• Title/Summary/Keyword: consumer price index

Search Result 139, Processing Time 0.036 seconds

Estimating the Determinants of foreign direct investment of korea : A Panel Data Model Approach (페널 데이터모형을 적용한 한국의 해외 직접투자 결정요인 추정에 관한 연구)

  • Kim, Hee-Cheul;Shin, Hyun-Dae
    • Journal of the Korea Society of Computer and Information
    • /
    • v.13 no.4
    • /
    • pp.231-240
    • /
    • 2008
  • In respect complication, group and period, the foreign direct investment of korea is composed of various factors. This paper studies focus on estimating the determinants of foreign direct investment of korea. The region of analysis consist of 7 groups, that is, Asia, Europe, Central and South America, Oceania, Africa, Middle East. Analyzing period be formed over a 67 point(2002. 6${\sim}$2007. 12). In this paper dependent variable setting up an amount of foreign direct investment, explanatory(independent) variables composed of gross domestic product, a balance of current accounts, the foreign exchange rate, employment to population ratio, an average of the rate of operation(the manufacturing industry), consumer price index, the amount of export, wages(a service industry). For an actual proof analysis, LIMDEP 8.0 software, analysis model is random effect in TWECR The result of estimating the determinants of foreign direct investment of korea provides empirical evidences of significance positive relationships between employment to population ratio and wages(a service industry). However this study provides empirical evidences of significance negative relationships between the foreign exchange rate, censurer price index and the amount of export. The explanatory variables, that is, an average of the rate of operation(the manufacturing industry), gross domestic product and a balance of current accounts, are non-significance variables.

  • PDF

Market Power and Retail Price in Mobile Communications Industry: an International Comparative Study (시장지배력 수준과 요금인하 간의 관계분석: 이동통신서비스시장의 국제비교)

  • Choi, Saesol;Han, Sung-Soo
    • International Area Studies Review
    • /
    • v.18 no.3
    • /
    • pp.231-248
    • /
    • 2014
  • The relationship between market structure and social welfare outcomes has received considerable critical attention in the field of competition policy research. In particular, it is necessary to study in greater depth the impact of market power on social welfare in the telecommunications industry, which is highly likely to form a monopolistic market structure. This is because, when market powers are concentrated on few upper carriers, there are negative effects on social welfare due to an excess of profits. Against this background, the present study investigates the relationship between the market structure of the mobile communications industry (the level of market power) and social welfare outcomes (the retail rate cut) through an international comparison. The results demonstrate that both the market structure and competition status of the Korean market have had significant gaps in global trends. It also points out that the monopolistic market structure (when the leading provider has more than 50% of the market share) has significantly negative effects on consumer welfare (the retail price cut). In addition, the findings of this study suggest that the direction of competition policy should focus on not only improving market concentration(HHI), but also on mitigating the monopoly of power of a dominant operator.

A Study on the Export Competitiveness of Chinese ICT Items in Korean Market - Focused on the Computer and Peripheral Equipment Items - (중국산 ICT 품목의 대 한국시장 경쟁력 분석 - 컴퓨터 및 주변기기 품목을 중심으로 -)

  • Kim, Jiyong
    • International Commerce and Information Review
    • /
    • v.19 no.4
    • /
    • pp.127-145
    • /
    • 2017
  • The study focuses on the ICT industry, which is considered future growth engine. Tthe main objective of the research is to examine the extent of the competitiveness of the Chinese ICT industry, which is rapidly emerging as a competitor of the ICT industry in Korea. The ICT items subject to primary analysis of this study were computer and peripherals items. Analysis methods used were MSI (Market Share Index), EBI (Export Bias Index), and MCA (Market Comparative Advantage). The analysis period was from 2008 to 2016, and the analysis dater used were the export-import data provided by KITA. According to the study, Korean market share of Chinese computers and peripherals items has continued to increase, exports concentrated on the Korean market are intensifying, though the degree of competitiveness gained by the Korean market is quite strong. In particular, 852851, 847160 items have the largest competitiveness in the Korean market compared to other items used in this study. The implications of this study for the Korean market are as follows: i) improvement of quality with price ii) convergence product development between computer and peripherals items and consumer -friendly design development, and iii) marketing efforts to improve product awareness so that consumers recognize Korean computer and peripherals products.

  • PDF

Brand Equity and Purchase Intention in Fashion Products: A Cross-Cultural Study in Asia and Europe (상표자산과 구매의도와의 관계에 관한 국제비교연구 - 아시아와 유럽의 의류시장을 중심으로 -)

  • Kim, Kyung-Hoon;Ko, Eun-Ju;Graham, Hooley;Lee, Nick;Lee, Dong-Hae;Jung, Hong-Seob;Jeon, Byung-Joo;Moon, Hak-Il
    • Journal of Global Scholars of Marketing Science
    • /
    • v.18 no.4
    • /
    • pp.245-276
    • /
    • 2008
  • Brand equity is one of the most important concepts in business practice as well as in academic research. Successful brands can allow marketers to gain competitive advantage (Lassar et al.,1995), including the opportunity for successful extensions, resilience against competitors' promotional pressures, and the ability to create barriers to competitive entry (Farquhar, 1989). Branding plays a special role in service firms because strong brands increase trust in intangible products (Berry, 2000), enabling customers to better visualize and understand them. They reduce customers' perceived monetary, social, and safety risks in buying services, which are obstacles to evaluating a service correctly before purchase. Also, a high level of brand equity increases consumer satisfaction, repurchasing intent, and degree of loyalty. Brand equity can be considered as a mixture that includes both financial assets and relationships. Actually, brand equity can be viewed as the value added to the product (Keller, 1993), or the perceived value of the product in consumers' minds. Mahajan et al. (1990) claim that customer-based brand equity can be measured by the level of consumers' perceptions. Several researchers discuss brand equity based on two dimensions: consumer perception and consumer behavior. Aaker (1991) suggests measuring brand equity through price premium, loyalty, perceived quality, and brand associations. Viewing brand equity as the consumer's behavior toward a brand, Keller (1993) proposes similar dimensions: brand awareness and brand knowledge. Thus, past studies tend to identify brand equity as a multidimensional construct consisted of brand loyalty, brand awareness, brand knowledge, customer satisfaction, perceived equity, brand associations, and other proprietary assets (Aaker, 1991, 1996; Blackston, 1995; Cobb-Walgren et al., 1995; Na, 1995). Other studies tend to regard brand equity and other brand assets, such as brand knowledge, brand awareness, brand image, brand loyalty, perceived quality, and so on, as independent but related constructs (Keller, 1993; Kirmani and Zeithaml, 1993). Walters(1978) defined information search as, "A psychological or physical action a consumer takes in order to acquire information about a product or store." But, each consumer has different methods for informationsearch. There are two methods of information search, internal and external search. Internal search is, "Search of information already saved in the memory of the individual consumer"(Engel, Blackwell, 1982) which is, "memory of a previous purchase experience or information from a previous search."(Beales, Mazis, Salop, and Staelin, 1981). External search is "A completely voluntary decision made in order to obtain new information"(Engel & Blackwell, 1982) which is, "Actions of a consumer to acquire necessary information by such methods as intentionally exposing oneself to advertisements, taking to friends or family or visiting a store."(Beales, Mazis, Salop, and Staelin, 1981). There are many sources for consumers' information search including advertisement sources such as the internet, radio, television, newspapers and magazines, information supplied by businesses such as sales people, packaging and in-store information, consumer sources such as family, friends and colleagues, and mass media sources such as consumer protection agencies, government agencies and mass media sources. Understanding consumers' purchasing behavior is a key factor of a firm to attract and retain customers and improving the firm's prospects for survival and growth, and enhancing shareholder's value. Therefore, marketers should understand consumer as individual and market segment. One theory of consumer behavior supports the belief that individuals are rational. Individuals think and move through stages when making a purchase decision. This means that rational thinkers have led to the identification of a consumer buying decision process. This decision process with its different levels of involvement and influencing factors has been widely accepted and is fundamental to the understanding purchase intention represent to what consumers think they will buy. Brand equity is not only companies but also very important asset more than product itself. This paper studies brand equity model and influencing factors including information process such as information searching and information resources in the fashion market in Asia and Europe. Information searching and information resources are influencing brand knowledge that influences consumers purchase decision. Nine research hypotheses are drawn to test the relationships among antecedents of brand equity and purchase intention and relationships among brand knowledge, brand value, brand attitude, and brand loyalty. H1. Information searching influences brand knowledge positively. H2. Information sources influence brand knowledge positively. H3. Brand knowledge influences brand attitude. H4. Brand knowledge influences brand value. H5. Brand attitude influences brand loyalty. H6. Brand attitude influences brand value. H7. Brand loyalty influences purchase intention. H8. Brand value influence purchase intention. H9. There will be the same research model in Asia and Europe. We performed structural equation model analysis in order to test hypotheses suggested in this study. The model fitting index of the research model in Asia was $X^2$=195.19(p=0.0), NFI=0.90, NNFI=0.87, CFI=0.90, GFI=0.90, RMR=0.083, AGFI=0.85, which means the model fitting of the model is good enough. In Europe, it was $X^2$=133.25(p=0.0), NFI=0.81, NNFI=0.85, CFI=0.89, GFI=0.90, RMR=0.073, AGFI=0.85, which means the model fitting of the model is good enough. From the test results, hypotheses were accepted. All of these hypotheses except one are supported. In Europe, information search is not an antecedent of brand knowledge. This means that sales of global fashion brands like jeans in Europe are not expanding as rapidly as in Asian markets such as China, Japan, and South Korea. Young consumers in European countries are not more brand and fashion conscious than their counter partners in Asia. The results have theoretical, practical meaning and contributions. In the fashion jeans industry, relatively few studies examining the viability of cross-national brand equity has been studied. This study provides insight on building global brand equity and suggests information process elements like information search and information resources are working differently in Asia and Europe for fashion jean market.

  • PDF

The Relationship between Socioeconomical Status and Incidence of Facial Bone Fracture (최근 10년간 안면골 골절의 발생 양상과 사회경제학적 상황간의 연관성 분석)

  • Yang, Eun-Zin;Kim, Chang-Yeon
    • Archives of Plastic Surgery
    • /
    • v.38 no.3
    • /
    • pp.263-272
    • /
    • 2011
  • Purpose: The human face is the most exposed part of the body, and in patients with simple or complex trauma from traffic accidents, industrial calamities, sports injuries, human assaults, and daily accidents, facial trauma occupies an important portion. The etiology of facial trauma vary on a society's economic, cultural, and environmental status. Methods: Regarding patients who were admitted from between the years 2000 to 2009 at the Hanyang University hospital, the authors studied how the changes in the economic status in the past 10 years of our country influences the incidence of facial bone fractures. Results: In this study, 1) The unemployment rate showed a strong negative relationship with the total number of inpatients with facial bone fractures, the number of male patients, the number of female patients, the number of patients with facial bone fractures caused by fall down, the number of patients who were admitted for shorter than 7 days, and the number of the facial bone fracture patients with their age in the twenties. 2) The consumer price index showed a strong positive relationship with the number of female patients, the number of patients who were admitted for shorter than 7 days, and the number of the facial bone fracture patients with their age in the teens and fifties. Conclusion: Looking at the results of correlation analysis and multiple regression analysis with economic indicators, the unemployment rate showed negative influence to the total number of inpatients with facial bone fractures, and the number of inpatients with facial bone fractures caused by fall down, with statistical significance.

A Study on the Factors Affecting the Arson (방화 발생에 영향을 미치는 요인에 관한 연구)

  • Kim, Young-Chul;Bak, Woo-Sung;Lee, Su-Kyung
    • Fire Science and Engineering
    • /
    • v.28 no.2
    • /
    • pp.69-75
    • /
    • 2014
  • This study derives the factors which affect the occurrence of arson from statistical data (population, economic, and social factors) by multiple regression analysis. Multiple regression analysis applies to 4 forms of functions, linear functions, semi-log functions, inverse log functions, and dual log functions. Also analysis respectively functions by using the stepwise progress which considered selection and deletion of the independent variable factors by each steps. In order to solve a problem of multiple regression analysis, autocorrelation and multicollinearity, Variance Inflation Factor (VIF) and the Durbin-Watson coefficient were considered. Through the analysis, the optimal model was determined by adjusted Rsquared which means statistical significance used determination, Adjusted R-squared of linear function is scored 0.935 (93.5%), the highest of the 4 forms of function, and so linear function is the optimal model in this study. Then interpretation to the optimal model is conducted. As a result of the analysis, the factors affecting the arson were resulted in lines, the incidence of crime (0.829), the general divorce rate (0.151), the financial autonomy rate (0.149), and the consumer price index (0.099).

Analysis of consumption expenditure in urban household budgets -Using time series data- (도시 노동자가계의 소비지출분석 - 時系列 자료를 중심으로-)

  • 김정숙
    • Journal of Families and Better Life
    • /
    • v.10 no.2
    • /
    • pp.19-36
    • /
    • 1992
  • The purpose of this paper is to analyze empirically the tendency of household consumption expenditure according to the change of social and economical condition, and the factor which influences consumption expenditure of urban household. The data used in analysis are time-series. The data are statistic form Urban Household Economy Survey published by the Economic Planning Board, dating form the first quarter of 1970 to the fourth quarter of 1989. The income of household and consumption expenditure materials were deflated as consumer price index to exclude the influence of prices and the influence of household composition are considered to deflated as the size of the household under assumption of homogeneity. The consumption expenditure items were categorized to 12 relatively large range items. The time-series data were analyzed by using the Two Stage Least Squares and the Ordinary Least Squares. The following is the result of analysis. 1) Rather than the income increase of previous years. the average income increase for two years influences more significantly on consumption expenditure of household. In the case of influence on consumption expenditure for each item by increase in disposable income, such categories as furniture and utensils. clothing and footwear, housing, medical care, culture and recreation, and transportation and communication have significant influence. 2) Among consumption expenditure categories, the increasing factors were furniture and utensils, and clothing and footwear. And the decreasing factors were housing, medical care, culture and recreation ,and transportation and communication. The relative prices, however, had significant influence on categories such as housing, furniture and utensils, medical care , culture and recreation, and transportation and communication and all of them were the decreation factors. 3) Among with changes of social and economical conditions, miscellaneous showed the highest increase in marginal propensity to consume and foods was the lowest. Also culture and recreation and housing brought up a great change of the income elasticity of demand.

  • PDF

An Application of Machine Learning in Retail for Demand Forecasting

  • Muhammad Umer Farooq;Mustafa Latif;Waseemullah;Mirza Adnan Baig;Muhammad Ali Akhtar;Nuzhat Sana
    • International Journal of Computer Science & Network Security
    • /
    • v.23 no.9
    • /
    • pp.1-7
    • /
    • 2023
  • Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

An Application of Machine Learning in Retail for Demand Forecasting

  • Muhammad Umer Farooq;Mustafa Latif;Waseem;Mirza Adnan Baig;Muhammad Ali Akhtar;Nuzhat Sana
    • International Journal of Computer Science & Network Security
    • /
    • v.23 no.8
    • /
    • pp.210-216
    • /
    • 2023
  • Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

Predicting Economic Activity via the Yield Spread: Literature Survey and Empirical Evidence in Korea (이자율 스프레드의 경기 예측력: 문헌 서베이 및 한국의 사례 분석)

  • Yun, Jaeho
    • Economic Analysis
    • /
    • v.26 no.3
    • /
    • pp.1-47
    • /
    • 2020
  • This paper surveys research since the 1990s on the ability of the yield spread and its components (i.e., expectation spread and term premium components) for future economic activity, and also conducts an empirical analysis of their forecasting ability using the yield data of Korean government bonds. This paper's survey, particularly for the US, shows that the yield spread has significant predictive power for some macroeconomic variables, but since the mid-1980s, its predictive power seems to have declined, possibly due to stronger inflation targeting. Next, this paper's empirical analysis using Korean data indicates that the yield spread, and the term premium component in particular, has significant predictive power for industrial production (IP) growth, consumer price index growth, and the IP gap. An out-of-sample analysis shows that the prediction equations are unstable over time, and that in predicting IP growth, the yield spread decomposition makes a significant contribution to the prediction of IP growth.