1、中文 5694 字 ， 3500 单词， 18900 英文字符 毕业论文 外文翻译 外文题目： Causality between Export and Growth: Evidence from South Asian Countries 出 处： MPRA Paper No. 21027, posted 28. February 2010 / 13:01 作 者： Eusuf, M Abu and Ahmed, Mansur Causality between Export and Growth: Evidence from South Asian Countries M. Abu Eus
2、uf and Mansur Ahmed Abstract Strong economic growth accompanied with robust export performance leads many people to conclude that export sector of a country has pivotal role in the economic growth of that country. Empirical evidence on export growth nexus has been mixed and inconclusive. This study
3、examined whether there was any time series support for such export-led growth hypothesis for South Asian Countries. Engle-Grangers Error Correction Model (ECM) was used to test the Granger causality between export and output. The study had produced fairly mixed results, and did not find any conclusi
4、ve evidence in favor of export-led growth for South Asian Countries. While Pakistan, Srilanka and Bhutan were the cases of export-led growth, India, Nepal, and Maldives show the opposite result of growth-led exports. In one country, namely Bangladesh, the data had failed to detect any causality in e
5、ither direction which is attributed in low value addition in export. Key Words: Export-led growth hypothesis, Granger causality test, Unit Root Tests, Error Correction Model. I. Introduction: The export-led growth hypothesis (ELGH) postulates that export expansion is one of the main determinants of
6、economic growth. It holds that the overall growth of countries can be generated not only by increasing the amounts of labor and capital within the economy, but also by expanding exports. Economists behind export-led growth hypothesis consider exports can perform as an “engine of growth. This type of
7、 advocacy has been generated from the following reasons: First, expansion in demand for the countrys output through export growth facilitates the exploitation of economies of scale for small open economies. Second, exports expansion may relax a foreign exchange constraint which makes it easier to im
8、port inputs to meet domestic demand, and so enable output expansion. Third, expansion in exports may promote specialization in the production of export products, which in turn may boost the productivity level and may cause the general level of skills to rise in the export sector. This may then lead
9、to a reallocation of resources from the (relatively) inefficient non-trade sector to the higher productive export sector. The productivity change may lead to output growth. Finally, an outward oriented trade policy may also give access to advanced technologies, learning by doing gains, and better ma
10、nagement practices that may result in further efficiency gains Thus, international trade and development theory suggests that export growth due to export-oriented policies contributes positively to economic growth (measured by output growth). It should be noted that the theory also suggests that out
11、put can affect export. A one-way causality from output to exports is justified by, for instance, Kaldor (1967), Lancaster (1980), and Krugman (1984). They argue that output growth has a positive impact on productivity growth and improved productivity or reduced unit cost is expected to facilitate ex
12、ports. It could be interesting, from a policy making point of view, to study the causal nexus of exports and output in South Asian Countries. Though, scatter plots in appendix A show solid relationships between log of real export and log of real GDP among South Asian countries. Is there any time ser
13、ies support for the export-led growth hypothesis in South Asian Countries? Does any causality exist between exports and outputs? These are the main questions addressed in this study concerning India, Bangladesh, Pakistan, Srilanka, Nepal, Bhutan, and Maldives. Thus the purpose of this paper is to ex
14、plore the causal nexus of export and output in south Asian countries. In examining these issues, the study had been used Granger causality tests approach through cointegration and error- correction modeling.The relationship between exports and growth has been explored extensively in the literature.
15、Most of the early studies, including Michaely (1977), Balassa (1978), Tyler (1981), Feder (1983), Kavoussi (1984), Ram (1985), Sheehey (1990), Lopez (1991), Edwards (1993), and Ngoc et. al. (2003), were based on the Cross-section approaches and remarkably evidenced that exports have significant caus
16、al effect on economic growth. But these cross section studies contain an inbuilt drawback that these studies assume, rather than establish, that causality runs from export growth to GDP growth, while successful growth episodes in an economy can exhibits high export growth. These leads the authors, s
17、uch as Sheehey (1990) and Pritchett (1996), to raise questions about the validity of conclusions based on cross-country studies. Sheehey (1990) has been found that other production categories besides exports whose growth has a similar relationship to GDP growth. A number of studies including Jung an
18、d Marshall (1985), Chow (1987), Darrat (1987), Hsiao (1987), Bahmani-Oskooee et al. (1991), Kugler (1991), Dodaro (1993), Van den Berg & Schmidt (1994), Greenaway and Sapsford (1994), and Islam (1998) had adopted time series analysis for exploring the causal liaison between export growth and output
19、growth. Using the Granger (1969), Sims (1972), and Hsiao (1987) causality procedures, these studies were failed to provide a uniform conclusion about the export-led growth hypothesis. However, these time series studies were not free from disparagement. Although standard Granger or Sims tests are onl
20、y valid if the original time series are not cointegrated, none of these studies checked the cointegrating properties of the time-series variables involved. When time series are cointegrated, inferences based on traditional time-series modeling techniques will be misleading, as pointed out by Granger
21、 (1988), this is because traditional causality tests would miss some of the “forecastability” and, hence, reach incorrect conclusions about causality. Moreover all the stuedies reviewed above used growth of GDP and that of exports which is akin to first differencing and filters out long-run informat
22、ion. In order to remedy this situation cointegration and error-correction modeling have been recommended to combine the short-term as well as long run information. Bahmani-Oskooee and Alse (1993) took all these issues into account and employed quarterly instead of annual data for the eight countries
23、 studied. They found strong empirical support for two-way causality between export growth and GDP growth in eight out of nine countries. However, very few empirical studies have been done in the recent past to investigate the export- led growth (ELG) hypothesis for South Asian countries (Jung and Ma
24、rshall 1985； Bahamani, Oskooe and Alse 1993, Dodaro 1993； Khan and Saqib 1993； Chandra 2000, 2002 and Begum and Shamsuddin (1998). The available evidence in relation to export-led growth in South Asia appears rather mixed. In case of India, Chandra (2000, 2002) found bidirectional causal relationshi
25、p between export growth and GDP growth which is short-run causal relation, as cointegration between export growth and GDP growth was not found. In case of Pakistan, Bahamani-Oskooee and Alse (1993) and Khan and Saqib(1993) had done an exercise and found bi-directional causality between export growth
26、 and output growth, while Jung and Marshall(1985) observed that output growth had a perverse effect* on export growth and Dodaro (1993) failed to find any significant relationship in either direction. Both studies, Jung and Marshall (1985) Dodaro (1993), had failed to find any causal relation in eit
27、her direction for Sri Lanka. Abhayaratne (1996) confirmed the previous finding by using cointegration. Dodaro (1993) failed to find any causality either from export growth to income growth or vice versa for Nepal, while he found that export growth causes GDP growth. Begum and Shamsuddin (1998) had f
28、ound positive support for the export-led growth hypothesis for Bangladesh. The motivation for undertaking this study is thus threefold. First, by covering the entire South Asian region, it fills an important gap in the literature. Second, it tries to confirm the validity or otherwise of the mixed re
29、sults obtained in the empirical literature for South Asian as well as other countries. The causality directions between economic growth and exports have very crucial policy implications. Therefore, this study is conducted to investigate the relationship between output and export in the case of South
30、 Asia by using the recent econometric methodology, Engle- Granger Error Correction Model Granger causality test. Our specific objectives are as follows: (1) to examine the short run and long run causality relationship between output and exports； and (2) to suggest some policy implications . II. Meth
31、odology: 2.1. Cointegration, Error-Correction Modeling and Granger Causality Tests Before cointegration is applied, it is essential to test a time series for stationarity. A time series is stationary (in the sense of weak stationarity) if its mean, variance and covariance remain constant overtime. A
32、t a formal level, stationarity can be tested by determining whether the data contain a unit root. This can be done by the Dickey and Fuller (1979), Augmented Dickey-Fuller (ADF) and Phillips and Perron (1988) tests. The ADF test is used here for testing for stationarity as well as for the order of i
33、ntegration of a series. The logs of variables are taken so that the first differences can be interpreted as growth rates. If two variables LX (the log of real exports) and LGDP (the log of real GDP) are integrated to the order one, i.e. I(1), then the next step is to find whether they are cointegrat
34、ed. This can be done by estimating the following cointegrating equations by OLS and testing their residuals for stationarity. LGDP=+LX+u. (5) LX=+LGDP+e . (6) If LGDP and LX are both I(1), then for them to be cointegrated u and e should be stationary or I(0). To check whether there is valid long-run
35、/cointegrating relationship among the variables, we need to test the stationarity of residuals (i.e. linear combination of variables) employing the ADF test, which is given in (7). The ADF test statistics is the t-ratio on the term .The critical values for the test is given by McKinnon (1991). Et=Et
36、-1+Et-1+vt . (7) Where is the first difference, Et is the residual from cointegrating regressions and vt is the white noise. Once it is established that two variables are cointegrated, the next issue is that of which variable “causes” the other. Before the advent of cointegration and error-correctio
37、n modeling, the standard Granger tests were used widely to determine the direction of causality. However, as noted earlier, the standard Granger method is likely to be misleading if variables are cointegrated since the standard tests do not contain an error-correction term. The error-correction representation of the Granger causality model with two variables is formulated as follows: p1 q1 LGDPt = C1 + 1m LX t m + 1m LGDPt m + 1ut 1 . (8) m=1 m=1
