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In our teens and early twenties, we're still figuring out what we want to be when we grow up. As we get older, we start to settle into a career. In between, we switch jobs in the search. Based on data from the Current Population Survey, this is when people make the switches and the jobs they switch to.The chart above shows the rate by age, relative to the total number of people who switched to each job. So you see a lot of switching in the early years, and then things seem to settle down at older ages. If someone takes a new job when they're older, it tends towards management or jobs that require more education.

Machines are besting humans in more and more tasks; thanks to technology, fewer Americans make more stuff in less time. But today economists debate not whether machines are changing the workplace and making us more efficient - they certainly are - but whether the result is a net loss of jobs. The figures above may look dire. But compare the number of manufacturing jobs and total jobs in the chart below. Since 1990, the total non-farm workforce has grown 33 percent, more than accounting for the manufacturing jobs lost.

Christopher Pyne has overstated how well Australia is performing on jobs and growth compared to other major economies. IMF estimates for 2016 on GDP growth had put Australia ahead of the G7 countries. But the latest available data - which are actual figures as of the third quarter of 2016, not estimates - show that Australia's cumulative growth in 2016 so far is at the level of the G7 and not higher. So Australia is performing in line with the G7 and slightly worse than the OECD average.

Women continue to earn less than men, for a variety of reasons. Discrimination is one, research shows. Women are also likelier than men to work in lower-paying jobs like those in public service, caregiving and the nonprofit sector - and to take time off for children. Employers often base a starting salary on someone's previous one, so at each job, the gender pay gap continues, and it becomes seemingly impossible for women to catch up. Salary history bans are too new for researchers to have studied their effects extensively. But other research has found that people are overly influenced by an opening bid, something social scientists call anchoring bias. This means that if employers learn an applicant's previous salary and it's lower or higher than they were planning to offer, it's likely to influence their offer.

Our entire education system is faulty, claim experts. They worry that schools don't prepare kids for the world outside. But how could anyone prove what the future will be like? We set off on a round-the-world sleuthing trip to trace a statistic that has been causing headaches for students, teachers and politicians alike.

Algorithms decide which applications reach human managers' eyes. But they sort out people with unusual work histories or who lack college degrees

Journalists often make mistakes when reporting on data such as opinion poll results, federal jobs reports and census surveys because they don't quite understand - or they ignore - the data's margin of error. Data collected from a sample of the population will never perfectly represent the population as a whole. The margin of error, which depends primarily on sample size, is a measure of how precise the estimate is. The margin of error for an opinion poll indicates how close the match is likely to be between the responses of the people in the poll and those of the population as a whole. To help journalists understand margin of error and how to correctly interpret data from polls and surveys, we've put together a list of seven tips, Look for the margin of error - and report it. It tells you and your audience how much the results can vary. Remember that the larger the margin of error, the greater the likelihood the survey estimate will be inaccurate. Make sure a political candidate really has the lead before you report it. Note that there are real trends, and then there are mistaken claims of a trend. Watch your adjectives. (And it might be best to avoid them altogether.) Keep in mind that the margin of error for subgroups of a sample will always be larger than the margin of error for the sample. Use caution when comparing results from different polls and surveys, especially those conducted by different organizations.

In what feels like an increasingly polarised world, trying to convince the "other side" to see things differently often feels futile. Psychology has done a great job outlining some of the reasons why, including showing that, regardless of political leanings, most people are highly motivated to protect their existing views."Our results show why debates about controversial issues often seem so futile," the researchers said. "Our values can blind us to acknowledging the same logic in our opponent's arguments if the values underlying these arguments offend our own."

EVERYONE HAS A THEORY about who's to blame for the housing shortage that's driving up prices and chasing Bay Area families out of the region. A new poll offers surprising insights into where most of us point the finger: not at the government officials who control what homes are built where, but at the tech companies that have flooded this region with jobs and the real estate developers trying to maximize profits.

Nice article, don't just show the data, tell us what it means "I use a Misfit activity tracker to count my steps. The Misfit app does a decent job of showing me step counts per day and every month, misfit also sends me a summary of the previous month's activity. Unfortunately, the numbers in that summary are presented without any context, making that summary almost entirely useless."

Interesting discussion of a contentious issue related to the idea of a 'basic income' (but here, in a development context). Martin Ravallion (former Chief Economist of the World Bank, now at CGD) published a useful paper this week asking exactly this question. As he says, there's no simple answer - which is why the question is so interesting. Both 'the right to work' and 'the right to income' aim to secure a more fundamental right: freedom from poverty. Workfare has a long history, notably in India, where the National Rural Employment Guarantee Act (NREGA) guarantees (in theory) up to 100 days work per year, paid at a minimum wage, to anyone who requests it. Cash transfers (often with conditions) have expanded enormously in recent years, while the hot topic of Universal Basic Income (UBI) has advocates across the political spectrum. Which of these approaches is most cost-effective? Ravallion sets out the arguments clearly.

The proposal to drug test welfare recipients keeps on bouncing back. The most recent attempt, announced last week, is now the third proposal since 2017. But the tenacity with which the government is pursuing this agenda reflects, not necessarily a fixed policy position, but rather a moral stance. And this moral stance conflicts with that of the proposals' critics. Are we doomed to countless repeats of the same policy proposal? Or, as the Australian Social Policy Conference heard in Sydney this week, can we use philosophical arguments to help break the deadlock?

Interesting discussion of evidence on what does, and does not, work in tackling gender bias in recruitment and management processes. Evidence shows that skills-based assessment tasks (where candidates are given tests that replicate the work they'll actually do on the job) and structured interviews (where all candidates are given the same questions in the same order) have a positive impact on diverse recruitment. Unstructured interviews are more likely to allow unfair bias to creep in. Making promotion and pay processes more transparent can reduce pay inequality: when decisions are reviewed by others, managers realise they need to be objective and evidence-based. Evidence also shows women ask for less money than men. To encourage them to negotiate more, employers should make the possible salary range for roles clear. Studies indicate that women are put off negotiating when they're not sure what a reasonable offer is. "A lot of employers are genuinely really keen to reduce the gender pay gap, and also want to show they're making a change. But they're starved for information about what is likely to work,"

the use of historical data to train the AI gives 'a leg-up to people from groups who have traditionally been successful and grants fewer opportunities to minorities and women'.