By applying threshold analysis and quantile regression techniques, we investigate the linearity of the relationship between tourism receipts and economic growth. We find that a threshold exists, below and above which the relationship between tourism receipts and economic growth changes. In our sample, the threshold for tourism receipts is at 3.82% of gross domestic product. Specifically, our findings suggest that tourism receipts have a more pronounced effect on economic growth below the threshold than above the threshold. From the quantile regression analysis, we further find that countries have greater benefits from tourism at lower levels of economic growth. Thus, policy makers designing tourism policy may consider that the marginal benefit of tourism on growth wanes beyond certain levels in spite of the fact that tourism receipts are an important driver of economic growth at all levels of growth.
This paper investigates the nonlinear relationship between tourism and economic growth using a balanced sample of 58 countries in three continental samples (Africa, Asia, and Latin America) for the 2003–2017 period. First, we document an asymmetric threshold effect of tourism on economic growth. By utilizing an endogenous threshold regression model, we show that a single tourism threshold cutoff exists and that tourism receipts influence growth only till the threshold cutoff point in all three continental samples; however, this influence is nonexistent past the threshold point. Second, a quantile effect decomposition shows separate marginal effects for the tourism and economic growth relationship across the growth distribution. By using an unconditional quantile regression approach, we show that compared to their regional cohorts, slow- and medium-growth African countries, slow-growth Asian countries, and medium-growth Latin American countries exhibit substantially higher economic growth benefits from tourism. We explain these empirical observations and discuss their policy implications.
This study examines the effect of firm financial efficiency on executive compensation with an emphasis on the US apparel industry. We find that both annual efficiency levels and cumulative efficiency changes obtained from the Data Envelopment Analysis (DEA) are positively associated with CEO pay. The effect is stronger for technological changes and changes in scale efficiency. Our results seem to support the pay-for-efficiency paradigm, a stricter version of the pay-for-performance framework under the efficient contracting explanation for CEO pay.
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