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Globalization factors in income distribution and poverty in developing countries.

Abstract

The paper examines several globalization factors that determine income distribution and poverty in developing countries. Cross-country regression results indicate factors that make income distribution more equal and poverty lower are higher income per capita, rising number of internet users, lower telephone costs, and being among low-income countries. Those that render income distribution less equal are expanding volumes of airfreight transportation and increasing expenditures by international tourists. Poverty is negatively affected by the former but not the latter. As is consistent with other studies, foreign direct investment and trade have no effect on income distribution or poverty. However, if globalization contributes to economic growth then it will eventually lead to better income distribution and lesser poverty.

Keywords: Globalization, economic growth, developing countries, income distribution, poverty

JEL Classification: 040

I. INTRODUCTION

Income distribution and poverty within individual countries has been a major concern for globalization advocates. As globalization has accelerated in recent years, the question whether income distribution has been worsened and the poor become poorer has been debated heatedly. (Harrison, 2004). Krueger (1983) and Bhagwati and Srinivasan (2002) argue that trade reforms in developing countries should be pro-poor, since these countries are most likely to have a comparative advantage in producing unskilled-labor-intensive goods. Expanding trade opportunities should cut poverty and reduce inequality within poor countries.

However, Davis and Mishra (2004) argue that comparative advantage in producing goods made with unskilled labor is not absolute with respect to all countries but can fail vis-a-vis some countries with low-cost unskilled labor. Workers may gain from globalization depending on which sectors (import-competing or exporting) they are attached to. For instance, if globalization raises the prices of goods produced by the poor--such as agricultural products marketed by farmers--or lower those that are bought by the poor then poverty is likely to decline.

Easterly (2004) poses two opposite views. The "factor endowment" view: If poor countries are more endowed with (unskilled) labor, then relaxing constraints on global trade or factor flows will lead capital to channel into to poor countries and per capita incomes there should rise. The "productivity" view: if differences in per capita incomes stem from exogenous productivity differences across countries, rather than differences in endowment, then globalization will either have no impact on poverty or could exacerbate poverty, as capital is drawn away from low productivity countries towards high productivity regions.

Dollar and Kraay (2001), Sala-i-Martin (2002), and Prasad et al. (2004) contend that globalization could raise the incomes of the poor through a third channel: by increasing long run growth. Growing trade or capital flows could enlarge incomes of the poor by raising productivity through capital accumulation via foreign direct investment or imports of new machines embodying new technology.

At the empirical level, a number of cross-country studies find that globalization leads to increasing inequality. For example, both Milanovic and Squire (2004) and Easterly (2004) find that increasing globalization is associated with increasing inequality as the growth gains from trade have been completely offset by the adverse distributional outcomes for the poor. In addition, while the poor in the import-competing sectors are the losers following the liberalization of trade, those in the export sectors have not advanced that much because of the impediments by the developed markets.

The diverse and sometimes opposite outcomes indicate that relationship between globalization and income distribution and poverty depends not just on trade or financial globalization but on the overall response of the host country to the challenge of globalization. This includes changing social, legal, and political institutions (so-called soft infrastructure), building physical and human capital, and hard infrastructure such as roads, sewage systems etc., and application of policies to promote macroeconomic stability. (For a complete set of requirements, see, for instance, Tran [2007a]).

In most studies cited above, authors have used trade, foreign direct investment, and tariff as globalization indicators. In this paper, we examine several globalization factors that determine income distribution and poverty of developing countries. Cross-country regression results show that factors that make income distribution more equal are rising income per capita, greater number of internet users, lower telephone costs, and being low-income countries; those that cause it to be less equal are expanding volumes of airfreight transportation and higher expenditures by international tourists. Similarly, factors that worsen (do not worsen) income distribution also worsen (do not worsen) poverty (except international tourism). As is true with other studies, foreign direct investment and trade have no effect on income distribution or poverty in our model.

The paper is organized as follows. Factors influencing globalization are discussed in section II. Attempt to measure globalization is presented section III. A regression model to relate income distribution and poverty to globalization indicators is introduced in section IV. Regression results are discussed in section V. Finally, concluding remarks are given in section VI.

II. GLOBALIZATION FACTORS

Globalization is the process whereby the world and its economic and socio-cultural systems are made more uniform, integrated, and interdependent. Recent technological advancements--the internet, communications, and transportation--reduce the distances between countries and make the world smaller. The combination of technological and geopolitical forces renders previously close social and political systems more open and susceptible to change.

An essential factor in promoting globalization is the recent reduction in transport and communications costs (Kasper, 1994). Revolution in telecommunications through fiber optics and satellites allows the development of worldwide networks connecting personal computers to huge databases of computer programs and all kinds of information. Information as a part of capital becomes easily accessible and can be used to substitute for workers. Telecommunication advance allows international firms to move money, materials, products, and economic assets, around the world in seconds resulting in the establishment of a single global capital market. Firms can outsource or subcontract certain activities in a low cost country such as banking, credit, and customer services. Cheaper and faster transportation and communication open new markets, facilitate access to inputs, suppliers and contractors, allowing narrower forms of specialization in "fragmented" production processes. They enable a low-wage developing country to provide only the production or assembly phase of a product.

Individual economies are increasingly being globalized through globalization of finance, foreign direct investment, operations by the multinational corporations (MNCs), global specialization in the location of production, globalization of the tertiary sector of the economy, globalization of the office function, and global tourism.

Foreign direct investment (FDI) is instrumental in bringing the developing countries into the globalization process. FDI grew faster than world trade and four times faster than total world output. With brain power replacing mechanical power in products and the increasing creation of new man-made materials, the improvement in transportation and communications reduces the need for firms to be located at the source of raw materials. As a result, more final goods are being produced at the point of consumption. This explains why in 1998, 75 per cent of foreign direct investment went to large markets in developed countries. The United States and Canada alone absorbed 30 per cent of FDI and the U.S. was the largest recipient of FDI from other countries.

The multinational corporations play a crucial role in globalization of the individual economies by establishing subsidiaries and plants in places where they find most profitable. The MNCs are able to compete on a worldwide scale rather effectively since they can operate with greater information efficiency and share them with their subsidiaries and branches via the internet, satellites and fiber-optic communication systems. They have advantage on information over competing national firms since they possess knowledge on the worldwide markets, products, consumer preferences in different regional markets, characteristics of national labor forces, and business opportunities. The MNCs also hold huge amount of capital, great technological capability, highly effective managerial skills, and overall economies of scale. They act as an important agent in innovation and technology transfers between more developed countries (MDCs) and less developed countries (LDCs). Finally, they maintain leadership in the creation of tightly-knit global value chains in which they establish specific procurement and distribution systems that include certain sourcing countries of their choice.

One of the most salient features of globalization is expanding tourism across the globe. Many developing countries rely on tourism as a major export and foreign exchange-earning industry. Tourism in these countries grows much faster than manufacturing industry and its share in their GDP has steadily increased over time. Due to improvement in communications, transportation, and information technology, tourism becomes the largest global industry in which world travelers spent about $15 trillion in gross output in 1995. It employed 250 million people and produced 14 per cent of world GDP.

III. GLOBALIZATION INDICATORS

Although globalization has been frequently mentioned in economic development, there exists little literature about measurement of globalization and its effects on growth and development. Direct measures of globalization are difficult to obtain. For instance, the uniformity, integration, and interdependency of the economic and socio-cultural systems in the world would have to be measured over time. While waiting for these measures to be constructed, we can use factors that promote globalization as its indicators.

In this paper we use some indicators of globalization for which data are available to measure their effects on income distribution and poverty in developing countries. Expanding trade is an important factor in globalization process. Trade allows the transmission of knowledge either directly through technology embodied in imported equipments or indirectly through research and development stemmed from international competition. Innovations in the form of variety of machinery and in enhancing their quality can offset the tendency to diminishing returns of capital. The transfer of knowledge about production engineering and changing product patterns is likely coming through trade. Technology either imported or developed indigenously requires improved human capital. This is an important argument in the new growth theory in favor of trade as an engine of growth. Trade also offers larger markets which stimulate domestic investment and facilitate economies of scale. This, in turn, might generate externalities that prevent diminishing returns to capital. Finally, trade is an agent shaping the uniformity in consumption of standardized goods which promote a common lifestyle throughout the world. Hence, the greater the growth in trade, the broader the globalization becomes.

Foreign direct investment brings to the home country not only physical capital but also technology embodied in the machines and production and management structures. Thus FDI acts as an agent of knowledge and technology diffusion which allows the developing countries to change the shape of the production functions and their attendant productivities. FDI also contributes directly to trade since, for developing countries, without the former their production capability in manufactures may not be raised to expand trade. Most of foreign direct investments in developing countries are geared toward exports. Investments to exploit raw materials in home countries account for only a small proportion of total foreign investments. Greater exports frequently require more imports of raw materials or inputs, raising the total trade. Another aspect of foreign direct investment is that it is an instrument of globalization of industry by the multinational corporations (Moran, 2002). To enhance their competition in the international market, the MNCs have to provide their subsidiaries with the cutting edge of industry practices and generate backward linkages to the local suppliers. These steps create spillovers and externalities that, according to the new growth theory, can push the development of the host economy to a new level beyond the expectation of its leaders. The resulting economic successes encourage more FDI, and the cycle continues indefinitely proving that the greater the foreign direct investment is, the wider the globalization.

Traded goods have to be transported across borders. Falling sea and air transportation costs allow poor countries located far away from the market to enter into worldwide competition. In some large countries or underdeveloped continents, lack of transportation is a serious obstacle to input supplies, final goods distribution, and market formation. Thus rising volumes of land and sea transportation such as container transportation, and of air passenger and airfreight transportation, are crucial to the expansion of the world market.

The world is brought closer not only by increasing trade but also by improved communication. Expenditures on overall telecommunication systems, in particular fiber optics and satellites, and expenditures on information and communication technology in general are another contributor of globalization. As mentioned above, international tourism and the number of internet users should be considered among the factors that unify the world.

We select seven measures of globalization factors some of which partly on the basis of data availability: airfreight transportation, foreign direct investment, total trade as per cent of GDP, and per capita income, telephone costs to the U.S., international tourism, and internet user population. For instance, a more appropriate measure should be total shipping from all modes of transportation. However, until its data forthcoming, airfreight is used instead.

Airfreight transportation is a proxy for capacity to reach out to the global marketplace. Its expansion helps the export and import industries of the home country, particularly the high technology sectors. It is expected to increase the income inequality among different occupational groups, particularly between workers producing trade and non-traded goods. To the extent that airfreight transportation carries high-technology goods or low-weight but high-value commodities, it might have negative effect on the poor groups who are largely unskilled. Thus, we would expect this variable to raise income inequality and poverty.

Foreign direct investment enhances competitive advantage of home country through increasing its manufacturing capability, particularly its export industries. Again, it is expected to raise the income inequality among different occupational groups. On the one hand, as most foreign investment is concentrated in low-wage and labor-intensive industries, it might benefit the poor. Consequently, so long as FDI contributes to economic growth, it should lessen inequality and poverty. On the other hand, productivity level required in these industries tends to help the trained and skilled workers. Thus, it is difficult to identify the direction in the relationship between FDI and poverty. Indeed, Prasad et al. (2004) did not find any relationship between the two in many empirical studies.

Trade quantifies degree of a country's openness which, in turn, depends on the extent of trade liberalization carried out, specifically on how they lower tariff and non-tariff barriers and how they implement WTO rules. As indicated above, trade tends to raise income inequality and poverty if it favors skilled and high-productivity workers. However, previous empirical studies found no evidence in the aggregate data one way or another as to whether trade reforms are good or bad for the poor (Harrison, 2004).

Growth in per capita income tends to lower both income inequality and poverty as pecuniary benefits of growth eventually trickle down to low income groups.

Access to a telephone that can call overseas at lower costs indicates greater global participation. It bridges the geographical division and allows global communications between cultures. In particular, telephone costs to the U.S. affect how a large potential market can be accessed and communicated. As lowering this telephone costs would benefit the general population as well as small businesses participating in the world market, we would expect this trend to lower income inequality and poverty.

International tourism promotes greater understanding between cultures and facilitates tolerance and respect between peoples of different backgrounds. However, its expenditures would benefit mainly urban centers where most of the service is provided to tourists. Thus international tourism tends to raise both income inequality and poverty.

Number of internet users represents the portion of a country's population that are integrated to the global community. The internet provides a venue through which information can be disseminated at minimal costs. It promotes freedom of expression and human rights. Developing countries are behind in development because of, inter alia, lack of technical knowledge and knowledge of attributes. The latter refers to knowledge about clients, consumers, producers, sellers, products, services, employees, market, characteristics, and the like. In fact, one can get information on anything one wants through the web. Greater population of internet users means greater skills and knowledge are spreading among the population at large and thus it tends to lower income inequality and poverty.

IV. REGRESSION MODEL AND DATA

Income distribution is expressed by Gini coefficient (GINI) and poverty by income share held by the poorest 40 of the population (LOW40). We regress GINI or LOW40 against AFTY, FDID, TRAY, YPC, TELC, ITEX, IUTP, D1LIC, D2LMC, D3UMC, and D4OEC as in the equation (1) below.

Y = [[beta].sub.0] + [[beta].sub.1]AFTY + [[beta].sub.2]FDID + [[beta].sub.3]TRAY + [[beta].sub.4],YPC + [[beta].sub.5]TELC + [[beta].sub.6]ITEX + [[beta].sub.7]IUTP + [[beta].sub.8]D1LIC + [[beta].sub.9] D2LMC + [[beta].sub.10]D3UMC + [[beta].sub.11]D4OEC + u (1)

where

Y = GINI or LOW40.

AFTY = Air freight transportation (tons per kilometer) as per cent of its GDP (Purchasing Power Parity [PPP], constant 2000 international $).

FDID = Net inflows of foreign direct investment as per cent of its domestic gross fixed capital formation as in Welfens (Table A9, p. 46).

TRAY = Trade (% of GDP) average of preceding 5 years inclusive of the current year.

YPC = GDP per capita (PPP, constant 2000 international $).

TELC = Average cost of telephone calls to the United States (US$ per three minutes).

ITEX = International tourism, receipts (% of total exports).

IUTP = Internet users (per 1,000 people).

All these data are averages of the last 5 years inclusive of the current year.

Dummy variables:

D1LIC = 1 if the country is a low income country (LIC), = 0 otherwise

D2LMC = 1 if the country is a low middle income country (LMC), = 0 otherwise

D3UMC = 1 if the country is a upper middle income country (UMC), = 0 otherwise

D4OEC = 1 if the country is an OECD country, = 0 otherwise.

Because of data limitation each country has only one Gini coefficient and these coefficients of different countries are not of the same year. For example, the year which Gini coefficient is obtained for India is 2000 but for Ecuador it is 1998. The average of the last 5 years of the data depends on the years of the Gini coefficients (and the lowest 40% which are in the same years). For instance, if the year Of Gini coefficient is 2003, then the observations for independent variables will be average of observations for 1999, 2000, 2001, 2002, 2003. For some countries, if there are less than 5 years then we use the average of these years. All cross-country data come from the World Bank Development Indicators 2006.

In the regression equation for GINI, [[beta].sub.1] is expected to be positive; [[beta].sub.2] could be either positive or negative; [[beta].sub.3] could be either positive or negative; [[beta].sub.4] is expected to be negative; [[beta].sub.5] positive; [[beta].sub.6] positive; [[beta].sub.7] negative.

In the regression equation for LOW40, the signs are exactly opposite, i.e. [[beta].sub.1] is expected to be negative; [[beta].sub.2] could be either positive or negative; [[beta].sub.3] could be either positive or negative; [[beta].sub.4] is expected to be positive; [[beta].sub.5] negative, [[beta].sub.6] negative; [[beta].sub.7] positive.

The signs of the coefficients of the four dummy variables are indeterminate apriori, except for those of OECD countries where their high income status ensures a more equal income distribution (negative) and less poverty (positive).

V. REGRESSION RESULTS AND DISCUSSION

Table 1 shows a positive relationship between Gini coefficients and airfreight transportation, average cost of telephone calls to the U.S., and international tourism. The coefficients of these variables are significant at 1% level. But the relationship between Gini and income per capita and the number of internet users is negative and their coefficients are significant at at least 10% except for income per capita in model 3. This means that expanded airfreight transportation, and rising international tourism appear to make income distribution more unequal while higher income per capita, lower average cost of telephone calls to the U.S., and increasing number of internet users make it less unequal. Income distribution of low-income and OECD countries improves while that of the lower-middle-income and upper-middle-income countries worsens. If we measure Gini coefficient on the vertical axis and income per capita on the horizontal axis, the plot of Gini against income per capita would support an inverted Kuznets U-curve. The signs of the coefficients in Table I are as expected.

Table 2 shows a negative relationship between LOW40 and airfreight transportation and telephone costs the coefficients of which are significant at 1% level. The relationship between LOW40 and income per capita and number of internet users is positive with their coefficients being significant at at least 10% (except model 3). Expanding airfreight transportation reduces the income share of the lowest 40 per cent of the population while higher income per capita, lower average cost of telephone calls to the U.S., and rising number of internet users raise it. International tourism does not seem to have any effect on the poverty. Just like the case of income distribution, the poor group improves their income in the low-income and OECD countries but find themselves worse off in the lower-middle-income and upper-middle-income countries. So the relationship between poverty and income per capita also follows an inverted U-curve pattern. The signs of the coefficients in Table 2 are also as expected except [[beta].sub.6] of international tourism.

Cross-section results presented in both tables show that only expanding airfreight transportation has negative effect on both income distribution and poverty. But airfreight is too narrow a measure. A better indicator would be total shipping including container and rail shipping but no such data are available. We speculate that total shipping would be highly correlated with GDP per capita, which would contribute to ameliorate income inequality and poverty. Out of the three most important globalization factors, namely foreign direct investment, openness, and income per capita, only the latter is significant. Using time-series data for individual countries and groups of countries, Tran (2007b) has shown that globalization contributes to economic growth in LDCs. Thus, the combined results indicate that globalization will eventually improve on income distribution and poverty. However, globalization only helps the poor after the LDCs have gone through a worsening phase according to the Kuznets's inverted U-curve pattern. It has been established that investment in physical and human capital, transfer of technology and knowledge, and appropriate institutional reforms would enhance economic growth. Policy implication is that the LDCs should promote globalization and encourage their citizens and firms to participate in the world economy in such a way as to promote domestic and foreign investment in physical and human capital and knowledge, then income distribution will take care of itself.

VI. CONCLUDING REMARKS

In this paper, we examine several globalization factors that determine income distribution and poverty of developing countries. They are airfreight transportation, foreign direct investment, trade or openness, income per capita, telephone costs, international tourism, and number of internet users. Cross-country regression results show that factors that make income distribution more equal are higher income per capita, lower telephone costs, rising number of internet users, and being low-income countries or high-income OECD countries; those that cause it to be less equal are rising volumes of airfreight transportation, increasing expenditures by international tourists, and being low middle-income or upper middle-income countries. Similarly, factors that worsen (do not worsen) income distribution also worsen (do not worsen) poverty (except international tourism). As is consistent with other studies, foreign direct investment and trade show no effect on income distribution or poverty.

In summary, our study points to rising income level and greater degree of participation in the world community through internet access and telephone communication as a key to more equal income distribution and less poverty. Thus policies to promote growth and expanded communication will contribute to better income distribution and to relieving the poor's burden. But to achieve the income level of the OECD countries requires each developing country to pass through a stage that demands unequal distribution of income as a condition to provide incentives for work and investment. Further, evidence shows whether a country grows economically depends on its response to opportunities opened up by globalization forces. If a country mobilizes resources and initiates appropriate institutional reforms to take advantage of these opportunities, it will be launched along a growth trajectory. But if a country is not willing to take up changes demanded by globalization it will be left behind in the global competition, and its economic growth will be severely hampered. This, in turn, will make the country less able to solve the problem of unequal income distribution and high poverty.

References

Bhagwati, J. and Srinivasan, T. N. (2002), "Trade and Poverty in the Poor Countries", American Economic Association Papers and Proceedings, Vol. 92, No. 2, 180-183.

Davis, Don and Prachi Mishra (2004), "Stolper-Samuelson is Dead: and Other Crimes of Both Theory and Data", In Globalization and Poverty, Ann Harrison (ed.). Chicago: University of Chicago Press for National Bureau of Economic Research (NBER).

Dollar, David and Aart Kraay (2001), "Trade, Growth and Poverty", World Bank Development Research Group Working Paper 2615. Available at http://econ.worldbank.org/files/24986_wps2615.pdf

Easterly, William (2004), "Globalization, Poverty, and All That: Factor Endowment versus Productivity Views", in Ann Harrison (ed.), Globalization and Poverty, University of Chicago Press for NBER, Chicago.

Harrison, Ann (2004), (Ed.), Globalization and Poverty, University of Chicago Press for NBER, Chicago.

Kasper, W. (1994), Global Competition, Institutions, and the East-Asian Ascendancy, Institute for Contemporary Studies Press, San Francisco, California.

Krueger, A. (1983), "Trade and Employment in Developing Countries", Vol. 3: Synthesis and Conclusions, University of Chicago Press, Chicago.

Milanovic, Branko and Lyn Squire (2004), "Does Tariff Liberalization Increase Wage Inequality? Some Empirical Evidence", in Ann Harrison (ed.), Globalization and Poverty, University of Chicago Press for NBER, Chicago.

Moran, Theodore H. (2002), Beyond Sweatshops, Brookings Institution Press Washington, D.C.

Prasad, Eswar S., Kenneth Rogoff, Shang-Jin Wei, and M. Ayhan Kose (2004), "Financial Globalization, Growth, and Volatility in Developing Countries", in Ann Harrison (ed.), Globalization and Poverty, University of Chicago Press for NBER, Chicago.

Sala-i-Martin, Xavier (2002), "The World Distribution of Income (Estimated from Individual Country Distributions)", NBER Working Paper No. 8933.

Tran, Dang T. (2007a), "Globalization and Growth of Developing Countries", Chapter 23 in Thai, Khi V., Rahm, Dianne, and Jerrell D. Coggburn (eds.), Handbook of Globalization and the Environment, CRC Press, Boca Raton, Florida, 491-525.

Tran, Dang T. (2007b), "Globalization and Growth of Developing Countries: An Econometric Estimate", Paper presented at the International Atlantic Economic Conference, Madrid, Spain, March 14-18.

Welfens, Paul J.J. (1999), Globalization of the Economy, Unemployment and Innovation, Springer-Verlag, New York.

DANG T. TRAN

California State University, Los Angeles

Note

(1.) An earlier version was presented at Global Studies Conference, University of Illinois, Chicago, May 16-18, 2008. Many thanks for comments from participants of the session for this paper.

Table 1
Regression Results For GINI

                               Dependent variable: GINI
Explanatory
Variable          Model 1           Model 2           Model 3

ATFY                2.089 ***         2.56 ***          2.16 ***
                   (0.466)           (0.47)            (0.47)

FDID                5.05              2.65              2.68
                  (10.43)           (10.80)           (11.91)

TRAY               -0.038             5.019            -0.024
                   (0.031)          (10.43)            (0.036)

YPC                -0.00048 ***      -0.00021 ***      -0.0000021
                   (0.00011)         (0.000078)        (0.00021)

TELC                0.0205 ***        0.0200 ***        0.0129 ***
                   (0.0029)          (0.003)           (0.0031)

ITEX                0.00024           0.0024 ***        0.0022 ***
                   (0.00026)         (0.00023)         (0.00028)

IUTP               -0.0289 **        -0.031 ***        -0.028 *
                   (0.0132)          (0.010)           (0.015)

D1LIC              -8.56 ***
                   (2.31)
D2LMC                                 5.85 **
                                     (2.22)

D3UMC                                11.27 ***
                                     (2.36)

D4OEC                                                  -8.66 ***
                                                       (0.015)

C                  45.71 ***         35.94 ***         39.12 ***
                   (2.60)            (2.54)            (2.28)

[[bar.R].sup.2]     0.34              0.38              0.24

F-statistic         6.42 ***          6.90 ***          4.40 ***

n                 128               128               128

Note: (1) Superscripts ***, **, *, denote significance at 1%,
5%, and 10 % level, respectively.

(2) The numbers in parentheses beneath the coefficients are
their standard errors.

(3) [[bar.R].sup.2] is adjusted [R.sup.2], n = number of
observations, C = constant.

Table 2
Regression Results for Low40

                               Dependent variable: LOW40
Explanatory
Variable          Model 1           Model 2           Model 3

ATFY               -0.93 ***         -1.13 ***         -0.94 ***
                   (0.22)            (0.23)            (0.20)

FDID               -2.55             -1.41             -3.41
                   (5.04)            (5.28)            (5.82)

TRAY                0.019             0.015             0.011
                   (0.015)           (0.016)           (0.018)

YPC                 0.00022 ***       0.000073 ***     -0.0000015
                   (0.000055)        (0.000038)        (0.000096)

TELC               -0.00821 ***      -0.0080 ***       -0.0045 ***
                   (0.0013)          (0.0014)          (0.0015)

ITEX               -0.000076         -0.000047          0.0000034
                   (0.00013)         (0.00011)         (0.00014)

IUTP                0.0136 **         0.0140 ***        0.0131 *
                   (0.0066)          (0.0053)          (0.0074)

D1LIC               4.50 ***
                   (1.11)

D2LMC                                -3.29 **
                                     (1.08)

D3UMC                                -5.04 ***
                                     (1.04)

D4OEC                                                   3.64 **
                                                       (1.85)

C                  13.99 ***         19.94 ***         17.35 ***
                   (1.24)            (1.04)            (1.13)

[[bar.R].sup.2]     0.34              0.35              0.20

dd                  6.50 ***          6.22 ***          3.74 ***

dd                128               128               128

Note: (1) Superscripts ***, **, *, denote significance at 1%,
596, and 1096 level, respectively.

(2) The numbers in parentheses beneath the coefficients are their
standard errors.

(3) [[bar.R].sup.2] is adjusted [R.sup.2], n = number of
observations, C = constant.
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Date:Mar 1, 2010
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