Management & Economics Research Journal ISSN 2710-8856 (Online) ISSN 2676-184X (Print) Management & Economics Research Journal, Vol. 2 No.4 (2020), pp. 10-26 https://doi.org/10.48100/merj.v2i4.122 Faculty of Economics, Commercial & Management Sciences, Ziane Achour University of Djelfa, BP 3117, Djelfa - Algeria 10 www.mer-j.com The Impact of Tourism Industry on Economic Growth: The Case of Algeria (Cointegration & Causal Analysis) Boumedyen Taibi1, Khadidja Lamri2 1Associate Professor, University Dr. Tahar Moulay, Saïda (Algeria)  didenmajor@yahoo.fr 2PhD student, University Dr. Tahar Moulay, Saïda (Algeria)  Lamrikhadidja2222@gmail.com Published: 22-09-2020 Accepted: 15-07-2020 Received: 24-04-2020 Abstract: The objective of this research paper is to analyze the relationship between tourism and economic growth in Algeria during the period 1995- 2018. To this end, an economic model was estimated according to the econometric methodology (cointegration relationship test and vector error correction model “VECM” in addition to the Granger causality test). The results showed that the number of tourists has a negative impact on economic growth in Algeria, which means that Algeria depends on other variables to increase economic growth, such as oil revenues. Keywords: Causality, Cointegration, Economic Growth, Tourism, Algeria. JEL Codes: C1, O4, Z32. 1. Introduction Tourism is a human activity that satisfies the person and his values, society and objectives, in addition to the State and its budgetary ambitions. This human activity has been institutionalized over the generations since its emergence in the 19th century, passing from a shared individual need of a social class (the English aristocracy) to an aroused, oriented, cultivated, and democratized need. All of this explains the growing interest of small and large companies in the States for this significant source of income. Tourism has become a major driver of social and economic progress Corresponding author: University Dr. Tahar Moulay, Saïda (Algeria). [ didenmajor@yahoo.fr] https://crossmark.crossref.org/dialog/?doi=10.48100/merj.v2i4.122&domain=pdf&date_stamp=2020-09-22 The Impact of Tourism Industry on Economic Growth: the Case of Algeria (Cointegration & Causal Analysis) Boumedyen Taibi & Khadidja Lamri 11 through job creation and companies, infrastructure development, and earned export earnings. It is considered one of the leading service industries in the global economy. As the economic flows which result from international tourism have become vital factors in economic growth and international economic relations in many countries. Tourism is considered one of the most important economic activities for the Mediterranean countries. Due to their geographic position at the heart of three continents, these countries attract more than 30% of international tourist arrivals, which generates jobs and revenues. Algeria is part of the Mediterranean basin but it has not managed to develop the tourism sector with a very low number of foreign tourists (2657000 tourists in 2018) and to rank well compared to competing destinations like Morocco, Tunisia, Turkey, Spain, Italy…etc. Today, governments have focused their attention on the development of the tourism sector, thanks to these economic advantages. Therefore, the development of the tourism sector is a necessary priority for governments, which requires medium and long-term policies and planning to achieve tourism attractiveness. In Algeria, hydrocarbons are still the chosen sector so far, bearing in mind that the barrel of oil is somewhat volatile. Algeria must search for other sectors to rely on and search for new opportunities at the international level to diversify its sources, however, in the context of the current global economy, tourism is of particular interest. In this sense, our main problem in this paper is: What is the impact of tourism on economic growth in Algeria? To answer this problem, we propose four main hypotheses for our research, which are: - The tourism sector has a positive impact on economic growth in Algeria; - The tourism sector has a negative impact on economic growth in Algeria; - The NBT and REER caused the GDP; - The NBT and REER do not cause the GDP. pp. 10-26 Vol. 2 No. 4 (2020) Management & Economics Research Journal 12 2. Review of literature & the theoretical framework To lay the foundations of research, we addressed through this paper the presentation of some review of literature highlighting the most important results reached. 2.1. Review of literature In this section, we will try to present the most important results of the review of literature related to tourism and economic growth. 2.1.1. The Study of (Arslanturk & others, 2011) entitled "Time-varying linkages between tourism receipts and economic growth in a small open economy": Investigates the causal link between tourism receipts and GDP in Turkey for the period 1963-2006. The study uses the rolling window and time-varying coefficient estimation methods to analyze the Granger causality based on Vector Error Correction Model (VECM). The findings of the paper indicate that there is no Granger causality between the series, while the findings from the time-varying coefficients model based on the state-space model and rolling window technique show that GDP has no predictive power for tourism receipts. However, tourism receipts have a positive predictive content for GDP following the early 1980s. 2.1.2. The Study of (Ekanayake & Aubrey, 2012) entitled "Tourism development and economic growth in developing countries": Its objective is to investigate the relationships between tourism development and economic growth in developing countries using the newly developed heterogeneous panel cointegration technique. This study examines the causal relationship between tourism development and economic growth using Granger causality tests in a multivariate model and using the annual data for the 1995–2009 period. The study finds no evidence to support the tourism-led growth hypothesis. The results of the FMOLS show that, though the elasticity of tourism revenue with respect to real GDP is not statistically significant for all regions, its positive sign indicates that tourism revenue makes a positive contribution to economic growth in developing countries. The results of the study suggest that governments of developing countries should focus on economic policies to promote tourism as a potential source of economic growth. 2.1.3. The Study of (Ben Zaarour & Satour, 2017) entitled "Tourism and economic growth in Algeria": Evidence of Cointegration and causal analysis”. This paper investigates the relationship between economic growth and tourism development in Algeria. The purpose of this research is to test empirically the long-term relationship between economic growth and tourism The Impact of Tourism Industry on Economic Growth: the Case of Algeria (Cointegration & Causal Analysis) Boumedyen Taibi & Khadidja Lamri 13 development for annual data collected between 1995 and 2014. The study uses a multivariable model that includes gross domestic product and as a proxy of economic growth and a variable reflecting the tourism economy such as arrivals of international tourists, income from expatriate spending. Other relevant variables are added to estimate the econometric model. 2.1.4. The study of (Harrats & Ramdani, 2018) entitled " Studying the causal relationship between tourism investment and tourism growth in Algeria using Toda and Yamamoto methodology": This study aims to find the causal relationship between tourism investment and tourism growth in Algeria, where the Toda and Yamamoto methodology, using the methodology of Toda and Yamamoto, which is based on the Vector Autoregression (VAR) model, the study concluded that there is a one-way causality from tourism investment to domestic tourism raw production. 2.2. The theoretical framework During the 20th century, tourism gradually established itself as an essential element of social and economic life, first in Europe and North America, then in Asia and later in other parts of the world, in this section, we will present general tourism information. 2.2.1. Definition of tourism “Tourism is a new phenomenon that has emerged in everyday reality for less than half a century. But it experienced such rapid expansion and generalization in society as a commonplace and a naturally constitutive element of this daily life” (Cazes, 1989, p. 07). One of the earliest definitions of tourism was given by an Austrian economist, Hermann V, Schullard, in the year 1910 who defined it as: “the total of operators, mainly of an economic nature which directly relates to the entry, stay and movement of foreigners inside and outside a certain country, city or region” (Raj, 2002, p. 69). For the World Tourism Organization (WTO), “tourism is a trip away from your usual place of residence for more than 24 hours but less than 04 months, for leisure purposes, a professional purpose (business tourism) or a health goal (health tourism)"(Camilleri, 2018, p. 02). Professors Hunziker & Krap (1942) defined tourism as “the totality of the relationship and phenomenon arising from the travel and stay of strangers, provided the stay does not imply the establishment of a permanent residence and is not connected with remunerated activity” (Raj, 2002, p. 69). In 1991, the United Nations World Tourism Organisation declared that “Tourism comprises the activities of persons travelling to and staying in pp. 10-26 Vol. 2 No. 4 (2020) Management & Economics Research Journal 14 places outside of their usual environment for not more than one consecutive year for leisure, business or other purposes” (Camilleri, 2018, p. 02). 2.2.2. The tourism market The tourism market is defined by tourist demand and tourist supply. Figure 1. The tourism market Source: (Caccomo, 2007, p. 19) 2.2.2.1. Tourist demand Tourism demand is defined on the basis of total tourist spending on goods and services of domestic production In 1972, René Baretje introduced the function which links tourist demand to Xk factors like this: Y= f (X1, X2,..., Xk) with f’<0 (1) This translates to: An increase in prices will cause automatically a decrease in the quantities. This law applies to the tourist sector, so if the price of a stay increases its demand falls and vice versa. Price Tourist demand Tourist supply Tastes and fashions Quality Returned Price of substitutes Production factors Subsidy taxes Technology Production costs The Impact of Tourism Industry on Economic Growth: the Case of Algeria (Cointegration & Causal Analysis) Boumedyen Taibi & Khadidja Lamri 15 Figure 2. The tourist demand curve 2.2.2.2. Tourist supply The tourist supply can be defined as "the quantity of goods and services that can be presented on the market at a given price. Thus, the tourist offer is presented as a "basket" of goods and services, which are offered to consumers in order to satisfy their needs. What is offered to consuming tourists is in fact a set of by-products (accommodation, transport entertainment, and environment). " Y= f (X1, X2,..., Xk) with f’>0 (2) This translates to: An increase in prices will cause automatically an increase in the quantities. Figure 3. The tourist supply curve The tourism product consists of a set of activities. It is a basket made up of several products and services of a different nature and composition, with special characteristics. It is a composite product offering a set of P2 P1 Price Q1 Q2 Tourist supply P1 P2 Price Q1 Q2 Tourist demande pp. 10-26 Vol. 2 No. 4 (2020) Management & Economics Research Journal 16 material goods (hotel, craft products, etc.) and services of natural resources (landscape, beach, flora and fauna, etc.), socio-cultural resources (museums, etc.) and resources, technological (factory, nuclear power plants, etc.) and human relations, of the same importance. 3. Methods The estimation of the empirical model took place in four stages. First, we will perform unit root tests on each of the variables of the model to ensure the econometric approach to follow, secondly, we will look for the existence of a cointegration relationship between the variables and third place based on the cointegration relationship found, we will estimate a medium and long term model, in the end, we will evaluate the robustness of the model selected using the appropriate tests. As a final step, we will determine the trend of the relationship between economic growth and the tourism industry by the Granger causality test. 3.1 ADF Test Before the treatment of a time series, stochastic characteristics should be studied. If these characteristics - that is to say, its expectancy and its variance are modified over time, the time series is considered as non- stationary, in the case of an invariant stochastic process, the time series is then stationary. In a formalized way, the stochastic process yt is stationary if: (Bourbonnais, 2015, pp. 239-240) - E (yt) = E (yt+m) = μ ∀t and ∀m, the average is constant and independent of time. - Var (yt) < ∞ ∀t, the variance is finite and independent of time. - cov (yt, yt+k) = E [(yt − μ) (yt+k − μ)] = γk , the covariance is independent of time. It appears from these properties that a white noise process εt in which the εt are independent and of the same law N (0, σ 2 ε) is stationary. A time series is therefore stationary if it is the realization of a stationary process. This implies that the series have neither trend nor seasonality and more generally no factor changing over time (Bourbonnais, 2015, p. 240) The stationarity test used is that of Augmented Dickey-Fuller (ADF 1981). This test exists in three different versions: The first model without constant or deterministic trend presented generally as follows: ∆ t = t-1 − ∑ j ∆ t-j+1 + t The Impact of Tourism Industry on Economic Growth: the Case of Algeria (Cointegration & Causal Analysis) Boumedyen Taibi & Khadidja Lamri 17 The second model with constant and without deterministic trend: ∆ t = t-1 − j ∆ t-j+1 + + t The third model with the constant and deterministic trend: ∆ t = t-1 − j ∆ t-j+1 + + + t Where ΔY "t" is the change between periods t and t + 1. The null hypothesis H0 of non-stationarity is evaluated by testing the hypothesis φ=1 that means there is a unit root. 3.2 Johansen’s cointegration test (1988) The study of cointegration makes it possible to test the existence of a stable long-term relationship between two non-stationary variables, including delay variables and exogenous variables. There are several tests of cointegration, the most general being Johansen's. Whatever the test is chosen, it has significance only on non-stationary long series. Therefore, the cointegration analysis makes it possible to identify the true relationship between two variables, by looking for the existence of a cointegrating vector and eliminating its effect if necessary. Two series x and y are cointegrated if the following two conditions are satisfied: they are assigned a stochastic trend of the same order of integration and a linear combination of these series allows to reduce to a series of the order of integration inferior (Dupont, 2009). To determine the number of cointegration relationships, Johansen (1988) proposes two tests based on the eigenvalue of a matrix derived from a two-step calculation : Calculation of two residues ut and vt by making two regressions: The first : ∆ t = 0 + 1 ∆ t-1 + 2 ∆ t-2 + ⋯ + p ∆ t-p + t (3) The second : ∆ t-1 = 0′ + 1′ ∆ t-1 + 2′ ∆ t-2 + ⋯ + p′ ∆ t-p + t (4) pp. 10-26 Vol. 2 No. 4 (2020) Management & Economics Research Journal 18 With: t = 1,t 2,t ….…. k,t - ut and vt are the matrices of the residuals of dimension (k, n), with k: number of variables, n: number of observations. . = 1 t t′ . = 1 t t′ . = 1 t t′ . = 1 t t′ Extracting the k eigenvalues of the matrix M of dimension k: k in the following manner: = . . . . The number of cointegration relationships is tested by the "Trace" (Likelihood Ratio) statistic provided by Johansen, written: trace = − ∑ ln (1 − i) (5) With n: number of observations, i eigenvalue of the matrix M, k: number of variables, r: rank of the matrix. This statistic follows a probability distribution similar to x2 tabulated using simulation by Johansen and Juselius (1990). The trace test works by excluding alternative hypotheses: The rank of the matrix r = 0, ie H0: r = 0 against H1: r > 0, if H0 is refused we proceed to the next test (λ trace > the critical value read in the table), in the opposite case, the procedure is stopped and the rank of the matrix is r = 0. If after having rejected different hypotheses H0 at the end of the procedure, we test, H0: r = k-1 against H1: r = k. If we refuse H0 then the rank of the matrix is r = k and there is no cointegration relation because the The Impact of Tourism Industry on Economic Growth: the Case of Algeria (Cointegration & Causal Analysis) Boumedyen Taibi & Khadidja Lamri 19 variables are all I (0). The second test proposed by Johansen is given by the statistic of the maximum eigenvalue: max = − log (1 − r+1 ) (6) The test is carried out like the trace test, sequentially by excluding alternative hypotheses. In case of divergence of the two tests: trace and maximum eigenvalue, the test of the trace with the highest power is preferred. 3.3 Estimation of a vector error correction model (VECM) This type of econometric specification is known as the partial adjustment or error correction mechanism. The latter type of specification was popularized by Hendry under the general theme of error correction models (ECM) Davidson, Hendry, Srba & Yeo (1978) (Maurel, 1989, p. 105). The approach of this model allows us to determine both short-term and long-term properties at the same time. 3.4 Model validation To test the normality of the residuals, we use the J-B test from JARQUE and BERA. This test follows a distribution of "Chi-square" with two degrees of freedom. It is frequently used to determine whether the residuals of a linear regression follow a normal distribution. The test of J-B formulates the null hypothesis of normal distribution of the residues and this hypothesis is accepted only if the statistic J-B is lower than the critical value 5.99. This normality of the residuals is also accepted when the critical probability is superior to the 5% threshold (Mignon & Lardic, 2002, p. 275). The Jarque-Bera statistic is written (Damodar Gurrati, 2004, p. 149): = + ( ) (7) With: - n: Number of observations - k: Number of explanatory variables if the data come from the residues of linear regression. Otherwise, k = 0. - s: Coefficient of asymmetry of the test sample. - K: Kurtosis of the tested sample. s and K are defined by: pp. 10-26 Vol. 2 No. 4 (2020) Management & Economics Research Journal 20 = 3 3 = ∑ ( i ̅) ∑ ( i ̅) (8) = 4 4 = ∑ ( i ̅) ∑ ( i ̅) (9) With: ̂3, ̂4 the estimators of the third and fourth moments, ̅ : is the mean of the sample and is the variance of the sample. 3.5 Granger causality Causality in the sense defined by Granger (1969) and Sims (1972) is inferred when lagged values of a variable, say xt, have explanatory power in a regression of a variable yt on lagged values of yt and xt. The VAR can be used to test the hypothesis. Tests of the restrictions can be based on simple F tests in the single equations of the VAR model. That the unrestricted equations have identical regressors means that these tests can be based on the results of simple Ordinary Least Squares (OLS) estimates. The notion can be extended in a system of equations to attempt to ascertain if a given variable is weakly exogenous to the system. If lagged values of a variable xt have no explanatory power for any of the variables in a system, then we would view x as weakly exogenous to the system. Once again, this specification can be tested with a likelihood ratio test as described below the restriction will be to put “holes” in one or more matrices or with a form of F test constructed by stacking the equations (Greene, 2002, p. 592). 4. Results and discussion Globally, tourism has become widely recognized by governments, many international business houses and financing agencies as an effective way to raise the level of development of a country's economy; to the extent that emerging economies like India are beginning to think that they are an alternative source of economic growth, therefore, in this section, we will study the impact of the tourism sector on economic growth in Algeria for the period 1990-2018 and analyze the results. 4.1 Presentation of model variables 4.1.1 Presentation of the model In this study, we used the following reduced equation: = + t + + (10) The Impact of Tourism Industry on Economic Growth: the Case of Algeria (Cointegration & Causal Analysis) Boumedyen Taibi & Khadidja Lamri 21 With: - GDP: Gross domestic product - : Constant - NBT: Number of tourists - REER: Real effective exchange rate - : Error term The empirical model of equation 8 can be expressed in logarithmic form as follows: Ln = + t + + (11) The logarithmic form was used to linearize and homogenize the data. 4.1.2 Data sources and Variables The variables of this study are: - Gross domestic product (GDP), the number of tourists (NBT), real effective exchange rate (REER) - Annual data from 1995 to 2018 were obtained from the World Bank Development. 4.2 Empirical results In this section we will present our empirical results: 4.2.1 Graphical presentation of variables Before any analysis of time series, it is essential to study carefully the graph representing its evolution, because it provides a priori with a global idea on the nature and the characteristics of the processes generating this series, namely the seasonality trend etc. 3.19 3.20 3.21 3.22 3.23 3.24 3.25 3.26 3.27 96 98 00 02 04 06 08 10 12 14 16 18 Log GDP 2.56 2.58 2.60 2.62 2.64 2.66 2.68 2.70 96 98 00 02 04 06 08 10 12 14 16 18 Log NBT Figure 4. Gross domestic product Figure 5. Number of tourists Source: Results obtained from Eviews9 Source: Results obtained from Eviews 9 pp. 10-26 Vol. 2 No. 4 (2020) Management & Economics Research Journal 22 1.51 1.52 1.53 1.54 1.55 1.56 1.57 1.58 1.59 96 98 00 02 04 06 08 10 12 14 16 18 Log REER Figure 6. Real effective exchange rate Source: Results obtained from Eviews 9 The graphical representation of our raw series shows that there is a trend. So probably this series is not stationary, for confirmation we will apply the stationarity test. 4.2.2 ADF test The ADF test on our series is presented in the following table. We took the model with Intercept: Table 1. ADF test with Intercept on the GDP, NBT, REER series. Variable t-Statistic Critical values Probability Order GDP NBT REER -3.889942 -3.384542 -3.918533 -3.004861 -3.004861 -3.004861 0.0077 0.0229 0.0072 I(1) I(1) I(1) Source: Prepared by the researchers, based on outputs of Eviews 9 The result confirms that the series is not stationary in level, and are all integrated of order 1. They are therefore stationary in first difference. 4.2.3 Cointegration test “Johansen (1988)” The test relating to the number of cointegration relations is given by the value of the trace. We apply the test of Johansen (1988) to determine the number of cointegration vectors. The cointegration test is presented in the following table. Table 2. Trace test Hypothesized No.of CE (s) Eigenvalue Trace statistic Critical value 0.05 Probability None 0.687756 37.42953 29.79707 0.0055 At most 01 0.383108 11.82219 15.49471 0.1657 At most 02 0.052863 1.194846 3.841466 0.2744 Source: Prepared by the researchers, based on outputs of Eviews 9 The Impact of Tourism Industry on Economic Growth: the Case of Algeria (Cointegration & Causal Analysis) Boumedyen Taibi & Khadidja Lamri 23 According to table n° 02 and on the basis of Johansen statistics, we reject, at the 5% threshold (37.42953 > 29.79707 for the trace), the null hypothesis H0, absence of cointegration relation against the alternative hypothesis. (There is at least one cointegration relationship between the variables). So we accept the null hypothesis H0: there is at most 1 cointegration relation, against H1: there are at least 2 cointegration relations, at the 5% threshold (11.82219 <15.49471 for the trace). We accept H0: the presence of a single cointegration relationship at the 5% threshold. 4.2.4 Identification of the cointegration relationship = −0.977246 + 0.906738 − 15.76355 (12) [20.0121] [3.33255] The result of the estimation of the long-term relationship of the number of tourists confirmed that the NBT has a significant negative effect on the GDP (t-student> 1.96), on the other hand, the REER has a significant positive effect on NBT (t -student> 1.96). 4.2.5 Estimation of a vector error correction model (VECM) After the estimation of a VECM model we obtained the following dynamic equation: D(GDP) = - 1.01758524926*( GDP(-1) - 0.977245948913*NBT(-1) + 0.906737935779*REER(-1) - 15.7635465769 ) + 0.206520595523* D(GDP(-1)) - 0.0130659673321*D(GDP(-2)) - 0.210535429236*D(NBT(-1)) - 0.209656884331*D(NBT(-2)) + 0.499453850429*D(REER(-1)) + 0.972226834906*D(REER(-2)) + 0.087256864902 (13) The adjustment coefficient or the recall force is negative -1.01 and more significant (t-student > 1.96), so we conclude that there is a short-term long-term adjustment of 100% in the unit. 4.2.6 Model validation (J-B test) To test the normality of the residues, the J-B test was used. 0 1 2 3 4 5 6 -0.2 -0.1 0.0 0.1 0.2 0.3 Series: Residuals Sample 1995 2018 Observations 24 Mean -1.48e-16 Median -0.004589 Maximum 0.259722 Minimum -0.220274 Std. Dev. 0.135243 Skewness 0.249474 Kurtosis 2.362844 Jarque-Bera 0.654916 Probability 0.720754 Figure 7. J-B test Source: Results obtained from Eviews 9 According to figure 7, the J-B statistic is 0.65 with a probability of pp. 10-26 Vol. 2 No. 4 (2020) Management & Economics Research Journal 24 72%. We conclude that the residuals are normally distributed (the null hypothesis of normality is accepted). 4.2.7 Granger causality test After confirming the existence of a long-term relationship, we are going through another step represented in determining the trend of the relationship between economic growth and the tourism industry, and this is illustrated by the Granger causality test. Table 03. Granger causality test Null hypothesis Observation F- statistic probability NBT does not Granger cause GDP 22 5.44396 0.0149 GDP does not Granger cause NBT 22 0.27256 0.7647 REER does not Granger cause GDP 22 6.57027 0.0077 GDP does not Granger cause REER 22 0.43367 0.6551 Source: Prepared by the researchers, based on outputs of Eviews 9 According to this table, six hypotheses were tested simultaneously namely the causality between the two variables taken two by two, at the 5% threshold, it is clear from the results of the table that the causal relationship is in the same trend, meaning that the NBT causes economic growth (GDP), Furthermore, still at the 5% threshold, the REER has an influence on the GDP and not vice versa. 5. Conclusion This article has attempted to analyze the relationship between tourism and economic growth in Algeria since the period 1995-2018, to do this, four tests were used: the stationarity test, the Johansen cointegration test, the correction error test and the Granger causality test. The results showed that: - The series of variables, GDP, NBT, REER are stationary in the first difference - The three variables are cointegrated, they evolve together and consequently display a long-term relationship at least in one trend - The estimation of a VECM model shows that there is a short-term long-term adjustment of 100% - In Granger's sense, NBT causes GDP and not the reverse, and the REER has an influence on the GDP and not vice versa The Impact of Tourism Industry on Economic Growth: the Case of Algeria (Cointegration & Causal Analysis) Boumedyen Taibi & Khadidja Lamri 25 On the economic plan: To examine our four hypotheses posed previously: - The tourism sector has a positive impact on economic growth in Algeria - The tourism sector has a negative impact on economic growth in Algeria - The NBT and REER caused the GDP - The NBT and REER do not cause the GDP - The effect of NBT on GDP is negative, so we accept hypothesis 02 - The results showed that the NBT and REER cause GDP, so we accept hypothesis 03. The hydrocarbons sector dominates a large percentage of the Algerian economy, about 98%. Considering that oil is non-renewable energy and in view of the crisis that Algeria was exposed to in 1989 as a result of the collapse of hydrocarbon incomes and the decrease in exchange reserves. Thus, Algeria adopted new policies to revive the Algerian economy represented in the development of the tourism sector, in which this policy achieved a positive result. However, Algeria did not maintain this policy. It can be said that tourism development in Algeria has become imperative, despite what Algeria possesses of oil wealth, which must be used to strengthen the infrastructure for the development of non-oil economic sectors to achieve the principle of sustainable development and avoid financial crises resulting from fluctuations in the oil market. 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This is an open access article distributed under the terms of Creative Commons Attribution-Non Commercial License (CC BY-NC 4.0) which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. Recommended citation : Taibi, B., & Lamri, K. (2020). The Impact of Tourism Industry on Economic Growth: The Case of Algeria (Cointegration & Causal Analysis). Management & Economics Research Journal, 2(4), 10- 26. https://mer-j.com/merj/index.php/merj/article/view/122