WHEN LINDER MEETS GRAVITY MODEL: THE CASE OF USA, GERMANY AND JAPAN

Hrvoje JOŠIĆ University of Zagreb, Faculty of Economics & Business, Kennedy sq 6, Zagreb, Croatia [email protected]

Maja BAŠIĆ University of Zagreb, Faculty of Economics & Business, Kennedy sq 6, Zagreb, Croatia [email protected]

Abstract This paper brings into conjunction the gravity model of international with the . Both trade theories are “new trade” theories of established in the 60s and 70s of the last century after the Leontief testing of Heckscher-Ohlin theory. The gravity model of international trade is similar to Isaac Newton's gravity model while Linder hypothesis is a demand based theory. These two concepts are therefore mutually opposite. In order to confront two important theories of international trade, bilateral trade data for imports and manufactured imports specifically for three large World countries (the United States, Germany and Japan) in the period from 2000 to 2016 are collected. Panel regression models are constructed for both the gravity model and the Linder variable representing the Linder effect. The Linder variable is specified as an absolute difference between partner co untries GDP’s per capita. The results of the analysis have shown that trade data for all three observed countries comport with the gravity model of trade while the Linder effect could not be confirmed.

Keywords: Linder hypothesis, gravity model of international trade, panel data

JEL classification: C13, F14

Introduction

After the neoclassical theory of international trade, namely Heckscher-Ohlin theory or Factor proportions model (Heckscher, 1918 and Ohlin, 1931), and the Leontief test of Heckscher- Ohlin theory in 1950's (Leontief, 1953) new trade theories have emerged. Two important new trade theories which are subject of this paper are the gravity model of international trade (Tinbergen, 1962) and the Linder theory of similar preferences also known as the Linder hypothesis (Linder, 1961). The gravity model of international trade is based on Isaac Newton's theory of gravity. The trade between two countries is proportional to their size and reversely proportional to the distance between them. The augmented gravity model takes into account additional variables such as remoteness, colonial ties, common border, common language and dummy variables representing, for example, membership in WTO or regional trade integrations. On the other hand, according to the Linder hypothesis the greater the similarity between countries' economic structures, the greater the possibility of their mutual trade. These two concepts are therefore mutually opposite. The goal of the paper is confronting and bringing into conjunction these two new theories of international trade by investigating patterns of trade flows (namely imports) for three large world countries (the United States, Germany and Japan). The reason why these countries have been taken into

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consideration is the essence of the Linder hypothesis according to which trade occur between developed countries mostly in manufactured (industrial) products. It is contrary to the pattern of trade in Heckscher-Ohlin theory where trade happens between capital abundant (developed countries) and labour abundant (developing countries).

In this paper analysis will be conducted using panel OLS, fixed effects and random effects regression analysis in the period from 2000 to 2016. An appropriate panel regression model will be chosen with the help of the Hausman test and the redundant fixed-effect (log likelihood) test. In order to decide which theory of international trade is suitable to data, the signs of the regression coefficients for the Linder variable and the GDP of importing country will be calculated and inspected. The paper is structured in five sections. After the introduction, literature review presents and elaborates the theoretical and empirical features of both trade theories. In the third chapter methodology and data are described while in the fourth chapter the results of the econometric analysis are presented and discussed. The final chapter is a conclusion.

Literature review

In his 1961 seminal paper Linder (Linder, 1961) coined the demand-oriented theory of international trade, later named the Linder hypothesis. It was in contrast with supply-oriented classical and neoclassical theories of international trade. According to Linder a country will trade with countries of a similar level of economic development and similar demand structures. The Linder variable catches the difference between countries gross domestic products, the smaller the difference between them the higher expected trade between them should occur. The Linder theory predicted patterns of trade between highly developed countries in manufactured products (North-North trade) as opposed to trade between developed and developing countries in primary products (North-South trade). The term gravity model of international trade was first coined by Walter Isard (Isard, 1954) but was established into practice by virtue of the efforts of Jan Tinbergen (Tinbergen, 1962). The main critique of gravity model was that it is merely an econometric tool without a proper theoretical basis. Various economists have nonetheless shown that the gravity model can arise from various theories of international trade. According to Bergstrand (1985) gravity model is a direct consequence of the trade model based on monopolistic competition developed by , Krugman (1980). Eaton and Kortum have shown that the gravity model can be derived from a Ricardian type of model, Eaton and Kortum (2002). Helpman et al (2008) obtained it from the theoretical model of international trade in differentiated goods with firm heterogeneity (, 2012).

The Linder hypothesis and the gravity model of international trade have often been interconnected and studied simultaneously. In the next section empirical investigations on the Linder hypothesis will be presented and explained. Kennedy and McHugh (1980) tested the Linder hypothesis with the help of intertemporal approach with results not supporting the Linder hypothesis. Limitation of the study refers to the use of total trade data rather than data on trade in manufactures. Kennedy and McHugh (1983) found no support for the Linder hypothesis for the United States in the years 1963, 1970 and 1974 using correlations analysis. Arnon and Weinblatt (1998) tested the Linder effect in trade generally and in trade between developed and less developed countries. Empirical evidence was provided that the Linder effect can be found for both developed and less developed countries. McPherson, Redfearn and Tieslau (2001) examined the Linder theory in the case of OECD countries using panel

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dataset. The validity of the Linder hypothesis was approved for 18 out of 19 countries. Choi (2002) presented a favourable result in support of the Linder hypothesis using a modified gravity model with pooled trade data for 63 countries for various years in the period from 1970 to 1992. It seems that globalization may have strengthened the Linder hypothesis in 1990s as the coefficients of the Linder variable have a tendency to grow over time. The Linder hypothesis was also supported in the case of India, Pakistan and Bangladesh, Bukhari et al. (2005). Bohman and Nilsson (2007) introduced new methodology for testing the Linder effect using the income distribution approach. It identified the common market between trading partner countries by calculating the income overlap. The next step in the analysis was to relate the size of the common market to size of the home market forming the new Linder variable.

Hallak (2010) explained the reason for the failure of the Linder hypothesis in empirical evidence. Using trade data aggregated across sectors is often an inappropriate theoretical benchmark. Hallak proposed a theoretical framework in which product quality plays the central role. Rauh (2010) reaffirmed the Linder hypothesis for Germany in trade with other European countries using country panel and time fixed effects in the period from 2002 to 2007. Jian (2011) applied the gravity model on China-EU trade on a sample of 25 country- pairs and 250 observations with panel data used to disentangle the time invariant country- specific effects. Both the gravity model of trade and the Linder hypothesis seem to hold well in empirical analysis. Bo (2013) found support for the Linder effect in bilateral trade between China and its fourteen trading partners using the panel gravity model under fixed effects estimation. Differential GDP per capita was used as a proxy variable for the Linder effect. Kahram (2014) examined the Linder hypothesis for bilateral trade of Iran. Along with Linder effect factors that mostly affected trade pattern of Iran there were political factors, economic size, distance, common borders and others. Atabay (2015) applied the modified gravity model using panel data analysis on BRIC’s countries trade in the period from 1996 to 2010. Countries with smaller GDP per capita difference seem to tend to trade more. Steinbach (2015) investigated the Linder hypothesis for bilateral trade in agricultural and food products on a sample of 152 countries in the period from 1995 to 2012. He formed a similarity index analysing the estimates of the Linder term. His findings show that the similarity effect is strongest for processed products and weakest for bulk products. Jošić and Metelko (2018) tested the validity of the Linder hypothesis for Croatia using panel regression analysis. The results of the analysis pointed to the rejection of the Linder hypothesis, due to the fact that the pattern of Croatia's trade is in the line with gravity model of international trade.

Methodology and data

The gravity model is a work-horse of international trade. It was introduced in economics by Walter Isard in 1954 (Isard, 1954) but was established by Jan Tinbergen after his 1962 seminal paper (Tinbergen, 1962). The gravity model of international trade gives the relationship between country size and geographical distance. Equation 1 presents a formulation of the gravity model in multiplicative form: & ( $% $' !" = # * (1) )%' where !" represents trade flows (imports, or total trade), # is a constant, +! and +" are economic sizes of trade partner countries expressed as GDP, ,!" is the distance between

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country and country ! while ", # and $ are regression coefficients. The standard procedure for estimating the gravity model is using natural logarithms of variables:

%&' () = * + "%& ,-./ (0 + # ln1-./ )2 + $ ln1.() 2 + 3() (2)

The augmented gravity equation includes other variables such as adjacency, common language, colonial links, remoteness and dummy variables representing membership in WTO or regional trade agreements. The Linder variable is expressed as an absolute difference between trade partner countries and ! GDPs per capita.

( ) 4 &567 = 8-./ 9: ; -./ 9: 8 (3)

In Equations (5-8) linear relationship between variables is used because there is a problem of trade zeroes in data due to the absence of trade between some countries.

<>?@7AB ()C = * + "-./ ) + #DEFG + 3() (4)

<>?@7AB ()C = * + "4 &567 + #DEFG + 3() (5)

HIJKLI:CKMNO <>?@7AB ()C = * + "4 &567 + #DEFG + 3() (6)

Figure 1 presents the expected theoretical relationship between manufactured imports and the Linder variable and imports and the GDP variable. The larger the difference between countries’ GDPs is (the Linder variable), the decrease in manufactured imports should be larger. Furthermore, the larger the GDP of the exporting country !, the larger the imports from abroad should be. Obviously, these two concepts can be approached only on higher levels of GDP per capita while on lower levels of GDP per capita if the gravity model holds well, the Linder effect should disappear.

Figure 1: Expected relationship Imports vs. P-./ ) and <>?@7AB HIJKLI:CKMQ vs. Linder variable

Source: Authors' illustration

Econometric analysis will be conducted using a cross-country panel regression model starting with the pooled OLS regression model presented in Equation 7.

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Y ,! = "# + "$X$, ,! + % + "&X&, ,! + ' ,! (7) where Y is a dependent variable, X is an independent variable, (# is a constant, ($…() are regression coefficients, *--and . are indices marking individual entities and time periods while / is an error term. In order to take into account heterogeneity in data for different countries fixed and random effects modells are also constructed. A fixed effects model is presented in Equation 8. 23# Y ,! = "# + 0# 1 4# Dummy + "$X$, ,! + "&X&, ,! + 5 ,! (8)

A random effects model is shown in Equations (9-11).

Y ,! = "# + "$X$, ,! + % + "&X&, ,! + 6 ,! (9)

Y ,! = "# + "$X$, ,! + % + "&X&, ,! + ' ,! + 6 ,! (10)

Y ,! = "# + "$X$, ,! + % + "&X&, ,! + 7 ,! (11)

The redundant fixed effects test is used in order to differentiate between the pooled OLS and the fixed effects model while the Hausman test is used to differentiate between the fixed effects and the random effects model.

Results and discussion

Tables 1, 2 and 3 present the descriptive statistics of variables used in regression analysis. Imports and Manufactured imports are dependent variables. GDP, Linder and Distance are independent variables. Regressions are conducted individually for each of three countries, the United States of America, Germany and Japan, in the period from 2000 to 2016.

Table 1: Descriptive statistics of variables, United States of America, 2000-2016 Imports GDP Linder Manufact. imports Distance Mean 10470293 2.49E+11 34097.69 7684313 8869.16 Median 394260.2 2.00E+10 36056.53 113686.2 8601.8 Maximum 5.04E+08 1.12E+13 87250.09 4.87E+08 16350.4 Minimum 0 63101272 176.0650 0.000000 742.9 Std. Dev. 40480531 7.64E+11 13291.80 33179668 3582.497 Skewness 7.13 6.891 -0.557423 8.278704 -0.089511 Kurtosis 61.81 69.61 3.034744 89.01290 2.212661 Jarque-Bera 441017.8 557151.0 149.8089 923880.7 78.50590 Sum 3.03E+10 7.19E+14 98542331 2.22E+10 25631893 Sum Sq. Dev. 4.73E+18 1.69E+27 5.10E+11 3.18E+18 3.71E+10 Observations 2890 2890 2890 2890 2890

Table 2: Descriptive statistics of variables, Germany, 2000-2016 Imports GDP Linder Manufact. imports Distance Mean 5310339 3.15E+11 26245.51 3693909 6177.21 Median 167058.5 2.00E+10 27152.91 32653.50 6152.5 Maximum 1.18E+08 1.86E+13 94804.92 1.07E+08 18122.9 Minimum 0 63101272 24.93981 0 281.2 Std. Dev. 14477199 1.32E+12 12773.76 10779818 3953.18 Skewness 4.06 9.025760 0.12 4.52 0.46 Kurtosis 21.78 97.97 4.10 28.51 2.87

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Jarque-Bera 50440.21 1125485 154.74 88240.79 104.87 Sum 1.53E+10 9.10E+14 75849518 1.07E+10 17852144 Sum Sq. Dev. 6.06E+17 5.01E+27 4.71E+11 3.36E+17 4.51E+10 Observations 2890 2890 2890 2890 2890 Table 3: Descriptive statistics of variables, Japan, 2000-2016 Imports GDP Linder Manufact. imports Distance Mean 3411937 3.06E+11 23506.29 1750598 10058.08 Median 60958.71 2.02E+10 25128.32 4716.321 9583.5 Maximum 1.89E+08 1.86E+13 101063 1.71E+08 18555.1 Minimum 0 63101272 10.09 0 1154.8 Std. Dev. 13015388 1.29E+12 11710.93 10213429 3699.87 Skewness 8.30 9.46 0.67 11.63 -0.195060 Kurtosis 91.88 106.42 7.18 160.64 2.48 Jarque-Bera 978834.2 1323270 2311.24 3039884 50.70 Sum 9.80E+09 8.79E+14 67533585 5.03E+09 28896874 Sum Sq. Dev. 4.87E+17 4.78E+27 3.94E+11 3.00E+17 3.93E+10 Observations 2873 2873 2873 2873 2873 Source: Authors' calculations

The number of cross-sections in the analysis for the United States and Germany is 170. San Marino was excluded from analysis for Japan due to data unavailability so there are 169 cross-sections for Japan. Therefore, the total number of observations in the sample for the United States of America and Germany is 2890 and 2873 for Japan. Figures 2, 3 and 4 present scatter plot diagrams illustrating the relationship between dependent and independent variables.

Figure 2: Scatter plot diagrams for dependent and independent variables, USA, 2000-2016

Source: Authors' illustration

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Figure 3: Scatter plot diagrams for dependent and independent variables, Germany, 2000- 2016

Source: Authors' illustration

Figure 4: Scatter plot diagrams for imports and independent variables, Japan, 2000-2016

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Source: Authors' illustration

In the figures 2, 3 and 4 a positive relationship between imports and GDP and a negative relationship between imports and distance can be noticed as it could be expected from the previous studies of the gravity model. The fitted regression line is also showing a negative relationship between imports and the Linder variable suggesting the acceptance of the Linder effect. In order to finally conclude whether the patterns of trade for three large World countries are in accordance with the gravity model of trade or they are similar to the Linder hypothesis, panel regression analysis is conducted.

The results of the cross-country panel regression analysis for the United States of America, Germany and Japan in the period from 2000 to 2016 are displayed in Tables A1-A3 (in Appendix). In order to choose between the pooled OLS, fixed effects and random effects model, the Hausman test and the redundant fixed effects tests are conducted. The redundant fixed effects test (log likelihood test) is used in order to choose between the pooled OLS and fixed effects model. Cross-section F and cross-section Chi-Square statistics probabilities for all models were under 5 percent of probability indicating that the fixed effects model is preferable over the pooled OLS so there existed a heterogeneity in cross-section data. The Hausman test is used to choose between the fixed effects and random effects model. The random effects model is appropriate in all cases for the United States and Germany. The random effects model is acceptable for Japan under 10 percent of probability while in the case of imports of manufactures the fixed effects model was appropriate. GDP is statistically significant independent variable in regression under 1%, 5% and 10% percent of significance showing a positive relationship with the imports variable. The distance variable is statistically significant in the pooled OLS and random effects model with a negative relationship to dependent variable as predicted from the standard gravity model. However, the distance variable was excluded from the fixed effects model due to time invariant characteristics of data. The sign of the Linder variable for imports in Table A2 in the random and fixed effects model is positive indicating refusal of the Linder hypothesis although the pooled OLS model and scatter plot diagrams were suggesting the opposite. Applying the robustness check in

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Table A3 by taking only manufactured imports (as suggested in the Linder seminal paper) is also suggesting the refusal of the Linder hypothesis. It can be concluded that the United States, Germany and Japan import trade patterns follow the standard gravity model while the Linder hypothesis cannot be accepted.

Conclusion

The goal of the paper was to investigate trade patterns for three large World countries (the United States, Germany and Japan). The research question asked was whether trade data better fit the gravity model of trade or the Linder hypothesis. The method applied in the analysis was panel regression analysis consisting of pooled OLS, fixed effects and random effects models. The random effects model was preferable in most cases as most applicable to data. The results of the analysis have shown that trade patterns of bilateral trade for three large countries in the World behave according to the gravity model of trade. On the other hand, the validity of the Linder hypothesis could not be approved because signs of the Linder variable under the random and fixed effects models were positive leading to rejection of the Linder hypothesis. The limitations of the paper are related to the observance of only 3 large countries. Due to the paper length restriction it was optimal to analyse these three countries because there was a large number of observations on bilateral trade for each observed country. Also there was a problem of the distance variable which could not be included in the fixed effects model due to time-invariant characteristics of data. The research conducted can lead to the need for further research by inclusion of other countries into analysis, especially developing countries and countries in development. In that case the theory of gravity model and the Linder hypothesis should be on opposite sides, i.e., the acceptance of one theory should lead to rejection of other.

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Appendix

Table A1: Cross-country panel regression analysis for imports, GDP and distance variable, USA, Germany and Japan, 2000-2016 Dependent variable United States of America Germany Japan Total imports Independen Rando t Pooled Fixed Random Pooled Fixed Random Pooled Fixed m variable/M OLS effects effects OLS effects effects OLS effects effects odel 2809552 12082766 9974908 2748243 9407126 7844842 1034206 738910 9706496** *** ** *** *** *** *** *** 8*** Constant * (15.6742 (2.270519 (26.2533 (29.2111 (6.41213 (15.7464 (12.9037 (3.8485 (7.181151) 7) ) 9) 4) 3) 8) 9) 55) 3.08E- 3.10E- 6.40E- 8.14E- 7.97E- 6.83E- 7.77E- 7.68E- 3.92E - 05*** 05*** 06*** 06*** 06*** 06*** 06*** 06*** GDP 05*** (68.8128 (70.19313 (41.6206 (41.7459 (42.8359 (51.6575 (46.3624 (48.002 (59.69871) 6) ) 0) 7) 2) 9) 7) 48) ------1081.62 1069.490 648.5661 629.05 1012.818* 1052.309 3*** *** *** 74*** Distance ** * (- (- (- (- (- (- 1.891985) 21.1001 5.351585 14.06483 3.5140 7.238431) 4) ) ) 20) Adjusted R- 0.43425 0.92988 0.4469 0.557232 0.965194 0.629606 0.391700 0.508651 0.935665 squared 8 7 09 S.E. of 1088914 330260 26936061 7552181 7566725 3833385 3838177 9123301 3301276 regression 5 1 Prob. (F- 0.0000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 statistic) 0 Mean dep. 1047029 317401 10470293 5310339 5310339 479403 3411937 3411937 variable 3 738328.3 .6 S.D. 4048053 1447719 1447719 1301538 1301538 444076 dep.variabl 40480531 12433010 4921145 1 9 9 8 8 7 e Akaike info 35.2454 33.2137 crit. 37.05687 34.56992 7 4 34.89161 32.91486 Durbin - 0.02998 0.25068 0.4034 Watson 0.029163 0.371706 0.349072 2 4 0.235905 0.053089 0.428641 85 Observatio 2890 2890 2890 2890 2890 2890 2873 2873 2873 ns Hausman Chi-Sq. Statistic (12.129), Prob. Chi-Sq. Statistic (8.222882), Chi-Sq. Statistic (3.304931), test (0.0005) Prob. (0.0041) Prob. (0.0691) Cross-section F (205.167), Prob. Cross-section F (143.010854), Cross-section F (123.374346), Redundant (0.000 ) Prob. ( 0.00) Prob. ( 0.00) fixed Cross -section Chi-Square Cross -section Chi-Square Cross -section Chi-Square effects test (7575.263), Prob. (0.000) (6622.174), Prob. (0.00) (6204.6753), Prob. (0.00) Source: Authors' calculations T- statistics in parentheness, *** significant at 1 percent level, ** significant at 5 percent level, * significant at 1 percent level.

Table A2: Cross-country panel regression analysis for imports, Linder and distance

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variable, USA, Germany and Japan, 2000-2016 Dependent variable United States of America Germany Japan Total imports Independent Rando Pooled Fixed Random Pooled Fixed Random Pooled Fixed variable/Mo m OLS effects effects OLS effects effects OLS effects del effects 10465 367248 4140788 16824556 2065372 3362195 1115678 13343072 2168180 293** 79*** *** ** 1*** *** 6*** *** *** Constant * (14.690 (2.78530 (2.153384 (34.0540 (8.09605 (6.59837 (16.17824 (4.85619 (3.874 57 ) 2) ) 4) 8) 5) ) 5) 575) - - - 571.412 185.6285 158.1352* 392.7801 74.22770 53.67004 52.91167 45.793 79.64710 5*** *** ** *** *** *** *** 09*** Linder *** (- (4.31026 (3.728400 (- (4.80780 (3.53697 (2.83440 (2.511 (- 10.0920 6) ) 20.26503 4) 1) 3) 604) 3.954189) 6) ) - - - - - 763.401 - 815.0388 1174.485 808.28 801.2388 6*** 1324.398* *** *** 34*** Distance *** (- (- (- (- (- (- 3.63400 1.638131) 13.01380 5.181867 3.2461 12.56740) 4) ) ) 46) Adjusted R- 0.04436 0.0051 0.905226 0.004839 0.207526 0.885919 0.011917 0.056936 0.884846 squared 0 15 S.E. of 395724 1246206 1288774 44183 12489106 4889799 4932882 12639434 4416682 regression 93 3 6 41 Prob. (F- 0.0000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 statistic) 0 Mean dep. 104702 1047029 30522 838828.2 5310339 5310339 539961.6 3411937 3411937 variable 93 3 8.9 S.D. 404805 4048053 1447719 1447719 1301538 44296 12519434 4962539 13015388 dep.variable 31 1 9 9 8 85 Akaike info 37.8262 crit. 0 35.57162 35.58249 33.70055 35.54359 33.49702 Durbin - 0.01666 0.2556 Watson 9 0.168301 0.157774 0.030212 0.192319 0.177998 0.031672 0.271518 08 Observation 2890 2890 2890 2890 2890 2890 2873 2873 2873 s Hausman Chi-Sq. Statistic (13.543435), Chi-Sq. Statistic (52.096880), Chi-Sq. Statistic (3.156579), test Prob. (0.0002) Prob. (0.0000) Prob. (0.0756) Cross-section F (156.952283), Cross-section F (109.540136), Cross-section F (131.515652), Redundant Prob. ( 0.000) Prob. ( 0.00) Prob. ( 0.00) fixed effects Cross -section Chi-Square Cross -section Chi-Square Cross -section Chi-Square test (6864.929), Prob. (0.000) (5939.561), Prob. (0.00) (6367.6764), Prob. (0.00) Source: Authors' calculations T- statistics in parentheness, *** significant at 1 percent level, ** significant at 5 percent level, * significant at 10 percent level.

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Table A3: Cross-country panel regression analysis for manufactured imports, Linder and distance variable, USA, Germany and Japan, 2000-2016 Dependent variable Manufactu United States of America Germany Japan red imports Independe Rando Rando nt Pooled Fixed Pooled Fixed Random Pooled Fixed m m variable/M OLS effects OLS effects effects OLS effects effects effects odel 236017 912843 9002395* 118204 70273 2558776* 13962846* 2209195** 7231681** 98*** 2 ** 8*** 96*** Constant (1.915951 ** * * (11.421 (1.4216 (13.84167 (3.3623 (3.297 ) (30.17065) (7.157448) (5.559093) 05) 97) ) 98) 673) - - 404.301 150.3192 126.293 - 24.1871 19.321 56.57019* 43.01536* 66.76678 2*** *** 8*** 273.4162** 2* 91 Linder ** ** *** (- (3.885406 (3.3246 * (1.6455 (1.345 (4.929942) (3.809655) (- 8.63809 ) 14 ) (-18.48682) 37 ) 778) 4.203423) 4) - - - - - 240.354 648.363 - 569.78 755.4755* 564.9543 7 5 500.7099** 90*** Distance ** *** (- (- * (- (- (- (- 1.38410 0.97775 10.47737) 2.9002 4.327512) 11.23704) 0) 4) 71) Adjusted 0.02797 0.00334 0.88406 0.0028 0.886156 0.167756 0.886339 0.009988 0.047639 R-squared 5 8 3 42 S.E. of 327122 112139 347762 34784 11195080 9834145 3634274 3661201 9967184 regression 76 57 6 67 Prob. (F- 0.0000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 statistic) 0 Mean dep. 768431 674381. 175059 15628 7684313 3693909 3693909 362401.9 1750598 variable 3 4 8 5.2 S.D. 331796 112327 102134 34834 dep.variabl 33179668 10779818 10779818 3679623 10213429 68 79 29 20 e Akaike 37.4454 33.0189 35.35719 35.04166 33.10706 35.06854 info crit. 4 5 Durbin - 0.01285 0.10233 0.12111 0.1140 0.109027 0.024850 0.164442 0.152661 0.014136 Watson 3 1 0 31 Observatio 2890 2890 2890 2890 2890 2890 2873 2873 2873 ns Hausman Chi-Sq. Statistic (10.744202), Chi-Sq. Statistic (43.939104), Prob. Chi-Sq. Statistic (2.387618), test Prob. (0.001) (0.000) Prob. (0.1223) Cross-section F (129.865), Cross-section F (113.75034), Prob. Cross-section F (130.415225), Redundant Prob. ( 0.000) (0.000 ) Prob. ( 0.000) fixed Cross -section Chi-Square Cross -section Chi-Square Cross -section Chi-Square effects test (6372.93873), Prob. (0.000) (6034.826442), Prob. (0.000) (6346.177313), Prob. (0.000) Source: Authors' calculations T- statistics in parentheness, *** significant at 1 percent level, ** significant at 5 percent level, * significant at 10 percent level.

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