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International Journal of Social EconomicsEmerald Article: Analyses of FDI determinants in developing countriesRecep Kok, Bernur Acikgoz Ersoy

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To cite this document: Recep Kok, Bernur Acikgoz Ersoy, (2009),"Analyses of FDI determinants in developing countries", International Journal of Social Economics, Vol. 36 Iss: 1 pp. 105 - 123

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Analyses of FDI determinantsin developing countries

Recep KokEconomics Department, Dokuz Eylul University, Izmir, Turkey, and

Bernur Acikgoz ErsoySchool of Applied Science, Celal Bayar University, Manisa, Turkey

Abstract

Purpose – The purpose of this paper is to investigate the best determinants of foreign directinvestment (FDI) in developing countries.

Design/methodology/approach – This paper investigates whether FDI determinants affect FDIbased on both a panel of data (FMOLS-fully modified OLS) and cross-section SUR (seeminglyunrelated regression) for 24 developing countries, over the period 1983-2005 for FMOLS and 1976-2005for cross-section SUR.

Findings – The interaction of FDI with some FDI determinants have a strong positive effect oneconomic progress in developing countries, while the interaction of FDI with the total debtservice/GDP and inflation have a negative impact. The most important determinant of FDI is thecommunication variable.

Research limitations/implications – The limitations of the study are based on the development ofdata set which could be found uninterrupted for 30 years in 24 developing countries.

Originality/value – The main objective of this study is to define the main FDI determinants thatshow the capital flows to developing countries in a globalization framework. The secondary objectiveof this study is to assign countries’ convergence by using the same FDI determinants. FDI flow is oneof the main dynamics of globalization phenomenon thus FDI flow determinations will contribute tocountries’ process of political development.

Keywords Convergence, International investments, Developing countries, Globalization

Paper type Research paper

1. IntroductionTrade has traditionally been the principal mechanism linking national economies inorder to create an international economy. FDI is a similar mechanism linking nationaleconomies; therefore, these two mechanisms reinforce each other. The trade effects ofFDI depend on whether it is undertaken to gain access to natural resources, toconsumer markets or whether the FDI is aimed at exploiting locational comparativeadvantage or other strategic assets such as research and development capabilities.Most developing countries lack technology capability and FDI to facilitate technologytransfer and reduce the technology gap (TGAP) between developing countries anddeveloped countries. In fact, it is suggested that spillovers or the external effects fromFDI are the most significant channels for the dissemination of modern technology(Blomstrom, 1989).

FDI has innumerable other effects on the host country’s economy. It influences theincome, production, prices, employment, economic growth, development and general

The current issue and full text archive of this journal is available at

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JEL classification – F21, F47

Analyses of FDIdeterminants

105

International Journal of SocialEconomics

Vol. 36 Nos 1/2, 2009pp. 105-123

q Emerald Group Publishing Limited0306-8293

DOI 10.1108/03068290910921226

welfare of the recipient country. It is also probably one of the most significant factorsleading to the globalization of the international economy. Thus, the enormous increasein FDI flows across countries is one of the clearest signs of the globalization ofthe world economy over the past 20 years (UNCTAD, 2006). Therefore, we canconclude that FDI is a key ingredient for successful economic growth in developingcountries, because the very essence of economic development is the rapid and efficienttransfer and adoption of “best practice” across borders.

On the other hand, in general, foreign investors are influenced by three broadgroups of factors:

(1) The profitability of the projects.

(2) The ease with which subsidiaries’ operations can be integrated into investors’global strategies.

(3) The overall quality of the host country’s enabling environment (Christiansenand Ogutcu, 2002).

A large number of studies have been conducted to identify the determinants of FDI butno consensus has emerged, in the sense that there is no widely accepted set ofexplanatory variables that can be regarded as the “true” determinants of FDI. Theresults produced by studies of FDI are typically sensitive to these factors, indicating alack of robustness. For example, factors such as labor costs, trade barriers, tradebalance, exchange rate, R&D and tax have been found to have both negative andpositive effects on FDI. Chakrabarti (2001) concludes that “the relation between FDIand many of the controversial variables (namely, tax, wages, openness, exchange rate,tariffs, growth and trade balance) are highly sensitive to small alterations in theconditioning information set”.

The important question is “Why do companies invest abroad?” Dunning (1993)developed his theory by synthesizing the previously published theories, becauseexisting explanations could not fully justify the existence of FDI. According toDunning, international production is the result of a process affected by ownership,internalization and localization advantages. Dunning’s so-called OLI paradigm statesthat FDI is undertaken if ownership-specific advantages (“O”) like proprietarytechnology exist together with location-specific advantages (“L”) in host countries, e.g.low factor costs, and potential benefits from internalization (“I”) of the productionprocess abroad (Frenkel et al., 2004).

The latter is the most important: the factors based on which an investor selects alocation for a project. These include the factors affecting the availability of local inputssuch as natural resources, the size of the market, geographical location, the position ofthe economy, the cultural and political environment, factor prices, transport costs andcertain elements of the economic policy of the government (trade policy, industrialpolicy, budget policy, tax policy, etc.).

The main objective of this study is to define the main FDI determinants that showthe capital flows to developing countries in a globalization framework. The secondaryobjective of this study is to assign countries’ convergence by using the same FDIdeterminants. FDI flow is one of the main dynamics of globalization phenomenon thusFDI flow determinations will contribute to countries’ process of political development.

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2. The determinants of FDI: theory and evidenceFDI has been regarded in the last decades as an effective channel to transfertechnology and foster growth in developing countries. This point of view vividlycontrasts with the common belief that was accepted in some academic and politicalspheres in the 1950s and 1960s, according to which FDI was harmful for the economicperformance of less developed countries. The theoretical discussion that permeatedpart of the development economics of the second half of the twentieth century has beenapproached from a new angle on the light of the New Growth Theory. Thus, the modelsbuilt in this novel framework provide an interesting background in order to study thecorrelation between FDI and the growth rate of GDP (Calvo and Robles, 2003).

In the neoclassical growth model technological progress and labor growth areexogenous, inward FDI merely increases the investment rate, leading to a transitionalincrease in per capita income growth but has no long-run growth effect (Hsiao andHsiao, 2006). The new growth theory in the 1980s endogenizes technological progressand FDI has been considered to have permanent growth effect in the host countrythrough technology transfer and spillover. There is ongoing discussion on the impactof FDI on a host country economy, as can be seen from recent surveys of the literature(De Mello, 1997, 1999; Fan, 2002; Lim, 2001).

According to the neoclassical growth theory model, FDI does not affect thelong-term growth rate. This is understandable if we consider the assumptions of themodel, namely: constant economies of scale, decreasing marginal products of inputs,positive substitution elasticity of inputs and perfect competition (Sass, 2003). Withinthe framework of the neo-classical models (Solow, 1956), the impact of FDI on thegrowth rate of output was constrained by the existence of diminishing returns in thephysical capital. Therefore, FDI could only exert a level effect on the output per capita,but not a rate effect. In other words, it was unable to alter the growth rate of output inthe long run (Calvo and Robles, 2003).

As a consequence, of endogenous growth theory, FDI has a newly-perceivedpotential role in the growth process (Bende-Nabende and Ford, 1998). In the context ofthe New Theory of Economic Growth, however, FDI may affect not only the level ofoutput per capita but also its rate of growth. This literature has developed varioushypotheses that explain why FDI may potentially enhance the growth rate of percapita income in the host country (Calvo and Robles, 2003). However, the endogenousgrowth theory, which dispenses with the assumption of perfect competition, leavesmore scope for the impact of FDI on growth. In this theoretical framework, investment,including FDI, affects the rate of growth through research and development (R&D) orthrough its impact on human capital. Even if the return on investment is declining, FDImay influence growth through externalities. These may include the knowledge“leaking” into the local economy through the subsidiary (organization forms,improvement of human capital, improvement of fixed assets), as well as effects throughthe various contacts of the subsidiary with local companies (joint ventures,technical-technological links, technology transfer, orders, sale of intermediateproducts, market access, improved financing conditions, more intense competitiongenerated by the presence of the subsidiaries, etc.). These factors increase theproductivity of the subsidiary and of the connecting companies in the host economy.Technology transfer and the local ripple effects prevent the decline of the marginalproductivity of capital, thus facilitating longer term higher growth rates induced by


107

endogenous factors. Thus, the existence of such externalities is one of the preconditionsof the positive effect of FDI on the host economy (Sass, 2003).

The various theoretical schools attribute different impacts to FDI on economicgrowth. On the other hand the literature examines a large number of variables thathave been put forward to explain FDI. Some of these variables are encompassed informal hypotheses or theories of FDI, whereas others are suggested because they makesense intuitively. Most of the studies reporting a significantly negative coefficient onthe labor cost (wage rate) combine the theory with the growth rate, inflation and tradedeficit. Those reporting a positive coefficient combine wages with taxes and openness.The growth rate has been found to have a significantly positive effect on FDI if it iscombined with inflation, trade deficit and wages. Findlay (1978) postulated that FDIwould promote economic growth through its effect on technological progress.Empirical studies such as those by Blomstrom et al. (1992) and Borensztein et al. (1998)found that FDI is positively correlated with economic growth. Empirical studiesrelating economic growth to capital formation have concluded that gross domesticinvestment (GDI) exerts a major influence on economic growth. For instance, Levineand Renelt (1992) and De Long and Summers (1991) concluded that the rate of capitalformation determines the rate of economic growth. On the other hand, Graham (1995)surveys the theoretical and empirical literature on the determinants of FDI and theeconomic consequences of FDI for both host (recipient) and home (investor) countries.The paper concludes that FDI can have both positive and negative economic effects onhost countries. Positive effects come about largely through the transfer of technologyand other intangible assets, leading to productivity increases and improvements in theefficiency of resource allocation. Negative effects can arise from the market power oflarge foreign firms (multinational corporations) and their associated ability to generatevery high profits or from domestic political interference by multinational corporations.However, empirical research suggests that the evidence of negative effects from FDI isinconclusive, while the evidence of positive effects is overwhelming.

According to Sanjaya and Streeten (1977), FDI had a net positive effect on nationaleconomic welfare. The main determining factor of the remaining negative socialincome effects was the extent of effective protection granted firms. According to Sun(1998), FDI has significantly promoted economic growth in China by contributing todomestic capital formation, increasing exports, and creating new employment. Inaddition, FDI flows to China have tended to improve the productive efficiency ofresource allocation of the Chinese domestic sectors by transferring technology,promoting exports, and facilitating inter-regional and intersectional flows of labor andcapital. However, FDI flows to China have had also some negative side effects by:

. Worsening of environmental pollution.

. Exacerbating inter-regional economic disparities as a result of the unevendistribution of FDI.

. Transfer pricing.

. Encouraging round tripping of the capital of Chinese domestic firms recentliterature has also raised concerns about the harmful effects of flows of capital onthe recipient countries.

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Particularly, FDI displaces domestic savings (Papanek, 1973; Cohen, 1993; Reinhartand Talvi, 1998). In a seminal paper, Papanek (1973) showed the significant negativeimpacts of different types of capital on national savings. Based on a sample of 85developing countries, Papanek found that foreign capital displaced domestic savings.Specifically, he showed that foreign aid, private investment and other capital crowdedout national savings, and a reduction in domestic savings could lead to further increaseon the dependency on foreign capital (Baharumshah and Thanoon, 2006).

Another determinant, tariffs, has a positive effect on FDI if they are combined withthe growth rate and openness, but they produce a negative effect when combinedwith wages. The real exchange rate produces a positive effect when it is combined withopenness, domestic investment and government consumption. When domesticinvestment is excluded, the effect becomes negative.

This supports the argument that an efficient environment that comes with moreopenness to trade is likely to attract foreign firms. This conclusion is also supported byAsiedu (2002) and Edwards (1990). In this model, investment tax and wages have anegative impact on FDI, while infrastructure and market size have a significantlypositive impact on FDI. Generally, only in the case of export oriented FDI, cheap laborin terms of lower wages works as an incentive (Wheeler and Mody, 1992). On the otherhand Tomiura’ (2003) study confirms that the positive association between FDI andR&D is robust even if firms undertaking no FDI and/or no R&D are included. In thisrespect, Morck and Yeung (1991) hypothesize and provide evidence that FDI createswealth when an expanding firm possesses intangible assets, such as superiorproduction and management skills, marketing expertise, patents and consumergoodwill.

FDI determination effects in most of the studies can be seen from Table I.The UNCTAD’s classification of FDI determinants can be seen from Table II.

3. Data definitionThe indicators tested in this study are selected on the basis of FDI theories andprevious empirical literature. The indicators tested in the panel study and cross-sectionSUR, are the FDI determinants for which the data have been found for developingcountries for at least 30 years. Data sets related to a number of developing countries aresometimes discontinuous for some variables (i.e. not available for all 30 years). For thatreason while defining the main determinants of FDI in this study, 24 developingcountries for which uninterrupted data sets for 30 years at some variables could beused Developing countries list is reported in the Appendix. Hence, the forecasts relatedto main determinants of FDI in this study were obtained under these constraints. At thesame time, some variables referred as FDI determinants by UNCTAC and used inliterature were used in the same sampling. These are:

3.1 Gross foreign direct investment (GFDI)The gross inflows of investment to acquire a lasting management interest (10 percentor more of voting stock). A business enterprise operating in a country other than that ofthe investor. Data source: World Development Indicators (2007).


109

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(continued

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Table I.The effects of FDIdetermination

IJSE36,1/2

110

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Table I.


111

3.2 Electric power consumption (kwh per capita) (LOGELEC)Electric power consumption measures the production of power plants and combinedheat and power plants, less distribution losses, and own use by heat and power plants.Electric is particularly important for efficiency-seeking FDI. Data Source: WorldDevelopment Indicators (2007).

3.3 Total external debt, total (DOD, current US$) (LOGEXDEBT)Total external debt is debt owed to nonresidents repayable in foreign currency,goods, or services. Data are in current US dollars. Data Source: World DevelopmentIndicators (2007).

3.4 Technology gap (TGAP)TGAP is the difference of technology level between two countries. The TGAP ismeasured by the following formula (Blomstrom (1989):

GAPi;t ¼ ð ymaxt 2 yi;tÞ=yi;t; ð1Þ

where the GDP per capita of the Argentina is used as y maxi.

3.5 Total debt service (per cent of GDP) (TDSGDP)Total debt service is the sum of principal repayments and interest actually paid inforeign currency, goods, or services on long-term debt, interest paid on short-termdebt, and repayments (repurchases and charges) to the IMF. Data Source: WorldDevelopment Indicators (2007).

3.6 Inflation, GDP deflator (annual percent) (INFLATION)Inflation as measured by the annual growth rate of the GDP implicit deflator shows therate of price change in the economy as a whole. The GDP implicit deflator is the ratio ofGDP in current local currency to GDP in constant local currency. Data Source: WorldDevelopment Indicators (2007).

3.7 Domestic gross fixed capital formation (as a percentage of GDP) (GFCF)Indicates capital stock in the host country and the availability of infrastructure. DataSource: World Development Indicators (2007).

Determining variables ExamplesPolicy variables Tax policy, trade policy, privatization policy,

macroeconomic policyBusiness variables Investment incentivesMarket-related economic determinants Market size, market growth, market structureResource-related economic determinants Raw materials, labor costs, labor productivityEfficiency-related economic determinants Transport and communication costs, labor

productivity

Source: UNCTAD (2006)

Table II.The UNCTAD’sclassification of FDIdeterminants

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3.8 Telephone mainlines (per 1,000 people) (TELEPHONE)Telephone mainlines are telephone lines connecting a customer’s equipment to thepublic switched telephone network. Data are presented per 1,000 people for the entirecountry. Data Source: World Development Indicators (2007).

3.9 Market size – GDP per capita growth (annual per cent) (GDPpcgro)Annual percentage growth rate of GDP per capita based on constant local currency.GDP per capita is gross domestic product divided by midyear population. Data Source:World Development Indicators (2007).

3.10 Trade (per cent of GDP) (TRADE)Trade is the sum of exports and imports of goods and services measured as a share ofgross domestic product. Data Source: World Development Indicators (2007).

3.11 Gross capital formation (annual per cent growth) (GCF)Annual growth rate of gross capital formation based on constant local currency.Aggregates are based on constant 2000 US dollars. Data Source: World DevelopmentIndicators (2007).

4. MethodologyPanel data techniques has been used to estimate the FDI equations because of theiradvantages over cross-section and time series in using all the information available,which are not detectable in pure cross-sections or in pure time series[1].

In this study, the pool data (cross-section time series) has been created for 24countries over 1975-2005 periods. T denotes the number of periods and N denotes thenumber of observations for each period. For making the evidence more reliable, fivebasic models were set up for analyses. After reliable estimators were derived from SURand fully modified OLS (FMOLS) models, b convergence model was defined; countriesthat capture less FDI are converging to countries capturing more FDI.

5. Empirical results5.1 The FDI equationPanel data modeling applications have only been increasing over the past few yearsand there is no doubt that the range is going to expand further (Krishnakumar andRonchetti, 2000). Panel data refers to the pooling of observations on a cross-section ofcountries, households, firms, etc. over a number of time periods. We use panel datatechniques, for example, to control for individual heterogeneity or to study thedynamics of adjustment. Panel data allows for more informative results, morevariability, more degrees of freedom and more efficiency (De Kock, 2007, p. 1).

With repeated observations of enough cross-sections, panel analysis permits theresearcher to study the dynamics of change with short time series. The combination oftime series with cross-sections can enhance the quality and quantity of data in waysthat would be impossible using only one of these two dimensions (Gujarati, 1992,p. 638). Panel analysis can provide a rich and powerful study of a set of people, if one iswilling to consider both the space and time dimension of the data.


113

5.2 Unit root testsBefore proceeding to estimate with panel data, we carry out unit root tests to examinewhether the variables are stationary. These tests are the most widely used methods forpanel data unit root tests in the literature.

We performed:. Levin, Lin and Chu t, Levin, Lin and Chu (2002).. Im, Pesaran and Shin W-stat, Im, Pesaran and Shin (2003).. ADF – Fisher x 2, PP – Fisher x 2, Maddala and Wu (1999) and Choi (2001).. Hadri Z-stat, Hadri (2000).

Unit root tests for stationarity are performed on both levels and first differences for allthe six variables in the model indicated in the preceding section.

5.3 The estimation resultsThe results of panel estimating in its most general form are presented in Tables IIIand IV.

Pooled EGLS SUR standard error results are reported in Table V. b Convergencefixed effects results are reported in Table VI. Unit root test are reported in Tables VIIand VIII.

6. ConclusionCompetition among governments to attract FDI has grown significantly. Manycountries have not only reduced or eliminated such restrictions, but also moved towardencouraging FDI with tax and other incentives.

Appropriate domestic policies will help attract FDI and maximize its benefit, whileat the same time removing obstacles to local businesses. Foreign enterprises, likedomestic ones, pursue the good business environment rather than the special favorsoffered to induce the foreign enterprises to locate in the incentive offering regions,transparency and accountability of governments and corporations are fundamentalconditions for providing a trustworthy and effective framework for the social,environmental, and economic life of their citizens. They bring huge domesticgovernance challenges not only for the benefit of foreign investors, but also fordomestic business and society at large as well.

When evaluating the two main model groups, namely the first model groupincluding Models I, II, and III, and the second model group including Models IV and V,developed by us according to the literature related to FDI determinants, we see thattheir results are parallel. Thus, according to the forecasters of the second expandedmodel group it could be more reliable to forecast and develop policy suggestions.

In Models IV and V, the interaction of FDI with some FDI determinants has a strongpositive effect on development in developing countries, while that of FDI with the totaldebt service/GDP and inflation have a significant negative impact. On the other side,trade, telephone, gross capital formation, and GDP per capita growth have positiveeffect on FDI. However, the best FDI determinant is communication (telephonemainlines) and it has strong positive effect on FDI.

In addition, we did b convergence to see the catch-up process using same equation.Thus, parameters of b convergence for the 24 countries are in agreement with each

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Table III.Estimation results(first model group)


115

Dependent variable GFDI GFDIMethod Pooled EGLS (cross-section SUR) FMOLSVariables Model IV Model V

INFLATION 20.000125 (26.264648) 0.0000 20.01 (22.39e þ 03)LTELEPHONE 0.012599 (53.91920) 0.0000 2.88 (2.28)GCF 0.008344 (16.17362) 0.0000 0.03 (89.77)TDSGDP 20.004428 (21.784010) 0.0748 20.08 (264.25)TRADE 0.016691 (51.69897) 0.0000 0.01 (169.09)GDPPCGRO 0.002721 (1.509252) 0.1317 0.00 (1.82e þ 11)Weighted statistics Panel statisticsR 2 0.928417 Panel v-stat. 23.326071Adjusted R 2 0.927916 Panel rho-stat. 4.203005SE of regression 1.002957 Panel pp-stat. 26.353022Mean dependent var. 1.163867 Panel adf-stat. 22.904839SD dependent var. 4.943927 Group rho-stat. 4.712018Sum squared resid. 718.2290 Group pp-stat. 212.03339Durbin-Watson stat. 1.812542 Group adf-stat. 22.115461Unweighted statisticsR 2 0.774862Sum squared resid. 2258.920Mean dependent var. 1.890734Durbin-Watson stat. 0.662822Periods N ¼ 24, T ¼ 30 N ¼ 24, T ¼ 30

Note: t-statistics in parentheses

Table IV.Estimation results(second model group)

Variable Standard error

INFLATION 3.67E-05LOGELEC 0.037695TGAP 0.043167LOGEXDEBT 0.012250GFCF 0.001684

Table V.Pooled EGLS(cross-section SUR)standard error

Fixed effects (cross) Fixed effects (cross)

_ARG–C 1.387440 _MALA–C 0.283992_BOLI–C 1.160332 _MEXIC–C 0.762962_BRA–C 20.336461 _MORO–C 0.279814_CHIL–C 0.596849 _PAKIS–C 20.632061_COL–C 0.311010 _PARA–C 20.500683_COS–C 20.005836 _PHILIP–C 1.328027_DOM–C 1.036318 _SRILA–C 1.823186_ECU–C 0.151749 _THAIL–C 20.391661_EGY–C 20.508506 _TUNU–C 0.166280_ELSA–C 0.225043 _TURK–C 22.060049_INDIA–C 21.197840 _URU–C 21.348913_ JORD–C 20.898742 _VEN–C 21.632250

Table VI.b Convergence fixedeffects (cross)

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other, indicating that the model is meaningful. With reference to the countries whichare capturing less FDI convergence more attractive ones in this period.

In the study the main determinants of FDI have been identified. It is possible for thecountries to develop policies particular to their own economic structure by looking atthe main FDI determinants. For example,; when looking at the “Gross CapitalFormation” indicator in country X, we can conclude that this country can developpolicies to encourage the import of investment goods instead of the import ofconsumption goods. Again country Y can improve intra trade policies by taking the“Openness” indicator into account. Or by taking the “Total Debt Service-GDP ratio”indicator into account, country Z can develop policies related to utilization of resourcesprovided from external debt in productive fields in order to cover the country’s capitalinadequacy. So, the role of FDI in country growth can be expressed by the effects ofeach of the determinants or by the effects of all determinants together. In this way, therole of FDI at the country growth can be used effectively.

Individual interceptLevel First differences

Variables Pool unit root tests Statistic Probability Statistic Probability

GFDI Levin, Lin and Chu 2.22961 0.9871 210.2374 0.0000Im, Pas, Shin 0.75804 0.7758 217.1986 0.0000ADF Fisher 63.454 0.0667 366.414 0.0000PP Fisher 104.319 0.0000 567.737 0.0000Hadri 11.3404 0.0000 20.97013 0.8340

INFLATION Levin, Lin and Chu 0.47228 0.6816 215.1518 0.0000Im, Pas, Shin 27.94431 0.0000 221.7625 0.0000ADF Fisher 163.723 0.0000 447.260 0.0000PP Fisher 203.674 0.0000 618.190 0.0000Hadri 20.34771 0.6360 9.42924 0.0000

LOGELEC Levin, Lin and Chu 27.84248 0.0000 28.65500 0.0000Im, Pas, Shin 22.44188 0.0073 211.6970 0.0000ADF Fisher 91.5570 0.0002 233.313 0.0000PP Fisher 141.913 0.0000 330.080 0.0000Hadri 17.2276 0.0000 5.50141 0.0000

TGAP Levin, Lin and Chu 21.13212 0.1288 211.4426 0.0000Im, Pas, Shin 21.27903 0.1004 217.4173 0.0000ADF Fisher 52.1272 0.2478 339.850 0.0000PP Fisher 51.4860 0.2678 369.648 0.0000Hadri 14.4478 0.0000 0.23963 0.4053

LOGEXDEBT Levin, Lin and Chu 217.8573 0.0000 25.85490 0.0000Im, Pas, Shin 215.2460 0.0000 28.06550 0.0000ADF Fisher 264.821 0.0000 159.688 0.0000PP Fisher 350.606 0.0000 169.643 0.0000Hadri 15.7603 0.0000 8.11807 0.0000

GFCF Levin, Lin and Chu 21.73604 0.0413 27.73516 0.0000Im, Pas, Shin 25.51316 0.0000 215.5416 0.0000ADF Fisher 109.457 0.0000 311.182 0.0000PP Fisher 69.1233 0.0245 425.409 0.0000Hadri 5.78336 0.0000 21.42021 0.9222

Notes: Automatic selection of lags based on AIC: 0 to 6; Andrews bandwidth selection usingBartlett kernel

Table VII.Pool unit root test:

summary


117

Note

1. See Baltagi and Kao (2000) for a detailed discussion about panel cointegration.

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Appendix

Corresponding authorBernur Acikgoz Ersoy can be contacted at: [email protected]

1 Argentina 13 Malaysia2 Bolivia 14 Mexico3 Brazil 15 Morocco4 Chile 16 Pakistan5 Colombia 17 Paraguay6 Costa rica 18 Philippines7 Dominican republic 19 Sri lanka8 Ecuador 20 Thailand9 Egypt 21 Tunisia

10 El Salvador 22 Turkiye11 India 23 Uruguay12 Jordan 24 Venezuela

Table AI.Developing countries list


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