In global economy, tourism is one of the most noticeable and growing sectors. This sector plays an important role in boosting nation's economy. An increase in tourism flow can bring positive economic outcomes to the nations, especially in gross domestic product (GDP) and employment opportunities. But our result shows that there is no long-run and no short-runcausalityfrom independent lnets, fdi, lnopt, lnemp and lnttr to dependent variable lngdp. Meaning that Tourismsector (number of tourism arrival (NTA), Expend on tourismsector (ETS), agriculture (AGR), Transport on tourismsector (TPT), Exchange rate (EXR), Openness trade (OPT), Foreign Direct Investment (FDI), Employment (EMP) and Technology Transfer (TTR) in COMESAcountries have no impact on Economicgrowth in those eight (8) selected countries . This result seems strange compared to what we see in other countries but the cause might be the presence of high level of corruption or mismanagement in Tourismsector in COMESAcountries. Government in COMESACountries should observe and control closely the TourismSector in their respective countries to avoid any abuse of tourists and negative impact on their GDP. So, efforts should be directed towards policies that will enhance economicgrowth, such as the business environment, and openness, in order to have a greater impact on Tourismsector which plays a great role on economicgrowth.
Although much literature exists to demonstrate the importance of tourism as a foreign exchange earner, little is known about how tourism expansion affects the economy of a developing country (LDC). This paper employs a Time series analysis to demonstrate the potential contribution of tourism for economicgrowth in India from the time period of 1991 to 2012. Tourism contributes growth of economies (tourism led economicgrowth) or (economic-driven tourismgrowth). Co- integration test has been done for ascertaining longrun relationship and VECM for shortrun dynamics. Granger Causality test has been applied to determine causal relationship between these variables. The evidence confirms the conventional the tourism led- Hypothesis, that tourism (represented by foreign exchange earnings) causes economicgrowth both in short and longrun. The results also conforms the longrun association between foreign tourist earnings and gross domestic product and the Granger causality indicates bi- directional causalitybetween these variables.
The nexus between financial developments an economicgrowth is a debatable issue. Some others define that finance an important component of economicgrowth (Schumpeter, 1934; Goldsmith, 1969; McKinnon, 1973; Shaw, 1973; King and Levine (1993), others like (Robinson, 1952; Lucas, 1988). Schumpeter (1934) consider as a minor growth factor. Greenwood and Jovanovic (1990) financial development helps for generate better information and improve resource allocation. A broad system of financial intermediation is able to allocate more capital to efficient investments and leads to faster economicgrowth. Taimi, et al (2001) investigated the relationship between financial development and economicgrowth for the selected Arab countries. The findings of the study suggest that there is no clear evidence that financial development affect or is affected by economicgrowth. Bencivenga and Smith (1991) examined that financial development, financial intermediaries boost productivity, growth and capital formation by improving corporate governance. Al-Youssif (2002) examined the relationship between financial development and economicgrowth for 30 developing countries. The findings suggest that there is no bidirectional relationship between financial development to economicgrowth and economicgrowth to financial development. This study also emphasised that the finance growth relationship can’t be generalised across countries. Arestis (2002) studied the impact of financial liberalisation on 6 developing countries. The study found that financial liberalisation is much a more complex process, the effect of financial development are ambiguous. Shan Jordan and Qi (2006) examined the impact of financial development and economicgrowth on China. The study concluded that financial development is the second force (after the contribution from labour) affects the growth. Rachdi H and Hussene (2011) concluded that financial development and real GDP per capital are positive and strongly linked. Kilimani N (2007) analysed that the removal of distortions in the financial sector stimulates economicgrowth for the country like Uganda. Demetriades and Hussein (1996), Demetriades and Luintel (1997), Luintel and Khan (1999) and Singh (2008) discovered bidirectional causalitybetween financial development and economicgrowth. Arestis, et al. (2002) reported that financial development was promoted by economicgrowth but there was no feedback fromeconomicgrowth to financial development in India.
(2010) examined the effects of localization, urbanization, and local competition on labor productivity through the use of establishment- level data related to the Korean manufacturing industries. Based on their findings, when an establishment was located in a more localized/specialized, more urbanized/diversified, and more competitive area, the workers, due to the external benefits from agglomeration, became more productive. Martin, Mayer, & Mayneris (2011) assessed the effect of spatial agglomeration of activities on plant-level productivity. To conduct their study, they used French firm and plant- level data from 1996 to 2004. They exploited short-run variations of variables by making use of GMM estimation which allowed them to control for endogeneity biases that appears in the estimation of agglomeration economies. The results showed that French plants benefited from localization economies; however, they found very little evidence for urbanization economies. Dehghan Shabani (2013) investigated the influence of density of economic activity, which is defined as the intensity of labor and physical capital per square kilometer, on labor productivity in 28 Iranian provinces. The empirical results indicated that a high density of economic activity led to an increase in labor productivity in the provinces over the period from 2001 to 2011. Hu, Xu and Yashiro (2015) used the dataset of manufacturing firms active in 176 three-digit industries and in 2860 counties in order to evaluate the role of industrial agglomeration in productivity growth of China's industrial sector. They found that congestion and fiercer competition offset the advantages of agglomeration for firms which were operating within agglomerated regions. They further stated that industrial agglomeration had contributed up to 14% to productivity growth in China's industrial sectorbetween 2000 and 2007. In another study, Azari, Kim, Kim & Ryu (2016) investigated the effect of agglomeration on urban labor productivity in the manufacturing sector of Korea. The researchers benefitted from a panel data analysis of 200 Korean cities during 2004 to 2008. Based on their results, labor density had a negative impact on urban labor productivity, while output density had a positive impact on urban manufacturing productivity.
Table 2 lists the descriptive statistics for all variables of IPOs sample. The underpricing (UP) is noticeably high in this period (48.54%), as the result of removing limit on price fluctuation on first-five trading day. The mean of Skew variable is positive, suggesting that recent industry stock return is favorable prior to the IPO issues. Therefore, investors may place high expectation on good future return of IPO in the same industry. The sample is dominant by number of electronic IPO stocks (79%), and it may raise the concern that the result in this study only applicable for Taiwanese electronic sector. In addition, the number of IPO listed on TSE is 23% (27 firms) compared with 77% listed on OTC (94 firms). Table 3 describes the correlation matrix between these variables.
Currently, in Pakistan, energy demand is an average of 17000 megawatts while shortage is around 4000–5000 megawatts. In the next coming years energy demand will increase further and about approximately near 500 megawatts in the next ten years . The electricity shortage reached 5500 megawatts in 2015, and the supply was 15500 megawatts with 23000 megawatts of installed capacity. The demand will rise in different sectors including construction, agriculture, education, manufacturing, and most importantly in the sustainable development to boost economicsector . During the period of 2014-15, the total electricity generation was 109059 GWh, which nearly two- thirds came from the thermal sources . The electricity demand in the Pakistan is driven by several issues such as rapidly growing population, electricity prices, economic expansion, urban resident flows and weather. However, the major specific problems in the country are the crisis that caused electricity shortages were caused by theft and excessive use of electricity in domestic and industrial sectors, resulting in huge loss of power lines, mismanagement and political controversy in mega- power projects . Pakistan has energy shortage due to production and supply. This study major objective was to explore and investigate the relationships among economicgrowth, electricity access to rural population, electricity access to urban population, electricity access to total population, rural and urban population growth, total population growth and energy usage in Pakistan. Time span data was used in this study and was collected from the World Development Indicators (WDI). We employed the ADF and P-P unit root tests to check the variables stationarity. Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration with analysis of long-run and short-run was used to check the dynamics causality among the study variables. Besides the introduction section the remaining paper is organized as: Section 2 provides the existing literature regarding electricity production. Section 3 is materials and methods section which shows the data sources and model specification. Section 4 represents the empirical estimation strategy, and Section 5 is the results and discussion section regarding results of the unit root tests, results of the cointegration test, covariance test results, long-run and short-run results. Section 6 is conclusion and policy recommendation.
The study has empirically investigated the short and long-run effects of non-oil trade export on economicgrowth in Nigeria between the period of 33 years which spanned from 1986 to 2018. According to statistical evidence, non-oil total trade, balance of trade and exchange rate have positive and significant effects on economicgrowth in Nigeria whereas inflation has positive and an insignificant effect on economicgrowth. The study concluded that non-oil trade export significantly contributed to economicgrowth in Nigeria both in the shortrun and longrun under the studied period. The study validated the study of Christopher, Omoniyi and Olufunke (2014) that non-oil export has positive and significant effects on economicgrowth in Nigeria. Thus, as evidenced from the finding, Government are advised to diversify into other non-oil sector (Agriculture, manufacture, mining, financial services, etc) to augment the revenues from the oil sector; more funds and improvement should be appropriated to the non-oil sector in order to make our produce compete in the world market. The study contributed to knowledge by building on recent time series data and also based on the significant result emanating from the finding.
After decades of military conflicts and economic stagnation, Vietnam began its process of economic revival, by transforming from a centrally planning economy to a market –oriented economy with the introduction of Doi Moi (renovation) policy in 1986. Thereafter, the country has increasingly integrated into the global economy. Since the introduction of first Law on Investment in December 1987, the country has made frequent revisions on laws governing Foreign Direct Investment (FDI) to make it more attractive to investors. Numerous bilateral and multilateral free trade agreements were signed, which created favorable conditions for export trade. Particularly, becoming a member of WTO in January 2007 is a milestone for the global integration process of the country. As a result of openness policies, the share of FDI and export in the GDP has increased significantly. In 1986, FDI and export accounted for only 0 % and 6.6 % respectively in GDP but in 2015 the respective shares of FDI and export increased to 6.1% and 89.8%. However, to state that the increasing dependence of the country on export and FDI is Asian Economic and Financial Review
Houssou and Heidhues (2005) stated that the international donor community has provided assistance to debtor countries to reduce their external debt trap to spur economicgrowth, alleviate poverty, and achieve external viability. This support has taken the form of concessional financing provision from IFIs, and debt relief from official donor creditors, such as the Paris Club. It should be pointed out that these measures have produced great success in reducing the external debt burden of most middle-income countries. Nonetheless, many poor countries continue to experience unacceptable levels of poverty and heavy external debt burden due to a combination of factors, including policies of inappropriate development, poor external debt management policy, give up the structural adjustment and reform economy, the decline of their terms of trade, and poor governance. In North Africa, high levels of external debt service have negatively affected savings and foreign exchange earnings resulting in the crowding out of public investment. This scenario, in return, has also affected the provision of social services for the populace in the affected countries. This study advances the argument that the real problem that impedes the process of economic development in the North African countries is the challenge of inadequate real resources for capital formation, due to high external debt servicing. In many instances, the countries are compelled to resort to high levels of foreign borrowing in order to mitigate the effects of worsening economic conditions. However, further foreign borrowings have aggravated the debt trap as most of these countries have a history of debt service arrears and difficulties.
2. Literature review: the impact of macroeconomic factors on asset prices Although different researchers analyse various macroeconomic factors, most of them examine the effect of gross domestic product or personal income. This is due to the fact that these factors have an intuitively justified influence on all assets. For example, due to the rise of GDP, the company’s production, profits and, as a result, the prices of its shares in most cases increase (Singh et al., 2013). Moreover, almost all scientists (Aper- gis, 2003; Ong & Chang, 2013; and others) found statistically significant and direct im- pact of country’s economic condition on real estate prices. Kohlert (2010) emphasized that GDP is an economic indicator mostly escalated in the mass media, so the change of GDP should have the biggest impact on people’s future expectations and real estate prices. In extreme cases this can even lead to the formation of asset bubbles and to fi- nancial instability. However, in the case of government securities, the effect of GDP can be twofold: on the one hand, in the environment of deteriorating economic situation governments tend to increase the amount of debt and thus the supply of government securities, so the prices of these securities fall, but on the other hand, the prices of other assets during the economic downturn generally fall even more.
As we have mentioned abobe, by adding the growth rate of output to the growth rate of the price level, we are in fact using a slight twist on the original method of Lucas (1980). Output was absent, at least explicitly, from the first formulation of the Quantity Theory of Money made by David Hume. Hume presented the theory as the following thought experiment: “Were all the gold in England annihilated at once, and one and twenty shillings substituted in the place of every guinea, would money be more plentiful or interest lower? No surely: We should only use silver instead of gold”. 7 It is not clear when output was first included explicitly, but it plays a prominent role in Fisher (1911) and in Friedman and Schwartz (1963). Writing about the Greenback period, 1867-1879, Friedman and Schwartz observe that prices decreased slightly, despite an increase in the money supply, and they attribute the difference as being substantially due to the large increase in real output that occurred during this period.
(19) However as noted above the correlation of growth rates of inputs and total factor productivity growth is not zero. However, a priori it is not clear that the appropriate decomposition implies equal split of the covariance term between inputs and TFP, nor to favor TFP as in the Klenow & Rodriguez-Clare capital intensity approach. Even if endogenous technological progress was the best model of TFP growth, it does not automatically follow that input accumulation is not logically prior to TFP growth. Not all societies purposely spend to invent new products, and processes. The societies that do accumulate new goods and processes are also the highest human capital economies and with the most physical capital. Thus we use another method of variance decomposition in order to allow the data inform us which economic theories of growth are more likely. This is not a Bayesian approach strictly, but one that produces what we term plausible shares or plausible explanations.
The concept of causality introduced by Wiener (1956) and Granger (1969) is defined in terms of predictability one period ahead. This concept can be generalized by considering causality at a given horizon h, and causality up to any given horizon h [Dufour and Renault (1998)]. This generalization is motivated by the fact that, in the presence of an auxiliary variable vector Z, it is possible that a variable Y does not cause variable X at horizon 1, but causes it at horizon h > 1. In this case, there is an indirect causality transmitted by Z. Another related problem consists in measuring the importance of causalitybetween two variables. Existing causality measures have been defined only for the horizon 1 and fail to capture indirect causal effects. This paper proposes a generalization of such measures for any horizon h. We propose nonparametric and parametric measures of unidirectional and instantaneous causality at any horizon h. Parametric measures are defined in the context of autoregressive processes of unknown order and expressed in terms of impulse response coefficients. On noting that causality measures typically involve complex functions of model parameters in VAR and VARMA models, we propose a simple method to evaluate these measures which is based on the simulation of a large sample from the process of interest. We also describe asymptotically valid nonparametric confidence intervals, using a bootstrap technique. Finally, the proposed measures are applied to study causality relations at different horizons between macroeconomic, monetary and financial variables in the U.S. These results show that there is a strong effect of nonborrowed reserves on federal funds rate one month ahead, the effect of real gross domestic product on federal funds rate is economically important for the first three months, the effect of federal funds rate on gross domestic product deflator is economically weak one month ahead, and finally federal fundsrate causes the real gross domestic product until 16 months.
It is important to note that causality testing based on the application of an unrestricted VEC model has got a serious drawback. In practical applications it is often necessary to use a relatively large number of lags in order to model the dynamic multidimensional process in a proper way and avoid the consequences of the autocor- relation of residuals. However, the more lags the less degrees of freedom, which in turn may have an undesir- able impact on test performance, especially for small samples. Furthermore, testing for linear causality using a traditional Granger test often suffers because of possi- ble multicollinearity, especially for dimensions higher than two. This is why the sequential elimination of in- significant variables is often additionally performed for each VECM equation separately in order to test for short and longrun linear Granger causality. Each step of this procedure leads to omission of the variable with the highest p-value (t-test). The procedure ends when all remaining variables have a p-value no greater than a fixed value (in this paper, it was 0.10). The reader may find more technical details of this approach in .
The objective of the paper is to test for such an intertemporal trade-off empirically for a broad set of 45 developed countries and emerging market economies. The paper distinguishes between the initial, short-run reaction and the medium- to long-run response of economies to financial liberalisation. It presents evidence that economies indeed tend to receive an initial boost and grow faster in the initial five years following financial liberalisation. However, after this short-run gain, economies tend to grow more slowly in the subsequent years. This suggests that there is indeed a trade-off over time, i.e. a short-run gain and a medium/long-run "pain". Given that most emerging markets liberalised only within the last decade, an important caveat is, however, that this cost in the medium-term may be only be a temporary one, and that countries may return to faster growth in the long-run, following the experience of most developed economies decades earlier. It therefore may be too early to tell whether developing countries will really gain in the long-runfrom liberalisation.
opportunities. This is the regression reported by Rajan and Zingales (1998). In other words, USGrowth is a purer reflection of growth opportunities, while USNeeds is a reflection of industry financing needs, which incorporates simultaneously elements of growth opportunities, financial dependence, and the form of f(.) in (4) above. Thus, while USNeeds may be used as a time-varying predictor of financing industry needs, we suggest that our USGrowth measure is a more direct proxy for growth opportunities, as in (1) above. 4 Hence, we suggest that when we include the USGrowth*FD interaction in addition to USNeeds*FD, this more direct measure of growth opportunities will dominate in the growth regression. This will not be the case in sectoral share regressions, where we expect the underlying industry characteristic of financial dependence to be the dominant explanatory factor. The main difference in our two approaches is the following: we argue that inherent needs for funds affect industry shares while RZ argued that they affect industry growth. In our model, growth is primary affected by temporary shocks to growth opportunities; the effect of underlying industry characteristics on sectoral growth is third- order.
The results highlighted in Table 5 are obtained from Maddala & Wu panel co-integration test (1999) and Kao test (1999). The study employed five variables that capture financial development and real sectorgrowth. Therefore, there are chances of existence of at most four co-integrating relationships among the variables. Results of both Likelihood ratio trace statistics and maximum eigenvalue statistics are given against hypothesis of none, at most one, at most two, at most three, and at most four co-integration relationships. Both these statistics determines the co-integrating vectors in the non-stationary panels. The null hypothesis is of No co- integration in the panel dataset against the alternative that there exists a co-integration in the series. Lag order has been found by various criteria, the majority of which gives a lag order of 3.But this study has taken the lag order as 2 from Schwarz information criterion to save the loss of degree of freedom. The Likelihood ratio trace statistic is 102.1 at r = 0 i.e. for none co-integrating relationship, 57.99 at r = 1 i.e. for at most one, 23.56 at
Madrid Stock Exchange and the findings did not support weak form efficiency. Ewing (2002) analyzed the interrelationships among five major S&P stock indices in order to determine their interrelationships and how shocks to one index are transmitted to the others. By and large, he found strong interrelationships amongst the five S&P’s stock indices. In other study, Wang et al. (2005) examined the patterns of information flows within and across sectors of the two Chinese stock exchanges in Shanghai and Shenzhen and suggested a high degree of interdependence, indicating that the sectors are highly integrated and sector prices reflect information from other sectors. Berument et al. (2005) examined the long-run relationship properties of the sector indices of ISE and could not find any significant correlation among these indices. Under a similar spirit, Mohamad et al. (2006) investigated the opportunity for diversification across different economic sectors for long-term investment using sectoral indices of the Malaysian Stock Exchange. The results pointed out that although the returns of different industry sectors tend to be highly correlated, this correlation relationship is not stable. Hassan and Malik (2007) used a multivariate GARCH model to simultaneously estimate the mean and conditional variance among different US sector indices and found significant transmission of shocks and volatility among different sectors.
362 in the early stages of expansion, investments are undertaken extensively by those who possess the physical and human capital resources that allow the economy to grow in terms of entrepreneurship and higher trade transactions, thus leading to higher income inequality. As economic freedom keeps rising and growth contin- ues, new economic opportunities are disseminated among all who could not be part of the growth process. Eventually, the low-level participants in economic distribu- tion are also capable of reaping the benefits of economicgrowth and, thus, income inequality starts to decline. According to Barro (2000), the link betweeneconomicgrowth and income inequality is based on the assumption that the “distribution of political power is more egalitarian than the distribution of economic power”, indi- cating the inability of policies fighting income inequality to induce improvements in equality, for reasons probably related to rent-seeking and corruption. Moreover, growth can lead to increased inequality due to the presence of mechanisms that affect the revenues used to finance redistribution policies. In particular, such reve- nues are raised through distortionary taxation that provides a disincentive to work. If the power of such disincentives is high, then particular groups in the population, especially those who are near the eligibility level for transfer programs, may be- come dependent on the government for transfers, which leads to stagnation in in- comes. By contrast, those who remain in the labour market continue to acquire higher levels of human capital and thus they can experience income gains; as a re- sult, an increase in income inequality is observed (Cox and Alm, 1995). Finally, Vedder et al. (1988) argue that growth could not reduce inequality due to the pres- ence of the crowding out of private sector charity and the capitalization of public transfer payments (Gruber and Hungerman, 2007; Tullock, 1986).
The results of DP sequential unit root tests suggest that real output contains a unit root for all countries, and the money series is integrated of order one except for Singapore and Sri Lanka, in which they are I(2) processes. In the notion of FS framework, these order of integration imply that the LRN restriction c(1)/d(1) is testable for Indonesia, Malaysia, Myanmar, Nepal, the Philippines, South Korea, Taiwan, and Thailand. At the same time, LRSN is the appropriate hypothesis to be tested for the economies of Singapore and Sri Lanka. However, not all of the countries are informative to the LRN (LRSN) tests. The λ-max statistics in Johansen and Juselius (1990) tests show that while most of the countries do not have longrun cointegrating vector with money, the null of no cointegration is strongly rejected in the case of Sri Lanka. This result implies that money is not exogenous and it has the ability to affect real economic activity in Sri Lanka. In other words, money is non-neutral in Sri Lanka. For the next step, we proceed to apply FS methodology by excluding Sri Lanka in our analysis. For those countries with one unit root for their money series (Indonesia, Malaysia, Myanmar, Nepal, the Philippines, South Korea, Taiwan, and Thailand), Equation (8) is used to test for LRN. For Singapore, where money is I(2), Equation (9) is utilized to test for LRSN. The estimated results are then presented in Tables 4 to