Two years after the country entered the Great Recession, there are signs the national economy has slowly begun to recover. Thus far recovery has meant the return of economic growth, but not the return of jobs. And just as some communities have felt the downturn more than others, recovery has not and will not be shared equally across the nation’s diverse metropolitan economies.

Within metropolitan areas, many communities continue to struggle with high unemployment and increasing economic and fiscal challenges, while at the same time poverty and the need for emergency and support services continue to rise. Even under the best case scenario of a sustained and robust recovery, cities and suburbs throughout the nation will be dealing with the social and economic aftermath of such a deep and lengthy recession for some time to come.

An analysis of unemployment, initial Unemployment Insurance claims, and receipt of Supplementary Nutritional Assistance Program (SNAP, formerly known as food stamps) benefits in urban and suburban communities over the course of the Great Recession reveals that:

• Between December 2007 and December 2009, city and suburban unemployment rates in large metro areas increased by roughly the same degree (5.1 versus 4.8 percentage points, respectively). By December 2009, the gap between city and suburban unemployment rates was one percentage point (10.3 percent versus 9.3 percent)—smaller than 24 months after the start of the first recession of the decade (1.7 percentage points) and the downturn in the early 1990s (2.2 percentage points).
  
  
• Western metro areas exhibited the greatest increases in city and suburban unemployment rates—5.8 and 5.6 percentage points—over the two-year period ending in December of 2009. Increases in unemployment rates tilted more toward primary cities in Northeastern metro areas (a 5.3 percentage-point increase versus 4.2 percentage points in the suburbs), while suburbs saw slightly larger increases in the South (5.0 versus 4.4 percentage points).
  
  
• Initial Unemployment Insurance (UI) claims increased considerably between December 2007 and December 2009 in urban and suburban areas alike. The largest increases in requests for UI occurred in the first year of the downturn—led by lower-density suburbs—with new claims beginning to taper off between December of 2008 and 2009.
  
  
• SNAP receipt increased steeply and steadily between January 2008 and July 2009 across both urban and suburban counties. Urban counties remain home to the largest number of SNAP recipients, though suburban counties saw enrollment increase at a slightly faster pace during the downturn—36.1 percent compared to 29.4 percent in urban counties.
  

Even as signs point to a tentative economic recovery for the nation, metropolitan areas throughout the country continue to struggle with high unemployment. Within these regions, the negative effects of this downturn—as measured by changes in unemployment and demand for safety net services—have been shared across cities and suburbs alike. Standardizing sub-state data collection and reporting across programs would better enable policymakers and services providers to effectively track indicators of recovery and need in the nation’s largest labor markets.

Read the Full Paper » (PDF)
Read the Related Report: Job Sprawl and the Suburbanization of Poverty »

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• Full Paper
• Appendix A
• Appendix B
• Appendix C

      

Boris Nemtsov’s murder may be a turning point in current Russian history. Unfortunately, it is almost surely not a turn to the better, but one to something bad or to something even worse. This point needs to be made clear from the beginning. It is an illusion to think that this event will lead to anything positive, such as a backlash in the population at large against nationalistic rhetoric or even some kind of liberal revolution, or “Russian Spring.” 

Liberalism in Russia was left near-dead once Putin prevailed over the wave of protests in late 2011 and early 2012. The geopolitical conflict with the West over Ukraine—both Russian President Vladimir Putin’s actions and the Western response of imposing sanctions—virtually guaranteed that liberal, democratic, pro-Western forces would remain irrelevant for now and, likely, for some years to come. Nemtsov’s murder only exacerbates this trend.

There are two probable scenarios going forward. Both begin with the realization that the most important conclusion from the Nemtsov murder is that it signals a lack of control by Putin over very dangerous elements in Russian society. Putin will respond by further tightening Kremlin control over Russian politics and society. The question is how forcefully and how successfully. In one scenario, Putin succeeds in stabilizing the political situation. This will result in an even more authoritarian, but somewhat stable, Russia. In the other, Putin fails to establish control and the result is a breakdown of all control and reversion to an even more extreme nationalism than now.

We do not know who murdered Boris Nemtsov. The most likely version is that it was rogue nationalist, anti-Maidan, forces. If so, this means that Putin has serious problems with his own security forces. They are either inexcusably negligent (because they allowed the murder to happen) or criminally complicit (if anyone inside the security forces in any way facilitated the crime).

Most everyone outside the Kremlin is obsessed with, as they say, the eternal Russian question of Kto vinovat—”Who is to blame?” Putin’s priorities are different. His first question is not, “Who did this?” but “Who let this happen?” So his primary concern is to solve the problem of the competence of and/or control over his security services.

Second, his main target now has to be extremist-nationalists inside Russia. They are the biggest threat to his regime, far greater than the demonstrators on Bolotnaya Square in 2011-2012. Despite the fact that it is the Kremlin itself that has been the biggest promoter of nationalism, it is aware that extreme nationalism is a danger. Last October, at the Valdai Club meeting in Sochi, Putin’s number two man, Sergey Ivanov, was asked about the threat of nationalist-extremists in Russia. He admitted there was a problem. “They do exist. They are a tiny minority. But they represent a clear risk. We need to think about that. And, especially, we need to do something about it. ... We have to make it clear to extremists that the law will be strictly enforced, and that committing illegal acts is a road that leads to nowhere.” And when we do act against those who violate the law, he said, “we will not be constrained by concerns about human rights.”

       

Last month, we experienced yet another breakthrough in the epic battle of man against machine. Google’s AlphaGo won against the reigning Go champion Lee Sedol. This success, however, was different than that of IBM’s Deep Blue against Gary Kasparov in 1987. While Deep Blue still applied “brute force” to calculate all possible options ahead, AlphaGo was learning as the game progressed. And through this computing breakthrough that we can learn how to better leverage the data revolution.

In the game of Go, brute-force strategies don’t help because the total number of possible combinations exceeds the number of atoms in the universe. Some games, including some we played since childhood, were immune to computing “firepower” for a long time. For example, Connect Four wasn’t solved until 1995 with the conclusion being the first player can force a win. And checkers wasn’t until 2007, when Jonathan Schaeffer determined that in a perfect game, both sides could force a draw. For chess, a safe strategy has yet to be developed, meaning that we don’t know yet if white could force a win or, like in checkers, black could manage to hold on to a draw.

But most real-life situations are more complicated than chess, precisely because the universe of options is unlimited and solving them requires learning. If computers are to help, beyond their use as glorified calculators, they need to be able to learn. This is the starting point of the artificial intelligence movement.  In a world where perfection is impossible, you need well-informed intuition in order to advance. The first breakthrough in this space occurred when IBM’s Watson beat America’s Jeopardy! champions in 2011. These new intelligent machines operate in probabilities, not in certainty.

That being said, perfection remains important, especially when it comes to matters of life and death such as flying airplanes, constructing houses, or conducting heart surgery, as these areas require as much attention to detail as possible. At the same time, in many realms of life and policymaking we fall into a perfection trap. We often generate obsolete knowledge by attempting to explain things perfectly, when effective problem solving would have been better served by real-time estimates. We strive for exactitude when rough results, more often than not, are good enough.

By contrast, some of today’s breakthroughs are based on approximation. Think of Google Translate and Google’s search engine itself. The results are typically quite bad, but compared to the alternative of not having them at all, or spending hours leafing through an encyclopedia, they are wonderful. Moreover, once these imperfect breakthroughs are available, one can improve them iteratively. Only once the first IBM and Apple PCs were put on the market in the 1980s did the cycle of upgrading start, which still continues today.

In the realm of social and economic data, we have yet to reach this stage of “managed imperfection” and continuous upgrading. We are producing social and economic forecasts with solid 20th century methods. With extreme care we conduct poverty assessments and maps, usually taking at least a year to produce as they involve hundreds of enumerators, lengthy interviews and laborious data entry. Through these methods we are able to perfectly explain past events, but we fail to estimate current trends—even imperfectly.

The paradox of today’s big data era is that most of that data is poor and messy, even though the possibilities for improving it are unlimited. Almost every report from development institutions starts with a disclaimer highlighting “severe data limitations.” This is because only 0.5 percent of all the available data is actually being curated to be made usable. If data is the oil of the 21st century, we need data refineries to convert the raw product into something that can be consumed by the average person.

Thanks to the prevalence of mobile device and rapid advances in satellite technology, it is possible to produce more data faster, better, and cheaper. High-frequency data also makes it possible to make big data personal, which also increases the likelihood that people act on it. Ultimately, the breakthroughs in big data for development will be driven by managerial cultures, as has been the case with other successful ventures. Risk averse cultures pay great attention to perfection. They nurture the fear of mistakes and losing. Modern management accepts failure, encourages trial and error, and reaches progress through interaction and continuous upgrading.

       

There has been no shortage of news about South Africa’s recent economic and political turmoil—from its plummeting currency and slowing economy, to President Zuma’s cabinet shake-up, to weeks-long student protests over rising tuition fees in October.

Understanding what is driving political volatility requires understanding the central economic challenge facing South Africa’s major metropolitan regions: insufficient labor market opportunities for young people.

A recent Brookings report found that the unemployment rate among youth (ages 15 to 34) in Gauteng, the home province of the Johannesburg region, was nearly 40 percent, exceeding the 37 percent national rate. Young people continue to flock to Johannesburg, and the broader Gauteng City-Region that surrounds it, in search of economic opportunity. But the city-region has only created jobs at a 1.3 percent annual clip since 2000, far lower than peer regions like Shenzhen (8.2 percent), Istanbul (2.8 percent), and Santiago (2.4 percent), limiting its ability to absorb young workers. At the same time, the skills demands of the labor market have shifted as the region’s economy has transitioned from mining to more advanced services, creating a mismatch between what education and training systems are providing and what the labor market demands. This employment crisis matters for both economic competitiveness (output per worker growth, a rough measure of productivity, has stagnated since 2010) and economic justice (the unemployment rate for black South Africans is four times the rate for whites).

At a recent Global Cities Initiative event in Johannesburg local private, public, and civic leaders discussed both the immense scale of the youth unemployment challenge and an ambitious proposed solution: the youth skills empowerment initiative “Vulindlel’ eJozi” (a Zulu phrase meaning “open the way in Johannesburg”) created by the city of Johannesburg in partnership with the Harambee Youth Employment Accelerator. Of the approximately 1.6 million Johannesburg residents aged 19-34, just under half are not engaged in employment, education, or training. Vulindlel’ eJozi’s seeks to “reach 200,000 of these young people to meaningfully include and engage them in our economy over the next year.”

Vulindlel’ eJozi stands out for at least two reasons. Most glaringly is its sheer scale. Through its work with Harambee and other initiatives, the city of Johannesburg provided over 45,000 opportunities for youth to move towards employment during the first quarter of 2015. Second, the partnership leverages the resources and competencies of the private and civic sectors. Harambee has successfully trained and placed 20,000 youth in sustained formal employment with over 200 employers and ambitiously wants to engage 500,000 South African youth in their training programs. Constant employer feedback on what skills are demanded is one of the accelerator’s hallmarks, helping Harambee achieve higher trainee retention rates than industry averages.  

Youth unemployment, of course, is not a problem unique to South Africa. Recent Brookings research found that labor force participation, employment, and median earnings among American teens and young adults all declined between 2000 and 2014. How effectively the city of Johannesburg can build the institutional architecture to engage with private and NGO actors on a youth employment initiative at this scale will ultimately determine its success. These lessons could serve other cities well as they seek to deliver economic opportunity to their young people.

      

       

       

Millions of Egyptians took to the streets in January 2011 chanting “‘ish, hurriyya, ‘adalah ijtima‘iyya,” or bread, freedom, and social justice. This simple chant captured protestors’ desire for a new Egypt defined by economic, political, and social change. Five years later, however, the attainment of those demands seems more elusive than ever. In the economic sphere, Egypt still faces the major challenge of high unemployment, particularly among educated youth. Why do so many of Egypt’s young university graduates struggle to find employment?

Read "Educated but unemployed: The challenge facing Egypt’s youth"

In this policy briefing, Adel Abdel Ghafar analyzes the roots of Egypt’s youth unemployment crisis, starting with the structural issues plaguing the country’s educational system. He then examines other contributing factors including neoliberal economic reforms, gender inequality, and the lack of entrepreneurship. Abdel Ghafar warns that failing to address the unemployment issue will increase the likelihood of another uprising.

Abdel Ghafar thus argues that the Egyptian government must urgently undertake reforms and devote extensive resources to dealing with youth unemployment. Specifically, he recommends ways in which Egypt can revamp public university funding, promote vocational training, stimulate entrepreneurship, and increase the participation of women in the workforce.

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• English PDF
• Arabic PDF

       

       

BPEA co-editor Justin Wolfers describes new research that found: people dropped out of the labor force before the recession started; there are better ways to forecast unemployment; homebuyer expectations helped inflate the bubble; the U.S. is not actually as politically polarized as most people think; central banks’ recent experiments with capital controls haven’t delivered results; and the U.S. is making inroads fighting poverty.

Video

• U.S. Not Actually as Politically Polarized as Most Think
• Poverty Has Fallen Much More than Previously Thought
• New Unemployment Model Can Outperform Forecasters
• Perceptions Matter: Homebuyer Expectations Helped Inflate Bubble
• Central Banks’ Recent Experiments with Capital Controls Haven’t Delivered Results

      

The headlines seem pretty good. Unemployment fell a tick to 7.3 percent. And jobs growth continued, with payrolls expanding by 169,000 in August, which is just shy of the 175,000 new jobs that analysts were expecting.

But beneath the headline: blech!

The most important news was the revisions to what we had previously thought was a healthy and perhaps self-sustaining recovery. Instead, jobs growth in July was revised from 162,000, to a weak 104,000, and June was also revised downward. Taken together, this month's revisions means we've created 74,000 fewer jobs than previously believed. And the previous jobs report subtracted another 26,000 jobs through revisions. Moreover, for reasons that remain a mystery, revisions have tended to be pro-cyclical, meaning that the healthy recovery we thought we were having might have been expected to yield further upward revisions. All this means that analysts are hastily revising their views.

The other bad news comes from the household survey, where employment fell 115,000, leading the employment-to-population ratio to decline by 0.1 percentage points. So the decline in the unemployment rate isn't due to folks getting jobs; instead, it's due to people dropping out of the labor force.

I have two simple metrics I use to measure the "underlying" pace of jobs growth. The first puts 80% weight on the (more accurate) payrolls survey, and 20% weight on the noisier household survey. That measure suggests employment grew by only 112,000 in August. The alternative is to focus on the 3-month average of payrolls growth, which suggests we're creating slightly around 148,000 jobs per month.

Bottom line: This report says that we're barely creating enough jobs to keep the unemployment rate falling from its current high levels. Policymakers have been looking for a signal that the recovery has become self-sustaining. This report doesn't provide it. And until we're confident that the recovery will keep rolling on, we should delay either any monetary tightening, further fiscal cuts, and definitely postpone the legislative shenanigans that Congress is threatening.