Scholarly Journal Articles about the Asian Tiger Economies Essays

Scholarly Journal Articles about the Asian Tiger Economies Essays Scholarly Journal Articles about the Asian Tiger Economies: Authors, Journals, and Research Fields, 1986-2001 By Trinity University San Antonio, TX 78212 and Trinity University San Antonio, TX 78212 May 2002 Scholarly Journal Articles about the Asian Tiger Economies: Authors, Journals, and Research Fields, 1986-2001 I. Introduction. The Asian Tigers arrival into the world economy has been extraordinary. Hong Kong, Indonesia, Malaysia, Singapore, South Korea, and Thailand have experienced dramatic changes over the past 20 years. Their economies have fundamentally changed from traditional agriculturally based societies to rapidly growing newly industrialized nations. Their incredible rates of growth were accompanied by significant structural changes. While most of the change has been positive, from time to time these nations have been rocked by economic growing pains. These transformations of the South East Asian economies have attracted considerable attention in popular and scholarly publications. This paper extends bibliometric research into an area neglected thus far: the East Asian economies. It also extends bibliometric research itself in a new direction by investigating how economics literature responds to changes in the underlying economies. There were approximately 4,200 scholarly articles written about the East Asian economies that were indexed by the Journal of Economic Literature from 1986 to 2001 and included on the CD-ROM EconLit. This paper studies the economic literature about each of the major East Asian nations individually and for all of them combined. In addition, the paper presents a Whos Who of this literature by identifying the leading authors, journals, and research fields. Concentration of articles among journals and authors is also explored in detail. Then the literature trends about the Asian Tiger economies are contrasted with those of other emerging market economies (Czech Republic, Hungary, Mexico, and Poland) and a developed market economy (Italy). Finally, the study attempts to find parallels between the growth in articles and the growth of the economies. II. Data. The data source for this study is EconLit, the CD-ROM database of the Journal of Economic Literature. Over 200,000 articles from over 600 scholarly journals from many countries and social science disciplines appear in this source from 1986 to 2001. For each country, articles were selected on the basis of whether they contained such words as Thai, Thailand, Malay, and Malaysian in the articles title, geographic indicator, subject descriptor, or abstract. Some 4,277 articles met these criteria. Where the record for an article indicates two or more countries, one article was counted for each country. Because 623 of the articles examined more than one of the countries, the total number of unique articles used in the study totals 3,654. III. Trends and Cycles of Publications It is clear that over the past 25 years economists have found a fertile ground for research in the East Asian economies. As Figure 1 shows, the annual output of articles in scholarly journals about these economies grew much faster than all articles in economics. In terms of each of these nations, the South Korean economy captured the most attention, followed by Indonesia, Hong Kong, Singapore, Malaysia, and Thailand. CountryArticlesJournalsAuthors South Korea1,1713061,254 Indonesia732166714 Hong Kong671223773 Singapore613206678 Malaysia591202682 Thailand499190652 Table 1 and Figure 2 describe the growth and annual variations in the numbers of journal articles about the Tiger economies from 1986 to 2000. As stated earlier, the economies of South Korea and Indonesia garnered the largest number of articles at the beginning of the period and maintained the lead at the end. For the six economies, the number of articles ranged from 11 to 29 in 1986 and from 61 to 106 in 2000. The number of articles showed no trend for all six countries from 1986 to 1992, with small declines offset by gains. After 1992-93, the number of articles for each country followed an upward trend, with minor declines for at least one year before 2000. Because lags exist between publication and listing in the database, the figures for 2001 are clearly understated and we ignore them in our analysis of trends and cycles. It may be that the declines for some of the countries in 2000 also represent delayed reporting. However, that publications about these economies grew sharply af ter 1993 is unmistakable. Relative growth rates are best seen in Figure 3 with articles for each country represented by an index number based on 100 in 1986. From 1986 to 1993, the growth

Example of Bootstrapping in Statistics

Example of Bootstrapping in Statistics Bootstrapping is a powerful statistical technique. It is especially useful when the sample size that we are working with is small. Under usual circumstances, sample sizes of less than 40 cannot be dealt with by assuming a normal distribution or a t distribution. Bootstrap techniques work quite well with samples that have less than 40 elements. The reason for this is that bootstrapping involves resampling. These kinds of techniques assume nothing about the distribution of our data. Bootstrapping has become more popular as computing resources have become more readily available. This is because in order for bootstrapping to be practical a computer must be used. We will see how this works in the following example of bootstrapping. Example We begin with a statistical sample from a population that we know nothing about. Our goal will be a 90% confidence interval about the mean of the sample. Although other statistical techniques used to determine confidence intervals assume that we know the mean or standard deviation of our population, bootstrapping does not require anything other than the sample. For purposes of our example, we will assume that the sample is 1, 2, 4, 4, 10. Bootstrap Sample We now resample with replacement from our sample to form what are known as bootstrap samples. Each bootstrap sample will have a size of five, just like our original sample. Since we are randomly selecting and then are replacing each value, the bootstrap samples may be different from the original sample and from each other. For examples that we would run into in the real world, we would do this resampling hundreds if not thousands of times. In what follows below, we will see an example of 20 bootstrap samples: 2, 1, 10, 4, 24, 10, 10, 2, 41, 4, 1, 4, 44, 1, 1, 4, 104, 4, 1, 4, 24, 10, 10, 10, 42, 4, 4, 2, 12, 4, 1, 10, 41, 10, 2, 10, 104, 1, 10, 1, 104, 4, 4, 4, 11, 2, 4, 4, 24, 4, 10, 10, 24, 2, 1, 4, 44, 4, 4, 4, 44, 2, 4, 1, 14, 4, 4, 2, 410, 4, 1, 4, 44, 2, 1, 1, 210, 2, 2, 1, 1 Mean Since we are using bootstrapping to calculate a confidence interval for the population mean, we now calculate the means of each of our bootstrap samples. These means, arranged in ascending order are: 2, 2.4, 2.6, 2.6, 2.8, 3, 3, 3.2, 3.4, 3.6, 3.8, 4, 4, 4.2, 4.6, 5.2, 6, 6, 6.6, 7.6. Confidence Interval We now obtain from our list of bootstrap sample means a confidence interval. Since we want a 90% confidence interval, we use the 95th and 5th percentiles as the endpoints of the intervals. The reason for this is that we split 100% - 90% 10% in half so that we will have the middle 90% of all of the bootstrap sample means. For our example above we have a confidence interval of 2.4 to 6.6.