The Unassuming Economist

From a LSE paper by Enrico D’Elia and Roberta De Santis:

“This paper analyses trade and financial openness effects on growth and income inequality in 35 OECD countries. Our model takes into account both short run and long run effects of factors explaining income divergence between and within the countries. We estimate, for the period 1995-2016, an error correction model in which per capita GDP and inequality are driven by changes over time of selected factors and by the deviation from a long run relationship. Stylised facts suggest that trade and financial openness reduce the growth gaps across the countries but not income inequality, and the effects of finance are stronger in high income countries. Nevertheless, low and middle income countries benefit more from international trade. Our contribution to the existing literature is threefold: i) we study the short and long run effects of trade and financial openness on income level and distribution, ii) we focus on developed countries (OECD) rather than on developing and iii) we provide a sensitivity analysis including in our baseline equation an institutional indicator, a trade agreement proxy and a dummy of global financial crisis. Estimates results indicate that trade openness significantly improved the conditions of OECD low income countries both in short and long run mostly, consistently with the catching up theory. It also decreased inequality, but only in low and middle income countries. Differently financial openness had a positive and significant impact only in the short run on middle income countries and increased income disparities within countries in the short term in low income countries and in the long term in high income countries.”

From Social Europe post by Monique Goyens:

“In the latest contribution to our series on ‘just transition’, Monique Goyens argues that it must address the people finding it hard to pay their energy bills.

The task of moving to a carbon-neutral society is herculean, but the benefits won’t just include saving the planet. There are also long-term economic gains, even for the most hard-up.

An estimated 50 million around the European Union struggle to keep their homes warm and pay their energy bills. Many suffer from the same problems: poorly insulated homes, ill-suited tariffs, insufficient advice on how to save energy or a combination of all three.

The lowest income earners in the EU spend an increasingly large share of their budget on energy—rising from 6 per cent in 2000 to 9 per cent in 2014. In the short term, for this group, it makes more sense to use energy more efficiently than to invest in solar panels, heat pumps or pellet stoves. It will also deliver faster savings.

The choice should not however be between having a warm home and having food on the table. In making their homes more efficient, people consume less energy to heat their living space and can more easily pay their bills.

But getting people to take action can be complex. Those at risk of energy poverty might feel overwhelmed and prioritise solving other problems, such as warmer clothing or food. Often, they might not know what solutions are out there.

For governments and energy advisers to upload advice about insulation to a website isn’t enough. That advice needs to get out to people. Human contact also helps.”

Continue reading here.

From Econofact post by Lant Pritchett:

The Issue:

Reducing poverty in developing countries has been a longstanding and central concern of development economics. Over the past two decades, there has been a noticeable shift in the approach taken by development economists to address this question. Researchers have increasingly focused their efforts on randomized controlled trials — an experimental approach commonly used in medical research — to determine the effectiveness of anti-poverty programs and interventions. This shift was highlighted by the 2019 Nobel Prize in Economics awarded to Abhijit Banerjee, Esther Duflo and Michael Kremer, which recognized their work for breaking down the issue of fighting global poverty into “smaller, more manageable, questions – for example, the most effective interventions for improving educational outcomes or child health.” One question that remains, though, is how much overall reduction in the poverty rate one can expect from projects, programs or policy interventions that raise the well-being of those in absolute poverty at a given level of income, versus how much poverty reduction comes from broad-based economic growth.

The Facts:

• In the last quarter century 1.1 billion people, about one-seventh of the world’s population, have been lifted out of extreme poverty. Yet progress has been uneven across different regions and significant challenges remain. A dramatic reduction of extreme poverty in East Asia, particularly in China, accounts for an important share of the advances in combating global poverty, with poverty reductions in South Asia also contributing their share. In contrast, progress has been much slower in Sub-Saharan Africa (see here). The World Bank counts all persons in a household as “poor” if the household per capita daily consumption or income is below a “poverty line.” It uses three different thresholds, $1.90, $3.20, and $5.50 per person per day, where local currency values are translated into 2011 dollar values, and taking into account the fact that goods and services are cheaper in poorer countries (so-called “purchasing power parity” currency conversion rates). The World Bank notes a marked reduction in extreme poverty (less than $1.90 per day) over the past quarter century, with a decrease from 36 percent in 1990 to 10 percent in 2015. Still, over 700 million people around the world continued to live in extreme poverty in 2015.

• One way to consider the relationship between economic growth and poverty is to look across countries and compare median incomes and the share of the population living in poverty. The median income is the income level at which half the population has an income higher than this amount and half the population has an income below this amount. Statisticians focus on median income rather than average income because a small number of people with very high levels of income alter the average much more than the median and can give rise to a distorted picture of the income profile of a country.

• In theory, two countries could have the same median income and yet have very different poverty rates. Differences in how income is distributed within countries could make it so that two countries with the same median income could have very different poverty rates. For an extreme example, consider a country with a median income of $5,000 per capita (the equivalent of about $13.70 per person per day); This country could have no one living with an income of less than $5.50 per day, effectively having a rate of zero poverty under a $5.50 dollar-a-day threshold; Alternatively, half of the population could be living with an income less than $5.50 a day (in which case no one would have an income in the range between $5.50 and $13.70) — and, in this case, there would be a 50 percent poverty rate. Thus, there is not necessarily a tight relationship between the median income in a country and its poverty rate.”

Continue reading here.

From an IMF article:

“South Africa suffers among the highest levels of inequality in the world when measured by the commonly used Gini index. Inequality manifests itself through a skewed income distribution, unequal access to opportunities, and regional disparities. Low growth and rising unemployment have contributed to the persistence of inequality.

The South African government has used different tools to tackle the stubborn levels of inequality that have plagued the country, including through progressive fiscal redistribution.

Efforts to reduce inequality have focused on higher social spending, targeted government transfers, and affirmative action to diversify wealth ownership and promote entrepreneurship among the previously marginalized. These measures need to be complemented with reforms that promote private investment, jobs, and inclusive growth.

Here are six charts that tell the story of South Africa’s inequality:

Inequality has remained stubbornly high. South Africa started the 1990s with already elevated inequality as the policy of apartheid excluded a large swath of the population from economic opportunities. South Africa’s Gini—an index that measures inequality—has increased further in the early 2000s and has remained high ever since. Meanwhile, its peers have been able to make inroads in reducing inequality.

 

Income distribution remains highly skewed. The top 20 percent of the population holds over 68 percent of income (compared to a median of 47 percent for similar emerging markets). The bottom 40 percent of the population holds 7 percent of income (compared to 16 percent for other emerging markets). Similar trends can be observed across other measures, such as the income share of the top 1 percent.

Significant disparities remain across regions. Income per capita in Gauteng—the main economic province that comprises large cities like Johannesburg and Pretoria—is almost twice the levels as that found in the mostly rural provinces like Limpopo and Eastern Cape. Being close to the economic centers increases job and income prospects.”

From a new paper on the effects of stock market dispersion:

“We study the extent to which stock market dispersion is related to unemployment and output growth for 16 countries over 20 years. Using panel vector-auto-regressions and panel dynamic regressions, we find increases in stock market dispersion across industries to induce future increases in unemployment and
future decreases in industrial production (IP) growth. Moreover, the responses of unemployment and IP growth following a positive shock to stock market dispersion are persistent and are robust to various controls, sample periods, and estimation methods. Our article provides cross-country evidence in support of the hypothesis that shifts in demand across industries negatively affect employment.”

“We present the impulse responses of the unemployment rate and the change in the interest rate to a positive one-standard-deviation shock to stock market dispersion in the upper subfigure of Figure 2. […] Overall, we find an increase in stock market dispersion to have a negative impact on the labor market in the short term, as evidenced by the positive response of the unemployment rate. Specifically, the unemployment rate increases by 0.02% following a positive one-standard-deviation shock to stock market dispersion. This increase peaks after the seventh month and gradually fades away after 15–20 months. Despite its modest magnitude at peak (0.02%), the impact of stock market dispersion on the unemployment rate is relatively long-lived, with the responses lasting beyond the 20-month horizon. Our result is consistent with previous studies that use U.S. data (e.g., Loungani, Rush, and Tave 1990, and more recently Angelidis, Sakkas, and Tessaromatis 2015) in the sense that unemployment significantly depends on the lags of stock market dispersion.”