Comunidad Energética

Este concurso busca promover la participación de la ciudadanía y la comunidad organizada en la acción climática, para contribuir en la transición energética hacia un desarrollo sostenible y bajo en carbono en sus territorios. Para ello, se dispondrá un total $115.000.000 para financiar iniciativas de hasta 5 millones cada una, asociadas a líneas de acción como: eficiencia energética y recursos hídricos, generación local, energías renovables y residuos, movilidad sostenible, reforestación, protección ambiental y mejoramiento de suelos.

Para postular, envía un video de máximo 90 segundos explicando el problema energético que tienes y la solución con la que quieres postular.

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3° Concurso de Implementación de proyectos de Inversión Energética Local en el marco del Programa Comuna Energética

Implementación de proyectos de inversión energética local en el marco del Programa Comuna Energética: Este concurso está dirigida a todas las comunas del país y tiene como objetivo fomentar el mercado de inversión energética local, con un enfoque en el desarrollo de la generación distribuida a partir de energías renovables y medidas de eficiencia energética, a través del financiamiento de proyectos de pequeña escala que promuevan la creación de esquemas asociativos y modelos de negocio innovadores para el Desarrollo Energético Local en las comunas de Chile. Así, se financiarán proyectos de infraestructura energética a escala local a ser implementados en los territorios, liderados por los Municipios junto a empresas privadas Para ello, se dispondrá un total de $400.000.000, para cofinanciar proyectos con un máximo de $55.000.000 para proyectos individuales, lo que corresponde al 50% del total del proyecto (necesario el financiamiento privado del 50% restante)

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Fuente: porelclima.cl

After millennia of relying on anecdotes, instincts, and old wives’ tales as evidence of our opinions, most of us today demand that people use data to support their arguments and ideas. Whether it’s curing cancer, solving workplace inequality, or winning elections, data is now perceived as being the Rosetta stone for cracking the code of pretty much all of human existence.

But in the frenzy, we’ve conflated data with truth. And this has dangerous implications for our ability to understand, explain, and improve the things we care about.

I have skin in this game. I am a professor of data science at NYU and a social-science consultant for companies, where I conduct quantitative research to help them understand and improve diversity. I make my living from data, yet I consistently find that whether I’m talking to students or clients, I have to remind them that data is not a perfect representation of reality: It’s a fundamentally human construct, and therefore subject to biases, limitations, and other meaningful and consequential imperfections.

The clearest expression of this misunderstanding is the question heard from boardrooms to classrooms when well-meaning people try to get to the bottom of tricky issues:

“What does the data say?”

Data doesn’t say anything. Humans say things. They say what they notice or look for in data—data that only exists in the first place because humans chose to collect it, and they collected it using human-made tools.

Data can’t say anything about an issue any more than a hammer can build a house or almond meal can make a macaron. Data is a necessary ingredient in discovery, but you need a human to select it, shape it, and then turn it into an insight.

Data is therefore only as useful as its quality and the skills of the person wielding it. (You know this if you’ve ever tried to make a macaron. Which I have. And let’s just say that data would certainly not be up to a French patisserie’s standard.)

So if data on its own can’t do or say anything, then what is it?

What is data?

Data is an imperfect approximation of some aspect of the world at a certain time and place. (I know, that definition is a lot less sexy than we were all hoping for.) It’s what results when humans want to know something about something, try to measure it, and then combine those measurements in particular ways.

Here are four big ways that we can introduce imperfections into data.

• random errors
• systematic errors
• errors of choosing what to measure
• and errors of exclusion

These errors don’t mean that we should throw out all data ever and nothing is knowable, however. It means approaching data collection with thoughtfulness, asking ourselves what we might be missing, and welcoming the collection of further data.

This view is not anti-science or anti-data. To the contrary, the strength of both comes from being transparent about the limitations of our work. Being aware of possible errors can make our inferences stronger.

The first is random errors. This is when humans decide to measure something, and then either due to broken equipment or their own mistakes, the data recorded is wrong. This could take the form of hanging a thermometer on a wall to measure the temperature, or using a stethoscope to count heartbeats. If the thermometer is broken, it might not tell you the right number of degrees. The stethoscope might not be broken, but the human doing the counting might space out and miss a beat.

A big way this plays out in the rest of our lives (when we’re not assiduously logging temperatures and heartbeats) is in the form of false positives in medical screenings. A false positive for, say, breast cancer, means the results suggest we have cancer but we don’t. There are lots of reasons this might happen, most of which boil down to a misstep in the process of turning a fact about the world (whether or not we have cancer) into data (through mammograms and humans).

The consequences of this error are very real, too. Studies show a false positive can lead to years of negative mental-health consequences, even though the patient turned out to be physically well. On the bright side, the fear of false positives can also lead to more vigilant screening (…which increases the chances of further false positives, but I digress).

Generally speaking, as long as our equipment isn’t broken and we’re doing our best, we hope these errors are statistically random and thus cancel out over time—though that’s not a great consolation if your medical screening is one of the errors.

The second is systematic errors. This refers to the possibility that some data is consistently making its way into your dataset at the expense of others, thus potentially leading you to make faulty conclusions about the world. This might happen for lots of different reasons: who you sample, when you sample them, or who joins your study or fills out your survey.

A common kind of systematic error is selection bias. For example, using data from Twitter posts to understand public sentiment about a particular issue is flawed because most of us don’t tweet—and those who do don’t always post their true feelings. Instead, a collection of data from Twitter is just that: a way of understanding what some people who have selected to participate in this particular platform have selected to share with the world, and no more.

The 2016 US presidential election is an example where a series of systematic biases may have led the polls to wrongly favor Hillary Clinton. It can be tempting to conclude that all polling is wrong—and it is, but not in the general way we might think.

One possibility is that voters were less likely to report that they were going to vote for Trump due to perceptions that this was the unpopular choice. We call this social desirability bias. It’s useful to stop to think about this, because if we’d been more conscious of this bias ahead of time, we might have been able to build it into our models and better predict the election results.

Medical studies are sadly riddled with systematic biases, too: They are often based on people who are already sick and who have the means to get to a doctor or enroll in a clinical trial. There’s some excitement about wearable technology as a way of overcoming this. If everyone who has an Apple Watch, for example, could just send their heart rates and steps per day to the cloud, then we would have tons more data with less bias. But this may introduce a whole new bias: The data will likely now be skewed to wealthy members of the Western world.

The third is errors of choosing what to measure. This is when we think we’re measuring one thing, but in fact we’re measuring something else.

I work with many companies who are interested in—laudably—finding ways to make more objective hiring and promotion decisions. The temptation is often to turn to technology: How can we get more data in front of our managers so they make better decisions, and how can we apply the right filters to make sure we are getting the best talent in front of our recruiters?

But very few pause to ask if their data is measuring what they think it’s measuring. For example, if we are looking for top job candidates, we might prefer those who went to top universities. But rather than that being a measure of talent, it might just be a measure of membership in a social network that gave someone the “right” sequence of opportunities to get them into a good college in the first place. A person’s GPA is perhaps a great measure of someone’s ability to select classes they’re guaranteed to ace, and their SAT scores might be a lovely expression of the ability of their parents to pay for a private tutor.

Companies—and my students—are so obsessed with being on the cutting edge of methodologies that they’re skipping the deeper question: Why are we measuring this in this way in the first place? Is there another way we could more thoroughly understand people? And, given the data we have, how can we adjust our filters to reduce some of this bias?

Finally, errors of exclusion. This happens when populations are systematically ignored in datasets, which can set a precedent for further exclusion.

For example, women are now more likely to die from heart attacks than men, which is thought to be largely due to the fact that most cardiovascular data is based on men, who experience different symptoms from women, thus leading to incorrect diagnoses.

We also currently have a lot of data on how white women fare when they run for political office in the US, but not a lot on the experiences of people of color (of any gender, for that matter), who face different biases compared to white women on the campaign trail. (And that’s not even mentioning the data on the different experiences of, say, black candidates compared to Latinx candidates, and so on). Until we do these studies, we’ll be trying to make inferences about apples from data about oranges—but with worse consequences than an unbalanced fruit salad.

Choosing to study something can also incentivize further research on that topic, which is a bias in and of itself. As it’s easier to build from existing datasets than create your own, researchers often gather around certain topics—like white women running for office or male cardiovascular health—at the expense of others. If you repeat this enough times, all of a sudden men are the default in heart-disease studies and white women are the default in political participation studies.

Other examples abound. Measuring “leadership” might incentivize people to be more aggressive in meetings, thus breaking down communication in the long run. Adding an “adversity” score to the SATs might incentivize parents to move to different zip codes so their scores are worth more.

I also see this play out in the diversity space: DiversityInc. and other organizations that try to evaluate diversity of companies have chosen a few metrics on which they reward companies—for example, “leadership buy-in,” which is measured by having a Chief Diversity Officer. In order to tick this box, it has incentivized a burst of behaviors that may not actually do anything, like appointing a CDO who has no real power.

Why we still need to believe in data

In the age of anti-intellectualism, fake news, alternative facts, and pseudo-science, I am very reluctant to say any of this. Sometimes it feels like we scientists are barely hanging on as it is. But I believe that the usefulness of data and science comes not from the fact that it’s perfect and complete, but from the fact that we recognize the limitations of our efforts. Just as we want to analyze things carefully with statistics and algorithms, we also need to collect it carefully, too. We are only as strong as our humility and awareness of our limitations.

This doesn’t mean throw out data. It means that when we include evidence in our analysis, we should think about the biases that have affected their reliability. We should not just ask “what does it say?” but ask, “who collected it, how did they do it, and how did those decisions affect the results?”

We need to question data rather than assuming that just because we’ve assigned a number to something that it’s suddenly the cold, hard Truth. When you encounter a study or dataset, I urge you to ask: What might be missing from this picture? What’s another way to consider what happened? And what does this particular measure rule in, rule out, or incentivize?

We need to be as thoughtful about data as we are starting to be about statistics, algorithms, and privacy. As long as data is considered cold, hard, infallible truth, we run the risk of generating and reinforcing a lot of inaccurate understandings of the world around us.

Source: qz.com

When you’re a leader — no matter how long you’ve been in your role or how hard the journey was to get there — you are merely overhead unless you’re bringing out the best in your employees. Unfortunately, many leaders lose sight of this.

Power, as my colleague Ena Inesi has studied, can cause leaders to become overly obsessed with outcomes and control, and, therefore, treat their employees as means to an end. As I’ve discovered in my own research, this ramps up people’s fear — fear of not hitting targets, fear of losing bonuses, fear of failing — and as a consequence people stop feeling positive emotions and their drive to experiment and learn is stifled.

Take for example a UK food delivery service that I’ve studied. The engagement of its drivers, who deliver milk and bread to millions of customers each day, was dipping while management was becoming increasingly metric-driven in an effort to reduce costs and improve delivery times. Each week, managers held weekly performance debriefs with drivers and went through a list of problems, complaints, and errors with a clipboard and pen. This was not inspiring on any level, to either party. And, eventually, the drivers, many of whom had worked for the company for decades, became resentful.

This type of top-down leadership is outdated, and, more importantly, counterproductive. By focusing too much on control and end goals, and not enough on their people, leaders are making it more difficult to achieve their own desired outcomes.

The key, then, is to help people feel purposeful, motivated, and energized so they can bring their best selves to work.

There are a number of ways to do this, as I outline in my new book Alive at Work. But one of the best ways is to adopt the humble mind-set of a servant leader. Servant leaders view their key role as serving employees as they explore and grow, providing tangible and emotional support as they do so.

To put it bluntly, servant-leaders have the humility, courage, and insight to admit that they can benefit from the expertise of others who have less power than them. They actively seek the ideas and unique contributions of the employees that they serve. This is how servant leaders create a culture of learning, and an atmosphere that encourages followers to become the very best they can.

Humility and servant leadership do not imply that leaders have low self-esteem, or take on an attitude of servility. Instead, servant leadership emphasizes that the responsibility of a leader is to increase the ownership, autonomy, and responsibility of followers — to encourage them to think for themselves and try out their own ideas.

Here’s how to do it.

Ask how you can help employees do their own jobs better — then listen

It sounds deceivingly simple: Rather than telling employees how to do their jobs better, start by asking them how you can help them do their jobs better. But the effects of this approach can be powerful.

Consider the food-delivery business I previously mentioned. Once its traditional model was disrupted by newer delivery companies, the management team decided that things needed to change. The company needed to compete on great customer service, but, in order to do so, they needed the support of their employees who provided the service. And, they needed ideas that could make the company more competitive.

After meeting with consultants at PricewaterhouseCoopers and some training, the management team tried a new format for its weekly performance meetings with the drivers.

The new approach? Instead of nit-picking problems, each manager was trained to simply ask their drivers, “How can I help you deliver excellent service?” As shown in the research of Bradley Owens and David Heckman, leaders need to model these types of servant-minded behaviors to employees so that employees will better serve customers.

There was huge scepticism at the beginning, as you can imagine. Drivers’ dislike of managers was high, and trust was low. But as depot managers kept asking “How can I help you deliver excellent service?” some drivers started to offer suggestions. For example, one driver suggested new products like Gogurts and fun string cheese that parents could get delivered early and pop into their kids’ lunches before school. Another driver thought of a way to report stock shortages more quickly so that customers were not left without the groceries they ordered.

Small changes created a virtuous cycle. As the drivers got credit for their ideas and saw them put into place, they grew more willing to offer more ideas, which made the depot managers more impressed and more respectful, which increased the delivery people’s willingness to give ideas, and so on. And, depot managers learned that some of the so-called “mistakes” that drivers were making were actually innovations they had created to streamline processes and still deliver everything on time. These innovations helped the company deliver better customer service.

What it comes down to is this: employees who do the actual work of your organization often know better than you how to do a great job. Respecting their ideas, and encouraging them to try new approaches to improve work, encourages employees to bring more of themselves to work.

As one area manager summarized: “We really thought that we knew our delivery people inside out, but we’ve realized that there was a lot we were missing. Our weekly customer conversation meetings are now more interactive and the conversations are more honest and adult in their approach. It’s hard to put into words the changes we are seeing.”

Create low-risk spaces for employees to think of new ideas

Sometimes the best way for leaders to serve employees — and their organization — is to create a low-risk space for employees to experiment with their ideas. By doing so, leaders encourage employees to push on the boundaries of what they already know.

For example, when Jungkiu Choi moved from Singapore to China to start his gig as head of Consumer Banking at Standard Chartered, he learned that one of the cultural expectations of his new job was to visit the branches and put pressure on branch managers to cut costs. Branch staff would spend weeks anxiously preparing for the visit.

Jungkiu changed the nature of these visits. Instead of emphasizing his formal power, he started showing up at branches unannounced, starting his visit by serving breakfast to the branch employees. Then, Jungkiu would hold “huddles” and ask how he could help employees improve their branches. Many branch employees were very surprised and initially did not know how to react. But Jungkiu’s approach tamped down employees’ anxiety and encouraged ideation and innovative ideas.

Over the course of one year, Jungkiu visited over eighty branches in twenty-five cities. His consistency and willingness to help convinced employees who were sceptical at first. The huddles exposed many simple “pain points” that he could easily help solve (for example, training for the new bank systems, or making upgrades to computer memory so that the old computers could handle the new software).

Other employee innovation ideas were larger. For example, one of the Shanghai branches was inside of a shopping mall. In the huddle, employees asked Jungkiu if they could open and close the same times as the mall’s operating hours (rather than the typical branch operating hours). The team wanted to experiment with working on the weekends. Within a few months, this branch’s weekend income generation surpassed its entire weekday income. This was not an idea that Jungkiu had even imagined.

These experiments paid off in terms of company performance. Customer satisfaction increased by 54 percent during the two-year period of Jungkiu’s humble leadership. Complaints from customers were reduced by 29 percent during the same period. The employee attrition ratio, which had been the highest among all of the foreign banks in China, was reduced to the lowest among all foreign banks in China.

Be humble

Leaders often do not see the true value of their charges, especially “lower-level” workers. But when leaders are humble, show respect, and ask how they can serve employees as they improve the organization, the outcomes can be outstanding. And perhaps even more important than better company results, servant leaders get to act like better human beings.

Female investors have always kept discreet lists of other women angels and VCs. We circulate local lists amongst us, created Google spreadsheets and Whatsapp groups in an effort to create community, share deals, and support each other in a male-dominated industry.

“Our mission with the global directory of women in VC is to give women around the world the tools to better find each other, connect, and collaborate.” —Jessica Peltz-Zatulove

These efforts have been effective—but fragmented and mostly very localized. For this reason, I was particularly excited when Jessica Peltz-Zatulove and Sutian Dong started the Women in VC community several years ago and catalyzed it this year with a searchable database and Slack group. The Global Women in VC Directory now includes 1700+ women investors—at institutional funds, CVCs, family offices, and impact funds—across 1,000+ funds in 46 countries and 110+ cities globally. More than 50% of all women in the VC ecosystem are part of the community.

At Techstars, we share many of the same objectives as Women in VC, including a desire to connect founders to a diverse set of mentors and investors. So today we are excited to announce that Techstars and Women in VC have partnered to grow the number of women mentors across the Techstars ecosystem!

“Our mission with the global directory of women in VC is to give women around the world the tools to better find each other, connect, and collaborate,” said Jessica Peltz-Zatulove, co-creator of the Women in VC directory and Partner at MDC Ventures. “We believe creating a community-based support system is necessary to set more women up for success; it’s not only about actively recruiting more women investors, it’s about keeping the ones we have in the industry—part of that is helping to elevate their voices within their local communities. We’re thrilled to be partnering with Techstars globally to support their mission of diversity and inclusion.”

I’ve been an avid user of the Women in VC Slack community since the launch and have connected with dozens of female investors from London to Singapore, Chicago to LA. Increasing access to other women investors means that I can share deals with them and get them involved with the founders that we work with.

“We believe creating a community-based support system is necessary to set more women up for success.” —Jessica Peltz-Zatulove

Techstars Includes is our initiative to increase diversity and inclusion in the Techstars Network and entrepreneurship communities. This partnership is intended to leverage the Women in VC community to invite and recruit more female investors to join the Techstars mentor network, working toward our continued commitment to diversity and inclusion.

Techstars is committed to developing and supporting underrepresented entrepreneurs by fostering an inclusive Techstars Network. For the initial roll out, we will focus on New York, Los Angeles, London, Singapore, Detroit, Atlanta, Seattle, Boston, Denver/Boulder, Berlin, and Austin. Women in VC will also be a part of the Techstars Affiliate Program—an opportunity for any global Women in VC member to refer founders to any Techstars mentorship-driven accelerator program and fast-track their application process.

Source: www.techstars.com

How do creative people come up with great ideas? Organizational psychologist Adam Grant studies “originals”: thinkers who dream up new ideas and take action to put them into the world. In this talk, learn three unexpected habits of originals — including embracing failure. “The greatest originals are the ones who fail the most, because they’re the ones who try the most,” Grant says. “You need a lot of bad ideas in order to get a few good ones.”

This talk was presented at an official TED conference, and was featured by our editors on the home page.

Source: www.ted.com

On Monday, July 29, we will be 209 days into the calendar year. And we will have used up all the resources the Earth could regenerate in 365 days.

At least, that’s according to the Global Footprint Network, a group that uses an array of mostly United Nations data to calculate what it calls Earth Overshoot Day: the day when humanity overshoots the planet’s ability to recover from what resources we consume within each year—like regrow the trees we cut down, absorb the carbon dioxide we emit, and replenish the seas with the fish we harvest, to name a few. At this rate, it would take 1.75 Earths to sustainably meet the current demands of humanity, according to the available data.

It’s a useful visualization. But here’s the most sobering part: Earth Overshoot Day is probably a vast underestimation of the actual level of unsustainable planetary wreckage, and the scientists behind the numbers are the first to admit it.

David Lin, the chief science officer of the Global Footprint Network, likes to use the analogy of a bank account: If you have $100 in the bank and spend $200, that puts you in the red. You’ll have a deficit of $100. If you keep living like that, spending what you don’t have, eventually you’ll be in trouble. It’s not a sustainable way to live.

Each year, the human population grows. We consume more natural resources than the planet can regenerate in a year, and emit far more carbon dioxide than our forests and oceans can possibly sequester. Thus, our deficit grows. We fall further and further in the red. Last year’s Earth Overshoot Day was Aug. 1, three days later than this year’s. The date has crept up by two months over the last 20 years.

The Overshoot Day calculation relies heavily on an array of country-level and global UN data about everything from the food produced and consumed in each country to how much waste is generated, timber is felled, and fossil fuels are burned. The calculation is limited by the quality of the global data, which in itself might have some big underestimations built in. Research has suggested that the UN Food and Agriculture Organization might be underestimating the size of the global seafood harvest by 30%. But the UN numbers are the best available, for now, so that’s what they use.

“What we do is very much accounting,” said Lin, one of four researchers who spend six months each year crunching the numbers. “Everything we consume, and all the waste we produce—we can map back over how much bioproductive area it would require.”

They calculate individual overshoot days for each country, too; the US’s overshoot day was way back on March 15. Australia’s was March 31. Qatar’s was Feb. 11.

If the world’s population consumed natural resources and produced waste at the rate that the US does, it would take five Earths to sustainably meet those needs.

It’s a striking figure. But the truth is that it probably barely scratches the surface. Earth Overshoot Day does not, for example, take into account soil degradation, or water contamination, or mass species decline—because it isn’t designed to do that. It is simply a calculation of some inputs and outputs as they are now, and not how much resources might be depleted by human activity in the future. Ecological damage that doesn’t impact humans isn’t reflected here, either.

“It frequently gets misunderstood,” Lin says. “People think it’s this sustainability indicator of everything, which it isn’t. Because no such thing actually exists. It’s theoretically impossible.”

The global data for those indicators don’t exist—and even if they did, scientists would have to choose a time parameter for that “sustainability.” Is it sustainable for five years? Or for 10?

Overshoot Day doesn’t take into account, for example, whether our current level of agricultural output is ruining soils for future generations, or whether the current rate of groundwater pumping will result in desperate water shortages a few years down the line. “It’s a measure of what is. Not what could be,” Lin says. It’s a snapshot of Earth’s current deficits, but the future survivability of Earth depends on a lot more than what can be captured by that. “You could sell your organs for money, and your bank accounts that year might look okay, but that’s not sustainable,” Lin says.

It’s safe to say the situation is a lot worse than Overshoot Day lets on. But there’s still a place for the metric as a benchmark for the bare minimum of the trouble we’re in. “In spite of these things, we are still in overshoot. And we don’t have a better global measure for looking at this. No one has suggested a better way yet,” Lin says.

Source: qz.com

We often hear employers and business leaders lament the unfortunate gap between what students learn in college and what they are actually expected to know in order to be job-ready. This is particularly alarming in light of the large — and still growing — number of people graduating from university: above 40% of 25 to 34-year-olds in OECD countries, and nearly 50% of 25 to 34-year-olds in America.

Although there is a clear premium on education — recent reports from The Economistsuggest that the ROI of a college degree has never been higher for young people — the value added from a college degree decreases as the number of graduates increases. This is why a college degree will boost earnings by over 20% in sub-saharan Africa (where degrees are relatively rare), but only 9% in Scandinavia (where 40% of adults have degrees). At the same time, as university qualifications become more commonplace, recruiters and employers will increasingly demand them, regardless of whether they are actually required for a specific job. So, while tertiary degrees may still lead to higher-paying jobs, the same employers handing out these jobs are hurting themselves — and young people — by limiting their candidate pool to college graduates. In an age of ubiquitous disruption and unpredictable job evolution, it is hard to argue that the knowledge acquisition historically associated with a university degree is still relevant.

There are several data-driven arguments that question the actual, rather than the perceived, value of a college degree. First, meta-analytic reviews have long-established that the correlation between education level and job performance is weak. In fact, the research shows that intelligence scores are a much better indicator of job potential. If we were to pick between a candidate with a college degree and a candidate with a higher intelligence score, we could expect the latter to outperform the former in most jobs, particularly when those jobs require constant thinking and learning. Academic grades are indicative of how much a candidate has studied, but their performance on an intelligence test reflects their actual ability to learn, reason, and think logically.

College degrees are also confounded with social class and play a part in reducing social mobility and augmenting inequality. Many universities do select students on meritocratic grounds, but even merit-based selection is conflated with variables that decrease the diversity of admitted applicants. In many societies, there is a strong degree of assortative mating based on income and class. In the U.S., affluent people are more likely to marry other affluent people, and families with more money can afford to pay for schools, tutors, extracurriculars, and other privileges that increase their child’s likelihood of accessing an elite college education. This, in turn, affects the entire trajectory of that child’s future, including their future career prospects — providing a clear advantage to some and a clear disadvantage to others.

When employers attach value to university qualifications, it’s often because they see them as a reliable indicator of a candidate’s intellectual competence. If that is their focus, why not just use psychological assessments instead, which are much more predictive of future job performance, and less confounded with socioeconomic status and demographic variables?

Having said that, universities could substantially increase the value of the college degree if they spent more time teaching their students critical soft skills. Recruiters and employers are unlikely to be impressed by candidates unless they can demonstrate a certain degree of people-skills. This is perhaps one of the biggest differences between what universities and employers look for in applicants. While employers want candidates with higher levels of EQ, resilience, empathy, and integrity, those are rarely attributes that universities nurture or select for in admissions. As the impact of AI and disruptive technology grows, candidates who can perform tasks that machines cannot are becoming more valuable — and that underscores the growing importance of soft skills, which are hard for machines to emulate.

In a recent ManpowerGroup survey of 2,000 employers, over 50% of organizations listed problem-solving, collaboration, customer service, and communication as the most valued skills. Likewise, a recent report by Josh Bersin noted that employers today are as likely to select candidates for their adaptability, culture fit, and growth potential as for in-demand technical skills (e.g. python, analytics, cloud computing). Additionally, employers like Google, Amazon, and Microsoft, have highlighted the importance of learnability — being curious and having a hungry mind — as a key indicator of career potential. This is likely a result of the growing focus on employee training — one report shows U.S. companies spent over $90 billion on it in 2017. Hiring people with curiosity is likely to maximize the ROI of these programs.

There is also a huge opportunity for colleges to restore their relevance by helping to fill the learning gap many managers face when they are promoted into a leadership role. Today, people often take on leadership positions without much formal management training. Often, the strongest individual contributors are promoted into management, even though they haven’t developed the skills needed to lead a team. But if more schools invested in teaching those skills, organizations would have a larger amount of candidates with leadership potential.

In short, we believe that market demands clearly call for a paradigm change. More and more students are spending more and more money on higher education, and their main goal is largely pragmatic: to boost their employability and be a valuable contributor to the economy. Even if the value attached to a university degree is beneficial to those who obtain it, companies can help change the narrative by putting less weight on “higher education” as a measure of intellectual competence and job potential, and instead, approach hiring with more open-mindedness.

Source: hbr.org

At ten o’clock on a weekday morning in August, Mark Zuckerberg, the chairman and C.E.O. of Facebook, opened the front door of his house in Palo Alto, California, wearing the tight smile of obligation. He does not enjoy interviews, especially after two years of ceaseless controversy. Having got his start as a programmer with a nocturnal bent, he is also not a morning person. Walking toward the kitchen, which has a long farmhouse table and cabinets painted forest green, he said, “I haven’t eaten breakfast yet. Have you?”

Since 2011, Zuckerberg has lived in a century-old white clapboard Craftsman in the Crescent Park neighborhood, an enclave of giant oaks and historic homes not far from Stanford University. The house, which cost seven million dollars, affords him a sense of sanctuary. It’s set back from the road, shielded by hedges, a wall, and mature trees. Guests enter through an arched wooden gate and follow a long gravel path to a front lawn with a saltwater pool in the center. The year after Zuckerberg bought the house, he and his longtime girlfriend, Priscilla Chan, held their wedding in the back yard, which encompasses gardens, a pond, and a shaded pavilion. Since then, they have had two children, and acquired a seven-hundred-acre estate in Hawaii, a ski retreat in Montana, and a four-story town house on Liberty Hill, in San Francisco. But the family’s full-time residence is here, a ten-minute drive from Facebook’s headquarters.

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