Manual Labor Market Adjustments in the Pacific Basin

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This approach is intended to eliminate movements in unemployment that are driven by changes in the composition of the labor force. The green line in Figure 1 shows the result of this type of demographic adjustment.

Issues with the conventional approach

This shift-share adjusted unemployment rate displays less of a downward trend than the published BLS unemployment rate, indicating that demographic changes have lowered the unemployment rate since the mids. But even with this adjustment, unemployment appears to be close to previous lows: As of February the shift-share adjusted rate stands at 5.


However, we believe this conclusion is premature. We find that the standard approach to demographic adjustment does not properly capture the full effects of demographic changes. In fact, once we address the shortcomings of the standard approach, the demographic-adjusted unemployment rate appears to be higher than all its previous lows since To illustrate why a shift-share analysis does not properly correct for the effects of labor market changes related to demographics, we explore the effect of a specific ongoing shift in the U. A shift-share analysis will appropriately capture this effect.

However, the delayed labor force entry of young workers will also affect the aggregate unemployment rate through another channel: A change in the unemployment rate of young workers. A shift-share analysis will miss this effect. If you turn on the tap and pull the plug, the level of water in the tub changes with the flow of water into and out of the bathtub. If the rate of water flowing into the tub slows by turning the faucet down, while the draining rate remains the same, the water level will go down. A shift-share analysis controls for demographic changes by holding labor force shares fixed in its definition of aggregate unemployment.

Thus, it will completely miss the fact that a demographic change—like delayed labor force entry—can also lower the unemployment rate of young workers. Since a lower unemployment rate for young workers also implies a lower aggregate unemployment rate, a shift-share analysis will, in this example, underestimate the correction needed to account for the effects of changing demographics on aggregate unemployment.

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  • خرید: Labor Market Adjustments in the Pacific Basin!

To address the issues of a traditional shift-share analysis, we apply a new demographic-adjustment method Barnichon and Mesters that uses worker flows directly instead of unemployment stocks. We construct an alternative scenario in which the aggregate unemployment rate is not driven by trends in worker flows specific to certain demographic groups only. Instead our method accounts for aggregate labor market forces, that is, movements in the flows that are common across all demographic groups.

To do so, we use a dynamic factor model of the worker flows, which is a statistical analysis of the historical pattern of flows between employment, unemployment, and nonparticipation by each of the 11 demographic groups noted earlier. This statistical model allows us to separate all demographic influences from aggregate labor market trends.

For instance, our procedure will identify the long-term decline in the tendency of young workers to flow into unemployment as a demographic change, because other demographic groups did not experience such a change.

Labor Market Adjustments in the Pacific Basin

Once we identify all the demographic trends, we can then purge the aggregate unemployment rate of their effects. Our demographic-adjusted unemployment rate red line, Figure 1 stands at 5. Our adjusted unemployment rate is no longer lower than those in the labor market peaks of and —in fact, our adjusted rate is higher than all its previous lows since , with lows of 4.

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  7. Thus, once adjusted for demographic changes, the current unemployment rate is still 0. Taking a longer-run perspective, we consider the effects of demographics on unemployment since the mids and their underlying causes.


    Figure 2 shows that demographic factors lowered the unemployment rate by about 2 percentage points over this period, according to our adjustment method. This number is substantially larger than that implied by a conventional shift-share analysis, which suggests demographics lowered unemployment by just over 1 percentage point. This finding raises an interesting follow-up question: Which demographic changes are responsible for the decline in unemployment since the mids? We have already mentioned the first two phenomena. The last one was first documented by Abraham and Shimer and corresponds to a secular decline in the propensity of working women to leave the labor force, for instance, to raise children.

    Thus, a shift-share analysis understates the effects of demographics because it does not take into account either the trend of fewer young workers entering the labor force or the lower propensity of women to leave employment, both of which lowered the overall unemployment rate. Technological innovations and their applications are rapidly changing the ways in which we communicate, conduct business and connect. Policies, frameworks, rules and regulations need to continuously remain agile as well as increase capacities in order to adapt to such changes.

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