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Aging, growing, and slowing

GeoFRED lends insight into global population trends

The U.N. estimates the world’s population may rise to over 11 billion by 2100. Much of this growth will occur in developing countries, while populations in nations such as Japan and Germany have already begun to decline. Current data in GeoFRED can help us visualize these changes in world population.

The first map shows global population growth as a yearly percent change. The contrasts between continents are striking: Populations in Eastern European nations declined nearly 1% in 2015, while populations grew over 3% in a substantial number of African and Middle Eastern nations. Fertility and mortality are key factors, as is migration. Lithuania, for example, saw nearly 170,000 people emigrate in 2012—a loss of over 5% of total population. Meanwhile Oman, which had the highest increase in the 2015 data, gained over 1 million immigrants in 2012. Given that the total population of Oman was under 3.5 million that year, to say that migration had a substantial effect on the population is a gross understatement.

The second map shows the percentage of the population under 14. The same European nations that saw populations shrink in 2015 had 15% or less of the population aged 14 and under. In some of the nations with high growth rates, over 9 in 20 inhabitants were below the age of 14. The inverse is also true: In nations where populations are in decline, the proportion of retired adults as a share of the overall population is substantially larger than that of faster-growing nations. This discrepancy presents a problem for governments and citizens alike: The costs of supporting the retired population, whose lifespans are lengthening, increase while the proportion of working-age citizens who pay into entitlement programs that support them decreases. For example, the share of adults aged 65 and over grew by nearly 4% in Poland last year, yet the overall population declined by 1/10 of 1%.

We can predict that, as growth slows, populations age, thanks to the principle of population momentum: Even if families have fewer children and population growth lessens, those already born continue to age, resulting in populations skewed toward the elderly. Economic development plays a role as well. The last map shows the same pattern of colors across the continents as the others, with south Asia and central Africa standing apart from Europe, North America, and parts of South America. Yet the data being represented have nothing to do with population. Rather, this map shows GDP per capita, which approximates a nation’s average standard of living. As living standards improve, individuals are more likely to have access to reliable medical services, including contraceptives, and may prioritize formal employment over parenthood as infant mortality rates decline and job opportunities grow. Thus, nations with higher GDP per capita tend to have aging populations and slower, if any, population growth as they progress through the demographic transition.

How these maps were created: The original post referenced interactive maps from our now discontinued GeoFRED site. The revised post provides replacement maps from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.

Suggested by Maria Hyrc.

A widening disconnect?

Disconnected youth in America since the Great Recession

In Sierra County, California, more than 57% of 16- to 19-year-olds were neither enrolled in school, employed, nor actively looking for a job in 2015. In Nemaha County, Kansas, that figure was less than 0.17%. Outside both the labor force and the education system, these teens are known as “disconnected youth,” and this measure has gained increasing attention in recent years: The ranks of disconnected youth rose during the Great Recession, given that the percentage of disconnected youth is closely tied to local economic and social conditions. FRED has county-level data for the percentages of 16- to 19 year-olds considered disconnected from 2009 to 2015.

The maps show the percent change in disconnected youth in 2015 and in 2010. The map for 2010 shows more red, and the map for 2015 shows more blue, which indicates the percentages increased markedly in many more counties in 2010 than 2015, likely due to the residual effects of the Great Recession. According to the Measure of America, youth disconnection tends to increase along with poverty and adult disconnection—both of which were characteristic in the high-unemployment, low-output environment of 2007-09. High ratios of disconnected youth can have devastating effects on both individuals and the overall economy: Without a work or school environment, many 16- to 19-year-olds lack mentorship, resources, and engagement. In addition, fewer workers in the labor force combined with lower tax revenues and higher strains on social assistance programs can decrease the economic potential of counties and states.

The more recent data show decreases in disconnected youth in many Western counties, especially in Idaho and Utah. However, much of Georgia and Texas saw increases in disconnected youth, some up to 23%. Overall, the picture looks better in 2015 than in 2010. The average percent change in disconnected youth in 2015 was -0.31%, compared with a rise of 0.18% in 2010. This shift from an average rise to an average decline in disconnected youth is statistically significant; however, the averages are not adjusted for county populations (values corresponding to large and small counties carried the same weight) and the slight increase does not necessarily reflect a continuing trend.

Like many economic indicators, the proportion of disconnected youth has improved slightly since the Great Recession, yet still has a long way to go.

How these maps were created: The original post referenced interactive maps from our now discontinued GeoFRED site. The revised post provides replacement maps from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.
Suggested by Maria Hyrc.

‘Tis the season for adjustment

What difference does seasonality make in the unemployment rate over time?

If you’ve spent time graphing in FRED, you’ve run across the term “seasonally adjusted.” Seasonal adjustment uses mathematical algorithms to adjust data for predictable seasonal variations using the changes between new observations and those from a year prior. When analyzing long-term trends in data such as unemployment, which tends to increase in January and June, seasonal adjustment helps economists see the cyclical trends underlying month-to-month patterns caused by school schedules, holidays, harvests, and a plethora of other factors. For more information on seasonal adjustment, see this explanation from the Federal Reserve Bank of Dallas.

The difference between the seasonally adjusted unemployment rate and the original is presented in the top graph, where subtracting the raw data from the adjusted data shows the impact of seasonality on the data over time. Overall, seasonal adjustment made a bigger difference in the unemployment rate in the 1950s than it has in the past several decades because of the recent trend toward year-round employment. The distribution of workers across various industries has shifted significantly over the past 60 years, and different industries are subject to varying degrees of seasonal variation.

The graph below shows the overall declines of employment in two U.S. industries: Agriculture, which hires more workers during harvest seasons, employed 4.4% of workers in 1970 but only 1.2% in 2012. Likewise, manufacturing employment has declined by 16.1% over the same span of time and also employs workers based on seasonal factors and fluctuating production needs. By contrast, the Bureau of Labor Statistics reports that, between 1939 and 2015, employment in the private education and health services sector, known as the least cyclical sector in the economy, grew from 4.6% to 15.6% of all nonfarm jobs. Other sectors that have grown in employment share include retail trade and financial activities, which employ most workers year-round and thus do not contribute to seasonal variation as much as the industries that have declined.

How these graphs were created: For the first graph, search for “monthly unemployment rate U.S.” and select the seasonally adjusted series. Click “Add to Graph.” Under “Edit line 1,” add another series by searching for “monthly unemployment rate U.S.” again and selecting the not seasonally adjusted data. Click “Add.” In the formula tab, enter a-b and click “Apply.” For the second graph, search for “percent unemployment in agriculture” and select the relevant series. Click “Add Line” in the “Edit Graph” page and search for “percent unemployment in manufacturing” and select the relevant series.

Suggested by Maria Hyrc.

View on FRED, series used in this post: UNRATE, UNRATENSA, USAPEFANA, USAPEMANA


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