Accepted Manuscript Title: Export Diversification and Agricultural Growth: Evidence from Developed Countries Authors: Reza Moghaddasi, Elham Sadeghi, Safdar Hosseini, Amir Mohammadi Nejad PII: DOI: Reference:
S1517-7580(17)30036-X http://dx.doi.org/doi:10.1016/j.econ.2017.07.001 ECON 124
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Export Diversification and Agricultural Growth: Evidence from Developed Countries Reza Moghaddasi, Elham Sadeghi, Safdar Hosseini, Amir Mohammadi Nejad
Elham Sadeghi, Ph.D Student, Agricultural Economics Department, Faculty of Agriculture and Natural Resources, Science and Research Branch, Islamic Azad University, Tehran, Iran. Postal address: End of Sattari Highway, Islamic Azad University, Science and Research Branch. Phone number: 00989127661564 Email: em.sadeghi64@ gmail.com Corresponding Author: Reza Moghaddasi, Associate Professor, Agricultural Economics Department, Faculty of Agriculture and Natural Resources, Science and Research Branch, Islamic Azad University, Tehran, Iran. Postal address: End of Sattari Highway, Islamic Azad University, Science and Research Branch. Phone number: 00989123842641 Email:
[email protected] Safdar Hosseini, Professor, Agricultural Economics Department, Faculty of Agriculture, Tehran University, Karaj, Iran. Postal address: Mesbah St, Agriculture and Natural Resources Campus, Karaj, Iran. Phone number: 00989122978436
Email:
[email protected]
Amir Mohammadi Nejad, Assistant Professor, Agricultural Economics Department, Faculty of Agriculture and Natural Resources , Science and Research Branch, Islamic Azad University, Tehran, Iran. Postal address: End of Sattari Highway, Islamic Azad University, Science and Research Branch. Phone number: 00989122080559
Email:
[email protected]
Highlights
Openness, export diversification, rainfall, capital stock, total factor productivity and EU membership showed positive effect on agricultural growth. - Labor force revealed reverse relationship to agricultural growth. Evidence from the estimated regression supports a hump–shaped relationship between export diversification and economic growth in developed countries.
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Abstract This article provides evidence on the relationship between export diversification and agricultural growth using a panel data set of 20 developed countries during 2000-2014. The study adopted Herfindahl index for export diversity and Generalized Least Squares (GLS) to examine dynamic interactions among the variables. Main results showed that openness, export diversification, rainfall, capital stock, total factor productivity and EU membership had positive effect on agricultural growth while labor force was negatively related to it. This finding suggested that, to achieve agricultural growth under trade liberalization, countries should diversify their agricultural export and develop economic cooperation with other countries.
Keywords: Agricultural Growth, Developed Countries, Export Diversification, Herfindahl Index
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
1.Introduction Growth is the center of economic studies and is considered as a major issue for countries and economists all over the world (Hodey, 2013). Growth based on exports as an alternative to domestic-oriented policies had attracted many advocates over the past three decades (Azerbaijani et al, 2013). According to structural models of economic development, countries should diversify from primal exports into manufactured exports in order to attain sustainable growth (Chenery, 1979; Syrquin, 1989). In the recent years, many countries have paid special attention to the issue of export diversification implying increasing number of export products and decreasing the concentration on a single source of income (Sepehrdoust & Khodaee, 2014). In a general concept, export diversification may be seen as a change in the present export composition of an economy (Hodey, 2013). Dennis & Shepherd (2007) considered export diversification as a process of widening the range of export products in a country. Hausman & Klinger (2007) showed that country's export pattern is a good predictor of its future growth. Yokoyama & Alemu (2009) resulted that export diversification can be regarded as comparative advantage especially in economic growth of developing countries. 2
Review of the literature shows different conclusions regarding the type of relation between export diversification and economic growth. Some indicated a positive monotonic relationship between export diversification and economic growth (Herzer & Nowak-Lehman, 2006; Lederman & Maloney, 2007; Ferreira, 2009; Memarnejad et al., 2009; Arip et al., 2010; Azerbaijani et al., 2013), and others reported a non-monotonic (hump-shaped) association between two above mentioned variables (Imbs & Wacziarg, 2003; Klinger & Lederman, 2006; Aditya & Roy, 2007; Hesse, 2008; Cadot et al., 2011; Kadyrova, 2011). On the other hand rainfall, income, infrastructures and market demand are identified as main drivers of agricultural diversification (Joshi et al., 2003). A common feature of past studies is that they focused on economy-wide impacts of export diversification and little is done at sector level. So the main motivation of this paper is the lack of reliable applied (not theoretical) knowledge on the possible effects of expanding export products composition on agricultural growth, as the main provider of food, in selected developed nations ,which their data is more accessible. The remainder of paper is structured as follows. Section 2 presents the econometric model and source of data. Section 3 reports main results and finally section 4 provides discussion. 2.Materials and methods The data for this research is macro level for 20 developed countries1 during 2000-2014. The countries selection is based on gross national income per capita and agricultural value added. The dataset is compiled into a panel data from sources such as World Bank, Food and Agriculture Organization, World Trade Organization, U.S. Department of Agriculture, National Aeronautics and Space Administration. The main explanatory variable in the research is the export diversification that is calculated by Herfindahl index. Related data to the index is extracted from United Nations commodity trade statistics database (comtrade). The Herfindahl index is defined as the sum of the squares of the market shares that is calculated by the following formula:
1
Australia, Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, New
Zealand, Norway, Portugal, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States
3
𝑁
𝐸𝑖𝑡 = ∑(𝑆𝑗𝑖𝑡 )2
(1)
𝑗=1
where N is the number of commodities, 𝑆𝑗𝑖𝑡 is export value of commodity (j) as a share of total agricultural export value for country (i) in year (t). The closer 𝐸𝑖𝑡 is to zero, the more diversified is agricultural export products. For determining the effect of export diversification on agricultural growth Generalized Least Squares (GLS) method is applied.The method can be used to perform linear regression when there is autocorrelation or heterogeneity of variance between error terms of regression. If the number of cross section units exceeds time periods, it can be expected that there would be heterogeneity of variance in error terms. In these cases, ordinary least squares and weighted least squares can be statistically inefficient. In this research, GLS method has been used to determine the effect of the independent variables on agricultural growth. Following (Azerbaijani et al., 2013; Kadyrova, 2011; Hodey, 2013; Udah & Nwachukwu, 2014), the model is specified as : LnVit = αlnVi,t−1 + βLnLit + εLnCi,t−1 + ϵLnR it + θLnAAit + μLnOit + ρLnTFPit + γEit +δEit2 + Dit + εit
(2)
where: Vit , Lit , Cit , R it , AAit , Oit , TFPit , Eit are real per capita agricultural value added, agricultural labor force, agricultural capital stock, rainfall, agricultural areas, trade openness, agricultural total factor productivity and export diversification, respectively. Also, Dit is a dummy variable taking value one for EU members and zero otherwise and finally εit represents error term. Subscripts i and t denote country and time, respectively. Table 1 portrays expected sign of equation coefficients. (Insert table 1 here)
3.Results and discussion At the first step, it is necessary to check the stationarity of variables. As a robust check, the three unit root test statistics are reported in table 2. The null hypothesis of all tests is nonstationarity. Therefore, all variables could be treated as stationary. (Insert table 2 here) To choose between panel or pooled data model, Limer-F test is applied. In fact, this test reveals presence of any country specific effects in the data and its null hypothesis is 4
homogeneity of data. According to table 3, our sample countries are not homogeneous and should be treated as panel data units. .
(Insert table 3 here) The result of the Hausman test in table 4 shows that data should be analyzed as a random
effects model. Here, null is defined as superiority of random effects model. (Insert table 4 here) The calculated likelihood ratio (LR) statistic in table 5 rejects homogeneity of error terms and suggests the GLS as a proper method of estimation. (Insert table 5 here) Table 6 reports the estimation results of equation (2). The variable of interest (Eit) shows an indirect impact on agricultural growth implying that any increase (decrease) in agricultural export concentration (diversification) leads to reduction in sector growth. This result suggests , as is theoretically expected, that countries with higher export diversification would experience more growth because their export earnings is not dependent on a single product and more export ( more foreign demand) leads to higher agricultural growth. Positive coefficient of 𝐸𝑖𝑡2 reveals the hump-shaped relationship between mentioned variables. This finding is also reported in some previous studies (Codat et al., 2011; Kadyrova, 2011) . Rainfall, capital stock and trade openness all directly affect agricultural growth which is in line with theoretical expectations. The coefficient of TFP reveals the dominant role of productivity enhancement in agricultural growth, as one percent increase in TFP would result in 0.152 percent more output. The effect of agricultural area is not significant suggesting extensive agriculture as an inefficient policy in touching more agricultural growth. EU membership, as only dummy variable in the model, confirms that EU members, on average, experience more growth which might be a direct consequence of their policies (commonly known as CAP). Adequacy of the estimated regression is verified through different diagnostic tests.
(Insert table 6 here) 4.Conclusion This study examined the relationship between export diversification and agricultural growth using a panel data set of 20 developed countries during 2000-2014. The study used 5
Herfindahl index for export diversity and GLS method to examine dynamic interactions among the variables. Main results showed that openness, export diversification, rainfall, capital stock, total factor productivity and EU membership had positive effect on agricultural growth while labor force was negatively related to it. The positive effect of export diversification is in line with Herzer & Nowak-Lehman (2006), Kadyrova (2011), Hodey (2013), Memarnejad et al (2009) and Azerbaijani et al (2013). So governments should support entrepreneurship and provide all prerequisites to motivate investments in new kind of activities and products in agriculture. According to positive and significant effect of openness, more agricultural growth could be obtained by following more liberalized trade regimes. This result is also reported in some previous studies. ( Kadyrova, 2011; Hodey, 2013; Azerbaijani et al., 2013). As table 5 reports, capital stock plays an outstanding role in sector growth that supports findings of Kadyrova(2011) and Hodey(2013). The only unexpectable impact goes to labor force which might be due to capital-intensive nature of agricultural technologies in countries under study. Finally noticeable influence of TFP as a key indicator of efficient use of inputs, is previously reported by Udah et al( 2015).
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References Adity, A., Roy, S. (2007). Export diversification and economic growth: evidence from crosscountry analysis. J. Mimeo, 1–25. Arip, M, Yee L, Abdul Karim B. (2010). Export diversification and economic growth in Malaysia. MPRA paper 10/20588 Azerbaijani, K., Eshraghi, A., Ranjbar, H. (2013). Effect of export diversification and financial development on economic growth in the D8 countries. Proc of 1 National E- Conference on Future Perspective of Iranian Economy, Isfahan (Iran), Jan19 Chenery, HB. (1979).The process of industrialization: structural change and development policy. New York: Oxford, (Chapter 3). Cadot, O., Carrère, C., Strauss-Kahn., V. (2011). Export diversification: What's behind the hump? Review of Economics and Statistics. 93(2), 590-605. Dennis, A., Shepherd, B. (2007). Trade costs, barriers to entry and export diversification in developing countries. Working paper 07/40. Ferreia, G. (2009). From coffee beans to microchips: export diversification and economic growth in Costa Rica, Proc of Annual Meeting on Southern Agricultural Economics Association, Atlanta(Georgia), Jan31- Feb 03. FAO. Food and agriculture organization of the United Nations statistics division stat. (2016). http://www.faostat.fao.org/ Accessed 16.03.10. Hamano, M. (2014). "The Harrod–Balassa–Samuelson effect and endogenous extensive margins, J. the Japanese and International Economies, 31(C), 98-113.
Hausman, R., Klinger, B. (2007). The structure of the product space and the evolution of comparative advantage. Working paper 07/146 Hausman, Jerry A. (1978). Specification tests in econometrics. J. Econometrica, 46, 1251–1271. Herzer, D., Nowak-Lehmann, F. (2006). What does export diversification do for growth? An econometric analysis. J. Applied Economics, 38(15), 1825–1838. Hesse, H. (2008). Export diversification and economic growth. Working paper 08/21 7
Hodey, L. (2013). Export diversification and economic growth in Sub-Saharan Africa. Master’s thesis. Ghana. Accra. 98 pp. Imbs, J., Wacziarg, R. (2003). Stages of diversification. J. American Economic Review, 93(1), 63–86. Joshi, P. K., Gulati, A., Birthal, P. S., Tewari, L. (2003).Agricultural Diversification in South Asia: Patterns. _Determinants,and Policy Implications, Washington, DC: Food Policy Research Institute. Kaderova, A. (2011). The effect of export diversification on country growth. Master’s thesis. Central European, Budapest, Hungary.46 pp. Klinger, B., Lederman, D. (2006). Diversification, innovation and limitation inside the global technological frontier. Working paper 06/08 Lederman, D., Maloney, W. F. (2007). Trade Structure and Growth. In D. Lederman, & W. F. Maloney (Eds.), Natural Resources Neither Curse nor Destiny. California. Memarnejad, A., Shayeste, A., Emamverdi., GH. (2009). The effect of export diversity on Iran economic growth in the years after Islamic revolution. J. Applied Economics, 2, 85-100. NASA Giovanni. The bridge between data and Science. (2016). http: // www .disc. sci. gsfc. nasa. gov/ Giovanni/ Accessed 16.11.25. Sepehrdoust, H., Khodaee, H. (2014). The strategy of export diversification and economic growth in selected developing economies. J. International Economics Studies, 44(1), 47-57. Solow, R. (1956). A contribution to the theory of economic growth. J. Economics, 70(10), 65–94. Syrquin, M. (1988). Patterns of structural change. In H. Chenery, & T.N. Srinivasan (Eds.) ( pp. 203- 273). Amsterdam. Udah, S., Nwachukwu, I. (2015). Determinants of agricultural GDP growth in Nigeria. J. International Agricultural Research and Review, 3(3), 184-190. UNSD.Commoditytradestatisticsdatabase.(2016).http://data.un.org/Explorer.aspx?d=ComTrade& f=_l1Code%3a96/ Accessed 16.12.5 USDA. Data and statistics. (2016). www.ers.usda.gov/ Accessed 16.12.15
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World Bank. World development indicators. (2016). http://data. World Bank.org/ indicator/all/ Accessed 16.12.5. World Trade Organization. Statistics database. (2016). http://stat.wto.org. Yokoyama, K., Alemu, AM. (2009).The impacts of vertical and horizontal export diversification on growth: An empirical study on factors explaining the gap between Sub-Saharan Africa and East Asia's performances. International Relations and Area Studies, 7(3), 49-90.
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Table 1. Expected sign of variables’ effects Variable Expected sign
𝐥𝐧𝐕𝐢,𝐭−𝟏
𝐋𝐧𝐋𝐢𝐭
𝐋𝐧𝐂𝐢,𝐭−𝟏 𝐋𝐧𝐑 𝐢𝐭 𝐋𝐧𝐀𝐀 𝐢𝐭 𝐋𝐧𝐎𝐢𝐭 𝐋𝐧𝐓𝐅𝐏𝐢𝐭
+/-
+
+
+
+
10
+/-
+
𝐄𝐢𝐭 -
𝐄𝐢𝐭𝟐 +
𝑫𝒊𝒕 +
Table 2. Results of the unit root tests Variables
Levin, Lin & Chu
PP-Fisher
Chi-
square ADF- Fisher Chi-square
statistic
statistic
statistic
lnVit
-4.58***
57.94**
70.81***
LnCit
-11.14***
57.58**
53.84*
LnLit
-10.84***
59.15**
85.96***
LnR it
-6.84***
103.42***
52.06*
LnOit
-8.01***
65.5***
65.5***
Eit
-31.41***
74.17***
87.58***
Eit2
-3.29***
67.15***
81.46***
LnTFPit
-7.04***
128.93***
234.33***
LnAAit
-6.46***
110.28***
150.63***
*,** and *** denote significance at 10,5 and 1 percent, respectively.
Table 3. Result of the Limer-F test H0: no country specific effects
Statistic 8.76**
Result Presence of country specific effects
** :Significant at 5%
Table 4. Result of the Hausman test H0:Superiority of random effects model
Statistic
Result
13.65n.s
Data should be modeled as REM
n.s: not significant
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Table 5. Result of Likelihood ratio test H0: Homogeneity of error terms
Statistic
Result
41.23**
Heterogeneity of error terms
**: Significant at 5%
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Table 6. GLS estimation results of agricultural growth model Variables
Coefficients
lnVi,t−1 LnR it
-0.933(-17.7)*** 0.017(2.48)*
Eit
-0.381(2.76)*
Eit2
0.087(1.32)*
LnCi,t−1
0.022(5.84)**
LnLit
-0.013(-3.27)**
LnOit
0.033(1.69)*
LnTFPit
0.152(3.74)**
LnAAit
0.292(1.01)n.s
EU Membership
0.028(2.54)*
Constant
0.405
R2
0.86
F- statistic
71.7***
*, ** and *** denote significance at 10, 5 and 1 percent, respectively. , n.s: not significant
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