Are there dynamic productivity gains from agricultural trade?

PurposeThe primary purpose of this paper is to take an in-depth look at the question of whether liberalizing trade in agriculture can generate dynamic productivity gains comparable to those in the manufacturing sector.Design/methodology/approachIn contrast to the manufacturing sector that has generated firm/plant-level trade data, there is a lack of farm-level trade data that are needed for empirical measurement of dynamic productivity gains. Therefore, the authors use thought experiments to analyze the sequence of events that would occur when trade is liberalized for agriculture; delineate the expected behaviors of the actors involved in the trade and draw inferences about whether there would be dynamic productivity gains from agricultural trade.FindingsThe central finding is that there would be little dynamic gain from agricultural trade at the farm level due to the limited role of producers in shaping their international competitiveness. Yet, agricultural trade may generate dynamic gains if states or input supply corporations respond to the freer trade environment by making more investments for research and development (R&D). Further, when intraindustry prevails, there can be productivity gains at the industry level due to the transfer of resources from less to more efficient farm producers.Originality/valueThe findings of the paper are expected to present insights into value for researchers working in the area of agricultural trade; for agricultural trade policymakers in developing countries and for trade negotiators engaged in reforming or designing World Trade Organization (WTO)’s trade rules for agriculture.

Using marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi

Anopheles stephensi is an efficient malaria vector commonly found in South Asia and the Arabian Peninsula, but in recent years it has established as an invasive species in the Horn of Africa (HoA). In this region An. stephensi was first detected in a livestock quarantine station near a major seaport in Djibouti in 2012, in Ethiopia in 2016, in Sudan in 2018 and Somalia in 2019. Anopheles stephensi often uses artificial containers as larval habitats, which may facilitate introduction through maritime trade as has been seen with other invasive container breeding mosquitoes. If An. stephensi is being introduced through maritime traffic, prioritization exercises are needed to identify locations at greatest risk of An. stephensi introduction for early detection and rapid response, limiting further invasion opportunities. Here, we use UNCTAD maritime trade data to 1) identify coastal African countries which were most highly connected to select An. stephensi endemic countries in 2011, prior to initial detection in Africa, 2) develop a ranked prioritization list of countries based on likelihood of An. stephensi introduction for 2016 and 2020 based on maritime trade alone and maritime trade and habitat suitability, and 3) use network analysis to describe intracontinental maritime trade and eigenvector centrality to determine likely paths of further introduction on the continent if An. stephensi is detected in a new location. Our results show that in 2011, Sudan and Djibouti were ranked as the top two countries with likelihood of An. stephensi introduction based on maritime trade alone, and these were indeed the first two coastal countries in the HoA where An. stephensi was detected. Trade data from 2020 with Djibouti and Sudan included as source populations identify Egypt, Kenya, Mauritius, Tanzania, and Morocco as the top five countries with likelihood of An. stephensi introduction. When factoring in habitat suitability, Egypt, Kenya, Tanzania, Morocco, and Libya are ranked highest. Network analysis revealed that the countries with the highest eigenvector centrality scores, and therefore highest degrees of connectivity with other coastal African nations were South Africa (0.175), Mauritius (0.159), Ghana (0.159), Togo (0.157), and Morocco (0.044) and therefore detection of An. stephensi in any one of these locations has a higher potential to cascade further across the continent via maritime trade than those with lower eigenvector centrality scores. Taken together, these data could serve as tools to prioritize efforts for An. stephensi surveillance and control in Africa. Surveillance in seaports of countries at greatest risk of introduction may serve as an early warning system for the detection of An. stephensi, providing opportunities to limit further introduction and expansion of this invasive malaria vector in Africa.

Essays on modern economic issues in international trade, exchange rates and housing

<p>The common theme of the three papers in this thesis is the focus on the impact of data choices on empirical research in Economics. Such choices can be about the source of data; should we source the data from country A or country B in a bilateral trade relation? Is there a way to reconcile the discrepancies in international trade data? In investigating the impact of exchange rate on trade, should we choose high-frequency or low-frequency data? What does the choice of a certain frequency imply for the econometric analysis? In assessing the impact of housing wealth on household consumption, what are the benefits of choosing household-level data? How can we take advantage of aggregate data on house prices to circumvent the endogeneity arising from household-specific confounding factors? This thesis shows that data choices can strongly affect our conclusions regarding several modern economic issues. The first paper is titled ‘Reconciling International Trade Data.’ International trade data are filled with discrepancies–where two countries report different values of trade with each other. We develop an index for ranking countries’ data quality based on the following notion: the more a country’s reports on bilateral trade differ from the corresponding reports of its partners, the more likely it is a low-quality reporter. We calculate the comparative quality for each country’s imports and exports separately for every year from 1962 to 2016. We reconcile international trade data through picking the value reported by the country with higher quality in every bilateral flow. The findings include: (a) global trade was under-reported by roughly 5% over the past five years as countries with low data quality under-report both, their imports and exports; (b) erroneous reporting is prevalent among low-quality reporters; (c) importers’ data are less likely to be in error; (d) the level of development and corruption are possible determinants of trade data quality; (e) low-quality reporters are 14% more open to trade using reconciled data than using self-reported data (f) China tends to under-report its exports and over-report its imports, while there is only a small difference between US self-reported and reconciled data. The reconciled trade dataset is made freely available for future studies to use. The second paper is titled ‘Why You Should Use High Frequency Data to Test the Impact of Exchange Rate on Trade.’ The paper suggests that testing the impact of exchange rate on trade should be done using high frequency data. Using different data frequencies for identical periods and specifications between the US and Canada, the paper shows that low frequency data suppresses and distorts the evidence of the impact of exchange rate on trade in the short-run and the long-run. The third paper is titled: ‘Housing Leverage and Consumption Expenditure: Evidence from New Zealand Microdata.’ The paper investigates how household debt affects the marginal propensity to consume out of housing wealth. The paper uses New Zealand household-level data on spending, income, and debt over the period 2006–2016. The main empirical challenge is to identify exogenous variation in house prices to determine how consumption evolves with movements in household wealth. This identification problem is complicated by the presence of unobserved household characteristics that are correlated with housing wealth. The paper uses a detailed house sale dataset to derive local average house prices and use it as an instrument. The empirical results show that the estimated elasticity of consumption spending to housing wealth is about 0.22%. In dollar terms, the average marginal propensity to consume out of a one-dollar increase in housing wealth is around 2.2 cents. The empirical results confirm that household indebtedness, especially mortgage debt, acts as a drag on consumption spending, not only through the debt overhang channel, but also through influencing the collateral channel of the housing wealth effect.</p>

Essays on modern economic issues in international trade, exchange rates and housing

<p>The common theme of the three papers in this thesis is the focus on the impact of data choices on empirical research in Economics. Such choices can be about the source of data; should we source the data from country A or country B in a bilateral trade relation? Is there a way to reconcile the discrepancies in international trade data? In investigating the impact of exchange rate on trade, should we choose high-frequency or low-frequency data? What does the choice of a certain frequency imply for the econometric analysis? In assessing the impact of housing wealth on household consumption, what are the benefits of choosing household-level data? How can we take advantage of aggregate data on house prices to circumvent the endogeneity arising from household-specific confounding factors? This thesis shows that data choices can strongly affect our conclusions regarding several modern economic issues. The first paper is titled ‘Reconciling International Trade Data.’ International trade data are filled with discrepancies–where two countries report different values of trade with each other. We develop an index for ranking countries’ data quality based on the following notion: the more a country’s reports on bilateral trade differ from the corresponding reports of its partners, the more likely it is a low-quality reporter. We calculate the comparative quality for each country’s imports and exports separately for every year from 1962 to 2016. We reconcile international trade data through picking the value reported by the country with higher quality in every bilateral flow. The findings include: (a) global trade was under-reported by roughly 5% over the past five years as countries with low data quality under-report both, their imports and exports; (b) erroneous reporting is prevalent among low-quality reporters; (c) importers’ data are less likely to be in error; (d) the level of development and corruption are possible determinants of trade data quality; (e) low-quality reporters are 14% more open to trade using reconciled data than using self-reported data (f) China tends to under-report its exports and over-report its imports, while there is only a small difference between US self-reported and reconciled data. The reconciled trade dataset is made freely available for future studies to use. The second paper is titled ‘Why You Should Use High Frequency Data to Test the Impact of Exchange Rate on Trade.’ The paper suggests that testing the impact of exchange rate on trade should be done using high frequency data. Using different data frequencies for identical periods and specifications between the US and Canada, the paper shows that low frequency data suppresses and distorts the evidence of the impact of exchange rate on trade in the short-run and the long-run. The third paper is titled: ‘Housing Leverage and Consumption Expenditure: Evidence from New Zealand Microdata.’ The paper investigates how household debt affects the marginal propensity to consume out of housing wealth. The paper uses New Zealand household-level data on spending, income, and debt over the period 2006–2016. The main empirical challenge is to identify exogenous variation in house prices to determine how consumption evolves with movements in household wealth. This identification problem is complicated by the presence of unobserved household characteristics that are correlated with housing wealth. The paper uses a detailed house sale dataset to derive local average house prices and use it as an instrument. The empirical results show that the estimated elasticity of consumption spending to housing wealth is about 0.22%. In dollar terms, the average marginal propensity to consume out of a one-dollar increase in housing wealth is around 2.2 cents. The empirical results confirm that household indebtedness, especially mortgage debt, acts as a drag on consumption spending, not only through the debt overhang channel, but also through influencing the collateral channel of the housing wealth effect.</p>

PECULIARITIES OF CURRENT WORLD SCIENTIFIC RESEARCHES ON MARITIME ECONOMICS AND SPECIFICALLY SEABORNE TRADE

The article analyzes and systemizes current studies of leading world scientists on maritime economics and seaborne trade with the aim to reveal current trends and venues for future researches in this field. Special attention is paid to researches evaluating how the outbreak of coronavirus pandemic impacted shipping industry as a main global supplier of goods. All studies under review are conceptually grouped into two main branches. The first branch comprises papers focused on the world seaborne trade data dynamics, including official maritime reports. As opposed to Ukrainian and Russian maritime economics papers which predominantly describe and portray the statistical data available in official maritime reports issued by international organizations and shipping services providers, leading world scholars use this statistics as a baseline for individualized researches, mainly focused on investigation of correlation between various shipping indicators and prediction of same. The second branch comprises papers investigating trade of certain types of cargo, such as containers, crude oil, dry bulk. Several general peculiarities of both branches of researches are defined. Almost all of them attempt to provide an insight into the nature of a freight rate and to forecast the development of either general freight market or specific cargo related one. The utilized methodology is also identical. Depending on the aim of research and data availability, scholars employ various models of regression analysis, a standard tool of statistical modeling, which estimates the average relationship between two or more variables. No matter which freight market is under investigation, studies usually try to examine the connection of this market with others by evaluating the spillover effects between vessel types and vessel sizes. Distinguishing features of researches lie in the target stakeholders who could benefit from, either the industry in general or particular groups of market participants. In addition, nowcasting trade data is a real problem raised by the industry to modern science, which tries to tackle it by proposing innovative digitalized solutions.

A New Class of Revealed Comparative Advantage Indexes

This paper draws upon a critical analysis of the three RCA indexes in Vollrath (1991) to propose a new class of RCA indexes. The baseline RCA index in this new class rests on the overall structure of trade, is symmetric, avoids size bias and is compatible with the Kunimoto-Vollrath principle. Possible modifications of the baseline RCA index are subsequently suggested to take into account GDP per capita data and to use adjusted trade data with the aim of better measuring comparative advantages. These modified versions together with the baseline RCA index give rise to a whole new class of RCA indexes. An application to the Euro area indicates that this new class is able to rank countries according to their respective levels of comparative advantages in a more consistent way than alternative RCA indexes. Furthermore, the new class of RCA indexes provides second-best solutions for time stationarity and the desirable distributional characteristics of an RCA index.

IMPACT OF DATA ANALYTICS IN FINANCIAL MARKETS

Analysing the data in a specic goal oriented manner can yield great insights which be used to ensure smooth running and regulation of the markets while avoiding manipulations which can have adverse impact on other market participants and damage the integrity of nancial market framework. Large institutional players often use unethical methods to move the markets in there favour, one of the the example is freak trades. This issue can be countered by making it mandatory for brokers to share live trade data which can be thrown into manipulation detecting models specially for freak trades. Machine Learning and deep learning models can be made for detection of manipulative occurences which includes fake news detection, manipulation of bid ask depth. Data analysis shows immense potential for the process of regulation of nancial markets

Whose Policy Uncertainty Matters in the Trade between Korea and the U.S.?

Since the introduction of the news-based policy uncertainty measure, a few studies have looked at its impact on trade flows by using panel models and aggregate trade data. In this paper we consider the short-run and long-run response of 61 2-digit U.S. exporting industries to Korea and 49 2-digit Korean exporting industries to the U.S. to policy uncertainty measures of the U.S. and Korea. We find that both measures have short-run effects on exports of almost one-third of industries in either direction. In the long run, however, while nine U.S. exporting industries (with a trade share of 9%) are negatively affected by the Korean uncertainty measure, only five industries (with 6% export share) are affected by the U.S. uncertainty measure. As for the Korean exporting industries, we find that three industries with a 31% export share are affected positively by the Korean uncertainty measure and six industries with a 7% export share are affected positively by the U.S. uncertainty measure.