Information about Asia and the Pacific Asia y el Pacífico
Back Matter

Back Matter

Published Date:
October 2006
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    Information about Asia and the Pacific Asia y el Pacífico
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    1The main author of this chapter is Varapat Chensavasdijai.
    2See Chapter III of the IMF September 2006 World Economic Outlook for an analysis of the determinants of long-term growth in Asian economies and challenges ahead.
    3Empirical relationships between growth forecast errors of the United States and trading partner countries for 1990-2005 indicate that a 1 percentage point slowdown in the U.S. economy is associated with a ¼ percentage point reduction in growth in the rest of the world, and in Asia, a decline of ¾ percentage point in China and almost ¼ percentage point in Japan. (Details can be found in the Staff Report for 2006 Article IV Consultation with the United States, IMF Country Report No. 06/279.)
    4The main author of this chapter is Romuald Semblat.
    5The September 2006 Global Financial Stability Report provides an analysis of the correction in global markets.
    6In the Philippines, the rally was also driven by domestic investors’ buying through Unit Investment Trust Funds (UITF).
    7In narrow terms, the yen carry trade describes a transaction whereby international investors borrow in yen to invest in higher yielding currencies. Some broader uses of the term may also include purchases of high-yielding foreign currency bonds by Japanese investors, including the so-called “uridashi bonds”, foreign currency bonds sold in Japan for retail investors.
    8This figure compares with a daily volume of overall foreign exchange transactions of about $2 trillion and almost $350 billion for yen transactions alone. Precise estimates are difficult as many transactions are done through derivatives and Over The Counter (OTC) transactions (see Box 1.1 of the September 2006 Global Financial Stability Report).
    9The main author of this chapter is Andrea Richter Hume.
    10See Chapter I for a more detailed discussion of inflation performance.
    11Although consumption growth has been strong, much of China’s rapid growth has been driven by investment, which in some sectors had led to overcapacity and hence to falling prices. This has helped offset higher prices of services and of administered goods (such as energy), keeping headline inflation low.
    12These figures exclude off-balance sheet foreign exchange reserves of central banks, which in some cases (notably the Philippines and Thailand) have also increased in recent months.
    13It should be noted, however, that more recently the ASEAN economies (Indonesia, the Philippines, and Thailand) have seen a marked improvement in their current account positions.
    14The exact size of valuation gains cannot be calculated since the currency composition of reserves is generally not available. But if one assumed, for example, that 30 percent of Asia’s foreign reserves was held in euro, the valuation gains between end-2005 and mid-2006 would amount to roughly $60 billion, reflecting the 7½ percent depreciation of the U.S. dollar against the euro during this period.
    15Factors keeping core inflation low in India include cuts in import duties, and more generally competition from imports. Broader concepts of inflation (such as the wholesale price index) are likely to underestimate price increases due to (i) price controls and other measures to ease food price pressures (e.g., export bans on certain agricultural products, and custom duty reduction on others), and (ii) remaining controls on fuel prices. Even so, CPI inflation had risen to 7.9 percent in June, from 4.4 percent in January.
    16It should be noted, however, that to the extent that lower domestic interest rates boost equity valuations, this may actually encourage portfolio investment in equity.
    17Investor concerns about delays in tightening monetary policy (and adjusting fuel prices) contributed in part to the financial volatility experienced in Indonesia in August-September 2005.
    18Rapid credit growth is generally not of inflationary concern in Asia at the moment because it is starting from a relatively low base, reflecting the sharp contraction in corporate credit in the wake of the Asian crisis.
    19Chapter IV includes a discussion of the links between equity prices and the real economy, including the impact of asset-price swings on the net worth of financial institutions.
    20Interest payments (on domestic and external public debt) range from less than ½ percent of GDP in China, to 6 percent of GDP in India and the Philippines.
    21The average maturity of public external debt is roughly 13½ years for Thailand, and about 20 years for Indonesia and the Philippines.
    22The main authors of this chapter are Catriona Purfield, Hiroko Oura, and Charles Kramer. Andy Jobst and Jennifer Elliott contributed material on equity derivatives and smaller stock markets respectively.
    23Equity derivatives are mainly traded on organized exchanges rather than over the counter (OTC). Annual OTC equity trading in Asia is only around $100 billion (BIS (2005)).
    24In 2004, this figure excludes assets invested in Hong Kong SAR by overseas mutual funds.
    25Relatedly, Chapter V in the May 2006 Regional Economic Outlook discusses the hypothesis that increased uncertainty may have dampened capital investment in emerging Asia.
    26Box 4.1 finds that capital flows in emerging Asia have become more volatile in absolute terms, although it also finds that this has not necessarily resulted in greater vulnerability.
    27Households are also exposed to wealth effects from real estate price changes.
    28Because these calculations use aggregate data, volatility faced by individual households could well be higher.
    29The main authors of this chapter are Robin Brooks and Enric Fernandez.
    30For a survey of consumer finance in emerging Asia, see Box 5 in the May 2006 REO.
    31The long run impact of growth on the consumption-to-GDP ratio can be calculated by dividing the coefficient estimate on per capita output growth by one minus the coefficient on lagged consumption.
    32See, for example, M. Gabriella Briotti, “Economic Reactions to Public Finance Consolidation: a Survey of the Literature,” European Central Bank Occasional Paper No. 38, Oct. 2005.
    33The main authors of this chapter are Eritk Lueth and Murtaza Syed.
    34Ravallion (2001) finds that the median rate of decline in the $1 day poverty headcount index is around 10 percent a year for countries that combine growth with falling inequality, compared to just 1 percent for those where growth coincides with increased inequality.
    35As an example, imagine a society of four individuals earning 1, 2, 3, and 4, respectively. Now, the richer individual in each half of the income distribution transfers 0.5 to the poorer individual in that half, resulting in a distribution where two individuals have 1.5 and two individuals have 3.5. Inequality has fallen in the process, because the gainers were poorer than the losers, but polarization—defined as the emergence of distinct groups—has increased.
    36To keep the exposition simple, the terms income and consumption are often used interchangeably in this chapter.
    37The Gini coefficient is defined as the area between the Lorenz curve (which plots cumulative shares of the population, from poorest to richest, against the cumulative share of income they receive) and the 45-degree line, taken as a ratio to the area of the whole triangle. The values, which range from 0 in the case of perfect equality and 1 in the case of perfect inequality, are multiplied by 100 to obtain a range of 0 to 100 for the Gini index.
    38Despite the recent decline in its Gini index, Thailand remains among the most unequal and polarized economies in Asia.
    39Since the incomes of the very rich and very poor are hard to measure, the ratio of mean incomes of the 10th to the 1st decile is a less reliable indicator.
    40Like the Gini index, the Wolfson polarization index lies between 0 and 100, where 0 depicts a perfectly equal income distribution and 100 describes a situation in which half the population has zero income and the other has twice the mean.
    41PovCalNet, the World Bank database from which this and the following statistics are drawn does not include data for industrialized countries.
    42The finding of rising inequality and polarization in Asia is confirmed by earlier research, e.g., Ravallion and Chen (1997) and Firebaugh (2003). More recently, World Bank (2006a) reports that the rise in inequality is more pronounced than in previous growth episodes and extends to South Asia, traditionally a region with low inequality.
    43Although this may be driven by differences in the notion of the middle class. While our measures are based on relative incomes (i.e., the number of people with incomes a given distance around the median of the distribution), the popular press typically bases its assessments of the size of the middle class on absolute incomes, i.e. the number of people with incomes that allow for discretionary consumption of refrigerators, cars, cell phones, etc.
    44This database calibrates data from ILO’s October Inquiry, and allows comparisons across time and countries. It contains wage data for 161 occupations in over 150 countries from 1983 to 2003.
    45Wage dispersion is measured as the standard deviation of the logarithm of wages times one hundred and can be interpreted as the percentage deviation of a typical observation from the mean. For a given country, only wages of occupations that appear in both the initial and latest year are considered.
    46The exercise is mostly illustrative in that labor market coverage is very selective in each of the countries under consideration.
    47While we report regressions that use the Gini inequality index as the dependent variable, we obtained qualitatively similar results using the Wolfson polarization index.
    48The panel covers the period 1981-2004 and consists of the following countries: Bangladesh, Cambodia, Indonesia, Lao PDR, Malaysia, Mongolia, Nepal, Philippines, Sri Lanka, Thailand, and Vietnam. The data on inequality is published by the World Bank (2006b), which goes to great lengths to ensure some degree of international comparability.
    49However, the sample excludes Asia’s most developed economies with possibly major implications for the shape of the inverted U-curve. Moreover, causation may run from inequality to growth, resulting in inconsistent parameter estimates.
    50The unbalanced panel covers 13 countries over 1983-2003 and comprises 170 observations. The sample countries comprise Australia, Bangladesh, China, Hong Kong SAR, India, Japan, Cambodia, Korea, Sri Lanka, New Zealand, Philippines, Singapore, and Thailand.
    51Take-off dates are from WEO (2006).
    52In recent times, the information technology revolution is often cited as an example of this phenomenon.
    53In Korea, the equalizing forces discussed below may not have been as strong because it was the most urbanized and industrialized of the crisis economies, and the only country where poverty has an urban bias. Moreover, the crisis affected the urban poor disproportionately (Birdsall and Haggard, 2000).
    54The potential effects of demographics were also explored. In Japan, for example, growing income inequality is often attributed to population ageing—a recent study (OECD, 2006) finds that it explains almost half of the increase in inequality between the mid 1980s and 2000. However, we did not find such effects more generally—both old age and youth dependency ratios were not significant when added as explanatory variables to the regressions. This may reflect the fact that for 10 out of the 11 countries in the sample, the Gini index is based on consumption: while both the old and the very young tend to have relatively low incomes, their consumption is likely to be supported by intra-family transfers (and, in the case of the former, savings). In addition, population ageing is presently less pronounced in these countries. Hence, our results do not rule out the possibility that population ageing may also be an important determinant of income inequality and polarization.
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