Search results

In this chapter we demonstrate the construction of inverse test confidence intervals for the turning-points in estimated nonlinear relationships by the use of the marginal or first derivative function. First, we outline the inverse test confidence interval approach. Then we examine the relationship between the traditional confidence intervals based on the Wald test for the turning-points for a cubic, a quartic, and fractional polynomials estimated via regression analysis and the inverse test intervals. We show that the confidence interval plots of the marginal function can be used to estimate confidence intervals for the turning-points that are equivalent to the inverse test. We also provide a method for the interpretation of the confidence intervals for the second derivative function to draw inferences for the characteristics of the turning-point.

This method is applied to the examination of the turning-points found when estimating a quartic and a fractional polynomial from data used for the estimation of an Environmental Kuznets Curve. The Stata do files used to generate these examples are listed in Appendix A along with the data.

Empirical evidence on the relation between happiness (life satisfaction) and corruption is barely perceptible in the literature. The purpose of this paper is to contribute to closing this gap by presenting some estimates using a large cross-section of countries over the period 1996-2010.

The empirical model allows both corruption and per capita income to enter as arguments of a happiness “production function”. The correlation between happiness and corruption is presumed to be non-linear.

While the results do not support the existence of a Kuznets-type trajectory, the study finds that the level of per capita income determines whether happiness and corruption are related and in what way. The authors estimate cutoff income levels at which corruption has a discernible effect on happiness. The results show that corruption reduces happiness, but only for high-income countries – roughly the upper half of the income range in the sample.

Results nullify the oft-asserted statement that happiness is negatively linked to corruption in all countries. The nature of correlation is more complex.

The paper goes beyond simply testing whether happiness is related to corruption. It conjectures that the relationship between the two variables is non-monotonic. Thus, the analysis considers the notion that the association between happiness and probity is income dependent. A novel feature of the empirical model is that the estimated income cutoff levels are endogenously determined. That is, income thresholds are not pre-determined. The authors also test for the robustness of the results by addressing the issue of potential endogeneity of corruption.

The purpose of this study is to examine the effect of foreign direct investment (FDI) on economic growth contingent on the development level of the local financial system in emerging and developing Asia during the period 1996–2017.

The study adopts the threshold approach, namely the panel smooth transition regression (PSTR) model, for the annual data collection of 18 emerging and developing Asian countries in 22 years. The authors analyze the alternative PSTR models on different proxies of financial development (FD).

The results show new findings of two distinct thresholds of FD in the FDI–growth nexus. The growth-enhancing effect of FDI is realized only when the FD lies between the two threshold values. Notably, at very high levels of FD, the beneficial effect of FDI on growth is vanishing.

The authors provide new insights into the growth effect of FDI and the role of FD. The estimated nonlinear effect of FDI on growth and the thresholds of FD can be benchmarks for emerging and developing Asia in assessment of their situations. The results suggest important implications to the region in setting the long-run policies to boost the effect of FDI on economic growth.

The purpose of this paper is to present a nonparametric comparative study on the HCMS consumption of urban and rural households in China.

This paper applies the panel data for China's thirty provinces in 1991‐2007 as a sample and presents a local linear estimation to the nonparametric Working‐Leser panel data models on the HCMS consumption of the rural and urban households in China. The nonparametric Hausman test is applied to test the random effects specification against the fixed effects.

Both the parametric and nonparametric estimation of the Working‐Leser panel data models give us a similar result: that the HCMS commodity is quite elastic in both the rural and urban China. Nonparametric estimate also shows that the urban‐rural difference of the HCMS expenditure share in the total expenditure is mainly due to the large urban‐rural difference of total expenditure. Under the same total expenditure, the HCMS is more elastic in the urban than in the rural regions.

To decrease the urban‐rural difference in the HCMS consumption, the government should enhance the income or total expenditure level of the rural households, especially in the impoverished and remote areas in China. Urbanization plays a critical role in access to health care and can help make substantial changes in rural health care in China.

Compared to the parametric estimation, the nonparametric estimation gives us the added information that the expenditure elasticity of the HCMS consumption in China gradually declines as one moves up the per capita total expenditure distribution. The paper could make a contribution to the relatively thin literature on the Chinese medical and health consumption market.

Given the growing supply of wines and the large number of new consumers with purchasing power but lacking knowledge of the subtleties of high-quality wines, expert opinions are used for consumers as proxies for quality. This study aims to determine the determinants of prices in top-quality wine market. The authors also seek to estimate the role for country of origin, grape, producing region and winery in prices. And, finally, the authors try to show how countries, regions and wineries can help increase their position in international rankings.

The authors try to answer: What factors explain the price of top-quality wines (defined as best rated in a standardized ranking)? To some extent, in the hands of producers influence prices, which imply long-term decisions or large investments in land and marketing. Other variables that consumer value does affect prices. The authors try also to detect undervalued or overvalued wines, grapes, regions, wineries or producer countries. The authors estimate an econometric model of hedonic prices using a 14-year sample of the Wine Spectator’s 100 top-rated wines for the American market between 2003 and 2016, totaling 1,400 observations. The sample is a great cross-section because each wine is unique.

The authors’ contribution is twofold: the determination of the price explanatory values and the identification and attribution of price differences by country, grape, region and winery. Also, the authors detected grapes, countries, regions and wineries which are overvalued or undervalued with respect to the average prediction of the model.

The findings are useful to understand the role of price explanatory variables, as well as for making policy and managerial decisions. From the model, collective or managerial actions can be derived to increase particular wines’ positions in international rankings. The proxy for “quality” in the study is not the only possible definition.

In some cases, managerial choices could be conditioned by the policies or history. There is some room for collective action and public policies to improve regions’ and countries’ reputation.

There are clear synergies for policies that can raise the prestige of countries and regions and their spillovers on the brand name reputation of individual wineries.

The results, policy and managerial implications are of interest for business, countries interested in improving their position in international rankings and for consumers to make more informed decisions.

Fast food (FF) consumption increased dramatically through the 1990s in the USA, accounting for nearly 35.5 percent of total away‐from‐home expenditures in 1999. Given dramatic changes in food consumption, and heightened public concern about health and obesity, there is a considerable need for research to understand better the factors affecting US FF consumption. This paper aims to fill this gap.

In this paper, logistic regression is applied to analyze the socioeconomic and demographic factors influencing the likelihood of consuming FF using United States Department of Agriculture data from the Continuing Survey of Food Intakes by Individuals from 1994 to 1996 and the Supplemental Children's Survey of 1998.

In general, the expected likelihood of FF consumption increases until around 20‐30 years of age and then decreases; increases as household income grows until about $50,000‐60,000 and then decreases; and decreases as household size grows. Further, males from the Midwest and South regions that live outside central cities in Metropolitan Statistical Areas have the highest likelihood of consuming FF.

While much literature has addressed key questions about expenditure on food away from home, this study complements previous work by focusing on food items consumed from FF facilities in the 1990s. In addition, the results find highly significant and important (statistically and economically) interactions between the likelihood of FF consumption and age, income, and household size.

“Economics is a Serious Subject.” Edwin Cannan.

Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.

This chapter reviews recent developments in the density discontinuity approach. It is well known that agents having perfect control of the forcing variable will invalidate the popular regression discontinuity designs (RDDs). To detect the manipulation of the forcing variable, McCrary (2008) developed a test based on the discontinuity in the density around the threshold. Recent papers have noted that the sorting patterns around the threshold are often either the researcher’s object of interest or may relate to structural parameters such as tax elasticities through known functions. This, in turn, implies that the behavior of the distribution around the threshold is not only informative of the validity of a standard RDD; it can also be used to recover policy-relevant parameters and perform counterfactual exercises.