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The COVID-19 pandemic underscores the critical need for detailed, timely information on its evolving economic impacts, particularly for Sub-Saharan Africa (SSA) where data availability and lack of generalizable nowcasting methodologies limit efforts for coordinated policy responses. This paper presents a suite of high frequency and granular country-level indicator tools that can be used to nowcast GDP and track changes in economic activity for countries in SSA. We make two main contributions: (1) demonstration of the predictive power of alternative data variables such as Google search trends and mobile payments, and (2) implementation of two types of modelling methodologies, machine learning and parametric factor models, that have flexibility to incorporate mixed-frequency data variables. We present nowcast results for 2019Q4 and 2020Q1 GDP for Kenya, Nigeria, South Africa, Uganda, and Ghana, and argue that our factor model methodology can be generalized to nowcast and forecast GDP for other SSA countries with limited data availability and shorter timeframes.
The IMF’s 2019 External Sector Report shows that global current account balances stand at about 3 percent of global GDP. Of this, about 35–45 percent are now deemed excessive. Meanwhile, net credit and debtor positions are at historical peaks and about four times larger than in the early 1990s. Short-term financing risks from the current configuration of external imbalances are generally contained, as debtor positions are concentrated in reserve-currency-issuing advanced economies. An intensification of trade tensions or a disorderly Brexit outcome—with further repercussions for global growth and risk aversion—could, however, affect other economies that are highly dependent on foreign demand and external financing. With output near potential in most systemic economies, a well-calibrated macroeconomic and structural policy mix is necessary to support rebalancing. Recent trade policy actions are weighing on global trade flows, investment, and growth, including through confidence effects and the disruption of global supply chains, with no discernible impact on external imbalances thus far.
The Methodology review identified three broad areas for improving the EBA-Lite methodology: (1) expanding the fundamentals and policy determinants in the CA and REER regressions to better capture the external balance of EBA-Lite countries; (2) identifying alternatives to regression models for external assessments of large exporters of exhaustible commodities; and (3) a revised approach for the assessment of external sustainability in highly indebted economies. Accordingly, the revised methodology consists of three modules: 1) Regression Module 2) Module for External Assessments of Exporters of Exhaustible Commodities 3) Module for the Assessment of External Sustainability
The Asia-Pacific region was the first to be hit by the COVID-19 pandemic; it put a strain on its people and economies, and policymaking became exceptionally difficult. This departmental paper contains the assessment of the key challenges facing Asia at this critical juncture and policy advice to the region both to address the current challenges and to build the foundations for a more sustainable and inclusive future. The paper focuses on (1) adjusting to the COVID-19 shock, (2) using unconventional policies when policy space is limited, (3) dealing with debt, and (4) helping the vulnerable and greening the recovery. The paper first presents the different ways countries are adjusting to the COVID-19 shock.
China’s recovery is well advanced—but it lacks balance and momentum has slowed, reflecting the rapid withdrawal of fiscal support, lagging consumption amid recurrent COVID-19 outbreaks despite a successful vaccination campaign, and slowing real estate investment following policy efforts to reduce leverage in the property sector. Regulatory measures targeting the technology sector, intended to enhance competition, consumer privacy, and data governance, have increased policy uncertainty. China’s climate strategy has begun to take shape with the release of detailed action plans. Productivity growth is declining as decoupling pressures are increasing, while a stalling of key structural reforms and rebalancing are delaying the transition to “high-quality”—balanced, inclusive and green—growth.
China and Africa have forged a strong economic relationship since China’s accession to the WTO in 2001. This paper examines the evolution of these economic ties starting in the early 2000s, and the subsequent shift in the relationship triggered by the commodity price collapse in 2015 and by the COVID-19 pandemic. The potential effects on the African continent of a further slowdown in Chinese growth are analyzed, highlighting the varying effects on different countries in Africa, especially those heavily dependent on their economic relationship with China. The conclusion offers a discussion of ways how African countries and China could adapt to the changing relationship.
Vessel traffic data based on the Automatic Identification System (AIS) is a big data source for nowcasting trade activity in real time. Using Malta as a benchmark, we develop indicators of trade and maritime activity based on AIS-based port calls. We test the quality of these indicators by comparing them with official statistics on trade and maritime statistics. If the challenges associated with port call data are overcome through appropriate filtering techniques, we show that these emerging “big data” on vessel traffic could allow statistical agencies to complement existing data sources on trade and introduce new statistics that are more timely (real time), offering an innovative way to measure trade activity. That, in turn, could facilitate faster detection of turning points in economic activity. The approach could be extended to create a real-time worldwide indicator of global trade activity.
The Trans-Pacific Partnership (TPP) has reinvigorated research on the ex-ante impact of trade agreements. The results from these ex-ante models are subject to considerable uncertainties, and needs to be complimented by ex-post studies. The paper fills this gap in recent literature by employing synthetic control methods (SCM) – currently extremely popular in micro and macro studies – to understand the impact of trade agreements in the period 1983–1995 for 104 country pairs. The key advantage of using SCM to address selection bias – one of the persisting issues in trade literature – is that it allows the effect of unobserved confounder to vary with time, as opposed to traditional eco...
Global growth is forecast at 3.0 percent for 2019, its lowest level since 2008–09 and a 0.3 percentage point downgrade from the April 2019 World Economic Outlook.
In this paper we demonstrate the importance of distinguishing capital goods tariffs from other tariffs. Using exposure to a quasi-natural experiment induced by a trade reform in Colombia, we find that firms that have been more exposed to a reduction in intermediate and consumption input or output tariffs do not significantly increase their investment rates. However, firms’ investment rate increase strongly in response to a reduction in capital goods input tariffs. Firms do not substitute capital with labor, but instead also increase employment, especially for production workers. Reduction in other tariff rates do not increase investment and employment. Our results suggest that a reduction in the relative price of capital goods can significantly boost investment and employment and does not seem to lead to a decline in the labor share.