compared with the semiparametric estimator of Lancaster & Imbens (1996), which requires an auxiliary random sample from the population, our estimator is uniformly more efficient. We illustrate the versatility of the proposed method in three empirical applications: estimation of the conditional probability of testing positive for COVID-19 using data from British Columbia, the conditional probability of a single-vehicle car accident using crash records from Washington State, and the conditional probability of a drug overdose death using mortality records from the United States.

Trust in local institutions matters for trade, public goods provision, conflict resolution, and democratic consolidation. Using individual data from rounds 6 and 7 of the Afrobarometer surveys, I document that respondents in former British colonies are substantially more likely to trust traditional leaders than respondents in former French colonies. Cross-country comparisons of this kind are confounded by unobserved heterogeneity in pre- and post-colonial histories. To make progress on identification, I focus on Cameroon, which contains regions colonized by Britain and regions colonized by France within a single modern state, and I exploit the former colonial partition line as a geographic discontinuity, comparing respondents who live close to either side of the anglophone-francophone boundary. The theoretical argument is that the two colonial powers differed systematically in how they treated customary authority: British indirect rule preserved the governance functions of chiefs, while French direct rule subordinated chiefs to the administration and assigned them its most coercive tasks, taxation and forced labor above all, and these divergent experiences shaped the legitimacy of chieftaincy in ways that persist through institutional continuity and socialization. Consistent with this argument, respondents on the formerly British side are 22 to 26 percentage points more likely to trust traditional leaders than their neighbors on the formerly French side, and they are far more likely to have contacted a traditional leader in the previous year.