sadegh bafandeh imandoust; ali mofidi
Abstract
The GCI combines 113 indicators. These indicators are grouped into 12 pillars: institutions, infrastructure, macroeconomic environment, health and primary education, higher education and training, good market efficiency, labor market efficiency, financial market development, technological readiness, ...
Read More
The GCI combines 113 indicators. These indicators are grouped into 12 pillars: institutions, infrastructure, macroeconomic environment, health and primary education, higher education and training, good market efficiency, labor market efficiency, financial market development, technological readiness, market size, business sophistication and innovation. These are in turn organized into three sub-indexes, in line with three main stages of development; basic requirements, efficiency enhancers and innovation and sophistication factors. In this paper, the effect of global competitiveness index (GCI) on economic growth has been studied. To this end, panel data of 42 countries collected in the period 2010 to 2014, and the model is estimated. The model estimation results show that the GCI score has positive and significant effects on GDP per capita growth among selected developed and developing countries.
Methodology
Competitiveness is defined as the set of institutions, policies, and factors that determine the level of productivity of a country. Many determinants drive productivity and competitiveness. Understanding the factors behind this process has occupied the minds of economists for hundreds of years, engendering theories ranging from Adam Smith’s focus on specialization and the division of labor to neoclassical economists’ emphasis on investment in physical capital and infrastructure and, more recently, to interest in other mechanisms such as education and training, technological progress, macroeconomic stability, good governance, firm sophistication, and market efficiency among others. While all of these factors are likely to be important for competitiveness and growth. In economic theory of stages of development, the GCI assumes that, in the first stage, the economy is factor-driven and countries compete based on their factor endowments— primarily unskilled labor and natural resources. Companies compete on the basis of price and sell basic products or commodities, with their low productivity reflected in low wages. Maintaining competitiveness at this stage of development hinges primarily on well-functioning public and private institutions (pillar 1), a well-developed infrastructure (pillar 2), a stable macroeconomic environment (pillar 3), and a healthy workforce that has received at least a basic education (pillar 4). As a country becomes more competitive, productivity will increase and wages will rise with advancing development. Countries will then move into the efficiency-driven stage of development, when they must begin to develop more efficient production processes and increase product quality because wages have risen and they cannot increase prices. At this point, competitiveness is increasingly driven by higher education and training (pillar 5), efficient goods markets (pillar 6), well-functioning labor markets (pillar 7), developed financial markets (pillar 8), the ability to harness the benefits of existing technologies (pillar 9), and a large domestic or foreign market (pillar 10). Finally, as countries move into the innovation-driven stage, wages will have risen by so much that they are able to sustain those higher wages and the associated standard of living only if their businesses are able to compete with new and unique products. At this stage, companies must compete by producing new and different goods using the most sophisticated production processes (pillar 11) and by innovating new ones (pillar12). The Global Competitiveness Index (GCI) has been used by the World Economic Forum to assess the level of productivity of an economy. Hall and Jones (1996) have shown that around 89 percent of the variation in GDP per capita is due to variation in the level of productivity. As a result, GDP per capita can be used as a proxy for the level of productivity of a country. The regress of the log level of GDP per capita on the GCI score reveals that about two-thirds of the variation in GDP per capita can be explained by the GCI. However, estimating a bivariate relation between the growth rate and the GCI would be a mistake. The reason for that lies in what economists call the “conditional convergence effect”, which posits that, all other things being equal, there is a natural tendency for poor economies to grow faster—a phenomenon known as conditional convergence. In other words, if all countries had the same investment and population growth rates and the same levels of productivity, then we should observe poor countries growing faster than rich ones. Conversely, if all countries had the same level of income, then those that were more competitive would experience higher rates of long-term economic growth. In reality, however, countries differ both in their levels of income and their levels of productivity, and therefore it is very hard to predict the relationship between the growth rate and the level of productivity with a bivariate correlation analysis that includes the initial level of income. Formally, in a growth convergence equation, the growth rate of GDP per capita of country is a positive function of the GCI score and a negative function of GDP per capita.
Results and Discussion
The model estimation results show that the GCI score has positive and significant effects on GDP per capita growth among selected developed countries and a %10 increase in a country’s GCI score would lead to an increase in the economic growth by 17.32588 percentage points. This amount is 15.49522 for selected developing and emerging countries. Results of this paper show that “net growth rate” against the GCI score, revealing a positive and strong correlation, which is consistent with the view that the GCI is a good proxy for the level of productivity or competitiveness of an economy.
mahdi ghaemiasl; Sadegh Bafandeh Imandoust; Elham Dashti
Abstract
Underestimation of this high-demand services in today's world has resulted in the non-optimal allocation of resources and incorrect management and planning. In this research, focusing on Chalidareh Tourism Complex in Mashhad, a finite-horizon bayesian dynamic pricing model has been used to determine ...
Read More
Underestimation of this high-demand services in today's world has resulted in the non-optimal allocation of resources and incorrect management and planning. In this research, focusing on Chalidareh Tourism Complex in Mashhad, a finite-horizon bayesian dynamic pricing model has been used to determine the extent of willingness to pay for non-market regional natural resources. In so doing, based on Chen and Wu (2016), Gamma and Two-Point priors with exponential and normal WTP (Willingness to Pay) distribution have been used. The results showed that the average WTP for general exploitation of this complex is within the extent of 12230 IRR (as minimum) in Gamma prior and exponential distribution and 45270 IRR (as maximum) in the Two-Point prior and exponential distribution. Also, the average of WTP is 28750 IRR, while the WTP is 10623 IRR in non-Bayesian approach, which is lower than any of Bayesian estimations. Therefore, the application of Finite-Horizon Bayesian Dynamic Pricing (FHBD) algorithm in dynamic pricing can be an appropriate way to determine the threshold amount of WTP for the exploitation of natural resources.
Introduction
An important insight from the literature on dynamic pricing is that the optimal selling price of such products depends on the remaining inventory and the length of the remaining selling season (see e.g., Gallego & Van Ryzin, 1994). The optimal decision is, thus, not to use a single price but a collection of prices: one for each combination of the remaining inventory and the length of the remaining selling season. To determine these optimal prices it is essential to know the relation between the demand and the selling price. In most literature from the 1990s on dynamic pricing, it is assumed that this relation is known to the seller, but in practice, the exact information on the consumer behavior is generally not available. It is, therefore, not surprising that the review on dynamic pricing by Bitran and Caldentey (2003) mentions dynamic pricing with demand learning as an important future research direction. The presence of digital sales data enables a data-driven approach of dynamic pricing, where the selling firm not only determines optimal prices, but also learns how changing prices affects the demand. Ideally, this learning will eventually lead to optimal pricing decisions.
Theoretical Framework
In this paper, we focus on the dynamic pricing problem of selling a limited amount of inventory over a short selling horizon. In this regard, dedicating a certain number of periods for exploratory experimenting may be costly due to the limited time and inventory. Instead, a simultaneous optimization of pricing and learning is desired, which can be achieved by formulating the problem as a Bayesian dynamic program. However, computing the optimal policy for the dynamic program can be difficult, if not intractable due to the high dimensionality. Moreover, the binary customer choice model described above gives a rise to a two-sided censoring effect, that is, the observation of the customer’s WTP is censored either from the left or from the right side by the posted price. Because no simple conjugate prior distribution exists under the two-sided censoring (Braden & Freimer, 1991), one cannot resort to the conjugate prior technique to reduce the problem dimensionality.
Methodology
Consider a finite-horizon dynamic pricing problem for a single product. Inventory replenishment is not possible during the selling horizon, and the terminal value at the end of the horizon is zero. At the beginning of each period, given the available inventory quantity q, the seller determines the unit price p for the product. The goal is to maximize the expected total revenue over the finite horizon. Specifically, we divide the finite selling horizon into T periods to guarantee that there is one customer arrival in each period (e.g., Broder & Rusmevichientong, 2012; Talluri & Van Ryzin, 2004). Time periods are indexed in reverse order, with the first selling period being period T and the last period being period 1. The customer arriving in period t has WTP Xt, which is a random draw from an i.i.d. distribution with a continuous density f (x|θ), where x ≥ 0 is the actual WTP and θ ∈ Θ is an unknown parameter of the distribution. At the beginning of period t, the seller has a prior belief concerning the value of θ, denoted by πt (θ). For the ease of exposition, we assume that Θ is a continuous set and that πt (θ) is a density over this set. When Θ is a discrete set, all our analysis will carry through by treating πt (θ) as a probability mass function. We shall use πt (θ) and πt (θ) interchangeably and suppress the subscript t whenever appropriate within the context.
Results and Discussion
A Finite-Horizon Bayesian Dynamic Pricing Model base on Chen and Wu (2016), Gamma and Two-Point priors with exponential and normal WTP distribution have been used. Results showed that the average WTP for general exploitation of this complex is within the extent of 12230 IRR (as minimum) in Gamma prior and exponential distribution and 45270 IRR (as maximum) in Two-Point prior and exponential distribution. Also, the average is 28750, while the WTP is 10623 IRR in non-Bayesian approach, which is lower than all the Bayesian estimation results. Therefore, the application of FHBD algorithm in dynamic pricing can be an appropriate way to determine the threshold amount of WTP for the exploitation of natural resources.
Conclusions & Suggestions
In sum, we study the Bayesian dynamic pricing problem under two-sided censoring with a short time horizon and limited inventory. Upon comparing it with the exact-observation system, we found that having better information always improves the revenue performance, while the optimal price under the exact-observation system can be either higher and lower than that under the two-sided censoring system. When comparing the above two systems with the no-learning system, we discover a surprising result that learning can bring a negative value when the inventory is scarce due to the biased learning effect. A derivative approximation heuristic is then devised to numerically solve the two-sided censoring problem. We further develop a performance bound to compare our proposed heuristic with other benchmark heuristics. Numerical experiments demonstrate that our heuristic consistently outperforms others and is robust with respect to WTP distributions. The two-sided censoring effect in our problem is a result of the binary customer choice model. When a customer faces a choice among multiple products, a more general choice model that surveys in the substitution effect is needed.
sadegh bafandeh imandoust; ali mofidy
Abstract
Undobtedly, the Information and Communication Technology (ICT) has affected the country’s economic and social interactions. The Networked Readiness Index (NRI) comprising four subindexes that measure the enviroment for ICT’s, the readiness of a society to use ICT’s, the actual usage of all main ...
Read More
Undobtedly, the Information and Communication Technology (ICT) has affected the country’s economic and social interactions. The Networked Readiness Index (NRI) comprising four subindexes that measure the enviroment for ICT’s, the readiness of a society to use ICT’s, the actual usage of all main stakholders (Individual, Business and Government), and finally, the impacts that ICT’s generate in the economy and in the society, the three first subindexes can be regarded as the driver that establish the conditions for the results of the fourth subindexes, ICT impacts. These four subindexes are divided into 10 pillars composed of 54 individual indicators in total. Therefore, in this paper, the effect of Networked Readiness Index (NRI) on economic growth has been studied. To this end, panel data of 77 countries collected in the period 2007 to 2012, and the model is estimated by Generalized Method of Moments(GMM). The model estmation results show that the NRI score has positive and significant effects on GDP per capita growth in developed countries and this amount is equal 0.202848. This effect is positive and non significantin developing countries.
