Egypt's Exports to the ASEAN Bloc Using the Gravity Model (Comparison Between OLS, PPML)

International Journal of Social Science and Human Research ISSN (print): 2644-0679, ISSN (online): 2644-0695 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijsshr/v8-i12-20, Impact factor8.007 Page No: 9347-9354 IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9347 Egypt's Exports to the ASEAN Bloc Using the Gravity Model (Comparison Between OLS, PPML) Wafaa saad Ibrahim Yousef Economics and Foreign Trade Department, Faculty of Commerce and Business Administration-Helwan University, Cairo, Egypt ABSTRACT: This study aims to analyze Egyptian exports to ASEAN bloc countries during the period 2001– 2021 by applying the gravity model using two methods: OLS and PPML. The basic variables in the model include Egypt’s GDP, ASEAN’s GDP, Egypt’s population, ASEAN’s population, and geographic distance, which measure Egypt’s exports to the ASEAN bloc. The results of the gravity model show a positive relationship between Egypt’s GDP, ASEAN’s GDP, ASEAN’s population, and Egypt’s exports. Conversely, there is a negative relationship between geographic distance and Egypt’s exports. We also explain the differences in the effects of the basic gravity model factors on Egyptian exports to the ASEAN bloc by comparing the OLS and PPML methods. The results from the OLS method are larger than those from the PPML method because the OLS method cannot properly handle zero trade values between countries. KEYWORDS: ASEAN Bloc, Egypt, OLS, PPML, Gravity model, Zero-Trade. 1. INTRODUCTION Since the 1950s, countries worldwide (developing or developed) have tended to implement economic blocs as a method to increase growth rates, production, and market access. This approach boosts trade between countries, facilitates trade creation, and helps avoid the effects of trade diversion. The ASEAN bloc (association of southeast Asian) is considered one of the most important successful experiences of economic blocs globally, as it includes 10 countries: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam. ASEAN is a regional governmental organization that facilitates economic, political, security, and social integration among its member countries. It also provides a mechanism to resolve disputes between member countries through peaceful means. ASEAN is an official observer at the United Nations and represents a large market that attracts many countries, including Egypt. It has a population of 640 million people, which is approximately 5% of the world’s population. Egypt is working to benefit from the ASEAN bloc market to increase its exports and gain greater access to both ASEAN and African markets. Therefore, Egypt is striving to conclude many bilateral agreements with individual bloc countries, as well as agreements with the bloc as a whole. This study aims to analyze the basic factors of the gravity model that influence the increase of Egyptian exports to ASEAN bloc countries during the period 2001–2021. Since trade between Egypt and some ASEAN countries was zero in certain years, the impact values of the variables in the gravity model became larger than expected when using the OLS method. This is because OLS either excludes zero-valued trade flows or replaces them with a small constant. Therefore, the PPML method will be used for measurement. The gravity model is one of the most popular empirical tools for analysis in international trade studies (Anderson and Wincoop, 2003). Tinbergen was the first to propose using the gravity equation in international trade research (Tinbergen, 1962). Later, Anderson (1979) comprehensively examined the model, explaining that bilateral trade between two countries is positively influenced by each country’s GDP and negatively influenced by the distance between them. Therefore, the gravity model gained a new dimension in the literature beyond its theoretical foundations, focusing on “proper” econometric specification and extending the model by adding new variables to the equation (Golovka and Sahin, 2021). Olivero and Yotov (2012), Eaton et al. (2016), and Anderson et al. (2019) demonstrate the importance of dynamic gravity estimation frameworks. In addition, recent gravity literature is extended by numerous works on structural gravity frameworks (Arkolakis et al., 2012; Fally, 2015; Baltagi and Egger, 2016; Larch and Yotov, 2016; Yotov et al., 2016; Beverelli et al., 2018; Heid and Stahler, 2020). At the same time, many studies, such as Herman and Gee (2019), demonstrate significant differences in the effects of trade determinants over time and propose short-run gravity frameworks. The gravity model is estimated by OLS in this study, but recent literature argues that OLS is inappropriate for gravity applications for several reasons (Krisztin and Fischer, 2015). The longrun linear specification of the gravity model with OLS is unsuitable Egypt's Exports to the ASEAN Bloc Using the Gravity Model (Comparison Between OLS, PPML) IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9348 because bilateral trade often includes zero flows, making log-linearization infeasible. In many cases, trade between country pairs is zero, which creates problem for standard log transformations (Silva and Tenregro, 2006; Helpman et al., 2008; Burger et al., 2009). In general, two strategies are implemented in empirical studies to address “the problem of zeros.” One approach excludes all zero trade flows from the sample, assuming zeros contain no information. The other replaces zeros with a small positive number, assuming that zero values arise from statistical errors (Golovko and Sahin, 2019). However, excluding all zero trade flows from the data leads to the loss of valuable information in the model (Eichengreen and Irwin, 1998). Similarly, replacing zeros with a small positive number is an inefficient approach because the choice of this number is not based on theoretical or empirical justification (Linders and De Groot, 2006). At the same time, the literature shows that even small differences in the constant added to the dependent variable, “trade flows,” can significantly change the estimation results (Burger et al., 2009). Among the proposed alternative estimation methods, the PPML method suggested by Silva and Tenreyro (2006) has attracted the most attention. Using the log-linear form in empirical gravity models is a classical approach, but it presents several problems, such as heteroscedasticity and issues arising from zero trade flows. Since the logarithm of zero is undefined, trade flows with zero values are excluded from estimation, leading to bias caused by the logarithmic transformation. This study focuses on the issue of zero-valued trade flows. The log-linear model cannot handle zero trade flows effectively because the logarithm of zero is undefined. Frankel (1997) argues that the main reason for zero trade flows is the lack of trade between small and distant countries, which can be best explained by large associated variables and fixed costs. Rauch (1999) identifies other factors, such as low GDP per capita and the absence of cultural and historical ties, as possible explanations for the lack of trade between countries. Although the problems arising in the estimation of the log-linear gravity equation have been recognized for a long time, they have only recently been taken seriously (Helpman et al., 2008). Among the alternative estimation methods proposed, the Poisson pseudo-maximum likelihood (PPML) method suggested by Silva and Tenreyro (2006) has attracted the most attention. Silva and Tenreyro (2006) compared the PPML estimator to alternative estimation methods. The PPML estimator has been widely used in empirical applications of gravity equations because it effectively addresses problems caused by both heteroscedasticity and zero trade flows. This study will focus on zero trade flows and compare the results of OLS and PPML estimations for trade between Egypt and the Asian bloc. Due to the above, this study attempts to answer the following question: “To what extent do zero trade flows between countries affect the estimated values of variables when using the OLS method compared to the PPML method?” The rest of the paper is organized as follows: Section 2 analyzes Egyptian exports to the ASEAN bloc countries and the most important commodities exported from Egypt to these countries during the study period; Section 3 describes the variables and the gravity model; Section 4 presents the empirical results and discussion; and Section 5 provides the conclusions. 2. EGYPTIAN EXPORTS TO THE ASEAN BLOC It is clear from the data that the most important countries for Egyptian exports are Singapore, Malaysia, and Indonesia. In 2001, Egyptian exports to these countries were 86,125; 9,722; and 9,174 thousand dollars, respectively. These values continued to increase, reaching 112,780; 205,321; and 93,933 thousand dollars in 2010, and further rising to 505,410; 106,183; and 105,031 thousand dollars in 2021, respectively. This growth may be due to the large populations of these countries, as well as their commercial importance within the ASEAN bloc compared to other member countries. The countries with the lowest Egyptian exports are Laos and Myanmar, where exports amounted to 0 and 20 thousand dollars in 2001, respectively. These values remained nearly the same in 2010, reaching 0 and 21 thousand dollars, respectively. Egyptian exports to Laos showed a limited increase, reaching 57 thousand dollars in 2021. Table (1) Egyptian exports to the ASEAN bloc Year Brunei Cambodia Indonesia Laos Malaysia Myanmar Philippines Singapore Thailand Vietnam 2001 7 0 9.174 0 9.724 20 403 86.125 8.790 534 2002 27 6 15.579 0 11.257 18 942 60.486 15.612 228 2003 11 6 37.472 0 13.571 68 1.148 50.734 21.154 551 2004 94 772 20.557 11 10.504 0 10.045 135.245 53.875 1.122 2005 657 1.597 19.056 0 13.627 83 7.155 247.105 14.712 632 2006 249 69 24.015 38 9.108 432 615 53.239 3.605 846 2007 143 118 12.564 0 13.655 10 3.363 164.810 5.224 525 2008 185 39 73.402 0 36.520 0 2.288 285.815 12.372 3.020 2009 273 0 95.651 15 26.428 327 12.720 65.721 65.697 26.416 2010 258 14 93.933 0 205.321 21 12.501 112.780 24.461 18.373 Egypt's Exports to the ASEAN Bloc Using the Gravity Model (Comparison Between OLS, PPML) IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9349 2011 455 45 103.070 0 131.640 29 41.877 88.066 22.755 16.455 2012 928 10 97.141 0 150.271 0 15.473 9.825 31.369 12.731 2013 1.094 102 94.430 0 136.618 0 6.682 12.149 22.781 9.577 2014 2.315 204 61.858 3 88.478 22 3.930 46.117 21.674 12.662 2015 5.389 80 75.351 0 89.585 0 3.251 41.335 34.894 17.913 2016 898 417 66.447 0 65.097 0 6.637 126.749 63.034 24.319 2017 1.489 499 91.482 0 73.627 0 16.728 63.078 68.547 21.813 2018 4.579 671 117.048 0 245.345 0 33.662 59.476 28.978 33.534 2019 7.529 1.570 114.165 0 113.061 0 10.561 119.991 75.177 20.775 2020 3.955 1.273 104.937 5 77.219 17 5.946 132.115 25.935 35.028 2021 1.128 1.421 105.031 57 106.183 ---- 8.118 505.410 37.412 32.909 Source: www.ITC.org Figure (1)Egypts exports to ASEAN Bloc Source: www.ITC.org The most important commodities exported from Egypt to the ASEAN bloc are mineral fuels, mineral oils, and products of their distillation and bituminous substances, which reached 575,038 thousand dollars in 2021; salt, sulfur, earths and stone, plastering materials, lime, and cement, which reached 91,884 thousand dollars in 2021; and edible fruit and nuts, as well as peels of citrus fruit or melons, which reached 83,777 thousand dollars in 2021. Figure (2) Egyptian commodity exports to ASEAN Source: www.ITC.org commoditiy exports mineral fuels,mineral oils salt, sulphur,earth and stone edible fruitand nuts Egypt's Exports to the ASEAN Bloc Using the Gravity Model (Comparison Between OLS, PPML) IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9350 3. THE GRAVITY MODEL OF EGYPT’S EXPORTS TO ASEAN BLOC The gravity model of international trade explains bilateral trade flows through the size of trading partners’ economies and the distance separating them, which reflects trade costs. The gravity model, used in modern economics since Tinbergen (1962), hypothesizes that the gravitational force between two objects is directly proportional to the product of their masses and inversely proportional to the geographical distance between them. Thus, the classic gravity model can be expressed by the following equation (Tinbergen, 1962): 𝒚𝜶𝒊 𝒚𝜷𝒋 𝑻𝒊𝒋 = 𝒈( 𝑫𝒊𝒋 ) (1) Where, 𝑻𝒊𝒋 is bilateral trade of countries i and j, 𝒈 is the gravitation constant, yi (yj) denotes i (j) country economic size, Dij is the distance between i and j countries, α and ẞ are parameters. 𝑇𝑖𝑗 𝑦𝑗𝛽 In recent years, the gravity model has become one of the widely accepted trade models, especially following contributions by Anderson and Wincoop (2003), who added new variables such as population and trade barriers to the equation. Therefore, in this study, we use the classical gravity model equation with the population variable included, expressed in logarithmic linear form as an OLS equation: In 𝒕𝒓𝒂𝒅𝒆𝒊𝒋𝒕 = 𝜶+ 𝜷𝟏 + In 𝑮𝑫𝑷𝒊 + 𝜷𝟐 In 𝑮𝑫𝑷𝒋 + 𝜷𝟑 In 𝑷𝑰 +𝜷𝟒 In 𝑷𝑱 + 𝜷𝟓 𝑫𝒊𝒋 + 𝜺𝒊𝒋𝒕 (2) Where 𝒕𝒓𝒂𝒅𝒆𝒊𝒋𝒕 is the trade between I(Egypt) and j(ASIAN bloc) at time t (1999-2022). GDP it indicates GDP of country (i) (Egypt), GDPjt is GDP of countries j (ASIAN bloc), pi is population of country Egypt, pj is population of countries ASIAN bloc, Dij is the distance between Egypt and ASIAN bloc ‘every country of Asian bloc” , 𝜀𝑖𝑗 is the error term, 𝛽1, 𝛽2, 𝛽3, 𝛽4, 𝛽5 are the regression coefficients. 3.1Variables and data sources We use commodity exports, expressed in thousands of US dollars, as the indicator of bilateral trade flows. Data on bilateral exports are sourced from the International Trade Centre (ITC). The size of each country is represented by the GDP of Egypt and the ASEAN bloc, expressed in millions of US dollars, with data obtained from the International Monetary Fund (IMF). The expected effect of GDP on trade is positive. The populations of Egypt and the ASEAN bloc countries, obtained from the IMF and expressed in millions, are included, with the expected effect of population on trade being positive. Finally, the distances between Egypt and the ASEAN bloc countries are measured in kilometers between their capitals. This data comes from www.chemical-eclogoy.net, and the expected effect of distance on trade is negative. We estimate the equation for the period 2001–2022, focusing on trade between Egypt and the ASEAN bloc countries (Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Vietnam). 3.2 PPML Equation Several methods have been suggested to deal with the zero flows problem. For example, many empirical studies do not delete zero trade flows but modify the dependent variable using ln(𝑦𝑖𝑗 +1) or in (𝑦𝑖𝑗+0.1) to accommodate the logarithmic transformation (Krisztin and Fischer, 2015, p. 8). To overcome the zero-trade problem, we employed the most common approaches used in the gravity literature. Santos and Tenreyro (2006, 2010) and Martinez (2011) criticized the PPML estimator, suggesting that its results can be biased in the presence of zero trade combined with heteroscedasticity, which may lead to inconsistent estimates. The Poisson probability density is defined as follows: Pr(𝒚𝒊=j/𝒙𝒊)= exp(-𝝀) 𝝀𝒋 /𝒋𝒊 , j =0,1,2 (3) Where 𝜆= exp (𝑥𝑖,𝛽)=exp(𝛽0+𝛽1𝑥1𝑖+ ----) (Santos and tenreyro, 2010, p:1) The essential requirement for the PPML estimator to be consistent is the correct specification of the conditional mean E[𝑌𝐼|X]=𝜆=exp(𝑥1, 𝛽). For this reason, the data do not need to follow a Poisson distribution. More importantly, the PPML estimator is a consistent estimator even when the data are not integers. The vector of parameters of interest 𝛽 can be estimated by solving the following first-order conditions (Santos and Tenreyro, 2006, p. 645): 𝚺𝒊𝒏=𝟏 [𝒚𝒊exp ( 𝒙𝒊𝜷~)]𝒙𝒊=0 (4) Nevertheless, Poisson estimates tend to under predict the number of zero observations and are generally used in samples with a small number of zeros (Bosker and Garretsen, 2010, p. 209). The PPML estimator allows the estimation of variables without transforming them into logarithmic form (Santos and Tenreyro, 2006). Since there are zero trade flows between Egypt and some Asian countries, we use the PPML estimator to explain the differences between the OLS and PPML results when zero trade flows are present. The extended gravity model in exponential form, which will be estimated using PPML, is as follows: E (𝒆𝒙𝒑𝒐𝒓𝒕𝒊𝒋𝒕| x) = exp (𝜶 + 𝜷𝟏 In 𝑮𝑫𝑷𝒊𝒕 + 𝜷𝟐In 𝑮𝑫𝑷𝒋𝒕 + 𝜷𝟑In 𝒑𝒊𝒕+ 𝜷𝟒In 𝒑𝒋𝒕+ 𝜷𝟓In 𝑫𝒊𝒋 (5) All variables are defined in the variables section. Egypt's Exports to the ASEAN Bloc Using the Gravity Model (Comparison Between OLS, PPML) IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9351 Bilateral trade flows were arranged as balanced panel data to estimate the gravity model. Therefore, we conducted unit root tests on the variables using the Levin, Lin & Chu Unit Root Test (Levin et al., 2002, p. 5). Table (2) Unit root test results Variables Statistic Prob. E(Export) -2.978 0.001 GDPi -1.644 0.050 GDPj -3.093* 0.001 Pi -1.998 0.022 Pj -1.644 0.048 * The first different Based on the results in the table, all variables are stationary at level except for GDPj, which is stationary at the first difference. We also conducted a correlation test using the EViews program to determine whether there are correlations between the variables. Table (3) Correlation results GDPi GDPj Pi Pj GDPi 1 0.347 0.901 0.058 GDPj 0.347 1 0.359 0.774 Pi 0.901 0.359 1 0.062 Pj 0.058 0.774 0.062 1 Based on the results in the table, the variable (Pi) was excluded due to its correlation with GDPi As is known, two approaches are commonly applied to estimate panel data models: the fixed effects model and the random effects model. Therefore, we conducted the Hausman test: Table (4) Hausman test results Test summary Chi-sq statistic Chi-sq d.f Prob. Cross-section random 0.0000 3 1.000 Based on the results in the table, the p-value indicated that we should use the random effects model rather than the OLS method. 4-Empirical results We estimate the model using both the OLS gravity model and the PPML gravity model, as shown in Appendix Tables A.1 and A.2. Table 5 presents the estimation outcomes of the gravity model using OLS and PPML methods. Table (5) OLS and PPML results Comparison Variable Coefficient (OLS) Coefficient (PPML) t-statistic (OLS) t-statistic (PPML) Prob. (OLS) Prob. (PPML) C -3539 15.66 -0.0705 13.528 0.943 0.000 GDPi 0.0327 3.76E-09 1.124 3.187 0.262 0.001 GDPj 0.1379 7.00E-09 50.752 9.752 0.000 0.000 Pj 0.266 1.83E-08 2.641 7.880 0.008 0.000 Dij -3204.714 -0.00018 -0.5084 -1285 0.611 0.198 R2 0.35 0.31 The first column reports OLS estimates using commodity exports as the dependent variable. The second column presents PPML estimates using the same data, including zero trade flows between countries. It is noticeable that the OLS model explains 35%, and the PPML model explains 31% of the total variation (R²) in Egypt’s exports to the ASEAN bloc, both of which are statistically significant. The signs and magnitudes of the coefficients are generally consistent Egypt's Exports to the ASEAN Bloc Using the Gravity Model (Comparison Between OLS, PPML) IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9352 with previous studies. One important point to note is that the coefficients estimated by OLS tend to be larger than those obtained using the PPML estimation. The table shows the factors affecting Egypt’s exports to the ASEAN bloc: GDPi, GDPj, Pj, and Dij. Among these, three variables are highly significant at the 1% level. These include GDPj in both the OLS and PPML models, GDPi in the PPML model, and Pj in both the OLS and PPML models. The coefficients of these variables are positive, while the coefficient for distance (Dij) is negative but not significant at the 1% level. The estimated coefficient for GDPi in the OLS model is 0.0327, which means that if GDPi increases by 1%, Egypt’s exports to the ASEAN bloc increase by approximately 3.27%. In the PPML model, the coefficient for GDPi is 3.076E-09, implying that a 1% increase in GDPi leads to an increase in Egypt’s exports to the ASEAN bloc of about 3.07E-07%. The estimated coefficient for GDPj in the OLS model is 0.137, which means that a 1% increase in GDPj leads to a 13.7% increase in Egypt’s exports to the ASEAN bloc. In the PPML model, the coefficient for GDPj is 7.00E-09, implying that a 1% increase in GDPj results in an approximate 7.00E-07% increase in Egypt’s exports to the ASEAN bloc. The estimated coefficient for Pj in the OLS model is 0.266, which means that a 1% increase in Pj leads to a 2.66% increase in Egypt’s exports to the ASEAN bloc. In the PPML model, the coefficient for Pj is 1.083E-08, implying that a 1% increase in Pj results in an approximate 1.083E-06% increase in Egypt’s exports. Finally, the estimated coefficient for Dij in the OLS model is 3204.7, meaning that an increase of 1,000 kilometers in distance leads to a 320.47% decrease in Egypt’s exports to the ASEAN bloc. In the PPML model, the coefficient for Dij is 0.00018, indicating that an increase of 1,000 kilometers results in a 0.018% decrease in Egypt’s exports. This means that the OLS method generates larger estimates than the PPML method when dealing with zero trade flows between countries because OLS either excludes zero-valued trade flows or replaces them with a small constant. We therefore accept that the OLS method cannot adequately handle zero trade flows between countries, while the PPML method can address this issue and provide more accurate effects. 5CONCLUSION This study aims to demonstrate that the OLS method cannot properly handle zero trade flows between countries, while the PPML method can. We analyzed Egyptian exports to ASEAN bloc countries using the gravity model over the period 2001–2021 with two methods: OLS and PPML. The model included key variables such as Egypt’s GDP, ASEAN’s GDP, Egypt’s population, ASEAN’s population, and geographic distance to measure Egypt’s exports to the ASEAN bloc. We compared the effects of these basic gravity model factors on Egyptian exports using both OLS and PPML methods. The results from the OLS method are larger than those from the PPML method because OLS does not handle zero trade flows effectively. As shown in Table 6, there is a positive relationship between Egypt’s GDP, ASEAN’s GDP, ASEAN’s population, and Egypt's exports, while geographic distance has a negative relationship with Egypt’s exports. Table (6) comparison between OLS, PPML result Variable Coefficient (OLS) Coefficient (PPML) C -3539 15.66 GDPi 0.0327 -3.76E-09 GDPj 0.1379 7.00E-09 Pj 0.266 1.83E-08 Dij -3204.714 -0.00018 Finally, future studies can explore alternative solutions to the zero-trade problem between other countries and apply different methods within the gravity model framework. REFERENCES 1) Anderson. J.E, Larch. M, Yotov.Y.V, (2019), trade and investment in the global economy: a multicountry dynamic analysis, European Economic review, vol. 120, no. 103311. 2) -Anderson. J.E, (1979), a theoretical foundation for the gravity equation, the American Economic review, vol. 69, no. 1, pp: 106-116. 3) -Anderson.J.S, Wincoop.E, (2003), gravity with gravitas: a solution to the border puzzle, American economic review, vol.93, no.1, pp:170-192. 4) -Arkolakis.C, Costinot.A, Rodriguezcalare. A, (2012), new trade models. same old gains?, American Economic Review, vol.102, no.1, pp:94-130. Egypt's Exports to the ASEAN Bloc Using the Gravity Model (Comparison Between OLS, PPML) IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9353 5) -Baltagi.b.H, Egger.P, (2016), estimation of structural gravity quantile regression models, Empirical Economics, vol.50, no.1, pp:5-15. 6) -Beverelli .C, Keck.A, Larch.M, Yotov.Y, (2018), institutions trade and development: a quantitative analysis, CESIFO, working paper, no. 6920. 7) -Bosker,M. Garretesen,H. (2010), trade costs, market access and economic geography: why the empirical specification of trade costs matters.in P.A.G .van bergeijk and s. brakman(Eds), the gravity model in international trade . advances and applications (pp.0193-223)..New York: Cambridge university press. 8) Burder.M, Van Oort. F, Linders. G. j. (2009), on the specification of the gravity model of trade zeros, excess zeros and zero-inflation estimation, Spatial Economic Analysis, vol. 4, no. 2, pp: 167-190. 9) -Eation. J. Kortum.S, Neiman.B, Romalis.J, (2016), trade and the global recession, American Economic review, vol.106, no. 11, pp: 3401-3438. 10) Eichengreen, B, Irwin. D. A, (1998), the role of history in bilateral trade flows. In j.a. frankel, the regionalization of the world economy, pp:33-62, Chicago, university of Chicago press. -Fally.T, (2015), structural gravity and fixed effects, journal of international economics, vol.97, no.1, pp:76-85. 11) Frankel, J. (1997), regional trading blocs in the world economic system, Washington DC.: Peterson institute for international economics. 12) Golovko. N, Sahin,H, (2021), analysis of international trade integration of eurasian countries: gravity model approach, Eurasian economic review, p:15 13) Heid, B, Stahler.F, (2020), structural gravity and the gains from trade under imperfect competition, CESIFO, working paper , no. 8121. 14) -Helpman.E, Melitz.M, Rubinstein.Y, (2008), estimation trade flows: trading partners and trade volumes, the Quarterly Journal of economics, vol. 123, no. 2, pp: 441-487. 15) Herman.P, Lee.R, (2019), the short-run effects of product differentiation on trade, USITC office of economics working paper 2019-11-A. 16) -Krisztin.T, M.Fischer, (2015), the gravity model for international trade specification and estimation issues, Spatial Economic analysis, vol.10, no.4, pp: 451-470. 17) larch.M, Yotov.Y ,(2016), general equilibrium trade policy analysis with structural gravity, CESIFO, working paper no. 6020. 18) Levin. A, Lin,M , Chu.C,(2002), unit root test in panel data asymptotic and finite sample properties, "Journal of Econometrics", No. 108, pp: 1-24. 19) Linders, G.J, De Groot. H. l , (2006), estimation of the gravity equation in the presence of zero flows, Tinbergen institute discussion paper, No. 26-072/3.-Martinez. Z, I,(2011), the log of gravity model , Applied Economics, vol. 45, no. 3, pp: 311327. 20) Olivero.M.P, Yotov.Y.V, (2012), dynamic gravity: endogenous country size and asset accumulation, Canadian Journal of Economics, vol.45, no.1,pp: 633-648. 21) -Rauch,J.E,(1999), networks versus markets in international trade, journal of international economics, 48, 7-35. 22) Santos S, J. Tenereyro, S,(2006), the log of gravity, the Review of Economics and statistics, vol. 88, no. 4, pp: 641-658. 23) Santos S. J., Tenereyro, S,(2010), on the existence of the maximum likelihood estimates in poison regression, Economics letters, vol. 107, no. 2, pp: 310-312. 24) -Tinbergen. J, (1962), shaping the world economy, the International Executive, vol. 5, no. 1, pp:27-30. 25) -Yotov.Y,.Piermartini.R, Monteriro. J., Larch.M, (2016), an advanced guide to trade policy analysis:the structural gravity model, Geneva, world trade organization. www.IMF.org www.ITC.org 26) www.chemical-eclogoy.net Appendix Table A.1 OLS result Variable Coefficient t-statistic Prob. C -3539 -0.0705 0.943 GDPi 0.0327 1.124 0.262 GDPj 0.1379 50.752 0.000 Pj 0.266 2.641 0.008 Dij -3204.714 -0.5084 0.611 R2 0.35 Egypt's Exports to the ASEAN Bloc Using the Gravity Model (Comparison Between OLS, PPML) IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9354 DW 1.84 F-statistic 11.523 0.000 We estimate the model using the PPML gravity model to address the zero trade problems. The results are as follows: Table A.2 PPML result Variable Coefficient t-statistic Prob. C 15.66 13.528 0.000 GDPi 3.76E-09 3.187 0.001 GDPj 7.00E-09 9.752 0.000 Pj 1.83E-08 7.880 0.000 Dij -0.00018 -1285 0.198 R2 0.31 LR-statistic 5.56E+09 0.000 There is an Open Access article, distributed under the term of the Creative Commons Attribution – Non Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting and building upon the work for non-commercial use, provided the original work is properly cited.