Pierre Nguimkeu

Estimating the Productivity Impacts of Technology Adoption in the Presence of Misclassification

Abstract This article examines the impact that misreporting adoption status has on the identification and estimation of causal effects on productivity. In particular, by comparing measurement error‐ridden self‐reported adoption data with measurement‐error‐free DNA‐fingerprinted adoption data, we investigate the extent to which such errors bias the causal effects of adoption on productivity. Taking DNA‐fingerprinted adoption data as a benchmark, we find 25% “false negatives” and 10% “false positives” in farmers’ responses. Our results show that misreporting of adoption status is not exogenous to household characteristics, and produces a bias of about 22 percentage points in the productivity impact of adoption. Ignoring inherent behavioral adjustments of farmers based on perceived adoption status has a bias of 13 percentage points. The results of this article underscore the crucial role that correct measurement of adoption plays in designing policy interventions that address constraints to technology adoption in agriculture.

On the Estimation of Treatment Effects with Endogenous Misreporting

Participation in social programs is often misreported in survey data, complicating the estimation of the effects of those programs. In this paper, we propose a model to estimate treatment effects under endogenous participation and endogenous misreporting. We show that failure to account for endogenous misreporting can result in the estimate of the treatment effect having an opposite sign from the true effect. We present an expression for the asymptotic bias of both OLS and IV estimators and discuss the conditions under which sign reversal may occur. We provide a method for eliminating this bias when researchers have access to information related to both participation and misreporting. We establish the consistency and asymptotic normality of our estimator and assess its small sample performance through Monte Carlo simulations. An empirical example is given to illustrate the proposed method.

Some effects of business environment on retail firms

ABSTRACT This paper empirically tests how formal retail entrepreneurs’ perception about the business environment in Cameroon affects the performance of the retail sector. I use business owners’ responses from the 2009 Enterprise Survey to estimate an econometric model that corrects for heteroscedasticity. The results show that regulation costs, corruption, credit constraints, and lack of infrastructure negatively affect the gross margins of firms. In contrast, the competition of the informal sector – perceived by many formal entrepreneurs as a major constraint – is positively associated with the gross margins of formal firms. Policy implications are discussed.

Interval Estimation of the Stress-Strength Reliability with Independent Normal Random Variables

This article develops a procedure to obtain highly accurate confidence interval estimates for the stress-strength reliability R = P(X > Y) where X and Y are data from independent normal distributions of unknown means and variances. Our method is based on third-order likelihood analysis and is compared to the conventional first-order likelihood ratio procedure as well as the approximate methods of Reiser and Guttman (1986) and Guo and Krishnamoorthy (2004). The use of our proposed method is illustrated by an empirical example and its superior accuracy in terms of coverage probability and error rate are examined through Monte Carlo simulation studies.

Improved Inference for Moving Average Disturbances in Nonlinear Regression Models

This paper proposes an improved likelihood-based method to test for first-order moving average in the disturbances of nonlinear regression models. The proposed method has a third-order distributional accuracy which makes it particularly attractive for inference in small sample sizes models. Compared to the commonly used first-order methods such as likelihood ratio and Wald tests which rely on large samples and asymptotic properties of the maximum likelihood estimation, the proposed method has remarkable accuracy. Monte Carlo simulations are provided to show how the proposed method outperforms the existing ones. Two empirical examples including a power regression model of aggregate consumption and a Gompertz growth model of mobile cellular usage in the US are presented to illustrate the implementation and usefulness of the proposed method in practice.

Spatial and sectoral heterogeneity of occupational choice in Cameroon

This paper investigates the relationship between location, agglomeration, access to credit, informality, and productivity across cities and industries in Cameroon. Emphasizing the link between micro-foundations and the data, the paper develops and estimates a structural model of occupational choice in which heterogeneous agents choose between formal entrepreneurship, informal entrepreneurship, and non-entrepreneurial work. Their decision-making process is driven by institutional constraints such as entry costs, tax enforcement, and access to credit. The model predicts that agglomeration has a non-monotonic effect on formalization, and entrepreneurial profits increase with agglomeration effects. Estimating the model by the generalized method of moments, the paper finds that the returns to capital and labor are not uniform across sectors and cities. Manufacturing industries are highly constrained in capital and the elasticity of capital is higher in Yaounde and Douala, whereas labor elasticity is higher in Kribi. Counterfactual simulations show that an increase in roads provision can have a substantial impact in terms of output, formalization, and productivity. A reduction in the current interest rate has a large and significant impact on formalization and no significant effect on business creation. Likewise, while the current tax rate is suboptimal for most cities, a tax reduction policy would have a much greater impact on formalization than on business creation. These effects differ substantially across cities and sectors, suggesting that those policy instruments could be implemented accordingly to support formalization and business creation.

Robust learning in the foreign exchange market

Abstract This paper studies risk premia in the foreign exchange market when investors entertain multiple models for consumption growth. Investors confront two sources of uncertainty: (1) individual models might be misspecified, and (2) it is not known which of these potentially misspecified models is the best approximation to the actual data-generating process. Following Hansen and Sargent (Hansen, L. P., and T. J. Sargent. 2010. “Fragile Beliefs and the Price of Uncertainty.” Quantitative Economics 1 (1): 129–162.), agents formulate “robust” portfolio policies. These policies are implemented by applying two risk-sensitivity operators. One is forward-looking, and pessimistically distorts the state dynamics of each individual model. The other is backward-looking, and pessimistically distorts the probability weights assigned to each model. A robust learner assigns higher weights to worst-case models that yield lower continuation values. The magnitude of this distortion evolves over time in response to realized consumption growth. It is shown that robust learning not only explains unconditional risk premia in the foreign exchange market, it can also explain the dynamics of risk premia. In particular, an empirically plausible concern for model misspecification and model uncertainty generates a stochastic discount factor that uniformly satisfies the spectral Hansen-Jagannathan bound of Otrok et al. (Otrok, C., B. Ravikumar, and C. H. Whiteman. 2007. “A Generalized Volatility Bound for Dynamic Economies.” Journal of Monetary Economics 54 (8): 2269–2290.).

An Improved Selection Test between Autoregressive and Moving Average Disturbances in Regression Models

Abstract This paper proposes an improved likelihood-based method to test the hypothesis that the disturbances of a linear regression model are generated by a first-order autoregressive process against the alternative that they follow a first-order moving average scheme. Compared with existing tests which usually rely on the asymptotic properties of the estimators, the proposed method has remarkable accuracy, particularly in small samples. Simulations studies are provided to show the superior accuracy of the method compared to the traditional tests. An empirical example using Canada real interest rate illustrates the implementation of the proposed method in practice.