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Volume XVIII, Spring, Issue 1(79), 2023
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The present paper aims to demonstrate that the Ramsey’s Regression Specification Error Term Test (RESET) is very sensitive to the degree of nonlinearity between the variables of the under-specification functional form. This widely used test, for testing the functional specification of a model, is based on the notion that if nonlinear combinations of the explanatory variables have any power in explaining the predictor, the model is mis-specified and the data generating mechanism might be approximated by a nonlinear functional form. Using Monte Carlo techniques, we find that the power of the Ramsey’s RESET test is highly influenced and related with the degree of nonlinearity between the dependent and the independent variables of the under-specification functional form.
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A household’s preferences are usually assumed not to vary temporally or depending on the objects to which they are applied, but this assumption is often inconsistent with empirical estimates, for example, with the time-inconsistency problem of the time preference rate and the equity-premium puzzle. I show that these inconsistencies are generated because a household’s preferences vary depending on whether they are applied to permanent or temporary incomes. Preferences applied to permanent incomes are anchored to the steady state or a balanced growth path, but those for temporary incomes are not. Hence, the former is fixed and unchanged, but the latter can take various values depending on conditions.
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The returns to schooling are estimated for 28 European and Central Asian countries using the Mincerian function. Our results show that while the public sector pays on average more than the private sector, the effect of education on earnings is stronger in the private sector. However, the returns to tertiary education are higher in the private sector.
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The main purpose of the article is to study the impact of gamification on the phenomenon of perceived self-efficacy in managers in their work on the set goals related to the management of their team. The paper was prepared using an intervention based on the Superbetter method. The Generalized Self-Efficacy Scale (GSES) was used to measure the level of self-efficacy before and after the intervention in the study group and twice in the control group (as a pretest and a posttest). The authors of the scale of generalized self-efficacy are as follows: Ralf Schwarzer, Michael Jerusalem, Zygfryd Juczyński. As a result of the study, it was confirmed that the use of the Superbetter intervention increased the self-efficacy among managers subjected to it as part of the intervention in the research group, and thus increased their chances of introducing changes undertaken by them as a goal.
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The years-long academic discussion on financial literacy confirms that it promotes sound financial behaviors and increases financial inclusion. In this research work, I check if the latter still holds true during times of financial crises. I take the case of Lebanon, a country going through multiscale crises. By running two sets of surveys and using previous research data to answer my three research questions by using different statistical techniques, I find that regardless of people’s claims, only males acquire financial knowledge from the crises. I also find that people’s behaviors change throughout the financial crises in which previously advocated behaviors become redundant. I conclude my research by providing scholars and researchers with some opportunities for future research as on-topic discussion is scarce and vital to be done during such times.
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This study contributes to the literature on financial efficiency and growth. We show that banking development exerts a statistically significant and positive impact on local economic growth. We use the ARDL method to find the impact of institutional financial quality on agriculture sector growth in 14 Sub-Saharan African countries from 1990 to 2020. Our results show that land, rural population, and per capita agricultural income growth have long-run and significant (at 1% level) causal effects on the magnitude of agricultural value added as a percentage of GDP.
