Open access peer-reviewed chapter - ONLINE FIRST

Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries: Undertaking Hotel Sector

By Mohamed Salem

Submitted: February 12th 2020Reviewed: June 29th 2020Published: July 24th 2020

DOI: 10.5772/intechopen.93290

Downloaded: 18

Abstract

The study aims to examine the location drivers and barriers influencing the foreign direct investment (FDI) in the hotel sector in selected Middle Eastern and North African (MENA) countries. Data of study variables was selected from fDi Intelligence, Euromonitor International, World Economic Forum, and Datamonitor. Findings indicated a significant correlation of investor, quality, rule and law, infrastructure quality, corruption, politics, government effect, gross domestic product (GDP) growth, total tax rate, and real export GDP with FDI. However, FDI inflows were significantly determined by the level of investment freedom, investor protection, and political stability. The study concluded that investment freedom, market size, and stability of the country revealed the anticipated signs.

Keywords

  • barriers
  • drivers
  • FDI determinants
  • hotel sector
  • MENA countries

1. Introduction

Middle Eastern and North African (MENA) countries consist of a group of Middle Eastern and North African countries that are characterized as economically diverse regions. Among MENA countries, gross domestic product (GDP) per capita differs significantly from Qatar (46,598 US$) with the highest per capita income to Sudan, which has the lowest per capita income (719 US$). Egypt, Iran, and Turkey are the countries with the largest populations among MENA countries in terms of population size. However, GDP rate of Turkey is the largest, whereas Bahrain has the smallest GDP in terms of economic size. On the contrary, Jordan, Bahrain, Sudan, and Lebanon have the highest net of foreign direct investment (FDI) inflows, while Yemen, Syria, and Iran have the lowest net of FDI inflows in terms of GDP percentage [1]. A major challenge for resource-poor countries is represented from high inflation, importing meaningful accounts of fuel and food, while major resources of rich countries in the region are lacking. In addition, Turkey, Sudan, and Iran comprise the highest consumer price, whereas Bahrain, Saudi Arabia, Libya, and Morocco accounted the lowest consumer price in terms of the rate of inflation [2].

Financial sector development is important for the expansion and development of real estate and hotel sectors to improve inward FDI in developing and emerging markets. The most important determinants of FDI are the real estate market, market liquidity, market maturity and transparency, and institutional real estate market size [3]. Other drivers to FDI include economic and demographic factors, institutional factors, infrastructure quality, and sociocultural factors in real estate. In contrast, data availability, trading, currency, liquidity, portfolio construction, tax, and fund structuring and trading are the barriers experienced by real estate and hotel sectors [4]. However, major motivation behind the selection of real estate and hotel sectors of MENA was the paucity of empirical evidence in this region. In addition, there is very scarce academic literature in the body of context which is entirely associated with average FDI determinants. Exploratory evidence has shown interest regarding investment in specific MENA countries [5]. The availability of data in the selected countries is another influential factor.

Initially, the real estate sector is segregated into four asset classes, which include residential, industrial, retail, and office. In contrast, hotels have not been considered as a commercial real estate asset class for assorted reasons including complexity for a quick exit strategy; lack of understanding of the industry by investors, resulting from unstable cash flows; and volatility when compared to other property assets [6]. Hotel investors have different motives and barriers when venturing into these sectors. For example, hotel investors are more anxious toward the progression of the tourism industry. Government sectors are also seeking to attract capital so that they can design policies to improve and stimulate the entire investment environment and FDI, to enlarge their economies.

Previously, FDI flows were comparatively scarce in the MENA region, as compared to the European Union (EU) and other emerging and developing countries [7]. An important challenge can be experienced from several features of the MENA countries for the inward FDI performance. It is a fact that this region is highly fastened on oil, which deteriorates the economic foundation, has a high unemployment rate, has a high population growth, and portrays a deteriorated regional integration and the financial and capital markets persevere undeveloped [8]. In addition, the weight of the state in the country is still high, where the literature stresses the low rates of return on human and physical capital, the underdevelopment of physical infrastructure, and the lack of transparency in spite of the privatizations in the last years [9].

The examination of MENA institutional systems emerges to be specifically influential since a substantial number of these economies have been experiencing intense economic and institutional reforms [10]. In addition, trade relations are encouraged by the Euro-Mediterranean Partnership agreement along with the developed reduction of trade barriers. Some economies have created special regimes and liberalized investment regulatory framework for FDI. Tax and custom duty breaks, capital market reform programs, and lowering ownership limitations are included in reforms [11]. It is essential to study this subject considering the facts and the comparatively sparse empirical research on FDI in MENA countries. Therefore, this study aims to examine the location drivers and barriers influencing the FDI in real estate and hotel sectors in MENA countries.

This study has presented its novelty in different ways. Firstly, it has selected eight real estate and hotel markets in MENA countries and collected time series data during 2003–2009. The rationale behind the selection of this time period is that it provides the adequate analysis of financial development factors. These traditional factors are no longer sufficient to explain the FDI alteration; however, the quality of economic freedom is increasingly integrated into the direction of investors’ choices with increasing wave of globalization. In addition, suitable techniques are applied in this study to estimate the models based on a pooled tobit model. Secondly, this study has provided multidimensional evidence on the impact of location drivers and barriers on FDI in hotel sectors practically. Moreover, Dunning’s ownership, location, and internalization (OLI) paradigm is selected as a theoretical background to demonstrate the behavior of hotel foreign investors toward the selected MENA markets. Lastly, the application of economic models takes place in the emerging market. The key themes from the models are comprehensively used to cover political, sociocultural, and economic variables. This study has tried to aid governments of MENA countries to understand the drivers and barriers to sectoral-associated FDI in these markets and assist governments for reconsidering their policies by endowing specific recommendations to foreign investment policymakers.

This paper is organized as follows. The empirical literature associated with FDI and institutions, highlighting the research on the MENA countries, is reviewed in Section 2. The data utilized in the empirical study is presented in Section 3 along with some descriptive statistics on the institutional and economic variables in the MENA region. The econometric approach is presented in Section 4 along with the discussion of results. Section 5 presents the overall summary of this paper.

2. Theoretical approach to location drivers and barriers of FDI

The internationalization and traditional trade theories are embraced by the eclectic or OLI paradigm and systematized the advantages for firms that operate internationally [12, 13]. There are several benefits in selecting FDI when there are correspondingly ownership benefits such as location advantages, ownership advantages, and internationalization advantages. The significance of a firm owning assets is concerned in the ownership advantage such as exclusive productive procedures, patents, management skills, and pioneering technologies that can generate advantages in the future [14].

Location is considerable when a firm acquires from its presence in a predefined market by promoting from circumstances such as lower production, transport costs, access to protected markets, special tax regimes, and lower risks [14]. Internationalized operations, which allow a reduction in transaction costs related with risks of managing technology, can reduce market imperfections such as the imbalance of international resources allocation [12]. Therefore, the selection of a specific location is based on particular conditions that are in its preference [15]. The core objective of eclectic paradigm of Dunning to the literature was to bring forward the several complementary theories, which help in identifying a series of variables that reflect the activities of multinational enterprises [14]. The emphasis of this approach is to implement these variables for trading, for the international organization of production, and for international production. It shows that three modes of internationalization can be covered within the same analytical framework [15].

3. Related literature

3.1 Ownership-specific advantages

The OLI framework of Dunning was extended by Holsapple et al. [16] into the subject of international real estate investments. They claim that international real estate investments were hybrids of portfolio investments and direct FDI. The portfolio P subparadigm in the framework was included in the extended OLI framework for allowing the disadvantage of being international in an international environment to be comprehensively implemented. The modified framework divides ownership advantage dimension of Dunning into two subparadigms so that ownership is equivalent to ownership and portfolio in the ownership portfolio location framework.

Holsapple et al. [16] asserted that investors must assess both ownership and portfolio benefits when selecting on an investment in an international country. Further, it was demonstrated that the ownership advantages as the advantages possessed by enterprises in their host countries are transferable into international countries. It has been asserted that ownership must be taken into consideration where ownership is the advantage possessed by local enterprises to operate in the host country and claim that global investors must take into account the fixed costs of operating in a foreign environment.

3.2 Location-specific advantages

Location considerations are apparently at the core of FDI in real estate. It has been explained that real estate actors are interested in specific countries and majorly relied on a greater extent on the type of direct and indirect barriers, experienced by market actors in host countries [17, 18]. D’Arcy [18] has claimed that the business culture, institutional environment, and regulatory barriers are essential aspects when developing strategies for internationalization. Location advantages need investors for asking the where question in the OPLI extended framework in order to explore the factors such as monetary policies, host country political risks, and laws and fiscal policies. Holsapple et al. [16] claimed that location benefits must be estimated alongside the recurring costs of being international such as differential treatment in the host country or operating a long distance from the investment. Holsapple et al. [16] argued that enterprises can simply obtain passive interests in current real estate assets in the host country if they depend on portfolio advantages and the location selection is less essential.

3.3 Internalization-specific advantages

The internationalization advantage can be considered as an approach to exploit ownership by not contracting the related development activity but by objectively following it and maintaining control over it. It will be more advantageous for enterprises possessing ownership advantages to own the investment itself as compared to sell, franchise, or lease the advantage for foreign firms situated in the host country [16]. Internationalization of real estate activities is the process for determining the organizational mode by which stakeholders select to transfer capital across boundaries and intangible assets. Those intangible assets may entail human and management expertise, the reputation and knowledge of the internationalizing firm [18]. The capital transfer can be initiated and offered either from equity or debt positions, along with the financial structure related to the predefined transfer [18].

3.4 Empirical review

The location served as the central point for several researches, where it is generally highlighted as a motive for FDI. The significance of the location in FDI has been substantially supplemented by a number of studies [19, 20, 21]. Despite the immense work on the phenomena, the determination of the core location drivers for the FDI remained unknown. The study by O’brien and Williams [22] stated that the globalization and the liberalization of the national economic relations impact the significance of location which works as an important determinant for FDI. This aligns with the study of Mao and Yang [23], claiming the significance of one determinant may vary with time, as its importance declines with increase in significance of another determinant.

Theoretically, the selection of the location for the FDI has been promoted by various studies. For example, Mao and Yang [23] highlighted that FDI emerges as a consequence of the broad strategy formulated by the corporation in relation to the investment. It is based on maximizing profit while simultaneously perpetuating its global outreach. This is evident from the success of the United Kingdom, India, and Mexico for drawing hotel FDI in the periods 2005–2011 with respect to their size of the markets, taxes, and wages and degree of business regulations [24].

For Bayraktar [25], location serves as the main determinant for the FDI in terms of its investment decisions. The location drivers include land area, per capita income of the state, labor conditions, its production capacity, transportation, taxes, expenditures, as well as its agglomeration [26]. The review of the study by Yin et al. [27] illuminated that the conventional location theory, new location theory, and institutional environment regarding the labor cost, infrastructure, and market size as well as policy incentive serve as the major drivers of location for FDI.

Omoniyi and Omobitan [28] stated that the flow of FDI points toward the activities adopt with an intent to expand their profitability and competitive prospects. The activities carried out by these foreign firms are reflected as the strategies which overcome the economic gap and prevail in the domestic capital of the developing countries, simulating their economic growth. Lien and Filatotchev [29] argued that the FDI investment in terms of location is conditioned to the state capital, operations involved for its regulation, as well as parameters laid out for its repatriation of the profit and capital. Ma and Raimondos [30] further asserted that since the foreign firms are profit-oriented, therefore, the first priority is to assess return capacity of the state regardless of its host country social conditions. Location, where the possibility of capital loss prevails, is usually neglected by the firms irrespective of the industry. Falk [24] supplemented that the FDI decision is significantly related to the advantage, which the location offers to the firm. This is further corroborated by the research of Al-Shammari, Al-Halaq, and Al-Shammari [31], which adds that the advantage which the location offers serves as a catalyst for the FDI.

Snyman and Saayman [32] highlighted the characteristics of 42 host countries which influence the FDI in hotel and tourism industries. The study highlighted that political stability, health, safety, and infrastructure, i.e., airports and roads, along with factors related to cost and skills, as well as market sizes such as international tourism demand and GDP, are the main indicators of FDI location. Similarly, Brida et al. [33] highlighted the size and the past internationalization experience act as key drivers for the internationalization of Spanish hotel chains.

Phung [34] highlighted the locations’ market size, trade openness, and macroeconomic stability as the prime variables for FDI. These three variables have been supported by various empirical studies, which focused on the concept of FDI [30, 34]. The explanation behind was provided by Crescenzi and Petrakos [35] stating that the investor is concerned with the return, which is in direct relation with the host country customer base size, the availability of the resources, and the implementation of trade policy.

The labor market size and its low acquisition are also regarded as the location advantages for various developing countries. Phung [34] stressed upon these factors particularly for the developing countries since it is immobile as well as region-specific. The labor incentivizes the resources for investors as they are able to locate their function in the host country lowering their cost of production. In the hospitality sector, the estimation of labor force has found to be momentous in terms of the participation made by labor, its growth, and population stock [36, 37]. Wild and Wild [38] highlighted that due to the availability of the cheap labor in Mexico, various technology- and capital-rich investors in the United States shifted to Mexico for maximizing their profitability.

Another possible driver of location was highlighted by Bénassy-Quéré et al. [39]. According to them, the variation in terms of charged tax with regard to the offered good or service significantly impacts the flow of FDI in a state. The benefits in terms of agglomeration are further supplemented by Lien and Filatotchev [29] to improve the FDI flow in the country. Another study stated that when the location is successful in attracting FDI, it paves the path and serves as a catalyst for improving future FDI. These are similar to the stated results of Phung [34] highlighting the positive link of Japanese’s manufacturing plants in the United States to agglomeration when the location is being chosen.

Dunning and Lundan [14] also laid out factors in relation to the policy framework incorporating the specific policies related to the country FDI. Considering the model for general policy, it is suggested that the host country business environment should be stable in terms of its economy and political settings as well as social conditions. Reflecting upon the literature further highlighted various barriers for FDI in correspondence to the hospitality industry. Paudel and Tiwari [40] stated that the delay of approval in terms of FDI hotel serves as a major hindering block for the country hospitality sector. Evaluating the hotel and tourism industries, Bissoon [41] reported that inadequate guidelines in terms of tourism policy also impact the FDI flow in a country, particularly for its hospitality sector. Inadequate support from the regulatory institutes is also reported as the barrier for FDI.

Hayakawa et al. (2011) researched 93 countries constituting 63 developing countries and showed that the instability of the politics hinders the capability of the country for FDI inflow. Another research of Topal [42] concerning the developing country further highlighted that the reduction of the economic and political risks in terms of capital loss improves the country hotel FDI flow.

The reduction and restriction in FDI are inclusive of various factors such as legislative and regulatory frameworks, bureaucracy, protection of the investors’ finances, and restrictions on the foreign ownership [43]. Another research of Azémar and Desbordes [44] proposed that regulation in the product market of the host country, which may induce additional costs for businesses, serves as an FDI barrier for their entry.

4. Methods

The study adopted a quantitative causal research design to identify and examine the location drivers and barriers influencing FDI in real estate and hotel sectors. The data was collected from eight MENA countries, which include Algeria, Egypt, Morocco, Qatar, Saudi Arabia, Turkey, Tunisia, and the United Arab Emirates (UAE) during 2003–2009 (i.e., prior the Arab spring). The selection of the host countries was indicated from the availability and accessibility of the data. Moreover, the econometric analysis was conducted for both time series and cross-sectional data using the pooled tobit model technique.

The core purpose of this study is to examine the location drivers and barriers affecting real estate and hotel sector FDI location in the MENA countries. The study has employed a panel data, which is an authentic modeling strategy including both cross-sectional and time series analyses over a short period as selected in this study. The important characteristic of panel data that differentiates it from a cross section is the same as cross-sectional units followed over a predefined time period and allowed a study of the significance of lags in behavior or the outcomes of decision-making [45]. This information can be substantial as the number of economic policies can be anticipated to have an effect merely after some time has passed. Each independent variable is lagged once a year, considering the likely form of a cause and effect relationship. Pooled tobit, fixed effect, and random effect models and pooled ordinary least squares (POLS) were anticipated to a balanced panel of appropriate data for testing the effect of the selected location drivers and barriers.

The nominal FDI flows measure the dependent variable real estate FDI as provided by the FDI market database. The level of real estate investment in each country is normalized by distributing real estate FDI by the nominal GDP of the country. This allows the author for adjusting the level of investment for the size of the economy of each country. This modification facilitates for more direct comparisons between MENA countries as the size of the GDP for each country is potentially appropriate for the extent of real estate FDI received by each country. Euromonitor International was used to derive the nominal GDP data.

Spurious results are produced by regressions performed on nonstationary time series variables. It is therefore essential for confirming that variables are stationary, which indicates that the mean and variance and probability distribution do not change over time and do not follow any patterns. An autoregressive model was used to estimate whether a time series variable is nonstationary based on a unit root test. The commonly used root tests such as Phillips-Perron and augmented Dickey-Fuller (ADF) tests lack coerciveness to differentiate the unit root from stationary alternatives. The conventional ADF-type tests of unit root further experience from the issue of low strength in order to reject the null hypothesis of stationarity of the series, particularly for short-span data.

The study variables which include real visitor export, tourist arrival, gross domestic product, overall quality of infrastructure, total tax rates, corruption, government effectiveness, regulatory quality, rule of law, voice and accountability, levels of investors protection, and levels of human development were also treated as independent variables. The data for these variables were selected from fDi Intelligence, Euromonitor International, World Economic Forum, and Datamonitor.

5. Results

This section is divided into two major results: unit root test and panel estimation. Table 1 has summarized the results of the unit root test based on the propositions of Levin, Lin, and Chu (LLC) test. Findings have confirmed that the null hypothesis is rejected for all series investigated at their levels.

5.1 Unit root test

The unit root test results indicated that the model can be anticipated regardless of any differenced variables. All the variables are stationary at level I (1), so that they are included in their actual form.

5.2 Panel model estimation

The descriptive statistics and correlation matrix have been used to calculate the absolute values of the variables in the panel model estimation (Tables 2 and 3).

VariablesLLC test resultsConclusion
HFDIGDP−16.5351***I (0)
HUMANDEVELOPMENT−1.29020*I (0)
INFRAQUAL−4.32190***I (0)
INVFREEDOM−3.55790***I (0)
REALVEXPOTGDP−2.44533***I (0)
RGDPGROWTH−6.12141***I (0)
TAGROWTH−5.95415***I (0)
TOTALTAXRATE−3.04086***I (0)
PROTECTINVESTOR−3.55790***I (0)

Table 1.

Unit root test results.

***Significant at 1%, *significant at 10%.

HFDIGDP (−1)TOTAL TAX RATE (−1)REALVEXPOTGDP (−1)TAGROWTH (−1)RGDPGROWTH_1HUMAN DEVELOPMENT (−1)INFRAQUAL_1
Mean0.00749540.547920.0635549.8712505.8560830.7661040.490380
Median0.00432344.850000.0330757.6000005.3285000.7580000.493200
Maximum0.03306876.900000.18323735.9500020.835000.9100000.796320
Minimum0.00000011.300000.001485−18.000000.1280000.5830000.230400
Std. Dev.0.00834723.164530.06315810.677513.4675670.0943940.112819
Skewness1.315563−0.0227760.7051930.4128022.029346−0.1885570.135323
Kurtosis3.8178791.6064631.8606563.8103579.2354641.9933243.214573
Observations48484848484848
CORRUP (−1)GOVEFFECT (−1)POLITIC (−1)PROTECTINVESTOR (−1)REGQUALT (−1)RULELAW (−1)VACCOUNT (−1)INVFREEDOM (−1)
Mean0.3486110.5208330.5088544.6125000.7310610.7326390.30463546.25000
Median0.3333330.5000000.4625005.0000000.7272730.7916670.32325050.00000
Maximum0.5000000.7500000.7750005.7000000.9545451.0000000.47250070.00000
Minimum0.2500000.2500000.2500003.0000000.5000000.3333330.06250030.00000
Std. Dev.0.0770950.0868070.1475410.9350450.1421510.1396110.10468913.62491
Skewness0.3978631.3125460.206238−0.6993790.040110−1.120847−0.9085290.243874
Kurtosis2.4189047.3391002.1528111.9833071.9986224.0472333.3821132.134145
Observations4848484848484848

Table 2.

Descriptive statistics for variables employed in the hotel FDI panel.

Correlation123456789101112131415
HFDIGDP1
TOTALTAXRATE−0.395***1
REALVEXPOTGDP0.323**0.0641
TAGROWTH−0.0342−0.2168−0.03581
RGDPGROWTH0.238*−0.304**−0.0962−0.0671
HUMANDEVELOPMENT−0.11170.52***−0.0933−0.061−0.0491
INFRAQUAL0.321**−0.241*0.2213−0.0640.44***−0.00251
CORRUP0.369***−0.35***0.41***0.03890.1669−0.267**0.244*1
GOVEFFECT0.281**−0.296**0.122−0.0240.07−0.44***0.18520.05431
POLITIC0.427***−0.54***0.0918−0.0680.48***−0.38***0.47***0.20750.329**1
PROTECTINVESTOR−0.359***−0.1763−0.50***0.1196−0.02850.0114−0.336**−0.47***−0.0958−0.15731
REGQUALT0.372***−0.71***0.10070.0160.1623−0.50***0.19240.340**0.51***0.52***0.01361
RULELAW0.1862−0.49***0.44***0.18390.1495−0.311**0.253*0.65***−0.12550.2084−0.34***0.328**1
VACCOUNT−0.01690.47***0.0386−0.0920.11260.17730.10280.2106−0.1433−0.0341−0.294**−0.48***−0.1141
INVFREEDOM0.04990.42***0.242*−0.014−0.222*0.37***−0.41***0.1588−0.0778−0.39***−0.257*−0.328**−0.23*0.38***1

Table 3.

Correlation matrix for dependent, independent, and control variables for hotel FDI panel.

***, **, and * denote significant at 1%, 5%, and 10%, respectively.

Hotel FDI inflows have zero values, making the POLS, RE, and FE biased and inconsistent with respect to the pooled tobit test ([46], p. 616). Table 4 shows the correlation matrix between dependent and independent variables for hotel FDI panel. Results indicate a significant correlation of investor, quality, rule and law, infrastructure quality, corruption, politics, government effect, GDP growth, total tax rate, and real export GDP with FDI.

CorrelationHFDIGDP
HFDIGDP (−1)0.391034
TOTALTAXRATE (−1)−0.418039***
REALVEXPOTGDP (−1)0.345872***
TAGROWTH (−1)0.082825
RGDPGROWTH_10.270806**
HUMANDEVELOPMENT (−1)−0.056115
INFRAQUAL_10.355238***
CORRUP (−1)0.359171***
GOVEFFECT (−1)0.236522*
POLITIC (−1)0.478399***
PROTECTINVESTOR (−1)−0.381454***
REGQUALT (−1)0.335257***
RULELAW (−1)0.240289*
VACCOUNT (−1)0.010185
INVFREEDOM (−1)0.080180

Table 4.

Correlation matrix (dependent vs. all independent variables) for hotel FDI panel.

***, **, and * denote significant at 1%, 5%, and 10%, respectively.

Table 5 summarizes the results of pooled tobit regression for investigating the hotel barriers and determinants. From the findings, it is emphasized that the hotel FDI inflows are not significantly determined by control of corruption, regulatory quality, voice and accountability levels, government effectiveness, and rule of law. However, FDI inflows are significantly determined by the level of investment freedom, investor protection, and political stability (Table 5).

HFDI/GDPPooled tobit
123456789
Constant−0.015938−0.019560*−0.026673*−0.026624**0.038978−0.015241−0.005379−0.018302*−0.025179**
HFDIGDP (−1)0.0364980.0244370.002213−0.012777−0.0497570.036856−0.024509−0.010593−0.069064
TOTALTAXRATE (−1)−0.000181***−0.00017***−0.00018***−0.0001**−0.00025***−0.00018**−0.00023***−0.0002***−0.00021***
REALVEXPOTGDP (−1)0.055010***0.05024***0.05439***0.05448***0.046490***0.055139***0.071612***0.05693***0.037851**
TAGROWTH (−1)3.71E-053.81E-053.10E-056.12E-055.61E-053.58E-053.62E-052.11E-051.31E-05
RGDPGROWTH_10.000621**0.000591**0.000605**0.000530*0.000483*0.000619**0.000607**0.000495*0.000596**
HUMANDEVELOPMENT (−1)0.025201**0.025477**0.03073***0.031677**0.023776**0.02503**0.026764**0.027620**0.017054**
INFRAQUAL_10.0062300.0066780.005784−0.001205−0.0062500.0062440.0072130.0039660.022551*
CORRUP (−1)0.009782
GOVEFFECT (−1)0.013991
POLITIC (−1)0.017354*
PROTECTINVESTOR (−1)−0.00805**
REGQUALT (−1)−0.000622
RULELAW (−1)−0.014402
VACCOUNT (−1)0.016666
INVFREEDOM (−1)0.000241**
No. of observation484848484848484848
Left censored obs555555555
Uncensored obs434343434343434343
Log likelihood150.5809150.7858151.1739152.1154152.617150.5825151.5974151.6004153.1897
LR chi square26.8675227.2771728.0534329.9364930.9395926.8707128.900528.9063332.08509
Prob>Chi square0.00040.00060.00050.00020.00010.00070.00030.00030.0001

Table 5.

Determinants of hotel FDI (pooled tobit).

*, **, and *** denote significance at 10%, 5%, and 1%, respectively.

6. Discussion

The study shows an insignificant effect of control of corruption, regulatory quality, voice and accountability levels, government effectiveness, and rule of law but a significant effect of level of investment freedom, investor protection, and political stability on hotel FDI inflows. The findings indicate a positive but insignificant effect of corruption on the FDI inflows, which reveals that the role of corruption toward hotel FDI decisions is not critical.

Also, a negative and insignificant effect of regulatory quality on hotel FDI in MENA countries at 5% level of significance is identified, indicating it an unimportant determinant for hotel-related FDI decisions. Findings further indicated a negative and insignificant effect of rule of law on hotel FDI in MENA countries at 5% level of significance, indicating it an unimportant determinant for hotel-related FDI decisions. However, the findings have shown a positive but insignificant effect of voice and accountability on hotel FDI in MENA countries at 5% level of significance, indicating it an unimportant determinant for hotel-related FDI decisions. In contrast, findings provided a positive and significant effect of level of investment freedom on hotel FDI in MENA countries at 5% level of significance, referring it an ineffective determinant for hotel-related FDI decisions.

In this regard, Falk [24] indicated a positive but insignificant effect of corruption and tax rates on the hotel FDI projects in 104 host countries from 2005 to 2011. Nguyen et al. [47] have indicated a positive and significant impact of corruption on the FDI inflows in the service sector in Vietnam. The study has observed that the extent of corruption in Vietnam is lagging behind the country in terms of market institutions and the legal systems. In addition, Shah and Azam [48] also found the insignificant influence of corruption index on FDI inflows in MENA countries from 2003 to 2016.

Similarly, the present study showed a positive and insignificant effect of government effectiveness on hotel FDI, which indicates an unimportant determinant in hotel FDI decisions. In this regard, Shah and Afridi [49] have found a significant impact of government effectiveness on hotel FDI in SAARC countries from 2006 to 2014. Subramanian and Subramanian [50] showed a significant impact on government effectiveness in the service sector in India. It further indicated that the steps attempted by the government are effective in short run but can be successful in the long run if exporters concentrate on value addition, which offsets the rising domestic interest rates, and market development and calculated measures of restrictions are taken.

A positive and significant impact of political stability has been found on hotel FDI inflows, which shows that political stability acts as a core determinant in attracting hotel-associated investments in specific markets. Mao and Yang [23] found a significant and positive impact of political stability on FDI inflows in Chinese domestic hotels. It further provided significant spillovers in domestic hotels of Eastern and Western China. Findings provided in the study of Tekin [51] indicated the negative and indirect effect of political instability on FDI inflows of Russian and Turkish tourism industries. Maclean et al. [52] outlined that macro-political instability universalizes the growth of multinational hotel industry. The study provided that the postwar globalization and its associated discourses demonstrate the ideology of the hotel industry.

The panel model estimation showed a negative but significant effect of the strength of investor on the hotel FDI inflows at the 5% level of significance. There is empirical evidence in the hotel industry which shows a positive relationship between investors and FDI inflows. In addition, the study of Nam [53] indicated a significant effect of the strength of investors on the hotel FDI inflows by revealing a positive association between private and public hotels. The interest of investors toward the FDI inflows in Cambodian hotels is developed from the value-added benefits of each type of hotel investment. Kumar [54] on the other hand outlined a positive and significant impact of the strength of investors toward maintaining budget hotels and quality of hotel services.

7. Limitations

The study presented several limitations based on the model and findings. Firstly, it used data on an aggregate level, which augments the possibility that some information is lost during the data collection and transformation process. Secondly, only annual time series data is used to compute the panel models. Thirdly, data has been extracted from 2003 to 2009, which indicates doubts whether a vigorous econometric analysis can be conducted. It was also not possible for expanding more on the qualitative data end since the study period was based on only 6 years. However, this is an opportunity for future researchers to collect more primary data from policymakers, hotel markets intermediaries, and foreign investors. Lastly, public and private agencies have been used as potential sources to measure the accuracy of the data and, therefore, challenge the accuracy of such data. However, findings obtained through such data have provided significant empirical evidence for hotel investors and markets.

8. Conclusion

The study was aimed to investigate the location drivers and barriers of FDI determinants in MENA countries within the hotel sectors. In this regard, findings obtained from the econometric analysis of hotel FDI inflows have shown that hotel sector-specific variables and country-specific factors are influencing the FDI inflows in MENA countries, though the findings of the study are somewhat unsupportive. For instance, hotel FDI values are insignificant for FDI flows for the selected MENA countries, whereas investment freedom, market size, and stability of the country revealed the anticipated signs. In addition, the study showed an opposite sign of the investor protection, indicating that hotel investors are reducing such risks significantly and accomplishing a high extent of control through specific contractual agreements.

It further indicated three common barriers, which include taxation, level of investment freedom, and political instability. These barriers have explained why MENA countries attract hotel FDI at the least extent than to other countries at a similar stage of development. The level of investment freedom is found to be a significant and important barrier in explaining hotel-related FDI. Terrorism, political instability, and violence are shown as important barriers in restricting MENA countries toward hotel FDI inflows. Lastly, taxation also restricts hotels in MENA countries to attract toward FDI inflows.

9. Recommendations

Several recommendations are proposed for future research in this context. Firstly, it is recommended that the current research should be extended to investigate the influence of FDI determinants on economic growth of MENA countries. It will be of beneficial interest to indicate the significance of hotel sector to explain the wider economy and whether governments are making significant efforts to acquire explicit advantage for hotel sector as part of their economic growth. Secondly, it is recommended to undertake different regions or countries such as South East Asia or Eastern Europe. This may include hotel-related FDIs. Data sources could vary though, especially for FDI-related variables. Concerning independent variables, the current study already tried pragmatically a number of variables for the first time and thus suggests that variables from this study as well as other variables will be interesting to be empirically tested.

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Mohamed Salem (July 24th 2020). Identifying Location Drivers and Barriers of FDI Determinants in MENA Countries: Undertaking Hotel Sector [Online First], IntechOpen, DOI: 10.5772/intechopen.93290. Available from:

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