RamDasshole188 karma2019-10-24 17:39:38 UTC
I think what she means is that it's very hard to accurately predict a recession with a high degree of certainty before it actually occurs. Look at 2008. That hit a lot of really smart, educated economists with literally no warning. Of course there were a few people who predicted it might happen, but they couldn't say when. Also, people predict recessions that don't occur for years after they say it will happen. There's just too many variables to know for certain.
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RamDasshole54 karma2019-10-24 17:34:44 UTC
It probably won't be as bad as the last one.. hopefully.
Many recent students are at higher risk of being the first ones fired during a recession, have little to no savings and would therefore have a hard time making payments if they lost their jobs. Would you say that student loan debt is the most vulnerable to default during the upcoming recession?
RamDasshole20 karma2019-10-24 18:53:52 UTC
It's not speculation. It is probably different based on the company and industry, but overall:
•Between 1996 and 2007, men age 50 to 61 are 21 percent less likely than those age 25 to 34 to become displaced from their jobs each month, and men age 62 or older are 23
percent less likely.
•The story is similar for women: compared with those age 25 to 34, women age 50 to 61 are 30 percent less likely to lose their jobs, and those age 62 or older are 13 percent less
•The protective effects of age, however, derive solely from older workers‘ seniority with their employers. When we hold job tenure and other characteristics constant, we find that older workers are just as likely as younger workers to lose their jobs. In fact, men age 50 to 61 are significantly more likely to become displaced from their jobs than men age 25 to 34 with the same length of service with the employer.
RamDasshole5 karma2019-10-24 17:42:54 UTC
Dude, it's definitely more of an art on some levels. It's all based on statistical models and that is very much an art when dealing with so much data. What do you include in the model? Which type of model should you use? If you include too much data, you can easily overfit, too little and you don't have any predictive power. That's where it's an art form.
RamDasshole4 karma2019-10-24 20:24:08 UTC
Yeah, I would guess that it's a variety of factors. Friendship might be a big factor. Working with someone for 20 years vs 2 would make it much harder to let someone go. I think you're right with your points.
It might also be that a veteran employee might know the ins and outs and be very hard to replace. They know where the bodies are buried etc.
On the other hand, in industries like tech where things can change rapidly, being senior means much less as it can mean you're not up to date on the latest tech or can't adapt as quickly to change, while still commanding a salary premium.
The only way to truly find out would be to survey employers as to why they let people go.
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