Thursday, March 26, 2015

Development Targets: 169 or 19?

Back in 2002, the United Nations established a set of "Millennium Development Goals," which were phrased as a combination of overall "goals" and more specific "targets." For example, the first "goal" was "Eradicate Extreme Poverty and Hunger," but the first specific target under that goal was "Halve, between 1990 and 2015, the proportion of people whose income is less than $1.25 a day." (Thanks in large part to the explosive growth in the economies of China and India, this target was in fact reached five years earlier.) Many of the specific targets used the year 2015 as an end-date, and so the UN has been engaged in thinking about what the next set of goals or targets should be. Last summer, it settled on a list of 17 goals that include 169 targets.

The UN list seems open to two overall criticisms.  First, 169 targets is unwieldy--more a wish-list than a considered policy agenda. However, apparently now that it has been agreed upon altering the of 169 targets is politically impossible. More important, the UN list of goals and targets seems to imply that if we just agree on the 169 targets, we don't really need to discuss what actual policy choices would do the most to accomplish those goals and targets. But of course, if two policies seem likely to reduce poverty or improve health or protect the environment to the same extent, but one policy has much lower costs than the other, it makes sense to concentrate on the the cost-effective approaches first.

The Copenhagen Consensus Center has been taking on the job of commissioning research to evaluate policies to achieve the targets, when then draw on existing research about the costs and benefits of these policies. Based on these studies, which are available here, a group of three prominent economists--Finn Kydland, Tom Schelling, and Nancy Stokey--have just recommended a set of 19 policies and targets that all appear based on the existing research to have benefits that are at least 15 times greater than costs.

Here is the list of 19 priorities, quoted from the March 26 press release from the three economists. he underlying research papers with details about policies, costs, and benefits are available here.
1) Lower chronic child malnutrition by 40%. Providing nutritional supplements, deworming, and improving the balance of diet for 0-2 year olds will cost $11bn and prevent 68m children from being malnourished every year
2) Halve malaria infection. Distributing long lasting insecticide treated bed-nets and delaying resistance to the malaria drug artemisinin will cost $0.6bn, prevent 100m cases of malaria and save 440,000 lives per year.
3) Reduce tuberculosis deaths by 90%. Massively scaling up detection and treatment of tuberculosis will cost $8bn and save up to an additional 1.3m lives per year.
4) Avoid 1.1 million HIV infections through circumcision. Circumcising 90% of HIV-negative men in the 5 worst affected countries will cost $35m annually and avert 1.1m infections by 2030 with the preventive benefit increasing over time.
5) Cut early death from chronic disease by 1/3. Raising the price of tobacco, administering aspirin and preventative therapy for heart disease, reducing salt intake and providing low cost blood pressure medicine will cost $9bn and save 5m lives per year.
6) Reduce newborn mortality by 70%. Protecting expecting mothers from disease, having skilled medical staff support their deliveries, and ensuring high quality postnatal care will cost $14bn and prevent 2m newborn deaths per year.
7) Increase immunization to reduce child deaths by 25%. Expanding immunization coverage to include protection from forms of influenza, pneumonia and diarrheal disease will cost $1bn and save 1m children per year.
8) Make family planning available to everyone. Allowing women to decide if, when, and how often they become pregnant will cost $3.6bn per year, cut maternal deaths by 150,000, while allowing more attention and education to remaining children.
9) Eliminate violence against women and girls. Right now, every year 305 million women are domestically abused, costing the world $4.4 trillion in damages.
10) Phase out fossil fuel subsidies. Removing fossil fuel subsidies will lower carbon emissions and free up $548bn in government revenue to spend on for example, health, infrastructure and education.
11) Halve coral reef loss. Protecting marine habitats will cost $3bn per year but will prevent the loss of 3m hectares of coral reef, providing natural fishing hatcheries and boosting tourism.
12) Tax pollution damage from energy. Air pollution is the world’s biggest environmental killer, causing more than 7m annual deaths. Taxes proportional to the damage from air pollution and CO₂ will reduce environmental impacts efficiently.
13) Cut indoor air pollution by 20%. Providing more clean cookstoves will cost $11bn and prevent 1.3m deaths per year from indoor air pollution.
14) Reduce trade restrictions (full Doha). Achieving more free trade (e.g. the Doha round) would make each person in the developing world $1,000 richer per year by 2030, lifting 160m people out of extreme poverty.
15) Improve gender equality in ownership, business and politics. Ensuring women can own and inherit property, perform basic business needs like signing a contract and be represented in parliament will empower women.
16) Boost agricultural yield growth by 40%. Investing an extra $8bn per year in agricultural R&D to boost yields will reduce food prices for poor people, mean 80m fewer people go hungry and provide benefits worth $82bn per year.
17) Increase girls’ education by two years. Ensuring girls receive more education will increase their future wages, improve their health, reduce their risk of violence and start a virtuous cycle for the next generations.
18) Achieve universal primary education in sub-Saharan Africa. At a cost of $9bn per year, this target will ensure 30m more kids per year attend primary school.
19) Triple preschool in sub-Saharan Africa. Pre-school instils within children a life long desire to learn. Ensuring pre-school coverage rises from 18% to 59% will cost up to $6bn and will give that experience to at least 30m more children per year
What's the single best policy in terms of benefit-cost ratio? The background research paper by Kym Anderson suggests that completing the Doha talks for greater trade liberalization would have benefits for developing countries that are 2,100-4,700 times greater than costs. As Anderson points out, a growing body of work in international trade in the last couple of decades has pointed out that the static gains from trade--say, the U.S. trading wheat-for-oil with Saudi Arabia--are relatively small in the contest of an overall economy. Instead, the big gains from trade arise because of how trade leads to increases in productivity. For example, trade leads to spillovers of knowledge, or the spread of improved methods of management. Trade can stimulate international investment and growth of a financial sector, which has spillover effects for other firms. Global supply chains let producers specialize in the very specific areas where they have the greatest advantage. It lets producers in small countries take advantage of economies of scale when the produce for bigger markets, and lets consumers in small countries benefit from economies of scale when they import from other countries. Trade can provide an additional incentive for national governments to follow sensible macroeconomic and regulatory policies--which again can help all producers in an economy, not just exporters and importers.

I haven't read all the background paper, much less all the research cited in those papers, and so I don't have strong opinions about whether this list of 19 targets is necessarily the best one. But if the choice is between spreading out the scarce resources of time, money, and enthusiasm that are available for development efforts across 169 targets, or focusing those resources on the much smaller number of targets and policies with high payoffs, I know which approach is likelier to benefit low-income people around the world.

Wednesday, March 25, 2015

The Rise of Mortgages: Too Much House?

Economists sometimes argue that more choices must either be neutral or good. The logic is that if you don't want any of the additional choices, then don't take them, and you are equally well off. If you do want one of the additional choices, you are then better off.  Of course, this argument is not airtight. It assumes that there are no costs of evaluating more choices, and it presumes that the chances of choosing wisely and well are not diminished as the number of choices rises.

For one case in which these issues arise, consider the many changes in financial markets and government regulations that have made it vastly easier for people to take out a mortgage loan and buy a house. I certainly view the option to take out a mortgage loan as beneficial to me, because rather than spend years saving up enough money to buy a house outright, I can live in the house while paying down the mortgage over time. But this additional choice also brings dangers. People are often notably bad at evaluating situations where the costs and benefits are spread out over time. We find ourselves in situations where we would like to save money, or start exercising more, or eating healthier--but always starting tomorrow, never today. Many people find themselves running up credit card bills to have the benefits of consumption now, with the costs of paying shoved into the future. Thus, it wouldn't be surprising to find that when the option to take out a long-term mortgage becomes available, people are tempted to over-borrow.

My grandmother used to have a saying about people who bought all the house that the bank told them they could afford, and often a little more: "You can't eat bricks and mortar."

For example, one common rule-of-thumb when applying for a mortgage is that you can "afford" a house if the loan payments will be 30% or less of gross income. Of course, this rule is based on the likelihood that the loan will be repaid to the bank, not on whether you will feel good in a year or five years about how much you have spent. If someone told you what was the most you could spend on some other purchase--like a car or a vacation--you probably wouldn't feel semi-obligated to spend that actual amount.

Òscar Jordà, Moritz Schularick, and Alan M. Taylor explore some big-picture issues with this dynamic in "Mortgaging the Future?" written as an "Economic Letter" for the Federal Reserve Bank of San Francisco (March 23, 2015, 2015-09). From their abstract: "In the six decades following World War II, bank lending measured as a ratio to GDP has quadrupled in advanced economies. To a great extent, this unprecedented expansion of credit was driven by a dramatic growth in mortgage loans. Lending backed by real estate has allowed households to leverage up and has changed the traditional business of banking in fundamental ways. This “Great Mortgaging” has had a profound influence on the dynamics of business cycles."

Here's an illustration showing the ratio of mortgage lending to the total value of housing in the United States. In 1960 and in in 1990, with some bumps between, mortgages were equal to about 30% of the value of the housing stock: to put it another way, on average people were living in houses where 70% of the value of the house was their own equity in the home. By 2010, mortgages were 50% of the value of the housing stock. 

Across the high-income countries of the world, mortgage lending has become the dominant business for banks, rather than lending to businesses or making other kinds of loans to individuals. 

From an overall macroeconomic point of view, bank lending as a share of GDP is rising, and much of that lending is due to increases in housing lending. Jordà, Schularick, and Taylor present some correlations across data from 17 countries and reach this conclusion: 
The vast expansion of bank lending after World War II is one of the most extraordinary developments in the history of modern finance and macroeconomics.... [I]n the postwar period an above average mortgage-lending boom unequivocally makes both financial and normal recessions worse. ...  In contrast, booms in nonmortgage credit have virtually no effect on the shape of the recession in the same postwar period. Why the difference? At this point we can only speculate. A mortgage boom gone bust is typically followed by rapid household deleveraging, which tends to depress overall demand as borrowers shift away from consumption toward saving. This has been one of the most visible features of the slow U.S. recovery from the global financial crisis ... 
In other words, the tacit encouragement from regulators, the financial industry, and the tax code to buy a house doesn't just run a risk that some homebuyers will overborrow. It also makes recessions worse and, as in 2007-2009, can even threaten broader financial stability. 

At the household level, this sharpens the question of what we think of as the expected or normal amount of housing consumption. Back in  the early 1970s, the average new single-family house had 1,660 square feet, which peaked at over 2,500 square feet for a new house in 2007, and then declined a bit after bubble in housing prices popped.

Let me offer a speculation: Say that the rules for taking our a mortgage had been tighter over time. Imagine the standard was that banks would decide what you can afford based on 25% of your income, not 30%, or that mortgages were typically available for 15 or 20 years, not 30. My guess is that bank lending for mortgages would be smaller. The size of homes might well have increased, but not as quickly. Less of US capital investment would be allocated to housing, which would make it possible for more to be allocated to investments that can raise the long-term standard of living. The US economy would be less vulnerable to recession. People who were less stretched in making their mortgage payments would be less likely to face default or foreclosure. And my guess is that many of us would have adapted perfectly well to living in smaller homes, because the smaller size would be usual and typical and what we expect. The money we weren't spending on housing would easily be spent on other forms of consumption.

In short, the push for making mortgage loans more easily available is sometimes presented as if it can only make people better off. Either they can borrow the same amount as before, or they can decide that they would prefer to borrow more. But making mortgages more available also has number of tradeoffs, both for individuals who "can't eat bricks and mortar" and for the broader economy.

Tuesday, March 24, 2015

Singapore Snapshots and Lee Kuan Yew

There's a long tradition in political economy, stretching back at least to Plato's discussion of "philosopher-kings," which speculates that the best form of government might be a benevolent dictatorship. In a way, this argument is bulletproof, because any response that points out the real-world problems of dictatorship can be countered by saying that it's just not sufficiently benevolent. But in my lifetime, the best example anyone could cite of a benevolent dictatorship that seemed to be working pretty well was Lee Kuan Yew, who was the official leader of Singapore from 1959 to 1990, and the unofficial leader for some years after that. He died on Monday at the age of 91.  Thus, it seems a useful time for a quick review of the economy he left behind.

In some ways, the most straightforward case for Lee Kuan Yew is in the basic economic statistics of long-term growth. Here's a figure using World Bank data showing per capita GDP in Singapore, adjusted for inflation. During the 53 years from 1960 to 2013, Singapore's economy averaged more than 5% annual growth on a per person basis. For perspective, Singapore's per capita GDP was about 16% of the US level in 1960, and now is almost 80% of the US level.

What economic challenges is Singapore facing now? The IMF published a staff report on Singapore's economy in October 2014. The overall outlook for Singapore's economy remains strong. For example, the IMF writes:

"GDP tripled from less than US$100 billion in 2000 to almost US$300 billion in 2013. Strong growth and low unemployment and inflation have been achieved along with some strong social indicators (e.g. high life expectancy and home ownership). At the same time, however, compared to high-income peers, the labor share of income is low and inequality and the cost of living are high, adversely affecting some population groups. Key supporting factors for Singapore’s strong growth performance have been a stable macroeconomic environment, continuous reforms to upgrade transportation infrastructure and the broader business climate, and a liberal regime for the inflow of foreign workers. The economy remains well-diversified. Known as a financial center and trading hub, Singapore retains a competitive manufacturing sector which contributes more than one sixth of GDP. ... ... The authorities’ plan to raise social and infrastructure spending by 1−2 percent of GDP over the medium term should help reduce the large current account surplus. Financial regulation and supervision is among the best globally and Singapore is a frontrunner in implementing global regulatory reforms."

Here are a few other points that caught my eye. Singapore's population has risen by about one-third since 2000, most of that from foreign immigration (the birthrate in Singapore is quite low). In the last few years, the government has sought to slow the inflow of immigrants, and instead to provide subsidies for firms to make investments that can raise the productivity of land and labor--thus also seeking to assure that the economy remains more than just a financial center, but is diversified into other sectors. In 2013, unemployment fell to 1.9%.

Many of immigrants since 2000 worked in lower-wage jobs, and so inequality in Singapore has risen since 2000. The rise was particularly marked in the 2000-2006 period.

Overall, the level of income inequality in Singapore as measured by the Gini coefficient is roughly at US levels, although redistributive tax and spending policies do reduce that level somewhat.

Against Singapore's economic gains under Lee Kuan Yew, there has also been a legacy of repression of free speech and repression of political opposition. There have been laws about everything from spitting on the sidewalk to improper disposal of used chewing gum. For more severe crimes, there has been a threat of harsh legal penalties including caning and capital punishment. Back in the 1990s, one wag christened Singapore as "Disneyland with the death penalty."

Like dictators everywhere, Lee Kuan Yew made the claim that excesses were justified by the necessity of bringing civil order and economic prosperity to a potentially fragmented multi-ethnic and multi-religious country. Singapore had been a British colony, then controlled by Japan during World War II, then again under British control, then merged into Malaysia in 1963, before becoming a separate and independent country in 1965.  In a New York Times profile in 2010, Lee was quoted as saying: “I’m not saying that everything I did was right,” he said, “but everything I did was for an honorable purpose. I had to do some nasty things, locking fellows up without trial.” His obituary in the New York Times quoted this comment: "Many people say, ‘Why don’t we open up, then you have two big parties and one party always ready to take over?’ “ Mr. Lee said in a speech in 2008. “I do not believe that for a single moment.” He added: “We do not have the numbers to ensure that we’ll always have an A Team and an alternative A Team. I’ve tried it; it’s just not possible.”

On the other side, by the standards of murderous and genocidal dictators around the world--which of course is setting the bar dismally low--the level of political repression in Singapore was not very extreme. Lee Kuan Yew did start backing away from formal control of government back in the early 1990s, a quarter-century before his death. Opposition parties won a substantial share of the vote int he 2011 elections. While he was certainly well-to-do economically, he did not pile up personal riches beyond the dreams of avarice. He presided over a government that hired top managers from the public and private sector and strongly discouraged official corruption.

Lee Kuan Yew, taking his economic and political record as a whole, is probably about as close an example of a benevolent dictator as the real world  in modern times can offer.  I feel incapable of rendering any final judgement on his career. Singapore's economic and political path may not have been the best, but it was also far from the worst. Of course, such judgments are not up to me anyway, but instead are most appropriately made by the the people of Singapore as their country evolves after the passage of their nation's founding father.

Friday, March 20, 2015

Digging into Capital and Labor Income Shares

A wide array of evidence suggests that if you split all income in an economy into either labor or capital income, the labor share has falling in the last couple of decades. For example, here's a previous post referring to comments from the 2013 Economic Report of the President:
"The “labor share” is the fraction of income that is paid to workers in wages, bonuses, and other compensation. ... The labor share in the United States was remarkably stable in the post-war period until the early 2000s. Since then, it has dropped 5 percentage points. .... The decline in the labor share is widespread across industries and across countries. An examination of the United States shows that the labor share has declined since 2000 in every major private industry except construction, although about half of the decline is attributable to manufacturing. Moreover, for 22 other developed economies (weighted by their GDP converted to dollars at current exchange rates), the labor share fell from 72 percent in 1980 to 60 percent in 2005."
Or here's a post with comments from the Global Wage Report 2012/13 by the International Labour Organization:
"The OECD has observed, for example, that over the period from 1990 to 2009 the share of labour compensation in national income declined in 26 out of 30 developed economies for which data were available, and calculated that the median labour share of national income across these countries fell considerably from 66.1 per cent to 61.7 per cent."

Other posts on this subject have referred to a report from the Congressional Budget Office, reports from research by Federal Reserve economists, and a 2013 paper in the Brookings Papers on Economic Activity. While the overall fact pattern of a falling labor share seems well-established, digging down into what it actually means reveals some complexities that are often not much discussed. Matthew Rognlie considers some of these issues in "Deciphering the fall and rise in the
net capital share," presented today at the Brookings Papers on Economic Activity spring conference. Here are some of my own thoughts about the issues.

1) The capital labor movement isn't about inequality of incomes. 

All wages and compensation are counted as "labor income." Perhaps there are some underlying common reasons why the rising share of capital income and greater inequality of labor income are happening at the same time, but they are not the same thing.

2) Should self-employment income be considered as labor or capital?

If you own a business, then part of your income from that business is a return to your labor, while another part is a return to the risk-taking of business ownership, and should conceptually be considered a return to capital. At certain times, this distinction can make a big difference. Rognlie refers to a debate among economists back in the 1950s, after several decades of a falling number of farmers. Most of the farmers were self-employed, and thus their income was categorized as capital income. As more workers moved from farm to non-farm employment, their income then became treated as labor income. There are a variety of ways to split up self-employment income into labor and capital components, none of them fully satisfactory. However, there hasn't been a big upward trend in self-employment income in recent decades, so such measurement choices are not going to be much help in explaining the rise in share of income going to capital.

3) Should the focus be on net capital or gross capital? 

The difference between "gross" and "net" is that "net" takes depreciation of past  capital into account. Rognlie explains why the difference matters using an example from an industry where output is produced by short-lived software. As a result, the producer spends a lot on capital every year, but almost all of it is replacing the obsolete software that depreciated the previous year.

For instance, in an industry where most of the output is produced by short-lived software, the gross capital share will be high, evincing the centrality of capital’s direct role in production. At the same time, the net capital share may be low, indicating that the returns from production ultimately go more to software engineers than capitalists—whose return from production is offset by a loss from capital that rapidly becomes obsolete. Both measures are important: indeed, a rise in the gross capital share in a particular industry is particularly salient to an employee whose job has been replaced by software, and it may proxy for an underlying shift in distribution within aggregate labor income—for instance, from travel agents to software engineers. The massive reallocation of gross income in manufacturing from labor to capital, documented by Elsby et al. (2013), has certainly come as unwelcome news to manufacturing workers. But when considering the ultimate breakdown of income between labor and capital, particularly in the context of concern about inequality in the aggregate economy, the net measure is likely more relevant. This point is accepted by Piketty (2014), who uses net measures; the general rationale for excluding depreciation is pithily summarized by Baker (2010), who remarks that “you can’t eat depreciation.”
Here are two figures, with the first one showing the capital share based on gross capital, and the second showing the capital share based on net capital. Capital share is clearly rising in recent years with gross capital, as shown in the bottom figure. With net capital, the rise is more modest. It looks more as if there was a drop-off in capital shares in the 1970s which has since been reversed. Again, the underlying difference here refers to changes over time in how quickly capital investment is wearing out, and to changes in what share of current capital investment is replacing old depreciated capital vs. adding to the overall capital stock.

It's worth noting that the conclusion that the rising capital share mostly goes away if you look at net capital rather than gross capital is not at this stage a consensus finding. For an argument that using net capital makes the capital share fall, but by a more modest amount, you can check Loukas Karabarbounis and Brent Neiman, “Capital Depreciation and Labor Shares Around the World: Measurement and Implications,” Technical Report, National Bureau of Economic Research 2014.

4) Is the rise in capital share mainly housing? 

Perhaps the most striking finding from Rognlie's analysis is that all of the rise in the capital share can be accounted for by a rise in housing values. Here's a figure illustrating the point. the top yellow line shows the share of capital income using a "net" measure. The red line shows the measure of capital income with housing, the blue line, subtracted out. 

Many noneconomists don't think about owning a house as a form of capital ownership. But from the viewpoint of economic statistics, a homeowner is someone with a piece of capital--a house--that is providing a service. Of course, homeowners do not go through the formality of paying rent to themselves. Rognlie explains it this way: 
Income from housing is unlike most other forms of capital income recorded in the national accounts: in countries where homeownership is dominant, most output in the housing sector is recorded as imputed rent paid by homeowners to themselves. ... Indeed, imputed rents from owner-occupied housing should arguably be treated as a form of mixed income akin to self-employment income: in part, they reflect labor by the homeowners themselves. ... [H]ousing has a pivotal role in the modern story of income distribution. Since housing has relatively broad ownership, it does not conform to the traditional story of labor versus capital, nor can its growth be easily explained with many of the stories commonly proposed for the income split elsewhere in the economy—the bargaining power of labor, the growing role of technology, and so on.
The importance of housing in looking at movements of capital and labor income is not a new insight. For example, Odran Bonnet, Pierre-Henri Bono, Guillaume Camille Chapelle, Étienne Wasmer discuss the point in this readable June 30, 2014, note about their own research:"Capital is not back: A comment on Thomas Piketty’s ‘Capital in the 21st Century.’"

When thinking about the long-term evolution of capital and labor income, it becomes important to remember that capital income can mean different things at different times, and land and housing are part of capital, too. It's easier to provide an economic justification for  capital income being paid to those who invest in a productive, job-creating, profit-making firm than it is to justify capital income being paid to a lord from the 19th century who inherited large amounts of land and receives capital income from the rent paid by tenant-farmers. The capital income received by the owner of a factory with a huge and costly physical plant is also not identical in economic meaning to the capital income received by the owner of a firm where the capital depreciates to near-zero almost every year and the value of the firm is based in intellectual property. The rise in capital income as a result of a long-term rise in land and housing prices across the high-income countries is a phenomenon that isn't easily crammed into the usual disputes over whether capital owners are exploiting wage-earners.

Thursday, March 19, 2015

China's Consumption Transition

A standard pattern of long-term economic development is that a country goes through a period of higher savings and investment, along with correspondingly lower consumption levels. After the growth spurt, consumption levels rise again. For illustration, here's a figure from a chapter by
Jutta Bolt, Marcel Timmer, and Jan Luiten van Zanden appearing in the OECD report from last fall, How Was Life? Global Well-Being Since 1820, which can be read online here.

As the figure shows, countries like Japan and Korea had substantial drops in their ratio of consumption to GDP, reflecting rises in saving and investment, but then consumption as a share of rose again. Technological leaders like the US and German economies show much more stability in their consumption/GDP ratio looking back a couple of centuries. Poorer countries back in 1950 like China and Ethiopia have high consumption/GDP ratios, although it's intriguing that Ethiopia is showing a drop in consumption/GDP--and thus a rise in investment--over the last decade or so.

In this pattern, consider the peculiarity of China's consumption patterns. It's of course not unexpected that China would have a high consumption/GDP ratio back around 1950. It's not surprising that the various requirements for forced saving under the Mao regime pushed the consumption/GDP ratio down, often at extremely high human cost. It's not surprising that as China's economy liberalized in the 1980s, consumption/GDP fell and investment rose. But what is quite shocking is that as China's economy has grown rapidly, the consumption level has not kept up, and so the consumption/GDP ratio just keeps falling. China's rates of saving and investment are extraordinarily high.

The figure above looks at a broad measure of consumption that includes both household consumption and consumption done directly by government. In China, most of the decline in consumption is traceable to a fall in the household portion of consumption. Here are a couple of graphs with data going back to 1980, generated using data from the World Development Indicators from the World Bank. The first one shows the fall in household consumption as a share of GDP over time. The second shows government final consumption expenditure as a share of GDP, and it has not moved much over time, even as China's economy has grown explosively.

Here are a few thoughts on these patterns:

1) For perhaps a decade or so, there has been a strong argument the next stage for China's economic growth would involve "rebalancing" away from an economy that is so extraordinarily heavily on saving and investment and toward an economy more driven by consumption (see also here and here).

2) One channel through which a consumption rise could happen is through government spending on health, education, and assistance for the poor. However, while government spending on consumption has been rising in absolute levels, it has not increased as a share of GDP.

3) The other obvious channel through which consumption could rise is through higher wages and consumption levels by China's households. Again, while household consumption has been rising in absolute levels, it has not been keeping up with growth of GDP, and thus has been falling as a share of GDP.

4) China's economic resurgence is so unprecedented that making predictions is especially uncertain. The optimistic prediction would be that China's economy smoothly rebalances away from investment and toward consumption. The pessimistic prediction is that the extraordinarily fall in consumption/GDP ratios in China is sending us an important message.

In well-functioning economies, there is a connection where as firms grow and receive higher revenues and profits, those funds are then cycled back to the broader population in the form of higher wages, as well as higher returns that flow into savings accounts and retirement funds for future consumption. Of course, this process by which firms cycle revenues back to households is always a source of controversy. Various laws and institutions will shape the forms in which money flows back from the firm sector to the household sector, and the level of inequality of incomes that result. But in China's economy, this process of funds flowing from firms back to households seems not to be working very well.

The very low rates of consumption/GDP in China, and the corresponding high levels of saving and investment, are driving the amount of credit in China's economy sky-high (as illustrated here and here). China continues to have possibilities for rapid economic growth in the decades to come. But in the short-term or the middle-term, it also increasingly appears that because of the lack of rebalancing to consumption, China's economy is experiencing a credit and investment bubble in a number of sectors that will not end well.

Wednesday, March 18, 2015

Randomness is Lumpy: Pareidolia

"Pareidolia" refers to the common human practice of looking at randomness and seeing patterns. Some standard examples are when you see a basketball player make several shots in a row and interpret that as a "hot hand," not just the kind of streak that will happen every now and then among thousands of basketball players taking shots where each shot has a roughly 50:50 chance of going in. Or when you see a stock market adviser have several above-average years in a row and interpret that as evidence that future returns are likely to follow the same pattern, rather than as the kind of random streak that will happen every now and then when there are thousands of stock market advisers, each with a roughly 50:50 change of an above-average performance in any given year.

How good are you at perceiving randomness? Here's an example from Steven Pinker's 2011 book The Better Angels of Our Nature. This example and others were discussed in an article by Aatish Bhatia, "Empirical Zeal: What Does Randomness Look Like" in the December 21, 2012, issue of Wired magazine.

Consider the two panels with a bunch of points. The points on one panel are distributed randomly, but not on the other. Which is which?

The most common answer to say that the pattern on the right is random. The pattern on the left seems to have certain gaps and clusters and curves, which you can imagine as having some underlying meaning. But given the lead-in of the discussion here, you may be unsurprised to find that the random distribution is the one on the left. the distribution on the right is actually a representation of the pattern of glow-worms on a cave ceiling. The glow-worms compete for food and thus avoid being too close to each other. The greater evenness of the spacing is actually a giveaway that some underlying process is at work. Randomness is lumpy.

This may seem counterintuitive. After all, "random" refers to an equal probability of outcomes occuring--like where points occur in these panel. But an equal probability of something happening does not mean an equally spread out set of outcomes.

As an example, imagine that you flip a coin twice. On average, you expect to get one head and one tail. Now repeat this experiment of two coin flips 100 times. If every single time out of 100 you got one head and one tail, you could be extremely confident that you were not seeing a random outcome. After all, random chance suggests that one-quarter of the time you would expect to see two heads and one-quarter of the time you would expect to see two tails. In other words, if you don't see lumpy clusters, the odds are good that you aren't seeing randomness.

Separating what is random from what is an underlying pattern is of course the central task in figuring out what is happening in any complex system: the weather, outbreaks of disease, the path of an economy. Beware the dreaded phase, "It can't be just a coincidence." Sometimes, it can. Many people have a degree of pareidolia, and they will tend to assume that clusters must have an explanation other than randomness. A compelling reason for a course or two in statistics is to help people harness and shape their intuitions about what constitutes evidence of randomness or pattern.

Tuesday, March 17, 2015

Data Movement Mushrooms

I live my life a long hike away from the technological frontier. But a couple of white papers published by Cisco offer some glimpses of where information technology is headed. The two reports are "The Zettabyte Era: Trends and Analysis" (June 10, 2014) and "Cisco Visual Networking Index: Global MobileData Traffic Forecast Update, 2014–2019" (February 3, 2015).

As a starting point, Internet traffic can be measured in terms of the number of "bytes" of information transmitted. The current measures of annual global internet traffic are in exabytes, where the prefix "exo-" means 10 raised to the 18th power. According to Cisco, by the end of next year, annual IP (Internet protocol) traffic will reach 1000 exabytes, which is called a zettabyte--that is, is 10 raised to the 21st power. (In case you're wondering what label comes next, a yottabyte is 10 raised to the 24th power.)

What does that volume of traffic mean in more concrete terms? Cisco writes:
To appreciate the magnitude of IP [Internet protocol] traffic volumes, it helps to put the numbers in more familiar terms:
● By 2018, the gigabyte equivalent of all movies ever made will cross the global Internet every 3 minutes.
● Globally, IP traffic will reach 400 terabits per second (Tbps) in 2018, the equivalent of 148 million people streaming Internet HD video simultaneously, all day, every day.
● Global IP traffic in 2018 will be equivalent to 395 billion DVDs per year, 33 billion DVDs per month, or 45 million DVDs per hour. 
Personal computers have generated the majority of Internet traffic in the past, but explosive growth of smartphones and tablets means that in the next few years, personal computer will be accounting for less than half of Internet traffic. In this illustrative table, M2M refers to "machine-to-machine" Internet traffic, which is sometimes goes under the name IoE for "Internet of Everything."

The reports are full of detail about connection speeds, ways in which the Internet is being used and breakdowns across devices and regions. One figure offered a thought-provoking comparison about how much Internet traffic is generated by higher-end devices, compared to a basic-feature mobile phone. For example, a smartphone is 37 times the data volume of a regular phone, and a tablet is almost 100 times the data volume. Moving video is very data-intensive: "As in the case of mobile networks, video devices can have a multiplier effect on traffic. An Internet-enabled HD television that draws 50 minutes of content per day from the Internet would generate as much Internet traffic as an entire household today."

Although smartphones and tablets are currently the main devices altering how people generate internet traffic, the reports also include some predictions about what could come next. One big change could come through the wearable Internet. Cisco writes:
An important factor contributing to the growing adoption of IoE [Internet of Everything] is the emergence of wearable devices, a category with high growth potential. Wearable devices, as the name suggests, are devices that can be worn on a person and have the capability to connect and communicate to the network either directly through embedded cellular connectivity or through another device (primarily a smartphone) using Wi-Fi, Bluetooth, or another technology. These devices come in various shapes and forms, ranging from smart watches, smart glasses, heads-up displays (HUDs), health and fitness trackers, health monitors, wearable scanners and navigation devices, smart clothing, et al. The growth in these devices has been fueled by enhancements in technology that have supported compression of computing and other electronics (making the devices light enough to be worn). These advances are being combined with fashion to match personal styles ... By 2019, we estimate that there will be 578 million wearable devices globally, growing fivefold from 109 million in 2014 ... 

Another big change in Internet traffic will come from M2M or machine-to-machine connections. Cisco writes;
M2M connections—such as home and office security and automation, smart metering and utilities, maintenance, building automation, automotive, healthcare and consumer electronics, and more—are being used across a broad spectrum of industries, as well as in the consumer segment. As real-time information monitoring helps companies deploy new video-based security systems, while also helping hospitals and healthcare professionals remotely monitor the progress of their patients, bandwidth-intensive M2M connections are becoming more prevalent. Globally, M2M connections will grow from 495 million in 2014 to more than 3 billion by 2019... —a sevenfold growth.

Cisco also argues that its forecasts of future Internet traffic should be viewed as conservative. For example, "cloud gaming" refers to the situation where the graphics for high-powered games are done on a remote server and then transferred to the user, "If cloud gaming takes hold, gaming could quickly become one of the largest Internet traffic categories."Another possible game-changer for Internet traffic would be if people begin to get a large share of their television by "unicast," a separate stream coming to them, rather than by broadcast, which "carries one stream to many viewers." A final change would be a large move by consumers  to "ultra-high-definition" television.

It's intriguing to speculate about what very high levels of connectedness could mean. Easy access to movies and games is only the beginning, of course. High levels of connectivity may influence  where people will choose to live and work. It may mean that extraordinary levels of variety and customization become available. In a few years, downloading a file or movie from the cloud seems likely to be just as fast as downloading it from your personal computer or smartphone right now. The coming technology  means smart clothes. It may mean that buildings operate almost like a single machine: including homes, office buildings, and factories.It has applications for monitoriing of health and delivery of health care, transportation of goods and people, variable pricing, and the widespread availability of tracking what people watch, hear, and do. In  some very practical ways, our interactions with the world around us are going to feel quite different in just a few years.