Natural resource rents and public debts nexus in African resource-rich and most indebted nations: Issues with aggregation bias

Wang, Wencheng; Ning, Zinan; Yang, Shu; Riti, Joshua Sunday; Riti, Miriam-Kamah J.

doi:10.1016/j.resourpol.2023.103409

Cited by 5 publications

(3 citation statements)

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“…Many earlier studies show that aggregation bias occurs when data are aggregated at a higher level than the analysis level because data aggregation can cause the loss of information [31][32][33][34]. Pesaran, et al [31] propose a hypothesis test of perfect aggregation where aggregation validity is tested on either coefficient equality or the stability of the composition of regressors across micro units over time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Their results show large differences between preference estimates from aggregate data and those from exit polls. Lozada [34] derives aggregation bias in estimating Almost Ideal Demand System models with publicly available aggregate data under the assumption of a representative consumer. Results from this study show how restrictive and implausible the assumption is, particularly when the model is estimated with highly aggregated data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Such studies argue that the estimation of econometric models with typically available aggregate data leads to biased results because the assumption of a representative consumer's utility maximization or producer's profit maximization is not realistic and disregards the heterogeneity of individual behavior [33,34,36]. However, most data, particularly publicly available data, are available in aggregate form, and individual store/firm or household data are rarely available or are expensive (e.g., AC Nielsen and IRI data).…”

Section: Literature Reviewmentioning

confidence: 99%

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Estimating Market Power Exertion in the U.S. Beef Packing Industry: An Illustration of Data Aggregation Bias Using Simulated Data

Lee,

Chung

2024

Sustainability

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This study investigates data aggregation bias in estimating market power in the U.S. beef packing industry using New Empirical Industrial Organization (NEIO) models and shows empirical procedures that can alleviate the bias. Unlike many earlier studies in estimating market power exertion, our study examines the data aggregation bias when market-level data are used in place of firm-level data and show how the bias could be reduced. We first derive data aggregation bias analytically, then empirically investigate the aggregation bias by estimating both firm and aggregate industry models. Because the firm-level data are not available, we use simulated data generated from the Monte Carlo simulation method. Hybrid models, combining limited firm-level data with aggregate data, are also estimated to illustrate how the aggregation bias could be reduced. Our results show that aggregate models with industry-level data tend to underestimate market power exertion in the U.S. beef packing industry, and the aggregation bias is statistically significant at the 1% level. Comparing results from hybrid models with firm-level estimates, we find that hybrid models reduce the bias but do not remove the aggregation bias significantly. The sensitivity analysis shows that market power estimate and aggregation bias are sensitive to functional forms.

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