David N. Aratuo, Xiaoli L. Etienne, Tesfa Gebremedhin and David M. Fryson
The purpose of this study is to investigate the causal linkages between tourism and economic growth in the USA and determine how they respond to shocks in the system.
Abstract
Purpose
The purpose of this study is to investigate the causal linkages between tourism and economic growth in the USA and determine how they respond to shocks in the system.
Design/methodology/approach
The study uses a variety of time series procedures, including the bounds test, Granger causality test, impulse response functions and generalized variance decomposition to analyze the relationship between monthly tourist arrivals (TA) to the USA, real gross domestic product (GDP) and real effective exchange rates.
Findings
Results suggest that GDP Granger causes TA in the USA in the long run, indicating the economy-driven tourism growth hypothesis. Additionally, a shock to GDP generates a positive and significant effect on TA that persists in the long-run, while exchange rate shocks only have a significant effect in the first six months.
Research limitations/implications
Different tourism sectors may exert different degrees of influence on the economy. The use of aggregate data on TA in the analysis assumes homogeneity in the industry, thus, only represents the average relationship between tourism and GDP.
Practical implications
This study provides insight that shapes the investment, marketing, sustainability decisions of the public and private sectors aim at increasing tourist flows to drive economic development at the national, state and local levels.
Originality/value
Though several studies have examined the factors influencing the international tourist demand of the USA, this is the first to investigate the causal relationships between tourism, GDP and exchange rates for the USA. It is also the first in the US tourism literature to account for the nature of interactions between the three variables because of innovations in the system.
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Keywords
Xiaoli Liao Etienne, Scott H. Irwin and Philip Garcia
The purpose of this paper is to test for bubbles in the US hard red spring (HRS) wheat market from 2004 to 2014, with particular focus on 2007-2008 when the market experienced…
Abstract
Purpose
The purpose of this paper is to test for bubbles in the US hard red spring (HRS) wheat market from 2004 to 2014, with particular focus on 2007-2008 when the market experienced record-high price volatility.
Design/methodology/approach
The authors apply a recently developed bubble testing procedure to cash, rolling nearby futures contract, and individual futures contract prices of HRS wheat sampled at daily, weekly, and monthly frequencies. Two critical value (CV) sequences are derived to date-stamp bubbles, one from Monte Carlo simulations, and the other from recursive wild bootstrap procedure.
Findings
The authors find that regardless of the price series adopted, sampling frequency chosen, or CVs used, bubbles account for only a small fraction of the HRS wheat price behavior during 2004-2014. However, much sharper differences are detected regarding the key policy question of bubble behavior during 2007-2008. Individual futures contract prices during this period suggest only a minimal number of bubble days, while rolling nearby futures and cash prices indicate bubbles lasting much longer. Since theory suggests that prices for individual futures contracts are more likely to provide a clearer test of bubble components, the authors conclude there is little evidence that the spike in spring wheat prices to $25 per bushel in 2007-2008 was a bubble.
Originality/value
This paper is the first in the literature to examine the sensitivity of bubble testing to different types of data, sampling frequencies, and inference procedures.
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Juanli Wang, Xiaoli Etienne and Yongxi Ma
The purpose of this paper is to evaluate the technical efficiency and production risk in China's rice production and examine the effect of factor market reform on these two…
Abstract
Purpose
The purpose of this paper is to evaluate the technical efficiency and production risk in China's rice production and examine the effect of factor market reform on these two agricultural performance metrics.
Design/methodology/approach
Using an unbalanced farm-level panel data with 2,193 observations on 329 rice farms from 2004 to 2016, the authors estimate a translog stochastic production frontier model that accounts for both technical inefficiency and production risk. A one-step procedure through the maximum likelihood method that combines the stochastic production frontier, technical inefficiency and production risk functions is used to circumvent the bias problem often found in the conventional two-step model.
Findings
Estimation results show that both land and labor market reforms significantly improved the level of technical efficiency over the years, although the effect of land market deregulation is of a much higher magnitude compared to the latter. The land market reform, however, has also increased the risk of production. The authors further find that a higher proportion of hired labor in total labor cost helps lower production risk, while also acting to decrease technical efficiency. Additionally, agricultural subsidies not only increased the output variability but also lowered technical efficiency
Originality/value
First, the authors evaluate the effect of market deregulation on technical efficiency and production risk under a stochastic frontier framework that simultaneously accounts for both production performance metrics, which is important from a statistical point of view. Further, the authors exploit both cross-sectional and time-series variations in a panel setting to more accurately estimate the technical inefficiency scores and production risk for individual farmers, and investigate how the exogenous land and labor market reforms influence these two production performance measures in China's rice farming. This is the first study in the literature to analyze these questions under a panel framework.
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Xiaoli Liao Etienne, Andrés Trujillo-Barrera and Seth Wiggins
The purpose of this paper is to investigate the price and volatility transmission between natural gas, fertilizer (ammonia), and corn markets, an issue that has been traditionally…
Abstract
Purpose
The purpose of this paper is to investigate the price and volatility transmission between natural gas, fertilizer (ammonia), and corn markets, an issue that has been traditionally ignored in the literature despite its significant importance.
Design/methodology/approach
The authors jointly estimate a vector error correction model for the conditional mean equation and a multivariate generalized autoregressive heteroskedasticity model for the conditional volatility equation to investigate the interactions between natural gas, ammonia, and corn prices and their volatility.
Findings
The authors find significant interplay between fertilizer and corn markets, while only a mild linkage in prices and volatility exist between those markets and natural gas during the period 1994-2014. There is not only a positive relationship between corn and ammonia prices in the short run, but both prices react to deviations from the long-run parity. Furthermore, the lagged conditional volatility of ammonia prices positively affects conditional volatility in the corn market and vice versa. This result is robust to a specification using crude oil price as an alternative to natural gas price to account for the large transportation cost built into ammonia prices. Results for the period of 2006-2014 indicate virtually no linkage between natural gas prices and those of fertilizer and corn during that period, while linkages in price level and volatility between the latter remain strong.
Originality/value
This paper is the first in the literature to comprehensively examine the role of fertilizer on corn prices and volatility, and its relation to natural gas prices.