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Tourism is an important socioeconomic sector for many countries worldwide. The perishable nature of this industry requires highly accurate forecasts to support decision-makers with their strategies and planning. This study explores the relationship between time series data characteristics and the forecasting performance of the cutting edge Long Short-Term Memory (LSTM) neural network, along with benchmark methods. Such analyses are important to provide practical recommendations based on empirical evidence to support the development of more accurate forecasts. We analyze the case of inbound tourism in Australia from several country sources, including developed and developing economies from five continents. Findings from this study reveal that the LSTM deep learning approach achieves superior performance in most cases. However, we find that data characteristics, mainly unit root and structural breaks, are related to poor performance of LSTM forecasting model and, in such cases, the deep learning method is not recommended. The results reveal insights that can lead to a forecasting error reduction of around 40% in some cases. Further, more accurate results are found using univariate time series compared to models that employ regressor variables.

The main theme of this paper is to investigate the socio-economic impacts of climate change and adaptation; by comparing the losses with and without land reallocation for major staple foods as a key strategy of climate change adaptation in the farming system. In doing so, a computable general equilibrium (CGE) model for Nepal has been developed, incorporating the allocation of land for several agricultural crops, including the major staple foods, with specification of a nested set of constant elasticity of transformation (CET) functional forms. The set of land transformation elasticities facilitates switching from one crop to another; based on their agronomic characteristics and degree of climate change impacts. Using Global Trade Analysis Project (GTAP) data and Living Standard Survey (LSS) data, a social accounting matrix (SAM) for Nepal is developed and used to simulate three scenarios of climate change and land reallocation. The simulation results in the long run depict farmers as more likely to use land for crops that are comparatively less impacted by climate change, such as paddy. The results further show that land reallocation reduces the income inequality and poverty among rural and urban households by significantly controlling the income losses of marginal farmers.

Financial inclusion (FI) is increasingly becoming a global policy priority because it enables access to financial services, such as savings, payments, risk management, and credit, to households and firms with a wide range of needs. However, to the best of our knowledge, no study has yet been undertaken on measures of FI and the nexus between FI and bank market structure in Africa. We used panel data from 17 African countries from 2004 to 2018 to measure FI and examine its nexus with bank market structure in these countries. We employed two-stage unsupervised machine learning to index FI: its dimensions in the first stage and overall FI in the second stage. Then, we employed an endogenous panel threshold model to examine bank market power and asset concentration impacts on FI. We found that bank market power enhances availability and accessibility dimensions but reduces the usage dimension. The impact of a higher regime market power on usage is worse than that of a lower regime market power. Asset concentration impacts on overall FI, availability, and usage are more pronounced under a lower regime concentration. The results imply that bank asset concentration and market power beyond thresholds are not desirable for promoting FI. The results also imply that scrutinizing potential sensitivity of the results to FI dimensions is vital.

Assessment of the economy-wide impacts of tourism has increased importance due to the unprecedented impacts of COVID-19. Computable general equilibrium (CGE) approach is a versatile tool for estimating the economy-wide impacts of tourism, as opposed to alternative general equilibrium approaches and partial equilibrium approaches. However, there has not been any systematic review done on CGE applications in tourism literature. The article reviews CGE applications in tourism over the past 25 years using a systematic quantitative literature review approach. This review presents potentially important applications of CGE models in guiding post-COVID tourism. Further, the article highlights the new developments in CGE modelling, which are yet to be adopted in tourism economics. Existing studies show a strong geographical bias. Notable research gaps exist in the areas of poverty, inequality, gender, environmental and the climate change impacts of tourism. Future research in these areas will be vital to effectively guide post-COVID tourism recovery.

The impact of fiscal policy variables on economic growth and income inequality is estimated with data from a balanced panel of 19 high-income OECD countries for 1995–2015. A simultaneous equations model is estimated to test four hypotheses that link economic growth and income inequality to different components of fiscal policy under debt- and tax-financed fiscal strategies. The results do not provide statistically significant evidence for efficiency–equity trade-off. Regardless of the finance method, an increase in redistributive expenditures reduces income inequality but does not affect economic growth. The overall net impact of non-redistributive expenditures, direct taxation receipts and indirect taxation receipts is the increased income inequality. These results suggest increasing redistributive expenditures financed by direct taxes as a means of reducing income inequality. In addition, reducing non-redistributive expenditures promotes both efficiency and equity objectives, important for countries which are experiencing structural budget deficits.

This paper examines the impact of three fiscal policy shocks on per capita real GDP and income inequality in Australia during the period 1965–2014. A small structural vector autoregressive (SVAR) model is constructed for an open economy for contemporaneous identification and estimation purposes. Based on the evidence of one cointegrating vector among the variables, a structural vector error correction (SVEC) model is specified for the long run. Direct taxation, indirect taxation receipts and government spending are identified as permanent fiscal policy shocks. The convergent use of two different models (SVAR & SVEC) strengthens the credibility of the results. The results have three key policy implications. First, a reduction in direct taxation receipts increases per capita real GDP without increasing income inequality. Second, a reduction in government expenditure significantly increases income inequality. Third, the adverse effect of indirect taxation receipts on income inequality is greater than the redistributive effect of government expenditure, which questions the widely held fiscal policy strategy of using indirect taxation to finance redistributive expenditure.

We compare the long-horizon forecast performance of traditional econometric models with machine learning methods (Neural Networks and Random Forests) for the main energy commodities in the world using monthly prices provided by the International Monetary Fund (IMF). We study the case of Oil (Brent, WTI and Dubai Fateh), Coal (AU) and Gas (US and Russia). Models accuracy are measured using RMSE and MAPE and the M-DM test is applied to evaluate whether there is a statistically significant difference between the methods. We computed thousands of tests regarding the machine learning parameters combinations as there is no method to set the optimal structure for these models. The results show that machine learning methods outperform traditional econometric methods and also that they present an additional advantage, which is the capacity to predict turning points. This study adds further evidence for the discussion on the use of machine learning algorithms for the development of more accurate forecasts to support policymakers and help the decision-making process in the international energy market.

The recent mining boom represents one of the largest external shocks to the Australian economy in its post‐war history. Although most agree that it had a positive effect on the Australian economy as well as on people’s living standards, there is disagreement over the extent to which this boom has caused deindustrialisation and the so‐called Dutch disease in Australia. Our empirical findings support the presence of Dutch disease nationally for Australia using data for the period 1984–2013. Regionally, the results show that Dutch disease was concentrated mainly in the eastern and southern states of Australia—Victoria, Queensland, South Australia and Tasmania.

Purpose There is a large body of literature on the link between trade liberalisation, growth and poverty. However, less attention has been paid to the relationship between trade and regional disparities. The purpose of this paper is to identify and quantify the regional impacts of trade liberalisation, particularly in the war-affected regions and to understand to what extent trade reforms can contribute to the post-war recovery process and long-term economic and political stability in Sri Lanka. Design/methodology/approach The authors developed a single country multi-regional computable general equilibrium (CGE) model for the Sri Lankan economy to meet the need for a detailed country study as emphasised in the recent literature. Findings Both short-run and long-run results suggest that all regions including war-affected regions in the country gain from trade liberalisation, although gains are uneven across regions. Furthermore, the results suggest that war-affected regions gain more relative to some other regions in the long run. Originality/value According to the best of the authors’ knowledge within country regional impact of trade liberalisation using a multi-regional CGE model has never been attempted for Sri Lanka. The results of this study, even though based on Sri Lankan data, will be relevant to other developing countries engulfed in internal conflicts with regional economic disparities.

Financial inclusion is recognized as a key factor for promoting social development, inclusive growth, equal opportunity, and human development. Despite the consensus regarding the importance of financial inclusion, little is known about its nexus with cross-border banking in an ever-increasing financial globalization. We examine the effect of foreign bank presence on the various dimensions of financial inclusion using panel data for 17 African countries from 2004 to 2018. We find that foreign banks reduce financial inclusion when the degree of foreign bank presence is high. We show that the effect of foreign bank presence on financial inclusion depends on institutional quality, with its impact turning from negative to positive when institutional quality increases to a higher level. The results are robust to different sensitivity analyses. Our results imply that promoting institutional quality is essential to use foreign bank presence as an opportunity for ensuring an inclusive financial system.

The impact of climate change on agriculture has been one of the most discussed topics in the literature on climate change. Multi-regional computable general equilibrium (CGE) models have frequently been used to examine the impact of climate change on agriculture. However, these studies do not focus on country-specific issues related to the link between climate change and agriculture. This paper aims to address this gap by investigating the economy-wide impacts of climate change on Nepalese agriculture. Nepal makes an interesting case study as it has one of the most vulnerable agricultural economies in South Asia. This paper develops a comparative static multi-household CGE model to trace the direct and indirect impacts of climate change in Nepal. The results suggest that climate change has a significant negative impact on the overall Nepalese economy due to the induced loss of agricultural productivity. The results further reveal that rural households in Nepal, whose livelihoods primarily depend on subsistence farming, will face additional climate change–induced stresses due to already overstrained poverty and a weak social welfare system. The results indicate an urgent need to mainstream adaptation strategies to lessen the negative impacts of any climate change–induced loss of agricultural productivity in Nepal.

The export-led economic growth hypothesis is explored at the sectoral level for Australia and its regions. The merchandise exports data can be disaggregated into four broad sectors: (i) Agriculture, (ii) Mining and Fuels, (iii) Manufacturing and, (iv) Other. The results reveal that Mining and Fuels exports have played a crucial role in driving economic growth in Australia nationally and in three of its regions, New South Wales, Queensland, and Western Australia, and that the growth path of each region is unique.

This paper revisits the long standing controversy of trade and poverty linkage using a macro-micro modelling approach based on general equilibrium and microsimulation analytical frameworks. Sri Lanka, the first country in South Asia which undertook trade reforms more than three decades ago, is taken as a case in point in this study. The paper analyses the effects of trade liberalisation on income distribution and poverty in the urban, rural and estate sectors in Sri Lanka using the first ever microsimulation model built for the country in combination with a multi-household computable general equilibrium (CGE) model. The results reveal that without any fiscal policy adjustments a 100% tariff cut would lead to an increase in economic growth and a reduction in poverty incidence both in the short run as well as in the long run. However, when the tariff cut combined with the fiscal policy adjustments to maintain the budget neutrality, poverty outcomes showed mixed results. In contrast, results show that trade liberalisation increases the income inequality in Sri Lanka.

This paper investigates the feasibility of changes in cropland-use as an adaptation strategy to minimise the economy-wide costs of climate change on agriculture. Nepal makes an interesting case study as it is one of the most vulnerable agricultural economies within South Asia. We develop a comparative static multi-household computable general equilibrium (CGE) model for Nepal, with a nested set of constant elasticity of transformation (CET) functional forms, to model the allocation of land within different agricultural sectors. Land transformation elasticities in these CET functions are allowed to reflect the ease of switching from one crop to another based on their agronomic characteristics. The results suggest that, in the long run, farmers in Nepal tend to allocate land to crops that are comparatively less impacted by climate change, such as paddy, thereby minimising the economy-wide impacts of climate change. Furthermore, the results reveal that land-use change tends to reduce the income disparity between different household groups by significantly moderating the income losses of marginal farmers. Therefore, it is suggested that policy makers in Nepal should prioritise schemes such as providing climate-smart paddy varieties (i.e., those that are resistant to heat, drought and floods) to farmers, subsidising fertilizers, improving agronomic practices, and educating farmers to switch from crops that are highly impacted by climate change to those that are not, such as paddy.

This article contains the data related to the research article “Long-term forecast of energy commodities price using machine learning” (Herrera et al., 2019). The datasets contain monthly prices of six main energy commodities covering a large period of nearly four decades. Four methods are applied, i.e. a hybridization of traditional econometric models, artificial neural networks, random forests, and the no-change method. Data is divided into 80-20% ratio for training and test respectively and RMSE, MAPE, and M-DM test used for performance evaluation. Other methods can be applied to the dataset and used as a benchmark. Keywords: Oil, Natural gas, Coal, ANN

To effectively implement the proposed United Nations development agenda beyond 2015, Governments will need to focus on reducing inequality in their national planning strategies. In order to provide an evidence base for such planning in the Sri Lankan context, a systematic and comprehensive analysis on regional inequality is required. The present study begins to undertake this type of analysis using summary sigma convergence statistics, such as the coefficient of variation, the Gini coefficient and mean deviation scores. Regional gross domestic product (GDP) per capita data for the period 1996 to 2011 reveal that inequality with respect to this welfare indicator is considerable. Analysis of trends, over this 16-year period, indicates that while some beta convergence has occurred since 2000, this convergence (both conditional and unconditional) is not statistically significant. Indeed, the results suggest that based on current trends, it will take 15 years to halve the current inequality and about 30 years to achieve a regionally balanced economy. In this context, well-planned regionally inclusive development strategies are needed in order for Sri Lanka to move forward, especially since the current levels of regional disparity are argued by some to have contributed to the civil unrest and conflict that led to a reduction in national economic development over the three decades to 2009.

Due to an unfortunate oversight during the correction process the new affiliation of Chandika Gunasinghe has not been included. It should be read: Department of Economics, Faculty of Humanities and Social Sciences, University of Ruhuna, MH 81,000, Matara, Sri Lanka.

Summary Over last few decades, there has been a growing interest among researchers in understanding the link between trade liberalization and regional disparities within the context of an individual country. In this study, we develop the first ever single-country multiregional Computable General Equilibrium (CGE) model for the Indian economy to investigate this linkage. Overall our results suggest that, in the short run, trade liberalization has a beneficial impact on the rich and fast-growing middle-income states and a marginal or negative impact on the poor states.

The ready-made garment industry plays an important role in Sri Lanka both in terms of export earnings and poverty alleviation through employment generation. Following the removal of GSP Plus by EU in 2010 on the basis of alleged human right violations by the Sri Lankan government during the last stage of the civil war in 2009 and after, it currently faces serious challenges in exporting to the EU, its major market. Given the important role of the ready-made garment industry in poverty alleviation through employment generation, the impact of removal of GSP plus on the poor is examined in this paper. The empirical results of this study demonstrate that poverty and income inequality are expected to be exacerbated in Sri Lanka as a result of the removal of GSP Plus by the EU using non-economic reasons such as human rights violation. The results are also relevant for the renewed emphasis on trade preferences as a potential instrument for the Millennium Development Goals and the debate on how trade preferences are to be designed to maximise their effectiveness in stimulating a manufacturing supply response.

Purpose – There has been growing interest in recent years in modelling various poverty‐related issues. However, there have not been many attempts at empirical estimation of best‐fit income distribution functions with an objective of subsequent use in poverty focused models. The purpose of this paper is to fill this gap by empirically estimating best‐fit income distribution functions for different household income groups and computing poverty and inequality indices for Sri Lanka.Design/methodology/approach – The authors empirically estimated a number of popular distribution functions found in the income distribution literature to find the best‐fit income distribution using household income and expenditure survey data for Sri Lanka and subsequently estimated various poverty and inequality measures.Findings – The results show that the income distributions of all low‐income household groups follow the beta general probability distribution. The poverty measures derived using these distributions show that among...

The sharp rise in oil and food prices in 2007 and 2008 caused negative impacts on poverty and economic growth in many oil and food importing developing countries. Some analysts believe that these countries are under stress again due to a rise in crude oil prices, to a two-and-a-half year high in March 2011, which has also been partly responsible for higher food prices in recent months. However, there is a limited body of empirical evidence available from developing countries on the impact of high oil prices on growth in general and household poverty in particular. In this study, Sri Lanka is used as a case study and a computable general equilibrium (CGE) approach is adopted as an analytical framework to explore the growth and poverty impacts of high oil prices. The results suggest that urban low income households are the group most adversely affected by high global oil prices, followed by low income rural households. In contrast, estate low income households are the least affected out of all low income households. The energy intensive manufacturing sector and services sector are affected most compared to the agricultural sector.

The link between trade liberalisation and poverty has become one of the most debated topics in recent years. There is a growing body of empirical literature on this topic and many studies provide mixed results. In this study, Sri Lanka is used as a detailed case study and a computable general equilibrium (CGE) approach is used as an analytical framework to examine the trade–poverty nexus. The results suggest that, liberalisation of the manufacturing industries is more pro-poor than that of the agricultural industries. Overall, this study suggests that trade reforms may widen the income gap between the rich and the poor creating uneven gains across different household groups in Sri Lanka. While short-term complementary policies are needed to compensate vulnerable income groups, long-term policies are needed to make gains from trade liberalisation more inclusive and equitable to maintain economic and political stability in Sri Lanka.

Sri Lanka is the second worst affected country by the Indian Ocean tsunami (IOT) in absolute terms after Indonesia and the most affected country in relative terms of per capita deaths, missing and displaced people. In addition to the damages to public infrastructure and owner-occupied houses, some sectors of the economy such as fishing and tourism have been severely affected. This article demonstrates how a computable general equilibrium (CGE) model can provide some information on the effects of the IOT and on the likely effects of a reconstruction aid package on tsunami affected economies such as Sri Lanka, India and Indonesia. The article indicates that it is important to consider the combined effects of the IOT and the reconstruction package. While the IOT has produced negative economic effects on the economy, the reconstruction package would stimulate the economy.

In recent years there has been a trend in rising protectionism and a reversal of trade policy reforms in some developed and developing countries, particularly after the global financial crisis. Although some researchers and practitioners have discussed recent trends in trade policy reversal in both developed and developing countries in recent years, no serious attempts have been made to examine the effects of trade policy reversal in a developing country within an economy-wide framework. The current paper attempts to fill this research gap by answering the question: Can developing countries benefit from trade policy reversals? The study focuses particularly on the case of Sri Lanka. To address this central research aim the paper first reviews recent trends in import duty and para-tariffs in Sri Lanka, particularly after the global financial crisis. An economy-wide computable general equilibrium (CGE) model was then used to evaluate the effects of trade policy reversal on the Sri Lankan economy. The results of the Sri Lankan case study presented suggest that developing countries will not benefit from trade policy reversal at either the macro level or industry level. Sri Lankan Journal of Agricultural Economics Vol. (16) No. 1 (2014) pp. 35-60

This study examines the impact of trade liberalization and currency depreciation on the trade balance of Sri Lanka. Using the bounds testing ARDL approach for co-integration, which is more suitable for small finite sample cases, we found that there was a long run co-integrating relationship between the trade balance and its determinants; particularly the Trade Openness and Real Exchange Rate. Our findings suggest that 1% increase in trade openness leads to 0.48% deterioration, while 1 % depreciation of local currency leads to 0.45% improvement in the Trade Balance Ratio of Sri Lanka given all else remaining unchanged. These findings solve the fundamental dilemma, why Sri Lanka’s Trade Balance continued to deteriorate; despite of substantial currency devaluations/depreciations allowed during past five decades. Our findings conclude that a more powerful negative impact arising from trade openness fully offset the positive impact arising from currency depreciation; thereby leading the Trade Balance into deficit, eventually in the long run. Accordingly, we found trade liberalization and devaluation are counter-cyclical as policy tools.