FDI as a Factor of Globalization and its effects on Wages in Least Developed Countries Robert F. Wiley May 2008 1 Introduction Since World War II, the global economy has seen two periods of international integration and while different instances of globalization can be characterized by specific circumstances and effects, the current period (approximately 1980‐2003) is generally associated with the expansion of the international economy through trade, capital flow and technology sharing. 1 This expansion follows a trend of trade liberalization facilitated by several international organizations whose goal is to discourage protectionism and promote trade liberalization. The most notable of these organizations is the General Agreement on Tariffs and Trade, GATT, (1948) and its successor, the World Trade Organization, WTO, (1995). Proponents of globalization, sometimes called “globalists,” argue that global economic expansion leads to increased prosperity, and more efficient allocation of resources.2 The shining example is China, where the liberalization of economic policies in past years has corresponded with several years of high, sustained growth. The reality, however, is that China is an anomaly and a large number of countries and their people continue to struggle and live on dollars per day. As of March 2008, fifty countries remain on the United Nation’s Least Developed Countries list.3 Anti‐globalization advocates would argue that these poorer countries aren’t able to compete effectively (through subsidization) in the market for selling goods and are therefore forced to sell their goods at lower prices and this ultimately decreases their economic well‐being. The following research will not attempt to make an assumption about whether the effects of globalization have been either favorable or unfavorable, but rather to discern whether there exists a meaningful relationship between factors of globalization and some aspects of developing economies for a given time period. While globalization can be measured as the result of four factors: capital and labor flow, trade openness, and technology sharing, the following research will only focus on capital flow in 1 | P a g e the form in the form of foreign direct investment. Additionally, the effects of globalization on any specific country can be widely varied, from political and legal to cultural and ecological. In order to obtain meaningful results then, we will focus on globalization’s effect on labor in LDCs with an emphasis on wages. FDI statistics will show a degree of endogeneity for wages in any given country because of the innumerable factors affecting investment and wage at the aggregate level. In order to create a usable model, an instrument will attempt to explain the variation in FDI while also showing no correlation with the error terms in the estimates of wage levels. This instrument represents a factor that may influence the level of FDI flow while not affecting the relative wage levels. Every year the World Bank collects data on a large number of investment related factors from around the world. Included in that database are investment risk ratings given for whole countries as rated by institutional investors. It is possible that these risk ratings will make a suitable instrument for the model because they are presumably highly correlated with the amount of investment inflow, while being uncorrelated with the wages. In the following pages we will first look at a short description of the World’s LDCs, followed by a brief theoretical framework outlining the possible relationship of FDI and wages with some empirical evidence on the topic. Additionally, the paper will outline and run the proposed model and finally we will examine the results with an emphasis on the instrumental variable regression. LDCs Since the late 1960’s the UN has paid special attention to the world’s LDCs. These countries represent the poorest and most vulnerable people in the world and classification as an LDC, now requires formal guidelines to be met. According the UN‐Office of the High Representative for the Least Developed Countries, the criteria for an LDC are as follows: “a low‐
income criterion, based on a three‐year average estimate of the gross national income (GNI) per capita (under $745 for inclusion, above $900 for graduation), a human capital status criterion, involving a composite Human Assets Index (HAI) based on indicators of: (a) nutrition: percentage of population undernourished; (b) health: mortality rate for children aged five years or under; (c) education: the gross secondary school enrolment ratio; and (d) adult literacy rate, an economic vulnerability criterion, involving a composite Economic Vulnerability Index (EVI) based 2 | P a g e on indicators of: (a) population size; (b) remoteness; (c) merchandise export concentration; (d) share of agriculture, forestry and fisheries in gross domestic product; (e) homelessness owing to natural disasters; (f) instability of agricultural production; and (g) instability of exports of goods and services.”4 Of the fifty countries satisfying the criteria, 34 are located in Africa, 15 in Asia and the Pacific, and 1 in Latin America.5 As of 2005 the population in the LDCs was approximately 750 million and almost 50% of that population are living on less than 1$ per day.6 While LDCs contain more than 10% of the world’s population, the countries accounted for only 1.6% of the world’s FDI inflow in 2001.7 While the factors of globalization have been increasing for the global economy, some argue that LDCs have been excluded from this expansion. Ajit Ghose examines this hypothesis in his book Jobs and Incomes in a Globalizing World. Ghose proposes that adverse effects seen by many of the world’s developing countries associated with globalization are often the result of exclusion from the global economic expansion rather than exploitation of cheap labor and commodities. In Jobs, Ghose focused his research on trade as a factor of globalization, rather than FDI, on the premise that trade makes up a much larger percentage of the world GDP than FDI. It is useful, however, to look at the trends that can be seen in both trade and FDI in the last 25 years. In 1980 the FDI stock to world GDP ratio was around 5 and by 1998 it had jumped to 14. In the same period trade‐GDP ratio first declined, then rose back to the 1980 level in 1992 and grew rapidly thereafter.8 These trends are evaluated with a focus on how the changes affect the labor market structure of the world. In the end, Ghose concludes that increased trade between industrialized and manufactures‐exporting developing countries has affected the structure of both countries’ labor markets. In regards to the labor markets, much of the research was concerned with income inequality and the types of jobs created or destroyed, but interestingly, Ghose found no evidence that trade had any effect on real wages for either type of country. FACTORS OF GLOBALIZATION It could be argued that international migration is the most contentious of the factors of globalization. It is often the view that migrants undermine employment prospects and wages for domestic workers and the result is that in an era of unprecedented levels of trade, technology and capital flow, migration restrictions are as stiff as they have ever been. While international migration was the central feature of the nineteenth‐century period of globalization, the current 3 | P a g e expansion has not seen the large increases in labor flow of migration. From 1980 to 1998 the stock of the world migrant population as a percentage of the world population went from 2.2% to 2.4%.9 The debate over international migration becomes even more contentious when the topic of the so‐called “brain‐drain” is addressed. However, discussions of the South‐North migration and issues of skilled and un‐skilled labor will not be addressed here. In fact, migration as a factor of globalization will be viewed as a minor feature of globalization’s effect on LDCs and as such will not enter into the formal research. The advancement and sharing of technology is without a doubt a major contributor to all episodes of globalization. In the 16th century improvements in sailing and navigating technology led to the connection of the Old World with the New World. More recently, however, two factors have led to the current global economic expansion. The first has already been touched on briefly as the increase in trade liberalization policies. The second and equally important factor is the improvement in transportation and communication technologies. These improvements have decreased costs to the point that it is profitable to split production into smaller parts at locations throughout the world‐ a process called “supply‐chaining.” It can be argued that FDI and trade statistics can be indicators of the amount of technology sharing between countries because practices like “supply‐chaining” require elements of foreign investment and trade. It has also been documented that technology transfer is an effect of foreign direct investment. This and the fact that technology transfer presents measurement problems leads us to exclude technology transfer as its own factor from this evaluation. The idea of investment into a foreign country isn’t necessarily a novel concept, however, the rapid growth in the amount of FDI flow and stock and the increasing number of trans‐
national corporations (TNCs) can be viewed as a fairly recent development. Between 1982 and 1999 it is estimated that foreign divisions of TNCs doubled their share of gross world GDP. 10 The year 2007 saw the highest levels ever recorded for FDI, with significant increases in all major country groupings, presenting evidence that the trend is continuing.11 Traditionally, in economics, the impact of trade has been the focus of international relations, but it can now be useful to analyze the impact of investment as well as FDI plays an increasingly important role in the international economy. International trade involves the reciprocal transfer of goods and services across boundaries and this differs from FDI in several ways. The first is that FDI occurs most often between a parent company located domestically and a subsidiary or affiliate located 4 | P a g e in a foreign territory and the transfer takes the form of capital. FDI is measured as the amount of investment made to acquire a lasting interest in a foreign enterprise with the intention of gaining a voice in the management. In this framework the “direct investor” makes the investment and the “direct investment enterprise” receives it. The important stipulation pertaining to the definition of FDI that will help in our analysis is that the investment must be made with the intention of gaining a voice in the management of the investment enterprise. This is important because we are attempting to look at the labor‐related effects of a foreign enterprise’s ability to make decisions in another country. Traditional economic theory generally aims to examine the effects of trade on labor, while it is a more recent development to examine the effects of FDI separately from trade. The prescriptive framework of international trade tells us that when two countries enter into trade with their only difference being the composition of the labor force, the result is an increase in the real wage for skilled workers (and a subsequent decrease in real wage for unskilled labor) in the country where skilled manufactures increases and the opposite in the country where unskilled manufactures increase. Empirical evidence has shown that these assumptions do not always hold true in the real world where access to technology is not homogenous and capital and labor are not fully mobile, but it is generally assumed that trade does have some impact on wage inequalities, either between or within countries. As outlined above, trade and FDI have distinct differences, so it is not unreasonable to assume that their relative effects on wages may also be different. Before we can examine the results of FDI on relative wage levels it would be prudent to examine whether FDI has a statistically significant effect on wages at all. The hypothesis of how FDI affects wages, starts with how we assume the investment will affect production. The presumption is that when a subsidiary receives investment their aim is to increase production. Increased production in developing countries can be associated first with increased employment and indeed, Ghose found that in some countries this held true. 12 Additionally, we can make the assumption that increased investment into developing countries will increase productivity. Ghose found that changes in wage levels were the result of productivity changes, not trade, and empirical evidence exists that suggests that FDI may have impact on wages through productivity changes. Yasin Mesghena of Morehead State University published research in 2006 examining the long run relationship between wages in manufacturing and globalization using panel data from three developing countries with globalization represented by FDI inflows and trade openness.13 His results show that there is evidence of a long run relationship between wages in manufacturing 5 | P a g e and both measures of globalization. In this basic model, falling transportation and communication costs lead firms to desire to supply chaining. In order for firms to stretch their supply‐chains to developing countries they will need to increase their capital investment there. The increased capital investment will result in increased output and higher productivities that will in‐turn lead to higher wage levels. 2 Variables To assess the impact of FDI on wages in LDCs requires a compilation of relevant statistics. This compilation must include variables that effectively account for the factors affecting the amount of investment a county receives as well as the determinants of wage levels in that country. The UNCTAD gathers data on FDI yearly for its World Investments Report as this information is seen as valuable to evaluating the changing tides of the global economy. FDI inflows per year will hopefully capture the degree by which globalization has increased or decreased across time. Wage level information can sometimes be difficult to come by for the World’s LDCs because of their inherent lack of infrastructure. Fortunately, a large amount of data is available through the Occupational Wages Around the World database (OWW) as kept by Richard Freeman and Remco Oostendorp. The wage data are derived from the International Labor Office’s October Inquiry by calibrating the data into a normalized wage rate for each occupation. This wage rate represents the average monthly wage for men in each industry. While the entire database covers 161 occupations in over 150 countries from 1983 to 2003, only 12 countries have been chosen from the LDCs list because of data availability. The sample will hopefully be representative of wage levels within the country as well as across all LDCs. Appendix i contains a list of the included industries and illustrates that the wage figures are representative of both skilled and un‐skilled types of employment. This database contains individual variables for the raw data as well as country‐specific calibrated data with lexicographic weighting and imputated data in order to increase the amount the number of observations. This obviously increases the degree of uncertainty, but the raw data statistics have not shown to have enough observations for this application. The inclusion of different industries presents a bit of problem when the other data represent yearly aggregates for the country. The data for each industry for each year and for each country have been given as separate observations. A similar problem exists in that the industries vary across countries. More specifically, we are presented with an equal contributions problems. The hope is the large 6 | P a g e number of observations created will keep the estimator consistent. Unfortunately, this is means that our evaluation will be based on pooled data rather than the intended panel‐style. A list of the included industries can be found in Appendix i. The independent variables here are attempting to explain variation in FDI and wages in order to find the ceteris paribus relationship of FDI and wages. The first variable is simply the GDP per capita for each country in each year. This variable is colloquially assumed to be a measure of a countries relative wealth. The variables inclusion addresses the idea that relatively richer countries may have different wages with everything else constant. Also included will be the total GDP level. The aim of this variable is to control for differences country size in regards to a number of variables including population, natural resources and location. The least developed of the World’s countries are also the most susceptible to economic crisis and this presents a problem with the price levels and other factors that might be affecting wages. To account for this, data were included that represent the yearly percentage changes in inflation. Although the trend over the last 60 years has been the liberalization of trade policies, the degree of trade openness varies with the country. Trade openness will be included then because it may indicate how conducive the country is to foreign investment as well as accounting for the possibility of global exclusion. The trade openness variable is given as the sum of exports and imports as a percentage of GDP. Similar to trade openness, but representative of the domestic side of investment is the inclusion of data on the percentage of GDP from investment. As A.K. Ghose surmised, global exclusion may result from a country’s inability to shift from agricultural commodities production to manufactures. Manufactures export may be correlated to the amount of FDI inflow. As a way to account for this factor of FDI, agricultural raw material exports as a percentage of merchandise exports will be included. While at the aggregate level it may be difficult discern, education can certainly be a factor of wage levels and as such, data on the level of educational attainment across a country has been included. For any TNC the object of FDI is to create output from a number of inputs, the most obvious of which is capital. It is possible that the amount of capital formation within a country can impact both the amount FDI inflow and the wage. It is useful then to include capital formation statistics when modeling FDI and wages. Included are the estimated ratio of physical capital to output as well as the FDI inflows as a percentage of gross fixed capital formation. It is possible that any given year in the set was more conducive to FDI than year preceding or following it. This could be the result of 7 | P a g e any of the economic, political, or social transgressions occurring during that year and to account for this possible impact on any given country, included is the total world inflow for each year. More detailed descriptions of the above variables can be found in Appendix i. 3 Descriptors We first look at some descriptive statistics in order to gauge the characteristics of the data. Appendix ii shows the twelve selected countries. While the selection was based on data availability, these countries show characteristics of being representative of the larger group of LDCs. None of the selected countries are categorized as “oil‐exporting,” by the UN‐OHRLLS. The six “oil‐exporting” LDCs received 70% of all the FDI inflow that went to all LDCs in 2004.14 The inclusion of any of the oil‐exporting LDCs without the ability to control for the data could result in an outlier problem and skew the estimations. Table 1 has some interesting information with economic significance. For instance the mean monthly wage for these twelve LDCs was only $138, while the minimum was less than $7. For comparison, the average monthly wage for all the 150 countries in the set was $732 and the maximum was $22650. The statistics are similarly bleak for GDP per capita. The twelve LDCs have a mean GDP/cap. of $289 per year with a maximum value of $722. For the same period, the United States averaged a GDP/cap. around $25500.15 Table 1 Wage FDI GDP/cap Mean 138.54 33.93 289.18 Std. Dev. 174.16 91.62 97.96 Min. 6.8 ‐140.311 120.94 Max 3695.32 578.7 721.77 8 | P a g e From a simple correlation matrix (Table 2) we can see that the log of wage (lwage) and level wage (x2wlus) have mild positive correlation with FDI (fdi), (.162 and .0489 respectively). The wage variables are only slightly less correlated with the instrument (invrat), but the direction or the correlations support the model and intuition. Table 2 Correlations lwage X2wlus Fdi GDP/cap. invrat LogWage 1 Wage X 1 FDI .1623 .0489 1 GDP/cap. .3280 .2946 .2217 1 invrat .1361 .0295 .8301 .0711 1 With separate observations for each industry within 12 countries each year for 21 years the total number of possible observations is 7095, but there is a downside to creating the large number of observations. Because there are multiple sets of observations for every year we effectively lose the ability to analyze the data in the panel format. Each year becomes a different set of observations for each country and its industries and as mentioned earlier this presents a problem with consistency. Another important characteristic is that the number of observations will change dramatically with the variables in the regression because of the availability of the data. Agricultural raw materials exports as a percentage of merchandise exports contains the least amount of data with 2954 observations. 4 The Model The model will start with a simple OLS estimation of the significance of a number of variables on the level wage (x2wlus), but as mentioned earlier we can assume that this 9 | P a g e estimation produces an inconsistent estimator. Further analysis will consist of a two‐stage least squares estimation with log wage as the dependent variable of the instrumented fdi and the other independent variables. The two‐stage least squares procedure is an attempt to account for a violation of the Zero Conditional Mean assumption in the data. In order to effectively create a two‐stage least squares model we need a legitimate instrument and a good instrument is characterized by having non‐zero correlation with the endogenous variable and zero correlation with the dependent variable. It is also necessary that the instrument be statistically significant as a factor of the endogenous variable. If we look at Table 2, the correlation matrix shows that the investment rating (invrat) is correlated with fdi with a coefficient of .83 and x2lus with a coefficient of .0295. These coefficients indicate that invrat may make a suitable instrument. When we regress fdi on the remaining variables (Table 3), invrat does indeed show statistical significance with a p‐value of 0.000. Table 3 Fdi Coefficient P‐value Invrat ‐12.89 0.000 Inf 1.711 0.000 Agex 3.64 0.000 Wfdi .0003059 0.000 Trd ‐.88 .278 Fxc 10.28 0.000 10 | P a g e 5 The Results In a OLS regression with robust standard errors of the wage on fdi, cgdp, inf, trd, att, ky, agex, ci, wfdi, fdi2, fxc, invrat and holding the years constant (Table 4) we can see that fdi holds no significance with a t‐stat and p‐value of 1.15 and .249 respectively. Together the variables explain 47% of the variation in the wage (x2wlus) and have an F‐statistic of 159.24. The usefulness of this estimation is limited in that it probably represents a violation of the ZCM assumption and we don’t get a consistent estimation of fdi’s impact on wages. Table 4 X2wlus Coefficient P‐value Fdi .036 .772 cgdp .5586 0.000 trd ‐5.502 .005 Att ‐44.304 .164 Ky 622.29 .009 Gdp ‐8.727 .001 agex ‐.6132 .558 When we estimate the relationship of fdi and some of the factors that may influence the inflow of investment (Table 3) several variables show significance, but most importantly invrat is highly significant with a t‐stat and p‐value of ‐11.81 and 0.00 respectively. The coefficient on invrat shows that for every 1 point decrease in the investment rating, the FDI inflow increases by $12.89 million and this makes sense because the investment rating variable is defined on a 11 | P a g e scale of 1‐100 where a rating of 100 represents the most risky investment. Several other variables show significance at the 1% level. The direction of the coefficients and the t‐stats for trade openness and capital to output ratio support intuitive outcomes. You would suspect that the higher the trade openness and the amount of capital to output, the more likely a country would be to receive FDI inflow. This regression has 2184 observations, holds the years constant and is run with robust standard errors. Educational attainment was left out of this regression. Using this information we then run a 2SLS estimation. Table 5 shows selected variables from the second stage of the regression with x2wlus as the dependent variable, fdi as an instrument of invrat, and the independent variables of cgdp, inf, att, ky, agex, ci, wfdi, fdi2, fxc, gdp, trd and y0 to control for the year. Here you can see that fdi is the only variable that shows significance at about the 5% level with a p‐value of .026. With a coefficient of 1.083 the interpretation is that a $1 million increase in FDI flow in a year increases the wage level across industries by $1 us dollar. However, the results show that both ky and fdi2 hold zero significance when accounting for the other variables and instrumenting fdi. The regression was run with robust standard errors and resulted in 1949 observations. Table 5 X2wlus Coefficient P‐value Fdi^ 1.083 .026 Gdp ‐10.234 .038 NO OTHER VARIABLES SHOWING SIGNIFICANCE AT ANY LEVEL LOWER THAN 24% Running the same regression with ky and fdi2 dropped (Table 6) results in the same number of observations at 1949 and so it would seem that these two variables don’t add any degree of explanation for the model. In this new regression fdi has a p‐value of .062 showing significance at the 15% level with a similar coefficient. The coefficient implies that a $1 million 12 | P a g e increase in FDI inflow in a year estimates an increase of $1.1 in the monthly wage. $1.1 is approximately .8% of the mean wage rate for all countries and industries. The R‐squared value tells us that this model explains 5.9% of the variation in the wage data. Table 6 shows some selected variables from this regression that show significance at various levels. Table 6 X2wlus Coefficient P‐value Fdi^ 1.11 .062 Gdp ‐10.44 .031 Inf ‐3.925 .081 Ci ‐38.33 .068 Wfdi ‐.00033 .094 6 Conclusions The results of the 2SLS model show evidence that there is a statistically significant relationship between wages and FDI inflows in some LDCs, however, the extent of that relationship is unclear based on the available data. We were able to control for a number of variables affecting the amount of FDI inflow as well as the relative wages between countries and their industries, but the directions of the coefficients on several variables do not support common intuition. In comparing the OLS regression with our 2SLS argument we can hope that the instrument has given us a consistent estimator despite the data issues. Below is a quick comparison reference. 13 | P a g e OLS 2SLS Based on this information we can assume that the 2SLS model has helped our estimator to become more consistent, however, there are a number of issues implying that the efficiency of the estimator is out of control. If we look at the R‐squared values for the OLS regressions of fdi on the independent variables, it shows that the model is explaining 97% of the variation in FDI inflows. It is unreasonable to assume that the chosen variables could account for nearly all of the factors that influence the decision to transfer capital. When we think about the potential journey that capital goes through from leaving the initial country to the point that it presents some influence on the foreign labor market and all the factors that have influence in between, it becomes increasingly clear that estimating the ceteris paribus relationship of FDI and wages is incredibly complex and difficult. Table 3 shows an interesting anomaly that contradicts the research of A.K. Ghose. Ghose proposed that developing countries who continue to export primarily agricultural commodities fail to engage in the global economic expansion and thus see adverse effects. In this regression, agricultural exports as a percentage total merchandise exports shows statistical significance at the 1% level, denoting more agricultural raw materials exports, coinsides with an increase in FDI inflow. Put another way, for LDCs, production of primary commodities indicates higher participation in globalization. This seems completely counter‐intuitive and explanation of these results is very difficult. One potentially beneficial idea is the possibility of a reciprocal relationship of between FDI inflows and wages. Here we make the argument that wage levels 14 | P a g e are a function of FDI inflows, but it could also be possible that firms looking to invest where the wages are the lowest and this implies that FDI inflow may be a function of wage levels. While the 2SLS results may hold some value, the characteristics of the data and the high degree of endogeneity for a relationship of this magnitude can be preventing us from closely estimating the true impact that FDI inflow has on the receiving labor market wages. 15 | P a g e Appendix i Variable Descriptions x2wlus Wage data has been sourced from the Occupation Wages from Around the World database which is derived from the ILO’s October Inquiry (http://laborsta.ilo.org, Table
01) by calibrating the data into a normalized wage rate for each occupation. The
normalized wages refer to average monthly wage rates for male workers in US
dollars. (http://www.nber.org/oww/). The variable x2wlus represents a wage with country‐specific and uniform calibration and lexicographic weighting. The database can be attributed to Remco H. Oostendorp of Tinbergen Institute, Amsterdam Institute for International Development and Free University Amsterdam and Richard B. Freeman of Harvard University, NBER and London School of Economics. lwage Log wage is the log of x2wlus. fdi Foreign direct investment observations are given as the yearly inward flow in US dollars at current prices in the millions for 12 countries from the years 1983‐2003. The data has been sourced from World Investment Reports 2007 compiled by the UNCTAD and accessed through the online database. (http://stats.unctad.org/FDI/). gdpcap GDP per capita observations are given as the Gross Domestic Product in US dollars divided by the population. Yearly values for 12 countries from 1983‐2003. The data has been sourced from Handbook of Statistics 2007 compiled by the UNCTAD and accessed through the online database. Observations for Ethiopia have been condensed between the former Ethiopia (1983‐1992) and Ethiopia (1993‐2003). (http://stats.unctad.org/handbook/). inf 16 | P a g e Inflation data is the annual % change inflation at average consumer prices, with an index for the year 2000= 100. Data has been sourced from the IMF World Outlook Database for October 2007. (http://www.imf.org/external/pubs/ft/weo/2007/02/weodata/index.aspx) invrat Institutional Investor’s Rating data is given on a scale of 0 to 100, with 100 representing the least amount of risk. Institutional Investors magazine is published monthly by Institutional Investors, Inc. which releases country credit ratings twice per year: March and September. The data presents September values and has been accessed throught the World Bank’s “New Database on Foreign Direct Investment.” (http://www1.worldbank.org/economicpolicy/globalization/data.html) trd Trade data representing the openness of each country is given as the sum of exports and imports of goods and services in current prices as a percentage of the gross domestic product. Data has been sourced from the World Development Indicators Online database available from the World Bank Data & Research. (http://www.worldbank.org/). att Attainment data represents the average educational attainment of individuals 25 years and older in the given year. The data were available from 1983‐2000. The data has been sourced from Barro, Robert J. and Jong‐Wha, Lee (2000), “International Data on Educational Attainment .” NBER working paper #7911. Data was accessed via Klenow, Peter S. and Rodriguez‐Clare, Andres. “Externalities and Growth.” Handbook of Economic Growth. Data contains linear interpolations for the years in between those ending in 0 and 5 as calculated by Klenow and Rodriguez‐Clare. (http://www.klenow.com). ky Physical capital data is given as the estimated ratio of physical capital to output for the given countries, but was only available through the year 2000. Data has been sourced from Klenow, Peter S. and Rodriguez‐Clare, Andres. “Externalities and Growth.” Handbook of Economic Growth. volume 1A, P. Aghion and S. Durlauf, eds., 2005, 817‐861 (chapter 11). (http://www.klenow.com). 17 | P a g e agex Data for agricultural raw materials exports as a percentage of merchandise exports was accessed through the World Development Indicators database available through the World Bank Data & Research. Data were available for 12 countries from 1983‐2003. (http://www.worldbank.org/). ci ci represents the percentage of total GDP attributed to investment in each year for 12 countries from 1983‐2003. The data were accessed through the Penn World Table. (http://pwt.econ.upenn.edu/) wfdi Data for the total World inflow of FDI were accessed through the UNCTAD’s “Key Data from WIR Annex Tables.” Document # 21. Data were available for 12 countries from 1983‐2003 and given in millions of US dollars. (http://www.unctad.org/Templates/Page.asp?intItemID=3277&lang=1). Fdi2 Data for individual country’s inflow of FDI as a percentage of World FDI flow were calculated by the author by taking fdi over wfdi. gdp GDP data are given in current prices in US dollars and accessed through the IMF’s World Economic Outlook Database for October 2007. (http://www.imf.org/external/pubs/ft/weo/2007/02/weodata/index.aspx) fxc fxc represents the FDI inflows as a percentage of fixed capital formation. Data were accessed through the UNCTAD’s “Key Data from WIR Annex Tables.” Document # 20. Data were available for 12 countries from 1983‐2003. (http://www.unctad.org/Templates/Page.asp?intItemID=3277&lang=1). 18 | P a g e Industries from OWW y3: industry code
AB Plantations
AD Logging
BA Coalmining
BC Other mining and quarrying
CB Manufacture of dairy products
CH Manufacture of bakery products
DB Manufacture of wearing apparel
(except footwear)
DD Manufacture of footwear
EB Manufacture of wooden furniture and
fixtures
FB Printing, publishing and allied
industries
GB Manufacture of other chemical
products
IA Iron and steel basic industries
JB Manufacture of machinery (except
electrical)
JD Shipbuilding and repairing
LA Construction
MB Retail trade (grocery)
NA Railway transport
NC Freight transport by road
NE Supporting services to maritime
transport
NG Supporting services to air
transport
OA Banks
OC Engineering and architectural
services
PB Sanitary services
PD Medical and dental services
AA Agricultural production (field crops)
AC Forestry
AE Deep-sea and coastal fishing
BB Crude petroleum and natural gas production
CA Slaughtering, preparing and preserving meat
CG Grain mill products
DA Spinning, weaving and finishing textiles
DC Manufacture of leather and leather products
(except footwear)
EA Sawmills, planing and other wood mills
FA Manufacture of pulp, paper and paperboard
GA Manufacture of industrial chemicals
GC Petroleum refineries
JA Manufacture of metal products (except
machinery and equipment)
JC Manufacture of electronic equipment,
machinery and supplies
KA Electric light and power
MA Wholesale trade (grocery)
MC Restaurants and hotels
NB Passenger transport by road
ND Maritime transport
NF Air transport
NH Communication
OB Insurance
PA Public administration
PC Education services
PF Repair of motor vehicles
Appendix ii Bangledesh Central African Republic Mali Rwanda Sierra Leone Zambia Benin Malawi Niger Senegal Uganda Mozambique 19 | P a g e 1
Bhagwati, Jagdish (2007). In Defense of Globalization. Oxford, New York: Oxford University Press. Sachs, Jeffrey (2005). The End of Poverty. New York, New York: The Penguin Press. 1‐59420‐045‐9 3
From The United Nations Office of the High Representative for the Least Developed Countries. http://www.unohrlls.org/ 4
From The United Nations Office of the High Representative for the Least Developed Countries. http://www.unohrlls.org 5
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Facts About Least Developed Countries, UN‐OHRLLS. http://www.unohrlls.org/en/ldc/related/63/ 8
Ghose, Ajit K., Jobs and Incomes in a Globalizing World. Geneva, International Labor Office, 2003. Ch.2, pp. 9. 9
Ghose, Ajit K., Jobs and Incomes in a Globalizing World. Geneva, International Labor Office, 2003. Ch.2, pp. 9. 10
Ghose, Ajit K., Jobs and Incomes in a Globalizing World. Geneva, International Labor Office, 2003. Ch.2, pp. 6 11
“FDI Surged to Record Levels in 2007.” UNCTAD Investment Brief, No. 1, 2008. (UNCTAD/PRESS/PR/2008/001). 12
Ghose, Ajit K., Jobs and Incomes in a Globalizing World. Geneva, International Labor Office, 2003. Ch.2, pp. 6 13
Yasin, Mesghena. “Globalization and Wages in Manufacturing in the Developing Countries: Evidence from Panel Cointegrations and Fully Modified OLS Regressions.” Journal of Economics (MVEA), 2006, v. 32, iss. 2, pp. 32. 14
Facts About Least Developed Countries, UN‐OHRLLS. http://www.unohrlls.org/en/ldc/related/63/ 15
Author’s calculation using data from the UNCTAD Handbook of Statistics 2007. 2
Deleted: ¶
. reg lwage y0 fdi cgdp inf invrat trd att ky agex ci wfdi fdi2 gdp fxc, robust
Linear regression
lwage
y0
fdi
cgdp
inf
invrat
trd
att
ky
agex
ci
wfdi
fdi2
gdp
fxc
_cons
Number of obs =
F( 14, 1934)
Prob > F
R-squared
Root MSE
Coef.
.1201045
.0008733
.0046001
-.0056647
.1011271
-.0424874
-.6538386
5.475018
-.0034258
-.2309147
1.19e-06
199.5056
-.0610209
-.0964128
-236.7168
Robust
Std. Err.
.0304347
.000757
.0005326
.003033
.0199201
.0123354
.2024173
1.524972
.0060708
.054594
5.68e-07
417.6339
.0163096
.0359117
60.80583
t
3.95
1.15
8.64
-1.87
5.08
-3.44
-3.23
3.59
-0.56
-4.23
2.09
0.48
-3.74
-2.68
-3.89
P>|t|
0.000
0.249
0.000
0.062
0.000
0.001
0.001
0.000
0.573
0.000
0.036
0.633
0.000
0.007
0.000
=
=
=
=
1949
159.24
0.0000
0.4706
.58573
[95% Conf. Interval]
.0604162
-.0006113
.0035554
-.011613
.0620599
-.0666794
-1.050818
2.484256
-.0153319
-.337984
7.49e-08
-619.5543
-.0930073
-.1668426
-355.9687
.1797929
.0023578
.0056447
.0002837
.1401943
-.0182954
-.2568597
8.465779
.0084803
-.1238454
2.30e-06
1018.565
-.0290346
-.025983
-117.465
20 | P a g e . ivregress 2sls
x2wlus cgdp inf att agex ci wfdi fxc gdp trd y0 (fdi = invrat), vce(robust) first
First-stage regressions
Number of obs
=
F(
11,
1937) =
Prob > F
=
R-squared
=
Adj R-squared
=
Root MSE
=
fdi
cgdp
inf
att
agex
ci
wfdi
fxc
gdp
trd
y0
invrat
_cons
Coef.
.5386586
2.977549
61.96269
5.825221
11.73466
.0004102
36.9347
13.44705
2.750148
-19.71207
.6108246
38235.52
Robust
Std. Err.
.0159686
.1641995
7.311852
.1922386
4.779864
.0000145
3.424725
1.773171
.8839918
4.755671
1.796032
9472.495
t
33.73
18.13
8.47
30.30
2.46
28.26
10.78
7.58
3.11
-4.14
0.34
4.04
P>|t|
x2wlus
fdi
cgdp
inf
att
agex
ci
wfdi
fxc
gdp
trd
y0
_cons
Instrumented:
Instruments:
Coef.
1.10821
-.1732138
-3.924734
-94.65063
-6.42971
-38.33574
-.0003336
-30.00766
-10.44485
.3590295
.5581458
(dropped)
Robust
Std. Err.
.5949279
.321278
2.250875
70.75012
4.270424
21.03915
.0001992
19.27976
4.845979
.4005255
.3288192
[95% Conf. Interval]
0.000
0.000
0.000
0.000
0.014
0.000
0.000
0.000
0.002
0.000
0.734
0.000
Instrumental variables (2SLS) regression
.5073411
2.655523
47.62277
5.448205
2.360441
.0003817
30.21817
9.96953
1.016472
-29.03884
-2.911535
19658.16
Number of obs
Wald chi2(
Prob > chi2
R-squared
Root MSE
z
1.86
-0.54
-1.74
-1.34
-1.51
-1.82
-1.67
-1.56
-2.16
0.90
1.70
1949
2519.64
0.0000
0.9201
0.9197
41.9025
P>|z|
0.062
0.590
0.081
0.181
0.132
0.068
0.094
0.120
0.031
0.370
0.090
.5699761
3.299576
76.30262
6.202237
21.10888
.0004386
43.65124
16.92458
4.483823
-10.3853
4.133184
56812.88
=
1949
11) = 1418.17
=
0.0000
=
0.0589
=
101.46
[95% Conf. Interval]
-.0578267
-.802907
-8.336368
-233.3183
-14.79959
-79.57173
-.000724
-67.79528
-19.9428
-.425986
-.0863279
fdi
cgdp inf att agex ci wfdi fxc gdp trd y0 invrat
2.274248
.4564795
.4868995
44.01705
1.940168
2.900246
.0000568
7.779971
-.9469072
1.144045
1.20262
21 | P a g e . ivregress 2sls lwage cgdp inf att agex ci wfdi fxc gdp trd y0 (fdi = invrat), vce(robust) first
First-stage regressions
Number of obs
=
F(
11,
1937) =
Prob > F
=
R-squared
=
Adj R-squared
=
Root MSE
=
fdi
cgdp
inf
att
agex
ci
wfdi
fxc
gdp
trd
y0
invrat
_cons
Coef.
.5386586
2.977549
61.96269
5.825221
11.73466
.0004102
36.9347
13.44705
2.750148
-19.71207
.6108246
38235.52
Robust
Std. Err.
.0159686
.1641995
7.311852
.1922386
4.779864
.0000145
3.424725
1.773171
.8839918
4.755671
1.796032
9472.495
t
33.73
18.13
8.47
30.30
2.46
28.26
10.78
7.58
3.11
-4.14
0.34
4.04
P>|t|
lwage
fdi
cgdp
inf
att
agex
ci
wfdi
fxc
gdp
trd
y0
_cons
Instrumented:
Instruments:
Coef.
.0153009
-.0045549
-.0536738
-1.62639
-.0892427
-.5925867
-4.79e-06
-.4174774
-.1157024
.010719
.0097128
(dropped)
Robust
Std. Err.
.00718
.0037712
.0260568
.8251468
.0504454
.2523571
2.33e-06
.2324258
.0579728
.0039037
.0039297
[95% Conf. Interval]
0.000
0.000
0.000
0.000
0.014
0.000
0.000
0.000
0.002
0.000
0.734
0.000
Instrumental variables (2SLS) regression
.5073411
2.655523
47.62277
5.448205
2.360441
.0003817
30.21817
9.96953
1.016472
-29.03884
-2.911535
19658.16
Number of obs
Wald chi2(
Prob > chi2
R-squared
Root MSE
z
2.13
-1.21
-2.06
-1.97
-1.77
-2.35
-2.06
-1.80
-2.00
2.75
2.47
1949
2519.64
0.0000
0.9201
0.9197
41.9025
P>|z|
0.033
0.227
0.039
0.049
0.077
0.019
0.040
0.072
0.046
0.006
0.013
.5699761
3.299576
76.30262
6.202237
21.10888
.0004386
43.65124
16.92458
4.483823
-10.3853
4.133184
56812.88
=
1949
11) = 2125.36
=
0.0000
=
.
=
.88035
[95% Conf. Interval]
.0012284
-.0119462
-.1047442
-3.243648
-.1881138
-1.087197
-9.35e-06
-.8730236
-.229327
.0030678
.0020106
fdi
cgdp inf att agex ci wfdi fxc gdp trd y0 invrat
.0293734
.0028365
-.0026034
-.0091321
.0096285
-.097976
-2.22e-07
.0380688
-.0020779
.0183702
.017415
22 | P a g e . ivregress 2sls lwage cgdp inf att agex ky fdi2 ci wfdi fxc gdp trd y0 (fdi = invrat), vce(robust) first
First-stage regressions
Number of obs
=
F(
13,
1935) =
Prob > F
=
R-squared
=
Adj R-squared
=
Root MSE
=
fdi
cgdp
inf
att
agex
ky
fdi2
ci
wfdi
fxc
gdp
trd
y0
invrat
_cons
Coef.
.192037
1.710944
74.35934
3.647206
40.69579
474126.4
10.26798
.0003059
10.28087
3.042798
-.8801053
-4.078911
-12.89403
7824.546
Robust
Std. Err.
.0862728
.3191257
32.79773
.599772
226.5445
13256.36
12.97003
.0000817
1.799564
.7932303
.8114959
3.19784
1.015618
6343.948
t
2.23
5.36
2.27
6.08
0.18
35.77
0.79
3.74
5.71
3.84
-1.08
-1.28
-12.70
1.23
P>|t|
lwage
fdi
cgdp
inf
att
agex
ky
fdi2
ci
wfdi
fxc
gdp
trd
y0
_cons
Coef.
.0149844
-.0043674
-.0523903
-1.560373
-.0862014
0
0
-.5799987
-4.69e-06
-.4074027
-.1131534
.0105068
.0094923
(dropped)
Robust
Std. Err.
.0058557
.0067591
.0341753
1.427929
.0517503
33.88556
.0816934
3.266818
.0000101
1.630847
.3637091
.3925593
.0038351
[95% Conf. Interval]
0.026
0.000
0.023
0.000
0.857
0.000
0.429
0.000
0.000
0.000
0.278
0.202
0.000
0.218
Instrumental variables (2SLS) regression
.0228396
1.085078
10.03674
2.470939
-403.6012
448128.2
-15.16873
.0001456
6.751586
1.487122
-2.471604
-10.35049
-14.88585
-4617.146
Number of obs
Wald chi2(
Prob > chi2
R-squared
Root MSE
z
2.56
-0.65
-1.53
-1.09
-1.67
0.00
0.00
-0.18
-0.46
-0.25
-0.31
0.03
2.48
1949
5225.15
0.0000
0.9839
0.9838
18.8065
P>|z|
.3612345
2.336811
138.6819
4.823473
484.9927
500124.7
35.70469
.0004662
13.81016
4.598474
.711393
2.192663
-10.90221
20266.24
=
13) =
=
=
=
1949
31.05
0.0033
.
.87021
[95% Conf. Interval]
0.010
0.518
0.125
0.275
0.096
1.000
1.000
0.859
0.643
0.803
0.756
0.979
0.013
.0035074
-.017615
-.1193727
-4.359063
-.1876302
-66.41447
-.1601162
-6.982844
-.0000245
-3.603804
-.8260103
-.7588953
.0019756
.0264614
.0088802
.0145921
1.238317
.0152274
66.41447
.1601162
5.822846
.0000151
2.788999
.5997034
.7799089
.0170091
Instrumented: fdi
Instruments:
cgdp inf att agex ky fdi2 ci wfdi fxc gdp trd y0 invrat
. reg lwage y0 fdi cgdp inf invrat trd att ky agex ci wfdi fdi2 gdp fxc, robust
Linear regression
lwage
y0
fdi
cgdp
inf
invrat
trd
att
ky
agex
ci
wfdi
fdi2
gdp
fxc
_cons
Number of obs =
F( 14, 1934)
Prob > F
R-squared
Root MSE
Coef.
.1201045
.0008733
.0046001
-.0056647
.1011271
-.0424874
-.6538386
5.475018
-.0034258
-.2309147
1.19e-06
199.5056
-.0610209
-.0964128
-236.7168
Robust
Std. Err.
.0304347
.000757
.0005326
.003033
.0199201
.0123354
.2024173
1.524972
.0060708
.054594
5.68e-07
417.6339
.0163096
.0359117
60.80583
t
3.95
1.15
8.64
-1.87
5.08
-3.44
-3.23
3.59
-0.56
-4.23
2.09
0.48
-3.74
-2.68
-3.89
P>|t|
0.000
0.249
0.000
0.062
0.000
0.001
0.001
0.000
0.573
0.000
0.036
0.633
0.000
0.007
0.000
=
=
=
=
1949
159.24
0.0000
0.4706
.58573
[95% Conf. Interval]
.0604162
-.0006113
.0035554
-.011613
.0620599
-.0666794
-1.050818
2.484256
-.0153319
-.337984
7.49e-08
-619.5543
-.0930073
-.1668426
-355.9687
.1797929
.0023578
.0056447
.0002837
.1401943
-.0182954
-.2568597
8.465779
.0084803
-.1238454
2.30e-06
1018.565
-.0290346
-.025983
-117.465
23 | P a g e . reg fdi
y0 cgdp inf invrat trd ky agex ci wfdi fdi2 gdp fxc, robust
Linear regression
fdi
y0
cgdp
inf
invrat
trd
ky
agex
ci
wfdi
fdi2
gdp
fxc
_cons
Number of obs =
F( 12, 2171)
Prob > F
R-squared
Root MSE
Coef.
.4287422
.0458683
.6281943
-5.275755
-2.015722
81.27426
.5345695
4.824656
.0003193
533214
-1.488478
-4.16757
-874.2832
Robust
Std. Err.
t
1.691192
.0161404
.1281006
.4466619
.3974353
21.93654
.1302167
2.104818
.000019
6239.094
.6144251
.763182
3371.281
P>|t|
0.25
2.84
4.90
-11.81
-5.07
3.70
4.11
2.29
16.84
85.46
-2.42
-5.46
-0.26
=
=
=
=
2184
2567.99
0.0000
0.9685
25.073
[95% Conf. Interval]
0.800
0.005
0.000
0.000
0.000
0.000
0.000
0.022
0.000
0.000
0.015
0.000
0.795
-2.887782
.0142162
.3769817
-6.151685
-2.795115
38.25545
.279207
.6969883
.0002821
520978.7
-2.693401
-5.664213
-7485.558
3.745267
.0775205
.8794068
-4.399826
-1.236328
124.2931
.7899319
8.952324
.0003565
545449.2
-.2835557
-2.670926
5736.992
. corr x2wlus fdi lwage cgdp inf invrat trd att ky agex ci wfdi fdi2 gdp fxc
(obs=1949)
x2wlus
x2wlus
fdi
lwage
cgdp
inf
invrat
trd
att
ky
agex
ci
wfdi
fdi2
gdp
fxc
1.0000
0.0489
0.8204
0.2946
-0.0663
0.0295
0.3198
-0.1276
-0.1864
0.1969
-0.1978
0.1086
0.0610
-0.3035
0.1807
fdi2
gdp
fxc
1.0000
0.5126
0.3224
fdi
1.0000
0.1623
0.2217
-0.0741
0.8301
-0.0768
0.1986
-0.1689
-0.2026
0.2753
0.5512
0.9711
0.4933
0.3315
fdi2
. summarize
Variable
gdp
1.0000
-0.3173
lwage
cgdp
inf
1.0000
0.3280
-0.1055
0.1361
0.4365
-0.2234
-0.2487
0.3251
-0.2375
0.2067
0.1748
-0.3592
0.2500
1.0000
-0.4780
0.0711
-0.1929
-0.2040
-0.5676
0.0687
0.3348
-0.2200
0.2738
0.1927
-0.2562
1.0000
0.0875
0.4886
0.5341
0.4948
-0.4366
-0.5644
0.3554
-0.0877
-0.2554
0.4167
invrat
trd
1.0000
0.0237
0.4355
-0.1581
-0.3857
0.0430
0.6608
0.8747
0.4558
0.4696
1.0000
-0.0013
0.2103
0.2568
-0.7628
0.4841
-0.0889
-0.8131
0.5826
att
1.0000
0.0779
-0.9396
-0.3778
0.5241
0.2150
0.3038
0.5212
1.0000
-0.0352
0.0229
-0.1479
-0.1924
-0.1509
0.0447
agex
1.0000
0.1247
-0.3308
-0.2337
-0.5142
-0.3158
ci
wfdi
1.0000
-0.4996
0.2735
0.6784
-0.6771
1.0000
0.4944
-0.0466
0.7551
fxc
1.0000
x2wlus fdi lwage cgdp inf invrat trd att ky agex ci wfdi fdi2 gdp fxc
Obs
ky
Mean
Std. Dev.
Min
Max
x2wlus
fdi
lwage
cgdp
inf
5273
7091
5273
7095
7095
138.5387
33.93132
4.513989
289.1792
26.25046
174.1627
91.61605
.8814226
97.95237
39.44694
6.797655
-140.311
1.916578
120.9437
-14.9
3695.318
578.7
8.214822
721.7669
215.4
invrat
trd
att
ky
agex
4937
7094
6931
6931
2954
14.29082
44.04487
2.256922
.7339658
14.51456
5.542042
17.65893
1.092092
.4121836
21.51094
4.1
14.32574
.582
.103853
.206838
28.5
91.37796
5.457
2.349445
92.06559
ci
wfdi
fdi2
gdp
fxc
6931
7095
7091
7095
7091
6.526117
249066.9
.0001163
7.09759
5.541499
2.644092
221519.4
.0003265
11.58565
21.17073
1.591315
50681.7
-.001583
.636
-121.7855
16.51379
1411366
.001403
54.476
96.01546
24 | P a g e