Nabaz T. Khayyat

Analysis of Landmine Fatalities and Injuries in the Kurdistan Region

This study analyzes landmine victim data in the Kurdistan Region during the period 1960 to 2005. A regression analysis is used to identify the determinants and impact of the probability of getting killed by mines and unexploded ordnances. The rates of killed/injured victims are explained using a set of socioeconomic variables. As the data are a repeated cross-section in which the individuals are observed when they are subjected to landmine incidents, and to account for the dynamic aspect of the process and heterogeneity by location as well as to control for unobserved location and time effects, a pseudo panel data are created where districts are observed over the entire time period forming a panel data. The results show that (a) males, children, and the elderly are more susceptible to a higher level of landmine risks; (b) landmine training and awareness programs do not reduce the rate of landmine mortality; and (c) the rate of incidents are declining over time. This result can be used in the planning, monitoring, and resource allocation for mine action, as well as labor market programs and rehabilitation activities.

Technology Management and Policy of Kurdistan Region of Iraq

In this chapter, the importance of upgrading KRI’s industrial structure to be able to generate more value-added products for maintaining the process of economic development is discussed. Khayyat claims that this process requires advancement in technological capabilities to employ and engross more urbane technologies. Thus, this advancement in technological capabilities needed to build a national innovation system, its activities should include but not limited to hardware and software purchases, industrial design and engineering activities, employing up to date machinery, equipment, and other capital goods, in-house software development, and finally the ability to conduct reverse engineering. In addition to this, Nabaz Khayyat shows the importance of investing in human development in terms of training and tertiary education, which should be applied in KRI for state building process.

Discussion of the Results and Policy Implications

This chapter summarizes the findings from the estimated models. The empirical results are summarized for (i) The mean in case of production function and energy demand function without risk, and (ii) for the mean and variance function in the case of energy demand accounting for risk , in which the findings are related to the theory of the competitive firm under production risk. The empirical results are also discussed in relation to the information available about the industrial sector for the data period. Furthermore, the chapter covers the implications and policy recommendations based on the estimated models for production function and energy demand.

Energy Demand Model II

This chapter introduces the second group of the econometric models estimation, namely the energy demand model. The model is constructed in two forms: The Cobb-Douglas and the Translog function to allow for consistency and comparability. It is worthy of mentioning that the estimated energy demand is a derived demand, the variable of energy is considered as one of the input factors of production. The energy demand is, therefore, derived from the demand for the industry’s output. Since the demand for energy depends on the output level, the possible substitutability between energy and other inputs is allowed by production technology and energy price.

Literature on Energy Demand

The substitutability and complementarity of energy input have been widely studied during the last four decades. The empirical results were mixed between energy-capital complementarity and energy-capital substitutability. From the previous literature a flexible functional form (Translog) was generally used to model production, cost, energy demand or a combination of them depending on the objective of cost minimization or output maximization. For their empirical analyses the different studies utilized data covering different countries, regions, industrial sector, and in few cases firm levels. The results in general indicated substitution between capital and energy, while complementarity between capital and energy was also frequently observed. The degree of substitutability and complementarity differ significantly by different dimensions of the data and the unit’s characteristics. Energy efficiency is hard to conceptualize and there is no single commonly accepted definition. From the literature, energy intensity at the national level is calculated as the ratio of energy use to GDP. This variable is often taken as a proxy for general energy efficiency in production. However, this aggregate energy consumption to GDP ratio is too simple to explain an economy’s energy use pattern, and may lead to difficulties and misunderstandings in interpreting the energy intensity indicators. The energy/GDP ratio includes a number of other structural factors that can significantly affect those indicators. Hence, it is necessary to fix the structural change effect in measuring energy intensity at the aggregated level in the industrial sector. The demand for energy is defined as a derived demand that arises for satisfying some needs which are met through the use of appliances. The response to change in the energy demand is partially characterized and explained by changes in the behavior of the decision maker. Thus, the elasticity of energy that respond to changes in the short run is incomplete, while in the long run it will be accumulated over time and fully captured. A key hypothesis required for determining demand for input factors of production is the profit maximization , which depends on the level of output and a limited combination of input factors that give a highest production output. This is called a production function, in which it explains the maximum level of production given a number of possible combinations of input factors used in the process.

The EUKLEMS Database

The data used in this study is obtained from the harmonized EUKLEMS Growth and Productivity Account database released in 2009. It includes variables that measure output and input growth , and derived variables such as multi-factor productivity at the industry level. The input measures include different categories of inputs: Capital, labor, energy, materials, ICT capital, and value added services inputs. The data sample composes a panel data of 950 observations taken from 25 South Korean industries observed for the period 1970–2007. Additional variables are also included such as the energy price, volumes, growth accounting, and some other control variables.

History of Economic Development in South Korea

South Korea is a new industrialized economy that has taken advantage from its technological development, thereby serving as an economic model for emerging economies. The South Korean government has applied a sequence of industrial and technological policy initiatives across different stages of its economic development. The focus of the South Korean industrial plan strategy has been redirected from a consumer industry to a heavy and chemical industry, and then to a technology intensive industry. The government’s intervention has changed from direct and sector-specific involvement to indirect sector-neutral functional support system. South Korea is completely energy import dependent, it has no crude oil production. It is placed as the fifths country with the biggest import of crude oil worldwide. As a consumer of crude oil South Korea is on place nine. The South Korean government has developed a set of five-year plan for rational utilization of energy since 1993. A basic national energy plan covers 2008–2030 was announced in an attempt to reduce the energy use intensity by the end of 2030. This Chapter provides details about the energy consumptions in the industry sector and their development over time, focusing on the energy consumption in the South Korean industrial sector, and sheds lights on the energy intensity and energy use efficiency programs, it further provides a detail description of the current status of the energy demand in the South Korean industrial sector.

Production Function Models Estimation

In this chapter the first group of econometric models the Cobb-Douglas production function and the Translog production function are estimated. The findings from estimating the Cobb-Douglas production function model reveal that (i) In general the South Korean industries are exhibiting increasing returns to scale , (ii) There is a slight substitution pattern between energy and ICT capital, and (iii) There is a significant and positive impact of energy use on the production level in the South Korean industries.

Econometrics of Panel Data Estimation

An issue not to be ignored in econometric modeling of production technology and firm behavior is the heterogeneity with respect to production technology and productivity, and heterogeneity with respect to input demand. Industries that use the same amount of input often experience different levels of output. The assumption of homogeneous firms in the neoclassical production theory may not be suitable for many industries. The heterogeneity should be accounted for in empirical studies with econometric modeling. The availability of panel data set makes it easy to use econometric panel data techniques to account for heterogeneity. The producer heterogeneity under risk can operate on several stages: The production process, the risk preferences, and the expectation formation with respect to price and output. However, only heterogeneity with respect to the production process is relevant for estimating production function. A two-stage approach is used to model industrial demand for energy. In the first stage, a model to determine the total demand for energy as a derived input factor of production is specified and estimated. Here, the demand for energy is considered as a dependent variable, and then a Translog production function model incorporating non-ICT capital, labor, and energy as input factors of production is estimated. Furthermore, elasticities of substitution are calculated. In this study three specifications of mean function of the risk model are specified and compared: A general production function where energy is an input, a Translog energy demand function where energy is a dependent variable, and a Translog energy demand model generalized to incorporate risk function.

Literature on Production Risk

A noticeable number of econometric studies on production technology and firm behavior have been conducted since 1970s, where the flexible functional form technique is introduced. These studies have mainly focused on two issues, first, in measuring the producer’s responses to changes in the price of input and output, and second in measuring the productivity growth. The majority of these studies have relied on one of the two assumptions: The assumption of deterministic setting which indicates that for a given level of inputs the output level will be certainly known, or the assumption of homoskedastic production technology which implies that inputs do not affect the variability of the output.