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Behaviour over time

Suggested tasks/questions for this sub section

Identify the potential worrying trends. What indicators/metrics can be used to track those trends? What should we keep an eye on?

What are the most relevant variables to track? Find key variables by looking at the issues of the problem situation.

Indicators can be based on negative or positive variables.

Indicators may be Quantitative (can be presented with a number or it can be Qualitative (cannot that easily be presented as a number).


If your challenge is about the obesity epidemic you would probably have overweight and obesity levels as percentage of the population as indicators.

Watch out for

  • Don't add indirect / causal indicators here. Causal variables will be added in later stages.
  • Don't get stuck with a fixed set of indicators. As you learn more about the problem you might find better indicators to track.

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Further reading for intermediate problem solvers:

The indicators are the most important variables in your challenge in the start phase. The indicators will help us to know what to measure and track.

Tracking these indicators over time is important in order to know if the problem situation is getting worse or better.

The indicators are evolved and expressed as more actionable Metrics further into the process.

Qualitative and quantitative data on these indicators will then be used to assess the system performance and to detect anomalies and/or undesired trends.

Choosing the right indicators relies upon a good understanding of what is important for the people involved in the problem situation as well as to the greater society. That means that you should keep an eye open for better indicators.

Suggested reading

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What are the measurable indicators of the problem and how have they changed over time?


Key Indicators of the Labour Market (KILM) 2013

Long-term unemployment, the new challenge for many countries

Job seekers are finding it increasingly difficult to secure a new job within six months or less, according to the new edition of the ILO's “Key Indicators of the Labour Market”.

News | 11 December 2013

GENEVA (ILO News) – Unemployment spells for workers are becoming longer in some countries compared to the pre-crisis situation in 2008, according to the new edition of the ILO Key Indicators of the Labour Market (KILM) 

“Headlines on a recent decline in unemployment rates hide the bitter reality that many jobless workers are finding it increasingly difficult to get into a new job within a reasonable period of time of 6 months or less,” says Ekkehard Ernst, chief of the ILO Employment Trends Unit. 

Where do job seekers have more chances to find a job within a year 

For example, in Spain, the United Kingdom, the United States, Serbia and Bulgaria, long-term unemployment has increased by 40 per cent or more in comparison to 2008. 

The latest edition of KILM – an online reference tool offering data and analysis on the world’s labour market – includes information about the dynamics of job losses and job creation in 70 developed and emerging economies. 

The new figures show that in countries with similar unemployment rates, there can be substantial differences in labour market trends. 

While both the United States and Germany had unemployment rates of around 6.3 per cent between 1970 and 2013, unemployment spells were on average shorter in the US labour market. In France, where unemployment rates have been about 30 per cent higher than in Germany since 1991, it takes on average less time for an unemployed worker to find a job than it does in Germany. 

In developing countries, the story is different. Workers move faster between spells of unemployment and employment than in advanced economies, but that’s because they transit frequently into informal employment. 

In Mexico, for instance, the number of people entering and leaving the labour market between 2001 and 2012 were 3.7 per cent and 69 per cent higher, respectively, than in the United States – one of the advanced economies with the highest labour market turnover. 

Where are workers more likely to become unemployed within a year 

“Unemployment rates only give a rough picture of the functioning of a country’s labour market. Our data will help countries adapt their policies to those categories of workers who are most affected by the dynamics of the labour market,” explains Ernst. 

The data on unemployment flows in the KILM cover, depending on the country, up to 30 years (1980-2012). It is the first time that such statistics have been collected to obtain a single, consistent picture of labour market dynamics in both developed and developing countries. 

Skills mismatches are widespread

Countries at all development levels find that adequate education and skills make the difference between inclusive growth and growth that leaves large segments of society behind. 

The report shows that the level of skills mismatch (the skills that workers have compared to what the market needs) in developing economies stood at an average of 17.1 per cent in 2012. During most of the past decade it was well below this level, particularly in advanced economies. 

The average incidence of over-qualification in developed economies was 10.1 per cent in 2010, up from 8.5 per cent in 2008, and particularly affected migrants, younger workers and persons with disabilities. Under-qualification in developed economies averaged 28.1 per cent in 2010 compared to 31 per cent in 2008. 

The report also shows that the incidence of over-education tends to increase over time. This is partly due to rising levels of educational attainment. In times of economic crises, when employment opportunities are scarce and unemployment rates are high, over-education tends to accelerate. 

In addition to employment, KILM data also includes information and analysis on wages, labour productivity, working poverty and other labour market issues. 


Add new key indicators

  • Enter your rationale for choosing this particular indicator.

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Suggested tasks/questions for this sub section

  • What is reoccuring?
  • What small events keep happening?
  • Where is it happening? Are some areas/demographics more or less affected?
  • What continues to happen (that should not happen)?
  • What seems to be going up and down at regular intervals?
  • What seem to be happening more often or less often?
  • What behavior or characteristics are getting more dominant or visible over time?
  • If you have access to larger datasets you may use statistical analysis tools like SAS to find patterns.

Watch out for

Advanced topics

Further reading for intermediate problem solvers:

The patterns sub section is used to record patterns in system behaviour.

In order to find causes the problem later on, it is useful to know the patterns within the problem situation.

This sub step helps us to get an overview of what is happening so we can begin to identify patterns of behavior which is imporant to understand the dynamics of the problem. Events give birth to trends.

Suggested reading

What are some patterns in the behavior of the problem?


Patterns of Unemployment

Let’s look at how unemployment rates have changed over time and how various groups of people are affected by unemployment differently.


Unemployment Rates by Group

Unemployment is not distributed evenly across the U.S. population. [link] shows unemployment rates broken down in various ways: by gender, age, and race/ethnicity.


Unemployment Rate by Demographic Group

The line graphs show how unemployment rates since 1972 have changed for various demographics. Unemployment rates for people over the age of 55 have remained lower than unemployment rates for people ages 16–54. While unemployment rates for all ethnicities tend to rise and fall around the same time, it is notable that the unemployment rate for whites has always been lower than that of blacks and Hispanics.

(a) By gender, 1972–2012. Unemployment rates for men used to be lower than unemployment rates for women, but in recent decades, the two rates have been very close, often with the unemployment rate for men somewhat higher. (b) By age, 1972–2012. Unemployment rates are highest for the very young and become lower with age. (c) By race and ethnicity, 1972–2012. Although unemployment rates for all groups tend to rise and fall together, the unemployment rate for whites has been lower than the unemployment rate for blacks and Hispanics in recent decades. (Source:


The unemployment rate for women had historically tended to be higher than the unemployment rate for men, perhaps reflecting the historical pattern that women were seen as “secondary” earners. By about 1980, however, the unemployment rate for women was essentially the same as that for men, as shown in [link] (a). During the recession of 2008–2009, however, the unemployment rate climbed higher for men than for women.

Read this report for detailed information on the recession of 2008–2009. It also provides some very useful information on the statistics of unemployment.


Younger workers tend to have higher unemployment, while middle-aged workers tend to have lower unemployment, probably because the middle-aged workers feel the responsibility of needing to have a job more heavily. Younger workers move in and out of jobs (and in and out of the labor force) more easily. Elderly workers have extremely low rates of unemployment, because those who do not have jobs often exit the labor force by retiring, and thus are not counted in the unemployment statistics. [link] (b) shows unemployment rates for women divided by age; the pattern for men is similar.

The unemployment rate for African-Americans is substantially higher than the rate for other racial or ethnic groups, a fact that surely reflects, to some extent, a pattern of discrimination that has constrained blacks’ labor market opportunities. However, the gaps between unemployment rates for whites and for blacks and Hispanics diminished in the 1990s, as shown in [link] (c). In fact, unemployment rates for blacks and Hispanics were at the lowest levels for several decades in the mid-2000s before rising during the recent Great Recession.

Finally, those with less education typically suffer higher unemployment. In early 2013, for example, the unemployment rate for those with a college degree was 3.7%; for those with some college but not a four-year degree, the unemployment rate was 6.0%; for high school graduates with no additional degree, the unemployment rate was 7.6%; and for those without a high school diploma, the unemployment rate was 10.3%. This pattern may arise because additional education offers better connections to the labor market and higher demand, or it may occur because the labor market opportunities for low-skilled workers are less attractive than the opportunities for the more highly-skilled. Because of lower pay, low-skilled workers may be less motivated to find jobs.

We hear about the Chinese economy in the news all the time. The value of the Chinese yuan in comparison to the U.S. dollar is likely to be part of the nightly business report. So why is the Chinese economy not included in this discussion of international unemployment? The lack of reliable statistics is probably the reason. This article explains why.However, cross-country comparisons of unemployment rates need to be treated with care, because each country has slightly different survey tools for measuring unemployment and also different labor markets. For example, Japan’s unemployment rates appear quite low, but Japan’s economy has been mired in slow growth and recession since the late 1980s, and Japan’s unemployment rate probably paints too rosy a picture of its labor market. In Japan, workers who lose their jobs are often quick to exit the labor force and not look for a new job, in which case they are not counted as unemployed. In addition, Japanese firms are often quite reluctant to fire workers, and so firms have substantial numbers of workers who are on reduced hours or officially employed, but doing very little. This Japanese pattern is perhaps best viewed as an unusual method for society to provide support for the unemployed, rather than a sign of a healthy economy.


Comparing unemployment rates in the United States and other high-income economies with unemployment rates in Latin America, Africa, Eastern Europe, and Asia is very difficult. One reason is that the statistical agencies in many poorer countries lack the resources and technical capabilities of the U.S. Bureau of the Census. But a more difficult problem with international comparisons is that in many low-income countries, most workers are not involved in the labor market through an employer who pays them regularly. Instead, workers in these countries are engaged in short-term work, subsistence activities, and barter. Moreover, the effect of unemployment is very different in high-income and low-income countries. Unemployed workers in the developed economies have access to various government programs like unemployment insurance, welfare, and food stamps; such programs may barely exist in poorer countries. Although unemployment is a serious problem in many low-income countries, it manifests itself in a different way than in high-income countries.

Key Concepts and Summary

The U.S. unemployment rate rises during periods of recession and depression, but falls back to the range of 4% to 6% when the economy is strong. The unemployment rate never falls to zero. Despite enormous growth in the size of the U.S. population and labor force in the twentieth century, along with other major trends like globalization and new technology, the unemployment rate shows no long-term rising trend.

Unemployment rates differ by group: higher for African-Americans and Hispanics than for whites; higher for less educated than more educated; higher for the young than the middle-aged. Women’s unemployment rates used to be higher than men’s, but in recent years men’s and women’s unemployment rates have been very similar. In recent years, unemployment rates in the United States have compared favorably with unemployment rates in most other high-income economies.


Add new pattern

Add a note about an observable pattern.

  • Consider learning about System archetypes and using that to see if you can identify patterns.

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Suggested tasks/questions for this sub section

How stable is the problem situation?

What is the changing magnitude of the problem? Is it variable? Are there turbulence at specific times? Are there wawes of change or alternations in how big the problem is?

  • Is the situation predictable?
  • Can it be characterized as chaotic?
  • Is it spiraling out of control?

Watch out for

Advanced topics

Further reading for intermediate problem solvers:

This sub section is used to give a textual summary of the behavior that is seen in the graphs.

Runaway change can have devestating effects and will usually impede an effective response. It is therefore important to know how drastically the situation is unfolding.

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How would we characterize the stability of the situation?


Unemployment and subsequent employment stability: does labour market policy matter?

Recent labour market reforms in Europe have been aimed at activating non-employed people and shortening unemployment duration. While this should indisputably be a central policy aim, the exclusive focus on quick re-employment neglects the importance of its quality and stability. Therefore, this paper analyses the effect of labour market policy on re-employment stability in Europe. Combining EU-SILC longitudinal survey data with macro-data on labour market policy, we conduct multi-level survival analysis. Empirical evidence suggests that countries with more generous unemployment insurance and higher active labour market policy (ALMP) spending achieve more sustainable reintegration of previously unemployed workers. These results point to a policy trade-off between the well-confirmed disincentive and locking-in effect of unemployment benefits and ALMP programmes on the one hand, and their positive effect on re-employment stability on the other hand.



Add new stability description

Add a description of the stability of the problem.

  • Is the situation getting out of control?
  • Can it be characterized as chaotic or unpredictable?

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