This paper offers ten guidelines for researchers to improve their analysis of regional integration and their approach to regional integration. By way of analysis, regional integration statistics are often misunderstood or poorly constructed. In one way or another this leads to an oversimplification of their meaning. With respect to the approach taken by policymakers to regional integration, the goals of Africa's regional integration have not been seriously interrogated; nor have the necessary national and regional preconditions for achieving even a minimal form of regional integration that is sustainable.
Using a simple Bayesian ‘mixed effects’ hierarchical model we provide econometric estimates of annual 2020 employment losses in the context of the COVID‐19 pandemic for 15 SADC member states on the basis of historical GDP data between 2000 and 2019 and 2020 forecasts. Our mixed effects model consists of country‐varying coefficients, as well as ‘fixed’ (pooled) coefficients. This allows us to fully explore variation between countries. The model provides estimates for losses in total employment and women's employment, from which we infer income losses. We find that roughly half of estimated SADC countries have total employment losses below or approaching 25% of all jobs, while the other half have total losses exceeding 25%. Around one‐third of all jobs for women risk being lost during 2020 for Madagascar, Comoros, Angola, Botswana, Namibia, and South Africa. Our model implies that most SADC countries will experience an equivalent loss of wage income in excess of 10% of GDP (whether through pure job losses and/or reductions in wages and working hours). Policy implications are briefly discussed.
We assess the impact of the coronavirus disease 2019 (COVID‐19) pandemic on the labour markets and economies of 16 SADC member states using a
qualitative risk assessment
on the basis of high‐frequency Google Mobility data, monthly commodity price data, annual national accounts, and households survey labour market data. Our work highlights the ways in which these complementary datasets can be used by economists to conduct near
real‐time
macroeconomic surveillance work covering labour market responses to macroeconomic shocks, including for seemingly information scarce African economies. We find that Angola, South Africa and Zimbabwe are at greatest risk across several labour market dimensions from the COVID‐19 shock, followed by a second group of countries consisting of Comoros, DRC, Madagascar and Mauritius. Angola faces relatively less general employment risk than South Africa and Zimbabwe due to more muted decreases in mobility, though faces large pressure in its primary sector. These countries all face high risk in their youth populations, with Angola and Zimbabwe seeing high risks for women. South Africa faces more sector‐specific risks in their secondary and tertiary sectors, as does Mauritius. Comoros, DRC and Madagascar all face high risks of employment loss for women and youth, with Comoros and Mauritius facing severe general employment risks.
Attempts by governments to curb the market power of ‘Big Tech’ (Alphabet, Amazon, Apple, Meta Platforms, and Microsoft) are impeded by limited public information on their diversified digital platform ecosystems. Big Tech’s annual 10-K financial reports disclose little about their globally dominant ‘free’ services, platform user numbers, and monetization practices, and suites of products. To support antitrust and regulatory oversight, we propose mandatory 10-K type disclosures covering Big Tech’s: (i) internally used operating metrics (e.g. monthly active users), which underpin platform market share and monetization; (ii) ubiquitous ‘free’ products which escape traditional ‘profit and loss’ reporting; (iii) ‘monetization’ processes detailing how platforms make money from user data and attention; and (iv) product-by-product reporting through updating segment reporting rules. Disclosures should be mandatory for digital ‘gatekeepers’ and eventually integrated into reporting standards for all digital platforms.
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