Income inequality has been a hotly debated topic in Egypt since the 2011 revolution. However, researchers remain divided over the “true” level of inequality in this country. A blog posted on Vox in August argued that inequality in Egypt was underestimated and could be better represented by using house price data to estimate the top end of the income distribution. This blog is a response to that article and seeks to clarify issues relevant to the measurement of inequality in Egypt and elsewhere.
The primary motivation for predicting data in economics, health sciences, and other disciplines has been to deal with various forms of missing data problems. However, one could also make a case for adopting prediction methods to obtain more cost-efficient estimates of welfare indicators when it is expensive to observe the outcome of interest (in comparison with its predictors). For example, consider the estimation of poverty and malnutrition rates. The conventional estimators in this case require household- and individual-level data on expenditures and health outcomes. Collecting this data is generally costly. It is not uncommon that in developing countries, where poverty and poor health outcomes are most pressing, statistical agencies do not have the budget that is needed to collect these data frequently. As a result, official estimates of poverty and malnutrition are often outdated: For example, across the 26 low-income countries in Sub-Saharan Africa over the period between 1993 and 2012, the national poverty rate and prevalence of stunting for children under five are on average reported only once every five years and once every ten years in the World Development Indicators.
This is the third of three blog posts on recent trends in national inequality.
In earlier blogposts on recent trends in inequality, we had referred to measurement issues that make this exercise challenging. In this blogpost we discuss two such issues: the underlying welfare measure (income or consumption) used to quantify the extent of inequality within a country, and the fact that estimates of inequality based on data from household surveys are likely to underreport incomes of the richest households. There are a number of other measurement challenges, such as those related to survey comparability, which are discussed in Poverty and Shared Prosperity 2016 – for a focus on Africa, also see Poverty in a Rising Africa, published earlier in 2016.
Conventionally the governing law should not affect the cost of borrowing in international markets. If it did, borrowers would use the cheaper jurisdiction. Also, if somehow the spread differed at the time of the launch of the bond, trading in the secondary market should eliminate the difference. A recent paper shows otherwise: Sovereign bonds issued under the UK law had a persistent higher spread than those under the US law, but only since the global financial crisis in 2008.
Historically, U.S. law issuances formed the dominant part of the volume of dollar-denominated central government bond issuances, barring 2012 when U.K. law issuances briefly overtook U.S. law issuances (Figure 1). There were also divergences in characteristics of dollar-denominated central government bonds issued across the two jurisdictions. Average spread at launch for bonds issued under U.K. law became distinctly higher after the global financial crisis in 2008 (Figure 2). On average, bonds issued under U.K. law also had weaker ratings and shorter tenors post-crisis.
The previous blog post in this series described the trend in the global and regional averages of national inequality for the period 1988-2013. Now we dig deeper into the trends in inequality at the country level. We describe changes in national inequality during two periods – around 1993 to 2008 and around 2008 to 2013. The long-run spells include all countries for which we have data on inequality around 1993 and 2008, and that data is computed using the same welfare measure (income or consumption). The short-run spells include countries for which we have inequality data around 2008 to 2013; this list is based on the World Bank’s Global Database of Shared Prosperity.
The Organization of the Petroleum Exporting Countries (OPEC) unsettled oil markets in September when it announced it would resume placing limits on oil production among its members, effectively reversing two years of unrestrained production.
But how much control can OPEC really exert over prices? History suggests that formal agreements to influence the price of a particular commodity eventually fall apart. OPEC’s own history also shows that the short term benefits of managing supplies become long term liabilities. In addition, the oil producing landscape has changed dramatically in recent years with the advent of nonconventional producers, notably the U.S. shale oil industry. These factors will test the oil exporting organization’s power to influence markets.
Prices for most commodities, including oil, are forecast to rise in 2017 as a long period of declining prices appears to be bottoming out, according to the October Commodities Markets Outlook.
Oil prices are forecast to rise to $55 per barrel next year from $43 per barrel in 2016 as markets readjust after an era of abundant supply that outpaced demand. Energy prices, which also include coal and natural gas, are forecast to jump 24 percent in the coming year. The decision in September of the Organization of the Petroleum Exporting Countries (OPEC) to resume limiting oil production is another important factor behind the higher price forecast.
Figure 1. Risks to Global Growth
Upside risks to global growth have increased since January while downside risks for current-year growth have reached post-crisis highs.
A 90% confidence interval implies a 90% chance of growth falling within the given range
Assessing economic forecast uncertainty and the balance of risks to the growth outlook is critical to effective policymaking. Lower-probability but high-impact events can lead to significant deviations from baseline projections, and this should be factored into policy design. The World Bank’s most recent Global Economic Prospects unveiled a tool to quantify uncertainty around global growth forecasts and presented it in the form of a fan charts (Figure 1)
The approach adopted in the Global Economic Prospects report consists of two steps.
First, a number of measurable risk indicators that are typical sources of forecast errors for global growth forecasts are selected. Three were chosen: equity price futures, oil price futures and bond term spreads (the difference between short and long term interest rates). For instance, greater volatility in oil price futures could be associated with rising uncertainty around global growth forecasts, while a downward trend in equity price futures could signal rising downside risks to growth.
Second, the probability distributions of forecasts for these three indicators are then mapped to the distribution of global growth forecasts. Both the degree of uncertainty and the balance of risks to the forecast are approximated by weighted averages of the standard deviation and skewness implied by the distributions of expectations for the risk indicators. The weights are estimated in a vector autoregression model (Ohnsorge, Some, and Stocker 2016). To account for potential asymmetry in the distributions of risks, a two-piece normal distribution is assumed, in line with other studies.
In 2014, Nikhil Chandra Roy was struggling to find and keep regular employment. He had extensive experience dating back to 1977, doing the work of an electrician. But because he had no formal training or certification, Nikhil couldn’t win the confidence of employers in Bangladesh to give him anything more than episodic, relatively low-paying work.
At age 55, just as he was giving up hope for career progress, Nikhil saw an advertisement that ended up turning his outlook and life around. The ad introduced him to the Recognition of Prior Learning (RPL) program, aimed especially at people like Nikhil, who have real skills and experience in a particular occupation but no formal, independently recognized qualifications.
Not long later, Nikhil participated in a three-day program, which entails one day of assessment and two days of training. That led to the recognition he had long awaited and needed to boost his career: a Government-endorsed skills certification from the Bangladesh Technical Education Board (BTEB) in electrical installation and maintenance.
“From that point on,” Nikhil said, “there was no looking back. With my years of experience, knowledge and now skills certification, I was ready to progress my career from just an electrician to an entrepreneur.”
Nikhil was one of the many vulnerable informal sector workers in Bangladesh who have no regular jobs and who work on ad hoc opportunities, making it difficult to sustain livelihoods. These workers, with enough experience to perform the technical work well but not the credential many jobs require, improve their employability and bargaining power in job markets when they get the proper certification. And with that certification, workers gain social status in their communities.
The RPL program, which evaluates the skills level of workers and issues government certification to workers who pass an assessment, has operated since 2014 as a pilot activity under the Skills and Training Enhancement Project (STEP). STEP aims to give more Bangladeshis the technical skills they need to compete successfully in domestic and international labor markets.
The demand for RPL certification has been enormous. Since its inception, RPL has assessed more than 9,000 applicants from all over Bangladesh. Every month, RPL offers 600 applicants certification trainings in electrical installation and maintenance; IT support; block, boutique and screen printing; sewing machine operation; tailoring and dress making; motorcycle servicing; plumbing; and welding.