#;Time And Relative Dimensions In Queue

[aealzx]

Striding forward a few steps, Raphael wasn’t bothered at all when Leon scrambled to detach Lil Mikey from himself and herd him near Donnie so he could turn to face Raphael with his hands held in a defensive barrier between his brothers and Raphael. Crouching before the three, Raphael raised a hand in offer, noting Loen eyeing it, then him, warily.

“Hey. Sorry for scaring you. I don’t want to fight anymore, okay?” Raphael apologized simply, keeping his hand out for a handshake. Leon could only look at him suspiciously, blinking at the offered hand, and then squinting suspiciously at Raphael. Was he trying to trick him somehow? When was the last time someone other than his family had apologized to him for scaring him?

While Leon was considering his options, and Raphael’s motivations, Raphael decided to change tactics, carefully raising his hand towards Leon. The kid’s eyes widened for a moment, and he looked about ready to dart. But thankfully he stayed relatively in place. It could have been because Lil Mkey had whispered to him that it was okay, but Raphael liked to think that Leon was taking a leap of faith that Raphael didn’t mean any harm. None of them had meant him harm in the first place, it was just a habit for them to be ready to fight when there was an intruder. But after realizing they were probably dealing with another cross dimension sibling they’d just wanted to get him to sit in one place long enough to talk, while not getting hurt themselves.

As Raphael’s hand got close to him Leon’s eyes snapped shut and he turned his head away with a soft hiss, as though he expected to get smacked. Raphael couldn’t blame him for reacting that way, and instead just motivated to be even more careful than he had already planned. There was a bandage on Leon’s head that hadn’t gone unnoticed, and Raphael was careful to avoid it as he tenderly rested his hand on the kid’s crown, gently rubbing back and forth. “It’s okay kid. I’m not mad,” Raphael assured, giving a rare, soft smile. “And I’m not going to hurt you, or any of your brothers. Okay? You’re safe here. We just want to help.”

He wasn’t sure how many more times they’d have to repeat it before Leon started to believe them, but he just kept gently petting the kid’s head. And when Leon opened his eyes again to peek at him, Raphael just smiled a bit bigger. “There you are. See? All good.”

The comment barely calmed Leon’s nerves, having fully expected to get scolded in some manner. But his shoulders did droop somewhat, and he finally turned more towards Lil Mikey to nuzzle his nose against Lil Mikey’s head in silent comfort. “...What happened? Why are you hurt?” Leon asked quietly, wrapping one arm around Lil Mikey to hold him close once more while the other arm did his best to do the same with Donnie.

“A nasty witch kidnapped you guys from another dimension,” Mikey chimed in, bouncing behind Raphael and causing Leon to flinch. “We went in to rescue them, but they ended up getting hurt while we were there. Don’t worry though. My awesome brother and best friend are patching them up just fine.” Don had to give a mildly embarrassed snicker at the compliment, and Leatherhead could only give Mikey a small smile.

Leon’s expression scrunched in a grimace of confusion and borderline offense at that response. Did Mikey think he was four? “Hurt how? What did you do to take care of it?” Leon persisted, brow furrowing in frustration. What kind of report was that? He didn’t even mention what injuries they had at all. He wasn’t a child that needed everything glossed over in such a vague manner.

“Uhhhhh….,” Mikey faltered, having not expected Leon to ask for details. He wasn’t even sure what all of the details were. 

Luckily Don took that as his queue to step forward and kneel next to Raphael, concern saturating his form but his hands decidedly held back in his lap. “A government agent that we’ve been monitoring to keep her from achieving inter dimensional travel accidentally triggered a reaction that brought you and your family here from your dimension. It also grabbed our Leo, and they sent Raph a message to try and get him to return what we’ve stolen. We broke into her base since there was no way we were going to let her keep any of us, our brother or yours. But your Mikey got caught in the crossfire, and ended up getting shot in the back of his arm,” he explained quickly, partially to be open with their guest but also to see if his inference about Leon was correct.

With the sucked in hiss, and quick, analytical, and repeated visual examination of Lil Mikey’s bandages that Leon did, Don had his answer and gained a small smile. Leon was gathering medical data for his own mental documentation, and Don was happy to provide. “I had Leatherhead surgically remove the bullet, clean the wound, and stitch him back up. Both Leatherhead and I have done the procedure many times before. Right now he’s probably still numb from local anesthesia, but we’ll be sure to get him on painkillers once that starts to wear off. He and Lil Donnie were also both also exposed to an airborne toxin that breaks down cells when inhaled. We’ve dealt with it personally many times before, so we had an antidote ready that we’ve already given them. It’ll just take some time to heal what was already damaged, but there should be no further degradation.”

Leon was mostly quiet as Don, and sometimes Leatherhead, continued to give a detailed explanation that had Raphael raising a brow at him. It was more of an explanation than Raphael was used to getting, other than when Don was rambling in a distracted daze, and he briefly wondered if it was pointless or not. But when he looked at Leon he had to marvel at how the kid seemed to be understanding everything. The kid was even asking questions like what the toxin was, and what antidote was used and how it was given, what antiseptics were being used and if they used any antibiotic salve on Donnie. The responses Don gave earned satisfied nodding for the most part, and Raphael could only grin in amusement as he shifted his weight back to somewhat pull out of the conversation, the others settling around Master Splinter behind him. Looked like Leon was the medic of the other group. It was an interesting difference, and Raphael was wondering what had caused it. But the conversation was way over Raphael’s head, so he ended up tuning them out for a moment. It wasn’t until Raphael heard Don’s words fill with reluctance to be voiced that Raphael started paying attention again.

“...He hasn’t woken up since we left the base. I’m not sure if that’s normal for him, so it has me concerned…” Don was apparently talking about Donnie, who was still unresponsive to external stimuli. Raphael and the others had to agree with the voiced concern since Lil Mikey had been quick to wake up again once they’d landed, but it almost seemed like Donnie was sedated.

“It’s okay. He does that sometimes.” Leon responded easily. “We started calling them reboot naps.”

“...Reboot naps?” Raphael repeated, raising a brow. Why did that sound like something that would be done with a computer and not a person.

“Yeah. You know how sometimes a computer gets overloaded and starts to overheat and stop functioning properly? And the best thing to do is just turn it off so it can stop running programs and cool down?” Leon explained, sounding like it was one that had been used at least once before. After the others gave somewhat confused nods that they understood so far Leon continued. “That’s kind of what it’s like, I guess. His big brain just works overtime all the time and sometimes everything becomes too much, so he takes a huge nap to recover. He’ll wake up eventually.”

“...Hm… I guess that makes sense,” Don responded, voicing the thought all of them had. The analogy made sense, they just hadn’t dealt with anyone that did this before. Not without serious physical injury to go with it. “How long does he usually sleep for?”

“I dunno. Depends on how stressed out he got before. This time will probably be a longer one though,” Leon responded, shrugging.

Raphael had to furrow his brow in confusion at that combination of comments. He knew Donnie had probably been incredibly stressed out from everything, but none of them had mentioned anything to Leon about what had happened other than the injuries. “How do you know that?” Raphael asked, not able to keep his voice from being incredulous.

“I just do,” Leon huffed, shrinking back slightly in a physical show of avoidance. He was quick to change the subject as well, so Raphael knew his reason was something he didn’t want them to know. “Anyway, where’s his battle shell? I’m surprised he’s not wearing it.”

“Uhhh…” Don faltered, glancing at his brothers. He didn’t remember then having a truck with them. So that probably wasn’t what Leon meant. ”...Battle shell?” Don repeated in question. Luckily Little Mikey spoke up for them from where he was sagging into Leon’s lap, mumbling slightly in his increasing sleepiness. “It’s in the other room. Where we were before,” he explained, forcing back a yawn while trying to not completely fall over. “Told them not to take it off. But he insisted he shouldn’t wear it while sleeping. And needed to check his back.”

That was enough to que Don in on what they were talking about, giving a small gasp and dropping a fist lightly into an open palm. “Oh! The device he was wearing on his back. Yes, it’s back in the infirmary. I haven’t done anything with it other than take it off. Augustine’s employees took samples from Donnie’s back too, so I had to make sure those were clean as-”

“They did what?” Leon suddenly hissed, gaze becoming borderline furious as he latched onto only one thing Don said. “Where is it? Where is that base?” he demanded, moving to get to his feet. Don had only mentioned that Donnie had been cut before, not that he’d been some science lab pet.

“Woah, hey, relax kid. There’s nothing left of the base, your brother made sure of it. He set off the self-destruct as we were leaving,” Raphael spoke quickly, raising his hands to halt Leon’s movement without actually grabbing him. “We’re going to make double sure of course, but I wouldn’t be surprised if Augustine herself ended up with brain damage after how hard fearless over there kicked her in the head,” he added, jerking a thumb over at Leo, whose eyes widened slightly in surprise at being called out so suddenly.

“It wasn’t THAT h-... Okay maybe it was- She had a gun pointed at Donnie. What was I supposed to do?” Leo sputtered, gesturing awkwardly.

Don winced at the mention of the gun, and quickly looked over to Leon to see his reaction while Mikey hissed and clamped his hands over Leo’s mouth. He wasn’t surprised to see the lad shift protectively towards Donnie again, a glower shadowing his gaze. “....Show me what happened…” Leon demanded, his tone not leaving any room for debate. “All of it.”

“Show you?” Leatherhead prompted for further clarification. Did Leon really want to see everything that had happened?

“Are you saying you don’t have a database of every video feed from the government agent you’ve been monitoring?” Leon shot back, missing the real reason Leatherhead was questioning him and thinking they were trying to avoid showing it to them.

Don was stunned into silence for a stretch, surprised Leon had made that connection. While they had only found Augustine’s main base recently, downloading all of her data was one of the first things he and Mom April had done. “Huh…” Don voiced, the only hint to his pleasant surprise. Then again, this was just an alternate dimension version of all of them. Donnie probably had similar habits. “Let me get a tablet,” he requested, getting to his feet to hurry to the main computer. “Also Mikey don’t forget you’re cooking,” he reminded on his way passed.

“My stew!” Mikey squawked, jumping to his feet and leaping over the edge of the upper level to run back to the kitchen.

___________________

Previous Next

___________________

I rewrote this part so many times, and fussed with the image so much/was so slow on it and it still doesn't feel right. But I'm to the point I don't hate it so it's good enough and I'm gonna throw it out here. =v=;;

[gallusrostromegalus]

Mayuri broke out of hell and his sword is his parole officer??? I need elaboration immediately, I have such a morbid fascination with Mayuri he's my horrifying little torture meow meow

So some important things about Hell in AEIWAM:

I haven't read any of Kubo's newer work and I do not have plans to unless someone can vouch that it's really, really cool

They REALLY don't want anyone to stay there, because Hell is functionally Rehab.

See, the function of Hell in AEIWAM is to act as a sort of repair shop for damaged souls- souls that harm others don't produce as much energy when they move from one plane to another, and prevent other souls from completing their cycles and that's bad for The Life Machine, so it's in The Machine's interest that those harmful souls Stop That ASAP. So in hell, a Soul that has say, done a bunch of murder is meticulously taken apart and examined by Demons, who figure out Why He Did That, and then come up with a treatment program to Make Sure He Doesn't Do That Next Time. Sometimes it's therapy, sometimes it's hard labor to undo the spiritual pollution you caused, sometimes they have to uh. vivisect a soul and remove an unhelpful segment of soul. There's no BAD souls or soul fragments, according to the Demons of Hell- what's dangerous egotism in one person is a healthy level of self-esteem in another person and you just gotta... swap the spiritual organs, as it were. The demons really, really want you to get better!

3. They also don't want you to stick around because Hell has a bit of an overpopulation problem.

See, when the Four-to-Five Noble houses dismembered the Soul King and used parts of his body to black off the spirit world according to political preference rather than any kind of functionality, they tried to block off Hell entirely, perhaps to evade the fate that awaited them.

But they fucked up, and now the only part of Hell that's Blocked is THE EXIT, and it's blocked by God's Divine Ass :/. Now, there is, technically, An Exit built in, as it were, but it's very small compared to the original exit, and now there is a Queue To Get Out.

This has created MANY problems for Hell- The Demons have a running Metric for "Does this soul REALLY need to go to rehab?"*, and even with it stripped down to the most generous assumptions of "this was probably more circumstances than your fault" and most limited definitions of "Harm" and "Danger", there's still a steady stream of souls entering Hell, and it's larger than the stream escaping out. So now the majority population of Hell is Perfectly Fine people who completed their Rehab, but can't leave because the airport is closed.

*The reason soul society doesn't attempt to reunite people in the afterlife is that they actually cannot- who goes where after death is the provenance of Hell, and shinigami don't have any input on the process, save to occasionally herd someone back into the afterlife queue via Konsho.

It's getting. Crowded.

It's getting crowded to the point that Hell is actually starting to Burst at the seams- which is a solution and a problem- these crack represent the dimension literally unravelling, but it's also an opportunity for The Ruler Of Hell to stabilize those cracks and make new exits and move a bit more of that Queue along.

It's during one of these stabilization projects that Mayuri makes his escape.

(Continued under the cut)

The thing is. Mayuri wasn't even in that much trouble! He'd been the Medieval Japanese equivalent of a Fry Cook in life and uh. Poisoned a few people trying out new recpies, mostly involving novel culinary mushrooms. His fault, if you had to pick the main one, was an overabundance of curiousity relative to his sense of caution and a minor problem of not being able to imagine the interiority of others. None of those are EVIL. Dangerous, sure! But entirely fixable! and Mayuri had been quite young when he died because he had gotten a little too curious and tried his latest recipe out himself.

So Mayuri had been assigned to Jizo. In Real Life, Jizo is a pretty cool religious figure- he's the Bohdisattva who's whole thing is that absolutely no-one is incapable of becoming a better person, and refused to achieve Nirvana until all the hells are empty. He's the last guy out when the universe ends, and the particular patron of dead children and orphans. He is associated with caterpillar imagery because he wears a long cloak that all the lost souls of children can take shelter under, and when they all trail out behind him, it looks a bit like he's a centaur with a caterpillar body from all the little legs sticking out from under him.

...Which is why Mayuri's Zanpaktou looks like that.

Jizo seems like a WEIRD spirit to be hanging out with Mayuri imho, unless Jizo was originally Mayuri's Guardian/therapist/parole officer, and Mayuri did something shitty.

I think Mayuri HAD been making a lot of progress in terms of "the scientific process is a PROCESS for a reason" and "Other beings have feelings too" and "Harming others is Bad", and he's a clever lad who could be doing a lot of good if pointed in the right direction, so Jizo advocated for Mayuri to be put on one of the Hell-Crack stabilization teams to give him a good outlet for his restless mind.

Unfortunately for Jizo, he miscalculated how much progress mayuri had actually made vs his desire to not go to rehab, and Mayuri pulled some sort of stunt that bound Jizo to a sword like an Asauchi, and absconded with him to the Spirit world through the crack, promptly got arrested for More Science Crimes in spirit world, got sent to the Maggots Nest, and eventually caught the attention of Urahara, who saw the Chemistry Brilliance of Mayuri and exactly NONE of the Red Flags.

As it stands Mayuri is... Sort-of the captain of the 12th division.

Sure, on paper he's The Captain, he gets to wear the Haori and has to go to the meetings, but R&D is only a fraction of what the 12th actually does- rememer, Urahara is the guy that STARTED Research and Development. Before that, I think the 12th division was 100% devoted with being the gotei-13's SUPPLIER- food, uniforms, medicine manufacture, weapons repair, gigai, soul candy, maps, communicators- if you got it for work, it was made in the 12th division.

I think Mayuri is aware of maybe 12% of what his division actually does, because the people who are in charge of manufacture were around before Urahara, took one look at that man and went "...Nah" and started quietly Not Telling Him About Things. When Mayuri took over, they went "Absolutely Not" and have been engaged in a century-long farce to prevent the captain of the 12th from knowing what his division actually does. Fortunately for them, it's extremely easy to lie to Mayuri. He's a suspicious bastard, but he LOVES good news, especially the kind of news that is good because it means he doesn't have to go to another boring-ass meeting. So things are JUST FINE down in manufacture, your latest improvements were TOTALLY IMPLEMENTED and are going GREAT. Everyone remarks on how much better the MREs are since we started adding live beetles to them Sir. Your Genius is Much Praised- Whoops Is That The Time? Gotta Go- the science never stops!

He's going to run into a bit of a stumbling block in Las Noches though. Not Sayzel, though Sayzel doesn't help. He's going to run into the consequences of a Former Experiment that are REAL FUCKIN' MAD AT HIM. No, not Uryuu, though Uryuu is FAR FROM PLEASED. Mayuri is going to have to face the consequences of a much worse experiment- one based on the procedures of disassembling and reassembling souls he learned while he was in Hell. Mayuri will have to face The Wrath Of Kon

[dontbooatme]

dp x bnha au

And if I ever did anything with this it'd be mostly Danny's POV, despite the focus in this post but..

Okay, hear me out:

Maddie Fenton is born into the world of bnha. She has a very rare quirk that's difficult to activate/has very specific requirements to activate: the ability to rip open portals into alternate dimensions. She activates it by accident and ends up lost in the Ghost Zone. She gets separated from the portal she made in some way shape or form, but it stays open. Maybe hostile ghosts show up and she runs, because she's young here. 7-8 probably. So she's lost in the zone--until she finds a natural portal into the dp universe.

And she ends up stuck in a world without quirks.

She grows up there, everything else about her history stays relatively the same. Except she has a new motivation for being obsessed with ghosts and the Ghost Zone and creating portals. Because she's only ever used her quirk once when she was in her world and she grew up the rest of the way without any form of quirk counseling she could have had in her first world.

And of course she's tried to use her quirk again. But it's fickle in the first place, and there are so many other universes than the one she looking for.

But veil between the dp verse and the Ghost Zone is thinner here in a way that's not true for her world. Natural portals open here without the help of a quirk. She's seen that. And the energy traces they leave behind after they close is something that can be studied.

So she turned to learning how to make a portal to the Ghost Zone through science.

But then she meets Jack and they ultimately end up having Jazz and Danny. And she's still desperate to build that portal. But now she has a stake in this world that she didn't have before.

She doesn't know what she's going to do when her and Jack's work is done. Take her family with her? Does she want to uproot their lives and move to a universe she hasn't been in since she was a kid? Has, probably, been pronounced dead in? Where her family won't have any documentation anywhere for any place under the sun? Does she just want the chance to see it before she slinks back to this world like an outcast?

She still ends up married to Jack, and they still have Jazz and Danny and her and Jack still end up successfully building a portal to the Ghost Zone when he's 14.

And Danny still has his accident.

But instead of coming out in the human world, he ends up trapped in the Zone. With an overloaded/exploded, non functional portal left behind in the lab.

Which leaves Maddie and Jack in the position of having to build the portal from scratch. Which takes time. But only after they collect the materials. Which also takes time and some of them aren't easy to get their hands on.

And of course, they first have to realize Danny's missing and why he's missing before they really get to work on rebuilding the portal.

And he happens across the very same portal Maddie created way back when.

Queue Danny, lost in Musutafu, unknowingly in a parallel to his mom's journey. And discovering he has a thing or two in common with this very strange world he's been dropped into. Quirks being one thing, when he figures out he had a quirk even before he had ghost powers. But cultural differences that are specific to the bnha verse and that Maddie brought back with her could be another. He just pops up into this world, maybe hesitant to backtrack into the zone because ghosts and definitely unlivable conditions, and he walks into town as a teenager with no knowledge of quirks and no control of the quirks he has. He.. causes a bit of a disturbance.

And queue a Madeline Fenton who's now more desperate than ever before to burst through the veil and get into the Ghost Zone.

Idk, this idea just hit me. There are definitely kinks to work out. Like where a gaping portal in bnha would have to be located so it isn't easy to find. Or how Danny ends up lost there despite a gaping portal back to the Zone. Maybe the issue is that the Fenton portal is what's not functional. So he has access to the Zone, but that's the cut off point. And he knows better than to trust the reliability of natural portals, due to his mom's research. And how much she stresses how dangerous they are. Unbeknownst to him, because she has personal experience on that front. Or smth idk yet

I think in this au Danny would originally have a regenerative/quick healing type of quirk. To explain why he had a seemingly quicker/easier recovery period than Vlad did after their accidents.

And maybe Jazz has a quirk too. But I think for this au they should both have quirks that are pretty subtle. Something that might make them weird in the dp verse, but something that doesn't set them apart too much. So when everything about the dimension hop/quirks come to light, everything about that revelation is a huge shock

Also the jumpsuits would probably be something Maddie treasures a lot. Something that feels familiar to her with the relation to pro hero costumes. Maybe she even had a parent who was a prohero. And the jumpsuit is the closest recreation she could make of the suit x parent wore. A suit that Danny now wears when he goes ghost.

Maddie got her quirk when she was younger, but she didn't activate it for the first time till she was 7-8 due to how fickle it is

And, obviously, we'd have to bring Kurogiri into this. Because, portals

Ooh, that would probably leave Danny wanting to seek out the league, Kurogiri specifically once he finds out theres someone else who can make portals. Whether or not Kurogiri would even be able to make portals to a different universe? That's a different story

[x-authorship-x]

RAINBOW TEXT WAWAWAWAWAWAWWAAWAA

AAAAAAAHHHHHHHHHHHH SPIDERVERSE FIC,,,,,,,,, WHAT WOULD HAPPEN IF SPIDER SHISUI OR MIGUEL MET THE OTHER SHISUI'S. I FEEL LIKE THEY ARE ALL MORE TRAUMATIZED BUT ALSO MORE POWERFUL CONSIDERING CHAKRA. AND ALSO,, Y'KNOW. SHISUI'S TRAGIC END IN MOST UNIVERSES. I FEEL LIKE NINJA SHISUIS MIGHT INITIALLY BE UNDERESTIMATED BY THE SPIDERS BUT THEN THEY GO CRAZY BECAUSE WHAT THE FUCK WHY DO YOU HAVE MAGIC NINJA POWERS ONCE AGAIN, I LOVE YOUR STORIES AND ALL THE CONCEPTS AND POTENTIAL IDEAS THAT SPROUT FROM THEM. ALSJFHLFDLJGDGDLGDJNHOILGNMJIOGDNOIVGMJNDO.

Omg rainbow!!! ✨🥰✨

I'm not sure if Spider-Shisui could meet other versions of himself, logistically in this AU, because it seems to be only spiderman dimensions connected but who knows!!! The other Shisui's would low-key be jealous that Konohattan isn't at literal war every decade or so but also yikes no chakra and all the mobsters.... guess it's relative, huh

I will say that the Naruto manga/anime does exist in some Spiderverses though so I might do a one shot of shisui introducing himself, another Spider freaking the fuck out, Shisui then freaking out that they know he's from a Yakuza family oh shit they're gonna blow my cover only to realise.... Oh wait no there's a manga??? That is disturbingly like Konohattan???? WAIT MY CHARACTER DOES WHAT?!? 🤣🤣🤣 queue hate-reading/watching and loudly complaining about his fucked up life lol

Other Spiders: hey wait so what kinda movies and books do you have over there?

Shisui: .... Um.... highest grossing movie of all time is... Sailor Moon? 🥺❤️‍🔥👉👈

Thank you, anon! Im super happy you're loving my works~ have a great day/night~~~~

[cfsjetsnc]

Why Use a Jet Broker?

Many business owners feel the need to invest in their aircraft. The time-consuming wait in queues at the airport and unexpected delays can jeopardize business interests. Therefore, buying a private jet that saves the user time and is also comfortable is essential. True, many companies and owners list their Learjet for sale today. ​ The reasons for selling off such an asset can be varied, with each private jet owner having their reasons. It may be a good idea for the prospective buyer to go through the specs of the listed Learjet before making the final decision. The Learjet 45 is regularly put up for sale, making the interested party consider it carefully. It may be advantageous to learn something about the illustration history of this particular jet before inquiring about the reasons behind selling it. The Learjet 45 came into existence sometime between 1995 to 2013. Popular as a small business jet, the Learjet was first manufactured by Bombardier Aerospace. The dimensions of the cabin are impressive, with 375 cubic feet available for the user. It can thus accommodate eight passengers comfortably, with another person being squeezed in if required. The baggage space is pretty extensive as well. Learjet 45 can traverse 2099 miles comfortably depending on the headwinds and fly at a maximum speed of 535 mph. The pair of ventral fins, along with the winglets, make the jet relatively stable and are also suitable for first-time flyers. It had been manufactured after closely looking at the necessitates and incorporating the desired features. Indeed Learjet had been a leading business jet of its time. Its ability to fly through icy conditions was highly appreciated during the late nineties and early 2000s. Customers who decide to buy Learjet 45 are excited by its seating capacity, high performance, and impressive fuel efficiency. People hoping to fly within the country and at close distances find the used Learjet 45 to be a good buy. The demand for a good quality Learjet 35 or Learjet 45 is at an all-time high at present. The investor may be able o get it for $2.7 million to $2.8 million. Sure, it sounds pricey, but the price is far less than a used Citation CJ3 or a Phenom 300. The operating costs per hour may be a trifle more than that of the abovementioned jets. The operating costs take the fuel burn, maintenance costs, and fees for the operating crew into account. The manufacturer has discontinued all Learjet variants since the early 21st century. The company is willing to provide maintenance support for the jets still in use. It would not do to embark on the path of buying a private jet alone. The best way to purchase a listed airplane is to hire the services of a hugely experienced jet broker or aviation expert. The professional will not only assist with buying & selling private jets but also help the buyer obtain financing if needed.

[cxlxurbliind]

✔️✔️✔️✔️✔️✔️ ( !!! am do this RIGHT BACK @ YOU—!!! )

Send me a “✔” if i’m one of your favorite blogs || Not Accepting

        ♫ - Gale You Are Aware That Is Against The Rules Right?

[sherlocked-to-the-tardis]

When you realise that in Dinosaurs on a Spaceship there are The Doctor, Rory, and Amy together with Lestrade, Arthur Weasley, and Filch... and Doctor Who suddenly becomes a Potterwholock crossover

[binary--sun]

hey!!! i hope its not too much to ask, but i was wondering how you did the text effect in the first gif of your mandalorian edit? it turned out so lovely !!!!!

hey it’s no problem at all!!!! thank you and i’m always open to sharing how i make my stuff 💖 

basically i use after effects to animate the text! i make and color the gif as usual on photoshop. then, i create a new composition with the same dimension as my gif, then drag the gif into it. for the text, i followed this tutorial beginning from around the 3 minute mark.

here is the final result

a summary under the cut

keep in mind i’m just showing you what i did! if you want a more in depth explanation about AE, there are many great tutorials all over youtube including the one i linked above!

basically you duplicate your main text and nudge it to your preferred position. 

press ‘q’ or select the shape tool (while having the text layer selected) to create a mask over the part of the text you want to show. the overlapping part will disappear. set the feather to 9 for the gradient effect. here’s some screenshot to help visualize it better 

don’t forget to set the character style to no fill and a 1 pt stroke. here’s the final result 

next we want to animate it so the text pops from underneath the main text. select the text you want to animate, go to the 1s mark (or any time at all you want the text to pop), press p, click on the clock icon besides to create a keyframe. then go back to around 10-20 frames before, and move the text until it’s covered by the main text. this will automatically create a new keyframe :)

for the spread out effect, click the little arrow besides animate -> select tracking. move the time indicator to your last position keyframe, then click on the clock icon next to ‘tracking amount’. move the time indicator a couple frames forward, and change the amount to 25, or whatever spacing you need.

this is how my keyframes look

basically i repeated the same steps for the rest of the text! adjust the tracking amount at the topmost text so it spread out more.

yay! to animate the text going back, basically sellect all the key frames, copy them (ctrl +c) move the time indicator to around thee end of your gif (anytime you want the text to move back), paste the key frames (ctrl v), then right click -> keyframe assistant -> time reverse keyframe. move the position keyframes to the right, after the final tracking key frame (because we want the text to contract before moving anywhere).

we want smooth animation! so select all the keyframes, then press f9 (or right click -> easy ease). 

now that all your keyframes are in order, you can adjust the timing to your heart’s desire! just drag the keyframes around and see what looks good :) to see the keyframes easier, select the text layer, and press u on your keyboard. here is how mine look:

since the text is above a gif, i select all the text, go to layers -> blending mode -> exclusion. i change the font color to white so it has a nice black and white exclusion color.

now to export the gif! you have to use adobe media encoder (as far as i know anyways), so go to file -> export -> add to adobe media encoder queue. it’ll open the app, where you can select the file type ‘animated gif’.

and that’s it! i hope this makes sense anon hkjfhakfhak if not, i’m sorry and i’m open to questions if you wanna know more :”) i don’t know how familiar you are with AE and it might be confusing if you don’t know about AE at all. i’m no pro and am also relatively new to AE to be honest 🙂, but there are lots of tutorials online that are so fun to learn and incorporate into your gif creations 💖

[inaccurate-janto-conversations]

Ianto in the TARDIS

The Doctor: Now before we go in you might want to prepare yourself for a bit of a surprise

Ianto: ?

The Doctor: *Opens the door and walks in Ianto following behind him*

Ianto:

The Doctor:

Ianto:

The Doctor: Weeeellllll?

Ianto: ?

The Doctor: What do you think?

Ianto: It's...interesting.

The Doctor: Just interesting!? It's your first time in the TARDIS and you just say it's "intresting." Don't you think it's a bit... oh I dunno, different on the inside?

Ianto: Well of course its different, the interior exists in a different, relative dimension to the exterior. Thus the inside appears to be larger than what the outside leads you to believe.

The Doctor:

Ianto: It's very simple technology.

The Doctor:

Ianto: You should've seen the loos in Torchwood One. Never a single queue.

The Doctor: ... *Calls Jack*

Jack: Hiddy Ho Doc!

The Doctor: Yes hi, I need you to come get your boyfriend, he's clearly broken beyond repair.

[theaspiescribe]

The 21st Night Of September

Do you remember the 21st night of September? Well I most certainly do! A night when I made one of the biggest breakthroughs of my life. In virgo season, obviously.

There was a period of my life when I struggled to leave the house. If I did have to do it, I would incur a great physical toll. Panic attacks, exhaustion, stomach pain and the like. It would take days to feel normal again. It was not fun.  

It’s a curse to feel trapped inside your own home, inside your own mind. You see other people going out like it’s nothing, you remember a period in your own life, when it was nothing. You wonder how this is what has become of your life. You ask yourself why can’t it be different.

My despair was amplified by the fact I couldn’t see a way around this. How are you supposed to overcome a phobia of this sort when the phobia itself prevents you from getting the help you need? 

For so long, I felt helpless and because I couldn’t go out, I couldn’t make friends, so I was alone too. I couldn’t wrap my head around the fact that this was my reality. 

In addition to this I was dealing with a myriad of other issues both mental and physical. Now, when I look back I realise I was fighting hard to survive, that I was a lot stronger than I ever knew.

The turning point was a storm from the east, you may remember that they called it the Beast From The East. A snow storm of epic proportion, and as an admirer of snow, I was marveling at the intensity of it. I was electrified by the prospect of it.

We had another storm called Ophelia a few months before, but there was no snow and it was quite dangerous. This was a lot more exhilarating.  

Snow like this hadn’t been seen for years, since before my biggest issues emerged. It reminded me of a time in my life when things were peaceful and I lived a relatively happy life. 

I had always been mesmerised by how serene everything feels when it’s snowing. Looking up at the sky seeing the little snowflakes falling is always a soothing sight to behold. Everything just looks better with a thick blanket of pure white snow covering it, it looks cleaner, it looks neat. It sounds quieter, and in fact snow can absorb sound because it is so porous. 

That whole week I felt reinvigorated by the beauty of the snow, as if a whole new world was still within reach. 

As Elsa from Frozen once said:

Let it go, let it go Can't hold it back anymore Let it go, let it go Turn away and slam the door I don't care what they're going to say Let the storm rage on The cold never bothered me anyway

In a way, the snow storm was like a dimension between two worlds: An old one where I felt powerless to overcome my fears and a new one where I felt a surge of determination to fight back. No more moping or quitting, I whispered to myself. I knew the extent of the challenges that were ahead of me but I kept reminding myself to take it one day at a time and to just try my best. I will forever remember that storm, a beast in more ways than one. 

Mother’s day was very soon after The Beast and it provided the perfect opportunity to make my first foray into the wild. I wanted to buy her this specific laptop but it could only be bought in Navan. Even though this was truly uncharted territory for me I felt a calm and confidence at the prospect of going to do it. So, on that fateful Friday, my Dad and I embarked on a journey that would mark the beginning of a new chapter in my life. I had many false dawns before, but this time felt different, like there was actual substance behind it. 

Getting the laptop and interacting with the nice computer man was fine even despite his persistence that I buy anti-virus software. Anyway, what a resounding success I thought. Mission accomplished. But much to my chagrin, the drive had taken so long, I now had to use the bathroom. This was certainly not part of the plan because there was nothing more I hated than using public bathrooms, especially being trans. But again, instead of looking at this from the perspective that it’s an issue, I tried to look at it as another chance to make a breakthrough. 

McDonalds was the obvious choice. But the timing was far from ideal. It was a Friday evening, kids had just got out of school and it was busy and noisy. The beginning did not go well, there was this kids outdoor play area and I thought this was the entrance, but it in actual fact was not. In the past, I would have panicked, but I tried so hard to just roll with it. Anyway, thankfully the bathroom wasn’t an issue apart from some disconcerting roaring. Then I queued for food. Fuck I hate queues, absolutely loathe them. There was also a lot of very rowdy kids around excited about getting their food. My mind was in a state of panic by all the noise and movements. But I was struck by how calm on the outside I was, as chaotic as it felt inside, I could keep control on the outside.

On the journey home, as I sipped my ice cold beverage, I realised that I had shown an ice cool temperament to get through this first test. I felt a sense of pride and I told myself that the most difficult part was over now. I had taken the first step. Something to build upon.

In the subsequent months, I did indeed go on more adventures, to supermarkets and other shops. Gradually, it got easier but as with anything there were setbacks. Progress is never linear, I knew that better than anyone.

Then in July, I had stumbled upon an advertisement for Saturday Night Fever in the Bord Gáis Energy theatre. I had seen this musical with my sisters as a child in the Gaiety theatre and I had great memories of visits to the theatre with them. Of all the great things taken away from me, trips to the theatre ranked quite highly on the list. Was I really ready for something as overwhelming as this, I pondered? Probably not, but feeling bolder than usual, I decided to go for it, and got the tickets for my sisters birthday. The date? The 21st night of September. 

As the day approached, I was overcome by trepidation and was not feeling great physically. My migraines were making sleep difficult and I was worried that I wouldn’t feel physically able to do it. On the Friday morning, I felt okay, not great but okay. I’m going to do this, I told myself. 

And so the time came, and I was a bundle of nerves. This was probably the equivalent of a football player before a world cup final, or someone performing on broadway for the first time. My heart was beating so fast, my mind was going through a million different scenarios and I was having a hard time thinking straight. But on the surface, I looked calm and ready. Once again, I was impressed at this capacity to keep everything under control externally despite everything happening internally.

The hard part of course was still to come. As I walked into the theatre, I wondered to myself would I make it out again in one piece. The beginning did not go too badly at all, there was quite a spacious waiting area which I appreciated and the glorious view of the grand canal took my attention away from intrusive thoughts. The calm before the storm, I joked to myself.

And then it was showtime. This was my first time in the Bord Gáis Energy theatre, and my overall impression was a positive one. Thankfully we were seated near the back of the theatre. This was a strategic decision on my part, so I could make a quick getaway if it was necessary. Once the show started, it did indeed get a bit overwhelming, the noise and flashing lights were as jarring as expected, but I was enjoying the music a lot so I could put up with it. The most challenging bit was actually the sitting, I find it almost impossible to sit for extended periods of time and I was getting restless after about 10 minutes. By the end of the show, I had felt like I had aged 50 years but I made it. I came, I saw, I conquered. There was no sense of pride this time, just sheer exhaustion but once I had recovered, I realised what an achievement this was. I never thought I would have had the courage to do something like this again, but from somewhere I found it. What else could I do, I wondered to myself? 

But this is why I will always remember the 21st night of September, my soul truly was singin’ on that unforgettable night. You know it's alright, it's okay, we’ll live to see another day.

[bluewatsons]

Katherine Weisshaar, From Opt Out to Blocked Out: The Challenges for Labor Market Re-entry after Family-Related Employment Lapses, 83 Am Sociol Rev 34 (2018)

Abstract

In today’s labor market, the majority of individuals experience a lapse in employment at some point in their careers, most commonly due to unemployment from job loss or leaving work to care for family or children. Existing scholarship has studied how unemployment affects subsequent career outcomes, but the consequences of temporarily “opting out” of work to care for family are relatively unknown. In this article, I ask: how do “opt out” parents fare when they re-enter the labor market? I argue that opting out signals a violation of ideal worker norms to employers—norms that expect employees to be highly dedicated to work—and that this signal is distinct from two other types of résumé signals: signals produced by unemployment due to job loss and the signal of motherhood or fatherhood. Using an original survey experiment and a large-scale audit study, I test the relative strength of these three résumé signals. I find that mothers and fathers who temporarily opted out of work to care for family fared significantly worse in terms of hiring prospects, relative to applicants who experienced unemployment due to job loss and compared to continuously employed mothers and fathers. I examine variation in these signals’ effects across local labor markets, and I find that within competitive markets, penalties emerged for continuously employed mothers and became even greater for opt out fathers. This research provides a causal test of the micro- and macro-level demand-side processes that disadvantage parents who leave work to care for family. This is important because when opt out applicants are prevented from re-entering the labor market, employers reinforce standards that exclude parents from full participation in work.

The decision to become a stay-at-home parent tends to be a constrained one. Today, it is rare for women and men to aspire to become stay-at-home parents; most people hold ideals of balanced work and family arrangements (Stone 2008; Williams, Manvell, and Bornstein 2006). However, balance is difficult to achieve within the modern labor market, in which employers seek candidates who can fulfill “ideal worker norms” of intense time commitment and perpetual availability for work-related tasks (Davies and Frink 2014; Kelly et al. 2010; Turco 2010). Overwork is increasingly common (Cha and Weeden 2014), as is spillover of job-related work into home life (Reid 2011; Turco 2010). These expectations for employees conflict with similarly intensive parenting standards for middle- and upper-class parents (Blair-Loy 2003; Jacobs and Gerson 2001), contributing to parents’ decisions to “opt out” of work to care for children full-time (Stone 2008).1 Opting out is a gendered process: over the past two decades, 18 to 20 percent of mothers did not work for pay in order to care for children for one or more years, compared to a peak rate of only about 1.2 percent among fathers (Flood et al. 2015). These departures from the labor force are usually temporary; for example, the median lapse in employment among mothers is about two years (Reimers and Stone 2008; Stone 2008).

Do parents face penalties when they seek to return to work after opting out? In this article, I examine how demand-side processes, in the form of employer preferences, influence hiring prospects for both mothers and fathers who have previously opted out. I argue that opting out signals to employers that potential employees prioritize family over work, and that the act of opting out violates the ideal worker expectations that are ubiquitous in modern workplaces. This violation of ideal worker norms leads to fewer job opportunities for job applicants who have opted out.

Despite fairly high rates of individuals leaving work for caretaking responsibilities, we know relatively little about the demand-side processes faced by these job-seekers after they decide to resume working (see Lovejoy and Stone 2012). Sociological research has examined historical trends in the rate of opting out (e.g., Boushey 2008); demographic characteristics of mothers who leave work (e.g., Percheski 2008); and supply-side decisions and preferences—for example, why caretakers leave work, and how they conceive of their employment decisions (Stone 2008; Williams et al. 2006). In contrast to the dearth of demand-side studies of opt out applicants, a substantial line of related research examines how another type of employment lapse—unemployment from job loss—affects job prospects (e.g., Eriksson and Rooth 2014; Nunley et al. 2017; Pedulla 2016; Winefield, Tiggemann, and Winefield 1992). Existing research also documents the “motherhood penalty” in the labor market—establishing that mothers face penalties in hiring and wages relative to fathers and childless women—but these studies typically examine mothers with continuous employment records (e.g., Budig and England 2001; Budig, Misra, and Boeckmann 2012; Correll, Benard, and Paik 2007).

What are the mechanisms through which a gap in employment leads to lower callback rates during the job application process? A theory of skill deterioration, derived from human capital theory, suggests that time out of work leads to skills becoming rusty and obsolete; employers prefer to hire applicants with continuous employment records to avoid high training costs (Mincer and Ofek 1982; Nunley et al. 2017). In contrast to skill deterioration theories, signaling theories posit that employment history signals information about the job applicant to the employer beyond skill decline; employers rely on assumptions or stereotypes based on employee characteristics or job history to make hiring decisions (Spence 1973; Stiglitz 2002). Signaling theories have been tested with respect to unemployment: a bout of unemployment “scars” the job applicant by signaling lower applicant competence, and it leads to reduced job opportunities for unemployed individuals (Eriksson and Rooth 2014; Pedulla 2016).

I propose a résumé signaling theory in which opting out for family reasons produces negative perceptions about applicants’ commitment and dedication to work. In this theory, opting out signals a violation of ideal worker norms, which is distinct from the unemployment scarring signal of perceived competence. Given that employers have rigid expectations for employees to dedicate themselves fully to work, violating these ideal worker norms by demonstrating a prioritization of family evokes a moral evaluation of applicants’ work-family choices. Potential employers thus perceive opting out as indicating lower dedication to work and, as a result, view opt out applicants as less worthy of a job.

To test that opting out signals a violation of ideal worker norms—and whether these signals are distinct from perceptions of unemployed and employed applicants—this article presents three empirical studies. In Study 1, I use an original national survey experiment of 1,000 U.S. respondents to test social-psychological perceptions of opt out, unemployed, and employed applicants—all parents. Respondents rated résumés on dimensions that align with ideal worker norm violation as well as unemployment scarring theories. The findings from Study 1 establish that opting out signals a violation of ideal worker norms: opt out applicants are perceived as less committed to work, less reliable, and less deserving of a job than are unemployed applicants. I further find that opt out fathers experience an even greater penalty on ideal worker norm violation measures compared to opt out mothers.

In Study 2, I test how these perceptions play out in the real labor market. I conducted a large-scale audit study in which 3,407 job applications were submitted to professional and managerial job openings across 50 metropolitan areas in the United States, recording callbacks for each application. The audit study tests how each type of résumé signal—unemployment scarring, ideal worker norm violation, and signals of motherhood or fatherhood—lead to differences in employers’ hiring preferences. The audit study findings show that, overall, opting out leads to fewer callbacks than does unemployment, and unemployment, in turn, produces fewer callbacks compared to the continuously employed. In the aggregate, I find no significant gender differences in the effects of employment history.

In Study 3, I exploit variation across the audit study cities to examine how these signals vary in strength as local labor market contexts vary. In labor markets in which job competition is relatively higher, there are longer job queues for each job opening. I predict that in these competitive settings, employers more readily enact preferences to distinguish between negative signals. Weaker negative signals that have less of an effect in low-competition environments will be more apparent in competitive contexts. I find that in competitive job markets, gendered signals become apparent: when there are longer job queues, the motherhood penalty emerges among employed applicants and opt out fathers fare worse than in less competitive cities.

The results of these studies collectively demonstrate that ideal worker norm violations convey stronger negative signals than does unemployment scarring. The effects of motherhood/fatherhood signals are variable across labor markets, with stronger consequences in competitive labor market contexts, and relatively muted effects in less competitive contexts.

This article contributes to scholarship in two key ways. First, I add to scholarship on family, gender, and work by testing to what extent signaling a commitment to family over work influences subsequent career opportunities. Second, scholars have long recognized the importance of both micro-level decisions on hiring processes (e.g., Correll et al. 2007) and macro-level contextual factors (e.g., Fallick 1996; Haurin and Sridhar 2003). To understand how opting out affects job prospects, this study draws on micro-level processes of résumé signals and macro-level labor market contextual variation to develop an integrated theory of signaling and queuing.

Theoretical Influences: Skill Deterioration and Résumé Signaling Processes

How do gender and labor market history influence the hiring process? Two broad theoretical perspectives propose possible mechanisms: human capital theories and signaling theories. Theories of skill deterioration suggest that applicants who have a decline in skills or human capital are less desirable employees and will be hired less frequently. Signaling theories claim that information on a résumé sends a signal to employers based on stereotypes or assumptions. In this study, I examine signals produced from three pieces of information: unemployment, opting out, and motherhood/fatherhood, each of which have the possibility to produce distinct signals for employers.

Skill Deterioration Theories

Skill deterioration theory draws on human capital theories to argue that differences in skills or abilities explain why applicants with employment lapses are less desirable than the steadily employed (Acemoglu 1995; Becker 1964). Human capital theories generally explain variations in job-related outcomes in terms of workers’ differing skills and competencies (Becker 1964). The logic behind this argument is that when individuals have gaps in employment, their skills and human capital deteriorate from lack of use and their skills may become obsolete. By hiring applicants with more recent work experience, employers avoid training costs (Becker 1964).

Skill deterioration incurred during an employment lapse is ostensibly gender neutral and invariant across the type of lapse. Human capital theories have proposed gender differences in the accumulation of skills, but there is no reason why skills, once attained, should decline at varying rates for men and women (Acemoglu 1995; Becker 1964). Skill obsolescence during a lapse should also occur similarly for unemployed and opt out individuals—the reason for a lapse ought not to matter, only the lapse’s duration. Holding constant the amount of time out of the labor force, skill deterioration theory predicts the following hypotheses:

Skill deterioration: Both unemployed and opt out applicants will fare more negatively than continuously employed applicants, but there will be no differences in the effects of opting out compared to unemployment, nor differences between mothers and fathers.

Signaling Theories

In contrast to skill deterioration theory, signaling theories predict varying negative effects for unemployment compared to opting out and for motherhood compared to fatherhood. Developed by economists who recognized an information asymmetry between job applicants and employers, signaling theories propose that résumés provide employers with various pieces of information that “signal” the quality of potential employees (Connelly et al. 2011; Spence 1973, 1981; Stiglitz 2002). Originally applied to theorize how high-quality applicants could signal their ability to potential employers, recent research has extended this theory to establish that résumé information can signal negative qualities as well (Pedulla 2016; Stiglitz 2002). Because employers have limited time and resources to devote to screening and interviewing job candidates, they use résumé information to make decisions about whether to move forward with a candidate. Employment history on a résumé can signal assumptions about the applicant’s quality, ability, and value (Spence 1981; Stiglitz 2002). Résumés can also provide information on applicant characteristics (e.g., education, gender, race, age, parental status), which lead to assumptions and biases about a job applicant based on widely held beliefs about said identity/characteristic (Ridgeway and Correll 2004).

One of the most heavily studied negative résumé signals is current unemployment. Whereas skill deterioration theory argues that unemployed candidates fare worse on the job market due to employers’ fears of reduced skill levels, studies based on signaling find that unemployment incurs penalties beyond what would be expected from skill deterioration. Unemployment scarring studies argue that employers are drawing from limited information on a job applicant, and a lapse in employment is perceived as a signal that the applicant is an inferior worker and less desirable as an employee (Kroft, Lange, and Notowidigdo 2013). Unemployment due to job loss is interpreted as a sign of an unstated negative characteristic and is said to “scar” the job applicant: employers may assume applicants lost a previous job and were unable to regain a job because they are lower-quality employees (Eriksson and Rooth 2014; Gangl 2004). This proposition has been tested empirically by using experimental designs to account for human capital (Pedulla 2016), and by assessing unemployment’s effect net of job tenure, specific skills, and lapse length (e.g., Arulampalam, Gregg, and Gregory 2001; Eriksson and Rooth 2014; Gangl 2004; Ghayad 2015; Kroft et al. 2013).

The reduced-quality signal produced by unemployment has not been operationalized consistently, and scholars tend to use it as an umbrella concept (e.g., Arulampalam et al. 2001). Employers may make any number of assumptions about quality for applicants with longer-term unemployment lapses. For example, these applicants could be perceieved as lower quality at the time of job loss—that is, there could be an unobserved negative trait that led to them becoming unemployed (Stiglitz 2002). This negative trait might be skill levels or on-the-job behavior, such as reliability or interpersonal skills (Clark, Georgellis, and Sanfey 2001). Furthermore, long-term unemployment itself could raise doubts about an employee’s quality, suggesting there is a reason that prevented the applicant from regaining a job over a number of months (Stiglitz 2002). In a recent audit study and survey experiment, Pedulla (2016) takes an important step toward theorizing how quality is perceived for unemployed applicants. Pedulla (2016) compared job applicants with one year of unemployment to applicants with other types of employment histories. This study found that overall, unemployed applicants—particularly unemployed men—received callbacks at substantially lower rates than did the continuously employed (5.9 percent compared to 10.4 percent, respectively). Pedulla theorizes that the scarring unemployment signal could operate through notions of either competence or commitment. Pedulla’s study finds that perceptions of competence mediate the lower callback rate among unemployed men, but he finds no significant effects of perceived commitment for unemployed compared to employed applicants.

How strong is the negative signal of unemployment in the context of other résumé signals? Unemployment scholars would suggest that unemployment scarring occurs largely because of assumptions made about the involuntary nature of unemployment (Kroft et al. 2013; Pedulla 2016). Applicants who have been unemployed for several months or longer not only provoke questions about why they lost their previous position, but why they have not found a new job (Arulampalam et al. 2001; Eriksson and Rooth 2014; Ghayad 2015). Opt out applicants, in contrast, could be perceived as voluntarily having a lapse in employment, and they might avoid the negative competence signals incurred by an involuntary lapse and lengthy job search. Unemployment scarring theories thus predict the following hypothesis:

Unemployment scarring: Unemployed applicants will fare worse than both opt out and employed applicants because of reduced perceived worker quality.

Alternatively, opting out may incur greater penalties than unemployment by signaling a violation of ideal worker norms, a signal that has yet to be considered in demand-side employment research. Ideal worker norms include the expectation that employees prioritize work over all other parts of their lives (Blair-Loy 2003; Davies and Frink 2014; Turco 2010). Professional and managerial jobs today demand intense time commitments, and employers expect employees to always be available (Davies and Frink 2014; Kelly et al. 2010; Rivera and Tilcsik 2016). Employees are increasingly likely to work longer hours (Cha and Weeden 2014), and technological changes have led to greater spillover of work-related tasks at home—such as checking email and responding to phone calls after leaving the office (Reid 2011; Turco 2010). Mothers and fathers alike report high levels of work-family conflict, finding it difficult to fulfill all expectations associated with work and with intensive parenting (Blair-Loy 2009; Davies and Frink 2014; Kelly et al. 2010). Opting out of work to care for children is a direct violation of these pervasive expectations for employees to prioritize work above all. By signaling their lower dedication to work, periods of opting out could undermine applicants’ efforts to re-enter the work force.

This prediction finds support in the caretaker bias literature. Studies have found that prioritizing caretaking tasks over work can result in a host of negative outcomes for employees in their workplaces. For instance, parents who use flexibility policies to try to reconcile work and family demands experience lower wages on average (Blair-Loy and Wharton 2002; Glass 2004), increased harassment (Berdahl and Moon 2013), fewer promotions (Cohen and Single 2001), and lower performance evaluations (Albiston et al. 2012). Scholars of cultural moral schemas argue that gender, work, and family (and their intersection) are areas of life rife with moral conceptions of how individuals ought to behave, and who is a worthy fulfiller of moral standards (Blair-Loy 2003, 2009; Blair-Loy and Williams 2013; Steiner 2007). Because ideal worker standards are proscriptive ideas about how employees should behave, violating these standards invokes moralistic judgments about the worth of the employee—judgments that go beyond strategic estimations of employee productivity, skill level, or availability (Blair-Loy 2009; Davies and Frink 2014; Townsend 2002).

Caretaker and flexibility bias studies focus on penalties for prioritizing family within workplace contexts, but it is reasonable to expect that such censuring would also be evident during the hiring process. Given that ideal worker norms are so pervasive in the professional and managerial occupations that are the focus of this study, I propose that violating these norms will produce large negative effects—potentially larger than the quality signal of unemployment. Ideal worker norm violation theories posit the following hypothesis:

Ideal worker violation: Opt out job applicants will experience worse job application outcomes than will unemployed and employed applicants.

Gender Heterogeneity in Signal Strength

The above theories describe hypothesized variation in signals sent by differing employment histories. Employers are also expected to respond to résumé signals of gender and parenthood. Research on the motherhood penalty in hiring has found that résumé information about motherhood produces reduced hiring chances for mothers relative to childless women and fathers (Correll et al. 2007). The motherhood penalty theory argues that motherhood is a status characteristic, that is, an identity that elicits a host of assumptions and stereotypes about an individual (Correll et al. 2007; Ridgeway and Correll 2004). Bias against mothers is rooted in perceptions of lower competence and commitment to work: mothers are viewed as more distracted, and employers assume that children’s demands will reduce mothers’ availability for work and their dedication to work-related tasks (Correll and Benard 2006; Correll et al. 2007; Ridgeway and Correll 2004). To date, the motherhood penalty literature has focused on the effect of motherhood among currently employed applicants (e.g., Correll and Benard 2006; Correll et al. 2007; Gangl and Ziefle 2009); it would yield the following prediction about motherhood as a résumé signal across other employment statuses:

Motherhood penalty: Within employed, unemployed, and opt out groups, mothers will face penalties compared to fathers.

Because the motherhood penalty involves perceptions of mothers’ lower commitment to work, and ideal worker norm violation theory also predicts lower perceived commitment for opt out applicants, opt out mothers signal lower commitment in two ways (Dumas and Sanchez-Burks 2015; Sallee 2012). This interaction suggests that the motherhood penalty will be amplified among opt out applicants:

Motherhood penalty for opting out: The motherhood penalty will be larger for opt out applicants than among unemployed or employed applicants.

An alternative prediction is that penalties for opting out are worse for fathers than for mothers. This potential “fatherhood penalty” finds support in literature on norm violation, which demonstrates that those who are most expected to hold a norm are more severely punished when they violate the norm. In an audit study of gay men, for example, Tilcsik (2011) found that the hiring penalty for gay applicants was largest when the job advertisements used highly masculine language. With respect to ideal worker norm violations, because fathers are expected to prioritize work and be breadwinners for their families (Rudman and Mescher 2013; Townsend 2002), fathers who opt out could face harsh penalties. Indeed, prior studies have found that evaluators are more willing to criticize and stigmatize parents in nontraditional positions, such as stay-at-home fathers, questioning whether they were making appropriate work/family decisions (Brescoll and Uhlmann 2005; Brescoll et al. 2012; Coltrane et al. 2013). Put another way, because fathers face greater pressure to work hard and commit to work compared to mothers, fathers who opt out could be perceived as highly uncommitted to work, because they violated more rigid ideal worker norms through their decision to leave work for family reasons (for a related discussion of men who request family leave, see Rudman and Mescher 2013). This fatherhood penalty leads to the following hypothesis:

Fatherhood penalty for opting out: Fathers who opt out will be viewed more negatively than mothers who opt out.

Because this study focuses on mothers and fathers, I can test for gender heterogeneity among parents in the effects of opting out.2

Theoretical predictions for how gender may interact with unemployment scarring are less clear. Pedulla (2016) found that unemployed men received lower callback rates than unemployed women; this gender difference was marginally significant in the audit study, but the gender gap was not reproduced in a follow-up survey experiment of mechanisms. Studies on time use document that upon unemployment, mothers increase housework and childcare time to a greater degree than do unemployed fathers (Berik and Kongar 2013). It is thus possible that employers interpret unemployment differently for mothers and fathers, and perhaps believe that mothers become more committed to family (and less committed to work) during their lapse. In this case, unemployed mothers would experience similar processes as opt out mothers. The theoretical processes concerning the gendered effects of unemployment are less clear, however, so I do not produce a priori predictions on this interaction.

The above signaling theories imply a two-step process for how signaling affects employment outcomes. First, a piece of information on the résumé triggers employers’ assumptions about the job applicant. Second, if employers think these (perceived) qualities are relevant to hiring, then in the aggregate, applicants with negative résumé signals will experience reduced callback rates when applying for jobs. The survey experiment presented in Study 1 tests the first part of the process: whether the signal itself produces different assumptions about job applicants. The audit study, presented in Studies 2 and 3, examines the second step—how employers in an actual labor market respond to each signal in their callback decisions.

Study 1: Perceptions of Applicants

Theory

In Study 1, I used an original survey experiment to test whether skill deterioration or signaling theories best predict how perceptions of opt out job applicants compare to perceptions of unemployed and employed applicants.

Skill deterioration theory proposes that perceptions of skills lost are the predominant reason why a gap in employment could produce negative outcomes. I asked survey respondents to rate applicants’ capability as a primary measure of skill level. If skill deterioration were the only process occurring and there were no additional signaling processes, this theory would predict that unemployed and opt out applicants will both experience negative capability ratings, relative to employed applicants, and capability will be the only perceived difference between intermittently employed and continuously employed applicants.

Unemployment scarring theories suggest that unemployment operates as a negative signal through perceived employee quality. The theoretical argument is that evaluators assume that applicants with a bout of unemployment are weaker employees overall—whether in their ability and skills or their day-to-day work output. Perceived quality can be operationalized in a number of ways. Capability (a measure of perceived competence) and reliability (measuring dependability and consistency in work) have been demonstrated to affect perceptions of unemployed individuals (Clark et al. 2001; Pedulla 2016). In the context of the survey experiment, the unemployment scarring theory thus predicts that unemployment will lead to reduced perceptions of capability and reliability, relative to both employed and opt out applicants.

Violating ideal worker norms by prioritizing family over work—as is the case with opt out applicants—signals reduced commitment to work and less reliability at work (Brumley 2014; Davies and Frink 2014; Dumas and Sanchez-Burks 2015; Sallee 2012). In other words, evaluators may be concerned that applicants will leave work again in the future, or that they will be less present on a daily basis—because of competing family demands—and thus will be less reliable (Fuegen et al. 2004; Rivera and Tilcsik 2016). To capture the moral assessment associated with violating ideal worker norms, respondents were asked how deserving of the job they perceived applicants to be. In contrast to opt out applicants, unemployed applicants are not predicted to be perceived as less deserving—all else being equal, unemployed applicants may garner sympathy and be thought of as more deserving of a job, because they did not voluntarily stop working and have expressed continued interest in working. Thus, if opting out corresponds to a violation of ideal worker norms, then opt out applicants should be rated as less committed, deserving, and reliable than unemployed and employed applicants.

Finally, Study 1 allows for a test of competing predictions for gender heterogeneity in the effects of opting out. On the one hand, opting out could be worse for mothers than for fathers. Because the motherhood penalty operates in part through perceived commitment (Correll et al. 2007; Fuegen et al. 2004), opt out mothers may be perceived as even less committed than working mothers. On the other hand, ideal worker norms apply more strictly to fathers (Townsend 2002), so opt out fathers who violate these norms may be penalized to a greater extent than opt out mothers. Thus, either opt out mothers or opt out fathers may be rated lower on ideal worker measures (commitment, deservingness, and reliability).

Survey Experiment Design and Methods

The survey experiment was designed to test the effects of unemployment and opting out, relative to same-gender continuously employed applicants. The experiment was fielded by YouGov to a sample of 1,000 U.S. respondents3 in January 2014. YouGov samples from a panel of approximately 1.8 million individuals in the United States and uses a matching algorithm to create a sample representative of the same population targeted by the American Community Survey (i.e., the noninstitutionalized adult population).

Survey respondents were told they were helping a large U.S. accounting firm evaluate job applicants for a midlevel accounting position, and they would be presented with applications for two of the final applicants for the position. I chose accounting because it is an occupation most Americans are familiar with, is a large and growing profession (Bureau of Labor Statistics 2016), and has been used in existing experimental studies (e.g., Pedulla 2016). All respondents viewed one continuously employed applicant and a second applicant who was either unemployed or had opted out. Within respondents, applicant gender was held constant, such that both résumés belonged to either two mothers or two fathers.

This experimental design allows for a strong causal test of the effects of opting out and unemployment. When considering two applicants who vary only on employment history, does the same decision-maker respond differently to intermittent employment compared to continuous employment? Within-subject estimates of the effect of unemployment and opting out, compared to continuous employment, allow for a test of how each type of intermittency leads to different perceptions about job applicants.

Respondents rated each fictitious applicant on several dimensions: commitment, reliability, capability, and deservingness. For example, respondents were asked: “How committed do you consider Name?” Response options ranged from 1 (not at all committed) to 7 (extremely committed).4 These measures were developed based on social psychological literature and existing findings about unemployment, motherhood, and ideal worker norms (e.g., Correll et al. 2007; Davies and Frink 2014; Pedulla 2016). Because applicant gender was held constant within respondents, the design of Study 1 tests signaling of employment history more precisely than motherhood or fatherhood signals.5 However, between-subject estimates of gender can give clues as to whether there are amplifying or muting effects of motherhood and fatherhood.

Study 1 Results: Micro-Level Perceptions

Table 1 presents findings from OLS linear regressions for each of the résumé ratings, with fixed effects for respondent. The treatment effects in these models can be interpreted as the within-respondent difference between intermittent employment (unemployed or opt out) applicant ratings, compared to a same-gender continuously employed applicant.

Table 1. OLS Regression Estimates of Experimental Condition on Résumé Rating Measures

With respect to the unemployment signaling theories, unemployed applicants were rated significantly lower than employed applicants on measures of commitment, capability, and reliability. These are all measures of quality, confirming theories of how unemployment scarring signals operate through perceived quality.

Opt out applicants were rated lower than employed applicants on measures of commitment, capability, deservingness, and reliability. Commitment and reliability directly correspond to the violation of ideal worker norm theories. Opt out applicants, but not unemployed applicants, were rated as less deserving of the job than employed applicants. This suggests that the act of opting out contributes to ideas that these applicants are less in need of a job. The deservingness penalty suggests a moral violation—individuals who work hard and are dedicated to work are perceived as deserving and worthy (Blair-Loy 2009), but opting out violates these ideals and thus these applicants are viewed as less worthy of a job.

Figure 1 displays the average predicted levels of each standardized rating measure, with 95 percent confidence intervals. Figure 1 is from the fixed-effects model (see Table 1, Model 1), with dependent variables standardized to allow for interpretation across measures. Opting out yields a predicted penalty of about .2 standard deviations from the mean across measures of capability, reliability, and deservingness (–.187, –.158, –.203, respectively). The largest penalty for opting out is produced through perceptions of commitment (–.459 standard deviation units). When testing for significance in the ratings for unemployed compared to opt out applicants, the opt out effect is significantly more negative than the unemployed effect on measures of commitment, deservingness, and reliability (p < .05). Compared to employed applicants, both the unemployed and opt out applicants incur penalties on capability ratings and are not viewed as significantly different on this measure.

Figure 1. Survey Experiment Ratings by Employment Condition. Note: Estimates are from a fixed-effects model (see Table 1, Model 1) with standardized dependent variables.

Gender Differences in Ratings

Because respondents viewed two résumés from applicants of the same gender, it is not possible analytically to use respondent fixed effects and test for main effects of mother/fatherhood in rating outcomes. In the bottom panel of Table 1, I present between-subject estimates of the effects of employment, gender, and employment × gender interactions. Standard errors are clustered by respondent.6

Overall, I find no significant gender differences in the effects of employment or unemployment. In contrast, opting out does produce some gendered effects. On measures of commitment and reliability, opting out is significantly less negative for mothers than for fathers. This finding provides support for the fatherhood penalty hypothesis for opting out, which predicted that opt out fathers will experience greater penalties for violating ideal worker norms than will opt out mothers.

Discussion of Study 1 Findings

Study 1 demonstrates three important findings. First, comparing unemployed to employed applicants, I find partial support for the unemployment scarring hypothesis: unemployed applicants were rated lower than employed applicants on capability, reliability, and commitment.7 However, opt out applicants were rated lower than unemployed applicants on perceptions of commitment, deservingness, and reliability. Because these measures correspond to ideal worker norm standards, Study 1 establishes that opting out signals a violation of ideal worker norms, and that ideal worker norm violations are stronger negative signals than is unemployment scarring, supporting the ideal worker norm violation hypothesis.

A second important finding is that both unemployed and opt out applicants incur similar penalties on perceived capability, which is a measure of perceived skill decline and competence. This finding suggests that skill deterioration is at play, and it provides partial support for the skill deterioration hypothesis: both types of lapses produce assumptions about potential skill decline. However, in contrast to a pure skill deterioration explanation, unemployed and opt out applicants are not rated lower solely based on capability; they additionally incur penalties on other dimensions.

These two findings suggest that if employers care most about skills and ability in sorting job applicants, then unemployment and opting out should have similar effects in real job application settings. If, however, employers prefer that employees uphold ideal worker norms, then opt out applicants will fare worse than unemployed applicants when attempting to gain a job. The audit study will test these processes.

The third key finding from Study 1 is that unemployment produced no gender differences in effects, but opting out was somewhat worse for fathers than for mothers. Because of the survey experiment design—in which respondents viewed two applicants of the same gender—it is possible that gender effects could emerge differently in a context with both men and women applicants. The audit study will test to what extent this fatherhood penalty among opt out applicants appears in real labor market settings.

Study 2: Audit Study Main Effects

Theory and Hypotheses

To examine how signals of unemployment scarring and ideal worker norm violation affect demand-side employer preferences in hiring, I conducted a large-scale audit study with the same six experimental conditions as used in the survey experiment. Audit studies, a type of field experiment, have been considered the “gold standard” for establishing employer preferences or discrimination in hiring (e.g., Pager, Bonikowski, and Western 2009). The audit study methodology combines the benefits of experimental research to assess causality with the benefits of observational studies that assess real-life effects outside the laboratory.8 By sending fictitious résumés and job applications in response to real job openings, experimentally manipulating particular qualities on the résumés, and recording callback rates across the experimental conditions, audit studies allow researchers to measure employer preferences in ways that are not observable in most types of survey data.

Based on the employment results from the survey experiment in Study 1, I argue that opting out signals a violation of ideal worker norms. If employers value ideal worker norms, they will view this violation as a meaningful negative signal. Opt out applicants will thus receive fewer callbacks than both unemployed and employed applicants. In Study 1, I found that unemployment signals lower quality relative to continuous employment, and unemployment scarring theories predict that unemployed applicants will receive fewer callbacks than employed applicants. In addition, I found that both unemployed and opt out applicants were rated similarly on measures of capability. If employers view capability signals as more important than ideal worker norm violation signals, then opt out and unemployed applicants should receive similar callback rates in the audit study.

The survey experiment found that opt out fathers were rated lower than opt out mothers on ideal worker norm violation measures. This suggests that in the audit study, opt out fathers will receive fewer callbacks than opt out mothers. Although I found no evidence of the motherhood penalty in the survey experiment, the experimental design was not well-suited to observe overall gender effects. It is thus possible that in a competitive environment with mixed-gender applicants (as is the case in the audit study), a motherhood penalty will emerge, either in the main effect or in amplifying the effect of opting out.

Audit Study Design

In this study, one job application was submitted to each of 3,407 job openings that were posted on a large job-listing website between August 2015 and January 2016. The job listings were sampled from 50 major metropolitan areas in the United States, allowing for a range of labor market contexts. This sample yielded about 600 jobs per experimental condition.

In the applications, experimental manipulations (gender and employment status) were signaled in two places: on the cover letter and on the résumé itself. Gender was signaled through the applicants’ names, which are common names and easily identifiable by gender. The names (Elizabeth/Joseph Anderson, Emily/Sam Harris) were pretested on Amazon Mechanical Turk, an online platform, and respondents rated names as similar in terms of gender recognition, assumptions about applicants’ race/ethnicity, and commonness.

All of the fictitious applicants are parents. To signal parenthood, the cover letters state that applicants are moving to a new city with their family, which is why they are seeking a new job. Applicant résumés also include a line stating that the applicant was a parent volunteer at the local elementary school. Opting out is signaled on the résumé by stating next to the most recent job that the applicant “left to take care of my children.” This is restated in a similar manner on the cover letter.9 Unemployed applicants’ résumés state that they were laid off due to downsizing from their most recent job.10 Résumés for both unemployed and opting out applicants state they were out of work for a period of 18 months, which holds constant the length of employment lapse across lapse type.

All applicants are college-educated and have held two jobs since college for a total of about 9.5 years of work experience. Across each employment condition (continuously employed, unemployed, and opt out), applicants have the same number of years of work experience, but the timeline shifts for those with employment lapses. For example, the unemployed applicant has experienced a contemporary bout of unemployment but has the same number of years of employment as the continuously employed applicant. This timeline implies that applicants are approximately 32 to 34 years of age, making it reasonable that they could be parents.

Job applications were sent to five types of positions, each of which requires a college degree but no additional licenses or degrees: human resources managers, marketing directors, accountants, financial analysts, and software engineers.11 Skills and language describing past work experience were tailored to the job type, but details of the cover letter and résumé were constant across condition. All the fictitious applicants had real emails, phone numbers, and addresses. The separate phone numbers for each name had a recorded voicemail with a male or female voice.

To sample across cities, I used a major job-posting website that accumulates job postings from multiple smaller websites. To determine which jobs to send applications to, I created a Python script that enabled web scraping of all relevant job openings within 25 miles of each of the 50 cities in the study. Each day that I sent out applications, I scraped all jobs that were listed since the previous application date (typically every weekday) for each city and job category. For instance, the script collected all jobs listed in each of the 50 metropolitan areas that matched search criteria for the five job types (e.g., software engineering in New York City). The information scraped included the full job description, the company, salary, job title, and application website. From this complete list of jobs, I randomly subsampled to select which job openings to send applications to. For example, in one day there might be more than 7,000 jobs posted across the five job categories and 50 cities, and I might sample 200 from this list to send applications to in that day. Because I collected information on both the sample and the full list, I was able to verify that the sampled jobs did not differ from the full population on characteristics such as salary, description key words, and length of time listed on the website. Some audit studies do not use computer-generated random samples and rely on researchers choosing relevant jobs. This yields the potential for researchers to unconsciously bias the selection process, a possibility that is untestable because data on non-selected jobs are not collected. My sampling process eliminates this possibility.

Measures and Analytic Strategies: Study 2

The dependent variable of interest is the callback rate. When employers responded to a submitted job application, responses were coded if they requested an interview with the applicant. For example: “Dear Joe, We appreciate your interest in a career with us. Congratulations on being selected for our initial screening. We think you are a strong candidate for our marketing team and would like to set up a phone interview. Please call us to discuss this opportunity further and find a time to interview.”12

For the majority of applications, no response was received. This is typical of audit studies—past studies have found about an 8 percent response rate (e.g., Pedulla 2016; Tilcsik 2011). In the overall sample, 9.45 percent received an interview request, and 8.34 percent received a formal rejection. The remaining applications received no response, which is a presumed rejection.13

Study 2 gives results from the main effects of the experimental conditions on response rates. Because of random assignment, any difference in response rates by condition can be attributed to the experimental manipulation, and simple t-tests of mean differences are adequate to test for significant differences. In addition, I conducted logistic regressions predicting a callback (0 = no callback, 1 = callback). The primary independent variable is the experimental condition (employment history and gender), and models control for job type.

Study 2 Results: Audit Study Main Effects

Figure 2 displays the mean callback rate by experimental condition, with 95 percent confidence intervals. The results show that employed fathers and mothers received the highest response rates. Among employed fathers, 14.6 percent received requests for interviews, compared to 15.3 percent of employed mothers; this small gender difference was not statistically significant. Relative to the continuously employed, unemployed applicants received about two-thirds as many callbacks: 8.8 percent of unemployed fathers and 9.7 percent of unemployed mothers received interview requests. Finally, opt out applicants fared the poorest in terms of callback rates. Only about 5.4 percent of opt out fathers and 4.9 percent of opt out mothers received interview requests. Relative to their unemployed counterparts, opt out applicants were about half as likely to receive an interview request (t-statistic = 4.03, p < .05).

Figure 2. Mean Response Rates by Experimental Condition in Audit Study, All Job Types Source: Audit study data, collected 2015 to 2016.

Table 2 presents logistic regressions predicting callback rate, with controls for job type. Model 1 includes the main effects of employment, and Model 2 interacts employment with gender. These results show that the employment effects are statistically significantly different, but there are no statistically significant gender differences.

Table 2. Logistic Regressions Predicting Callback from Audit Study, across Experimental Conditions

Discussion of Study 2 Results

In the audit study, unemployed applicants were penalized relative to the continuously employed, and the opt out applicants faced a greater disadvantage relative to the unemployed. With respect to the hypotheses, a pure skill deterioration explanation does not hold, because opt out applicants faced greater disadvantages than the equivalently qualified unemployed applicants. The scarring signal of unemployment is evident, but this signal is less damaging to hiring opportunities than is the violation of ideal worker norms that opt out applicants demonstrate. Within each employment condition, results are consistent by gender, with no evidence of the motherhood penalty in the main effects, and no evidence of the fatherhood opting out penalty in the main effects.14

Overall, these findings support the ideal worker norm violation hypothesis. The unemployment scarring hypothesis is partially supported, because unemployment produces a negative effect relative to continuous employment. However, in these occupational contexts, violation of ideal worker norm signals swamp quality signals of unemployment and produce greater negative results.

Why are there no observable gender differences in these effects? As I will discuss in more detail later, these résumés are relatively high quality—the employed applicants’ callback rate was higher than in several recent audit studies (including Correll and colleagues’ [2007] motherhood penalty study). Gendered assumptions of motherhood and fatherhood might be minimized by résumé quality. Or, motherhood and fatherhood might send relatively weaker signals than employment history, and as such these signals were obscured by the stronger employment history signals. If this were the case, then weaker signals would be observable only in certain conditions. I propose that variation in callbacks across local labor market context—particularly, the competitiveness of a labor market—allows for testing of signal strength. In a low competition labor market, weaker signals are not easily observable because there are fewer job applicants, and employers must prioritize ranking applicants with strong signals. In highly competitive markets, employers have more applicants to choose from, and in these contexts weaker signals can be used to rank applicants. Study 3 tests these propositions.

Study 3: Variation In Callbacks Across Local Labor Markets

Signaling could operate differently across local labor markets. Labor market scholars have proposed queueing theories to explain how hiring processes work across contexts. The queueing approach to hiring is as follows: job-seekers rank jobs by preference, and employers rank job applicants for a particular job opening (Blanchard and Diamond 1994; Fernandez and Mors 2008; Moscarini 2005; Reskin and Roos 2009). The extent to which these queues overlap determines job outcomes (callbacks and eventual hiring) (Reskin and Roos 2009). Because researchers control the job application strategy in audit studies, job-seekers’ interests are rendered irrelevant, allowing for a focus on employers’ perspectives. In queueing theories, if a certain characteristic is viewed as less desirable (e.g., motherhood), then a résumé signaling this characteristic will place the applicant further back in the queue (if all else is equal).

The queueing theory allows for a theoretical test of relative signal strength by examining to what extent the outcomes associated with résumé signals vary across labor market context. Queueing theories do not predict that a signal itself changes across context (Moscarini 2005) (e.g., opting out may produce a negative signal across all contexts), but that an applicant’s queue position as a result of the signal could change due to the size of the applicant pool. Thus, the observed effects of résumé signals can vary across labor market competitiveness, which allows for a test of relative signal strength.

The predictions of signal variation across labor markets are as follows. Local labor markets that are competitive—with relatively few job openings compared to the number of job-seekers—are associated with longer queues for any particular job (Fernandez and Mors 2008; Reskin and Roos 2009). In these competitive markets, a résumé trait that sends a relatively weak negative signal could push an applicant farther back in the queue in an absolute sense: even if their relative queue position remains constant, in competitive environments there will be more desirable applicants ranked higher in the queue (Reskin and Roos 2009). In contrast, a labor market with low competition may be more forgiving of negative signals; with shorter queues, fewer ideal applicants top the queue. Finally, signals could be invariant to labor market context; this might occur if a signal is so negative that it pushes an applicant toward the bottom of a queue no matter the context. If this is the case, there may be no observable differences in callback rates across context.15

The following hypothetical example helps illustrate the logic of this argument. Consider two signals, one weaker and one stronger. Suppose the weak signal moves an otherwise ideal candidate 10 percent lower in a ranking of job candidates, whereas the strong signal moves this ideal candidate 50 percent lower in ranking positions. In a less competitive context in which there are 10 applicants for a job, the weak signal moves a candidate from position 1 to position 2, whereas the strong signal moves the candidate down to position 6. If three applicants are called in to interview, the weak signal candidate gets a callback but the strong signal candidate does not. In a competitive context with 100 job applicants, the signals may produce the same relative effect but move candidates further down a queue in an absolute sense. Now, the weak signal with a 10 percent penalty moves the candidate from position 1 to position 11, and the candidate with a strong negative signal (50 percent) moves to position 51. When the top three applicants are offered an interview, neither candidate receives a callback. Thus, the weak signal is only observed to produce a negative effect in competitive environments, whereas the strong signal is observable across all contexts.

I propose that—in this study—employment history produced a strong negative signal, whereas gender (motherhood/fatherhood) within an employment group produced a weaker negative signal. As described in Study 1, the fatherhood penalty among opt out applicants could emerge in high competition contexts, as could the motherhood penalty among employed applicants, as Correll and colleagues (2007) found. Queueing theories thus produce the following hypotheses for the signals introduced in the prior theory sections:

Labor market context: In labor markets with high competition, relatively weak negative signals will be observable, whereas in labor markets with low competition, only stronger signals will be observed. Gender differences within the opt out and employed conditions may only be observable within high competition contexts.

Study 3 Design and Measures

Study 3 uses the audit study data and exploits variation across location. As mentioned earlier, the audit study was conducted across the 50 largest metropolitan areas in the United States. The dependent variable in this study is again the callback rate across experimental condition. To measure labor market context, I used American Community Survey (ACS) 2006 to 2015 data (Ruggles et al. 2015) to create city-level measures of job-seekers in the major occupation groups of the audit study jobs. The ACS asks whether an individual—employed or unemployed—is currently searching for a new job. I used this to create an occupation-specific job-seeker rate, which varies across city.16 For example, applications in the audit study for software engineering positions were assigned the job-seeker rate for “computer and mathematical occupations” for the metropolitan area in which the job was posted.17

I then used logistic regressions to predict a callback, interacting experimental condition with the local job-seeker rate, and controlling for job type. Standard errors are clustered by location. In Part 3 of the online supplement, I explain how I tested models with additional contextual controls and detail different coding options for the job market competitiveness measure. Because the marginal effects of interaction terms are not directly interpretable from logistic regression coefficients (Ai and Norton 2003; Norton, Wang, and Ai 2004), I display results graphically and present a linear probability model for ease of interpretation.

Study 3 Results: Interactions with Labor Market Context

Figure 3 shows the results of predicted callback rates as the local job-seeker rate varies, across experimental condition, derived from Table 3, Model 2. Job-seeker rates of these occupations vary from 2.9 to 7.8 percent in the 50 cities in my sample, with the average at 4.9 percent.

Figure 3. Callback Rates across Local Job-Seeker Rates, by Experimental Condition. Source: Audit study data, 2015 to 2016. Note: Job-seeker rates are from the American Community Survey 2006 to 2015, for the 50 metropolitan areas in the audit study.

Table 3. Callback Rates by Experimental Condition, Interacted with Local Labor Market Contexts

As the local job-seeker rate increased, employed fathers remained likely to receive interview requests, and there was no significant variation across local labor market in employed fathers’ callback rates. Employed mothers, in contrast, were less likely to receive callbacks in high job-seeker contexts than they were in cities with lower job-seeker rates. This suggests that in labor markets with high job-seeker rates and more opportunities for employer discretion, employed fathers benefit. In these locations, the motherhood penalty is striking: with a local job-seeker rate of 7 percent, employed fathers are predicted to have a 15.7 percent chance of receiving a callback, compared to employed mothers’ predicted callback rate of 7.9 percent. In less competitive markets with low job-seeker rates, employed mothers are given more callbacks, on average.

Turning to the unemployed applicants, I find no significant relationship between the local job-seeker rate and the callback rate for unemployed fathers or mothers. However, in high job-seeker contexts, the gap between employed and unemployed mothers is no longer statistically significant. This suggests that in contexts with increased competition, employment history distinctions matter less for mothers, because mothers from all employment groups fare relatively poorly in these contexts.

Finally, in examining the opt out applicants, I find a slight negative effect for fathers as job-seeker rates increase. Just 2.5 percent of opt out fathers are predicted to receive callbacks in competitive labor markets with job-seeker rates of 7 percent (CI: [.3 percent − 4.9 percent]). In less competitive markets (job-seeker rates of 4 percent), 7.2 percent of opt out fathers are predicted to receive callbacks (CI: [4.3 percent − 10.1 percent]). For mothers, however, callback rates among opt out applicants remain consistently low across the local labor market context. This finding suggests that even in labor markets with low competition, employers are not interested in hiring opt out mothers.

Table 3 shows the regression estimations predicting callbacks, from which Figure 3 is derived, and confirms the findings from Figure 3. Models 1, 2, and 3 are logistic regressions, and Model 4 is a linear probability model, which allows for simpler interpretation of interaction effects (Ai and Norton 2003). In Model 1, I present the experimental condition main effects (coded as a six-category variable rather than 3 × 2 employment × gender interaction so as to avoid cumbersome three-way interactions). Model 2 interacts experimental condition with the local job-seeker rate.

When considering job-seeker rates as a measure of local labor market competition, it is plausible that these rates are related to several additional factors that capture the city’s economic and occupational context. To ensure that related contextual measures do not explain the job-seeker findings, I added four city-level context measures to Model 3: the city’s occupational composition (measured as the percent of managerial and professional workers in the city’s workforce), mothers’ labor force participation rate, estimates of the number of new hires within each audit study occupation, and estimates of the change in occupation size from 2015 to 2016.18 Each contextual control variable is interacted with the experimental condition to ensure that the job-seeker interactions are not due to correlations with these other context measures. Because these measures are included as a robustness test, I do not discuss the coding or motivation of variables here, but I elaborate on these decisions in Part 3 of the online supplement. Finally, Model 4 in Table 3 presents the full model as a linear probability model, in which interaction effects are easily interpreted. This model confirms that there is a significant negative interaction of employed mother × job-seeker rate, indicating that relative to employed fathers, employed mothers fare worse as competition increases (p < .05). Similarly, opt out fathers fare worse as local job-seeker rates increase.

Study 3 Discussion

The variation across local labor markets demonstrates two important findings. First, although I observed no motherhood penalty in the overall callback rates, a motherhood penalty emerges in highly competitive markets for employed mothers compared to employed fathers. These results support the labor market context hypothesis for the motherhood penalty signal, among employed applicants. Considering a queueing approach to hiring, this result suggests that motherhood is a negative signal for employed applicants but is only observable in competitive contexts with longer queues. In contexts where there are fewer job-seekers (and shorter job queues), I do not find a significant motherhood penalty, and employed mothers fare relatively better in these contexts. These findings suggest that for employed parent applicants, employers differentiate by gender—preferring fathers—in longer queue contexts, when they are more easily able to enact their applicant preferences.

Opt out fathers receive fewer callbacks in contexts with high job-seeker rates than in less competitive environments. This again suggests that gender—in this case, fatherhood—is a relatively weaker negative signal among opt out applicants. Although opting out produces negative effects for both mothers and fathers, opt out fathers incur reduced callbacks in job markets with long queues, and they do somewhat better in job markets with less competition. These findings support the fatherhood penalty for opting out hypothesis: in competitive markets, fathers who violate ideal worker norms by opting out incur greater penalties than do mothers.

I find that employment status sends strong negative signals across labor market contexts. Unemployed mothers and fathers, as well as opt out mothers, do not experience detectably different callback rates across labor markets. These findings support the labor context hypothesis: strong negative signals place applicants toward the bottom of queues no matter the size of the pool of other applicants.

Conclusions and Discussion

In today’s labor market, jobs are increasingly demanding and require workers to fulfill ideal-worker norms, which involve being constantly available, working long hours, being highly dedicated, and putting work above competing life demands (Blair-Loy 2003, 2009; Cha and Weeden 2014; Turco 2010). Among workers who have family responsibilities—particularly caregivers and parents—the challenge of fulfilling ideal-worker expectations and balancing family demands can lead to career interruptions. Existing scholarship demonstrates that parents who opt out of paid labor do so because of the inflexibility of employment and the challenge of adequately fulfilling the demands of both intensive jobs and intensive parenting (Stone 2008). Among parents who leave work, the tendency is to remain out of the labor market temporarily and to return to work after several years (Percheski 2008; Stone 2008). Yet, existing scholarship does not satisfactorily assess the subsequent labor market consequences faced by individuals who opt out. In particular, there has been limited study of the demand-side processes that occur during the labor market re-entry process.

In this article, I developed a new signaling theory in which opting out signals a violation of ideal worker norms. In Study 1, a survey experiment, I demonstrated that opting out leads to more negative perceptions than unemployment on metrics of commitment, deservingness of the job, and reliability. In Study 2, an audit study conducted across 50 U.S. metropolitan areas, I tested the relative strength of the ideal worker norm violation signal, unemployment scarring signal, and motherhood penalty signal. I found that job applicants who had been out of work to care for children fared worse in terms of hiring prospects, compared to otherwise equivalent applicants who were unemployed because of a job loss. The unemployed, in turn, are disadvantaged relative to continuously employed applicants. In the aggregate, I found no gender differences in callback rates for mothers compared to fathers. These findings demonstrate that, among the occupations and cities in the sample, violating ideal worker norms by opting out sends a strong negative signal to employers—a signal that swamps the signals of unemployment scarring.

To further examine how signal strength varies across contexts, in Study 3 I integrated the signaling theory with a theory of labor markets as queues. I tested how callback rates differ across the 50 cities in the audit study, using job-seeker rates as a measure of market competitiveness. I argued that strong negative signals (e.g., opting out) place applicants toward the bottom of a job queue no matter the quality or quantity of the other job applicants. Less damaging negative signals will be observed only when labor queues are long, such as in competitive markets where employers have the option of indulging in taste-based preferences and ranking large numbers of applicants. Using this theoretical framework, I found that in competitive markets with higher job-seeker rates, a motherhood penalty was apparent among employed applicants, and employed fathers emerged as preferred applicants. Furthermore, opt out fathers faced additional negative penalties in competitive cities, suggesting that fathers face greater penalties for violating ideal worker norms.

These contextual findings show that macro-level processes across locations can affect hiring processes with systematic patterns. This is not a new idea, but it is rarely incorporated into field experiments on hiring (for important exceptions, see Kroft et al. 2013; Tilcsik 2011). The conclusions drawn from these experimental findings might have been quite different had I sampled only one or two cities. With new technology available to social scientists, including web scraping and “big data” approaches to data collection, it is now feasible to test whether audit study effects vary across contexts. My study incorporates these macro-level labor market contexts into the signaling theories tested in an audit study context. In short, I find that location matters, and it would be fruitful for future research to continue to extend audit study methodology along this trajectory. An important subsequent project could measure variation in gender attitudes at the city level (perhaps using social media data), and assess to what extent hiring processes for women and mothers vary across these dimensions.

This research leaves some unanswered questions, which could inspire future research ideas. First, the résumés in this study examine one method of signaling unemployment, opting out, and parenthood. To provide a clear causal test of reasons for employment lapses, I used several common methods of depicting a gap in employment; among real applicants, there is certain to be substantial variability in explaining an employment lapse. Résumé signaling theories rely on the idea that information (or lack thereof) on a job application stimulates assumptions about the job applicant. A natural question follows: Are there ways of signaling employment history or parenthood that do not produce negative assumptions, or do variations in signaling language yield different outcomes than those observed in the current study? For example, it would be worth testing whether giving no information or less information to explain a lapse yields different effects than informing employers that a layoff caused unemployment.

In a related area, future research would benefit from continued theorization of the multidimensionality of assumptions produced by signals. I argued that opting out signals are distinct from unemployment scarring signals, and although opting out produces a larger negative effect than unemployment, there is some overlap in the types of assumptions provoked by both types of lapses (e.g., capability, reliability). Future research could do more theorizing as to what additional perceptions are invoked by different résumé signals, including perceptions of likeability, interpersonal skills, short- versus long-term commitment, cultural capital, and other moral evaluations relating to the work-family intersection.

Next, the finding that opt out fathers are penalized as much or more than opt out mothers should be unpacked in future research. I argued that this fatherhood penalty occurs because fathers experience higher expectations to uphold ideal worker norms than do mothers, and they are punished to a greater extent when they violate these norms. Considering the dearth of stay-at-home fathers in today’s labor market, this finding raises important theoretical questions on the gendered nature of care work. For example, are stay-at-home fathers experiencing penalties for violating male breadwinner or other gender expectations in conjunction with violating ideal worker norms? If, in the future, stay-at-home fathers become increasingly normalized, would this increase the perceived status of opting out and reduce penalties associated with opting out for both mothers and fathers? Future research could work to distinguish theoretically between the gendered nature of care work, breadwinner expectations, and ideal worker norms, to develop a more comprehensive understanding of how employers perceive opt out fathers.

Future research could also extend the scope of this study to different occupations, racial/ethnic groups, and caregiver characteristics. The current audit study is limited to highly educated applicants who have college degrees and are perceived to be white. Less-educated and lower-income mothers, however, often stay at home to avoid the cost of childcare; an important next project might examine variation in the effect of family lapses among less-educated applicants. Assumptions about parenthood vary by race and ethnicity as well, and these assumptions could lead to different outcomes in the hiring context. Additionally, this article examines the effects of opting out for mothers compared to fathers, but not for childless individuals. It would be worthwhile to test whether similar patterns emerge for childless applicants who engage in other forms of care work (e.g., caring for a sick parent), and if there are gender differences under these conditions.

Finally, researchers should conduct qualitative interviews with employers about their perceptions of former stay-at-home mothers and fathers who returned to work. How much experience do employers have with these types of job applicants, and what are their perceptions of employees who had previously taken lapses for family care? Asking these types of questions will allow for a more precise test of whether opt out penalties follow from taste-based preferences or from statistical discrimination processes: employers might justify their decisions to penalize opt out applicants through a rational business perspective, arguing that these employees are less reliable and less committed to work. Assessing employer familiarity with these types of employees, and their perceptions of work quality and commitment post-lapse, could provide leverage toward understanding these processes.

This article suggests that the way we organize labor produces systematic disadvantages for primary caretakers and contributes to our understanding of how gender inequality in the labor market is maintained and reinforced. In our current gender system, mothers are more likely than fathers to interrupt employment to care for children. We know from existing literature that mothers are “pushed out” of work when workplaces are inflexible and intensely demanding (e.g., Stone 2008). My research shows that, after being pushed out, they are kept out and have reduced job opportunities when attempting to regain employment. When fathers opt out—challenging the normative gendered division of labor—they too face penalties, and in some contexts greater penalties than opt out mothers. These processes produce a reinforcing cycle: ideal worker norms limit job opportunities when caretakers are in work, which contributes to an increased likelihood of leaving work, but the same ideal worker norms are invoked to prevent re-entry back into work. To level the playing field, we may require a rethinking of the ideal worker norm that prevents caretakers from reaching their career goals. Until we reach such a point, it remains unlikely that these forms of inequality will change.

Notes

I refer to individuals who leave work to care for family or children as having “opted out” of work. This is a common term in both the media and academic scholarship. However, it is generally understood that exiting a job is rarely entirely voluntary, and many times these individuals feel “pushed out” or “forced out” because the workplace is not flexible about reconciling conflicts between family and work responsibilities (see, e.g., Stone 2008).

The term “motherhood penalty” has primarily been used in literature comparing mothers to childless women, referring to a within-gender comparison (Budig and England 2001; Budig et al. 2012). In experimental research, scholars have compared mothers to childless women and to fathers, theorizing the motherhood penalty as an interaction of gender × parental status (e.g., Correll et al. 2007). The current study’s experimental design compares mothers to fathers and does not include a comparison to childless women or men. The analytic consequences of this design are that childless women and men who leave work to care for family or who are unemployed could experience different processes that this study cannot directly test. In an online survey experiment not presented here, I found that parents experience greater hiring penalties than do childless applicants who have other family-related lapses (results available upon request). Future research would benefit from examining how these other family-related lapses (e.g., caring for a sick parent or partner) produce different assumptions in the hiring context than does caring for a child. Use of the terms “motherhood penalty” and “fatherhood penalty” in this article thus captures only the between-gender, within-parent comparison; this use deviates from how these terms have typically been applied in other research settings.

Out of the 1,000 respondents, I excluded 29 from the analysis because they spent less than 10 seconds reading both résumés.

For more details on measurement and survey experiment design, see Part 4 of the online supplement.

Because survey respondents are viewing two applicants of the same gender and told that these applicants are finalists for the position, responses to résumé ratings should be considered in the context of two same-gender applicants. It is possible that responses would change if respondents were rating two different-gender applicants or viewed the applicants in the context of a larger pool. The experimental design is helpful because it allows for a precise test of the effect of employment history, but the limitation is that any estimates of gender differences in ratings or effects are less straightforward to interpret.

In the experimental design in which gender of applicants is held constant within respondents, it is not possible to estimate the effect of applicant gender while using respondent fixed effects. There are two possibilities for estimation: (1) between-subject models estimating the main effect of applicant gender (clustering standard errors by respondent), and (2) respondent fixed-effects models estimating the interaction of gender with employment, but not estimating the main effect of gender. The first estimation strategy is shown in Table 1, Model 2. The second produces no substantive differences in effects, and is available upon request.

Pedulla (2016) found that unemployment did not lead to reduced perceptions of commitment to work, which is why commitment is not considered as an a priori prediction for the effects of unemployment. However, Pedulla’s study consisted of childless applicants. Parents might face higher expectations to work (and provide for their children) than do childless applicants, and thus are penalized in perceptions of commitment. Given that perceived commitment is a key dimension on which employers make decisions (Correll et al. 2007), it would be worthwhile to further examine the contexts in which unemployed applicants are perceived as less committed.

One critique of audit studies is that they are poorly suited to distinguish between statistical discrimination and taste-based discrimination. Statistical discrimination refers to cases in which businesses make decisions based on the productivity of a group; here, discrimination is justified from a business sense, because hiring decisions are made based on rational economic behavior (Correll and Benard 2006). Taste-based discrimination, on the other hand, is made due to stereotypes about the performance of particular groups, which bias how employers evaluate these groups (Correll and Benard 2006). Audit studies are sometimes critiqued because they are unable to test which of these processes are taking place (e.g., Heckman 1998). One can speculate about what types of biases are entering into the decision-making process, but this critique of audit studies cannot be entirely avoided.

Is it common practice to mention being a stay-at-home parent on one’s résumé? Based on results from an October 2017 Google search of “should I put stay-at-home parent on my résumé?”, the majority of blog posts and articles recommend to explicitly mention being a stay-at-home parent on the résumé and cover letter. Out of the first 50 Google search results, only five articles recommended not mentioning this in the résumé or cover letter, and another seven recommended putting it in the cover letter only but not on the résumé. The remaining 38 were either strongly in support of mentioning this in the résumé and cover letter (24) or were ambivalent (14) and said it is up to the job applicant, with no recommendation either way. Future research could more systematically study how common this practice is in real résumés and cover letters, but these results suggest that potential job applicants receive positive messaging and general encouragement to explicitly mention being a stay-at-home parent on their résumés and cover letters. The articles generally recommended being direct about the reason for leaving the most recent position, rather than creating a new position labeled “caretaker” or “stay-at-home mother” (see also Karsh and Pike 2009). Future research would benefit from examining whether employers perceive this information differently if listed on the résumé, cover letter, or in an interview, and whether there are ways to present this information positively to prevent employer bias.

The unemployment lapse was signaled through a layoff to correspond to an involuntary bout of unemployment. Some researchers have signaled unemployment by providing a gap in employment but no explanation for the lapse (e.g., Eriksson and Rooth 2014; Kroft et al. 2013; Pedulla 2016). Economists suggest that a layoff due to downsizing is perceived negatively, because presumably companies lay off lower-skilled personnel during downsizing (Charness and Levine 2002; Gibbons and Katz 1991). Additionally, it is rare for applicants to directly state on a résumé that they were fired, but much more common to list a layoff (Claman 2013). To clearly signal unemployment—rather than a gap for an unknown reason—I used a layoff as a common way to explain a job loss. However, it is possible that giving information about a layoff mutes the negative effects of unemployment, compared to a lapse due to firing or another involuntary reason, or relative to no reason given. This possibility should be tested more thoroughly in future research.

I chose these jobs because they are relatively common, vary in terms of gender composition, and exist across many different labor markets (Bureau of Labor Statistics 2016).

Occasionally, employers would respond in a more ambiguous way or ask for more information. For example: “Hi Elizabeth, Thanks for sending your résumé. I have a few questions: (1) Have you developed iOS apps before? If yes, are any currently in the app store? (2) It says you are moving to Los Angeles. Are you in Los Angeles now?” This was relatively rare and did not count as a callback. However, results are robust to including these responses, and these analyses are available upon request.

Using formal rejections could be attributable to features of the companies rather than the applicants, so I do not use this measure as a dependent variable in the analyses (these results are available upon request).

See Part 2 of the online supplement for interactions with job type and overall response rates by job type. The similar patterns across most of the jobs suggest that the effect of opting out does not vary widely by the feminization of jobs and is a persistent effect.

One possibility to consider is that the unemployment signal itself may produce different meaning in low versus high unemployment contexts, because employers are more or less forgiving of unemployment depending on whether it is common or uncommon in the local context. In other words, the unemployment signal strength could change across contexts, which would contradict the queueing theory assumptions of an invariant signal effect. Kroft and colleagues (2013) used an audit study to examine the effect of unemployment duration on callbacks across local labor markets, in the wake of the Great Recession. They found that unemployment duration has a stronger negative effect in low unemployment contexts, but when individuals have more than eight months of unemployment, the effect of unemployment is similar across low and high unemployment contexts. These findings suggest that the signal of longer periods of unemployment (in my study, 18 months) is invariant across labor market contexts, lending support for the queueing assumption of an invariant signal. It would be worthwhile to further examine whether rare or common résumé signals produce different effects across contexts to test this assumption more thoroughly.

(number of job-seekers / [number of job-seekers + number of non-job-seekers (employed)]) × 100 in the major occupational category. Note that this measure potentially misses job applicants who did not list an occupation on the ACS. However, the findings hold for alternative measures of local job competitiveness, including a city’s overall unemployment rate, the unemployment or job-seeker rate of college-educated individuals, and the non-manual occupation unemployment/job-seeker rate (see Part 3 of the online supplement).

City-level estimates of job-seeker context are based on the American Community Survey (ACS) PUMA (Public Use Microdata Area) definition. These consist of geographic areas with populations of 100,000 or more. Because the audit study contains the top-50 most populated cities in the United States, each of these cities corresponds to an ACS PUMA. One coding decision to note with respect to the constructed occupation-specific job-seeker rates is that accounting and financial analysts fall under the same major occupational group, per the 2010 Census occupational coding scheme (“financial specialists”). Both job types thus receive the same job-seeker rate values across cities. See Part 3 of the online supplement for additional details on contextual measures.

The first two measures are derived from ACS 2011 to 2015 data (Ruggles et al. 2015); the latter two are from the Quarterly Workforce Indicator (QWI) data (United States Census Bureau 2016), from the time period of the audit study (the last quarter of 2015 and the first quarter of 2016). See Part 3 of the online supplement for motivation and coding of these measures.

References

Acemoglu, Daron. 1995. “Public Policy in a Model of Long-Term Unemployment.” Economica 62(24): 161–78.

Ai, Chunrong, and Edward C. Norton. 2003. “Interaction Terms in Logit and Probit Models.” Economics Letters 80(1):123–29.

Albiston, Catherine R., Shelley J. Correll, Christina Stevens, and Traci Tucker. 2012. Laws, Norms, and the Motherhood/Caretaker Penalty (http://www.americ anbarfoundation.org/uploads/cms/documents/albiston_120512.pdf).

Arulampalam, Wiji, Paul Gregg, and Mary Gregory. 2001. “Unemployment Scarring.” Economic Journal 111(475):F577–F584.

Becker, Gary S. 1964. Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. New York: Columbia University Press.

Berdahl, Jennifer L., and Sue H. Moon. 2013. “Workplace Mistreatment of Middle-Class Workers Based on Sex, Parenthood, and Caregiving.” Journal of Social Issues 69(2):341–66.

Berik, Günseli, and Ebru Kongar. 2013. “Time Allocation of Married Mothers and Fathers in Hard Times: The 2007–09 US Recession.” Feminist Economics 19(3):208–237.

Blair-Loy, Mary. 2003. Competing Devotions: Career and Family among Women Executives. Cambridge, MA: Harvard University Press.

Blair-Loy, Mary. 2009. “Work Without End?” Work and Occupations 36(4):279–317.

Connelly, Brian L., Trevis S. Certo, Duane R. Ireland, and Christopher R. Reutzel. 2011. “Signaling Theory: A Review and Assessment.” Journal of Management 37(1):39–67.

Correll, Shelley J., and Stephen Benard. 2006. “Biased Estimators? Comparing Status and Statistical Theories of Gender Discrimination.” Social Psychology of the Workplace 23:89–116.

Correll, Shelley J., Stephen Benard, and In Paik. 2007. “Getting a Job: Is There a Motherhood Penalty?” American Journal of Sociology 112(5):1297–1339.

Davies, Andrea R., and Brenda D. Frink. 2014. “The Origins of the Ideal Worker: The Separation of Work and Home in the United States from the Market Revolution to 1950.” Work and Occupations 41(1):18–39.

Dumas, Tracy, and Jeffrey Sanchez-Burks. 2015. “The Professional, the Personal, and the Ideal Worker: Pressures and Objectives Shaping the Boundary between Life Domains.” Academy of Management Annals 9(1):803–843.

Eriksson, Stefan, and Dan-Olof Rooth. 2014. “Do Employers Use Unemployment as a Sorting Criterion When Hiring? Evidence from a Field Experiment.” American Economic Review 104(3):1014–39.

Fallick, Bruce C. 1996. “A Review of the Recent Empiri- cal Literature on Displaced Workers.” Industrial and Labor Relations Review 50(1):5–16.

Fernandez, Roberto M., and Marie L. Mors. 2008. “Competing for Jobs: Labor Queues and Gender Sorting in the Hiring Process.” Social Science Research 37(4):1061–80.

Flood, Sarah, Miriam King, Steven Ruggles, and J. Robert Warren. 2015. Integrated Public Use Microdata Series, Current Population Survey, Version 4.0. [Machine-readable database.] Minneapolis: University of Minnesota.

Fuegen, Kathleen, Monica Biernat, Elizabeth Haines, and Kay Deaux. 2004. “Mothers and Fathers in the Workplace: How Gender and Parental Status Influence Judgments of Job-Related Competence.” Journal of Social Issues 60(4):737–54.

Gangl, Markus. 2004. “Welfare States and the Scar Effects of Unemployment: A Comparative Analysis of the United States and West Germany.” American Journal of Sociology 109(6):1319–64.

Gangl, Markus, and Andrea Ziefle. 2009. “Motherhood, Labor Force Behavior, and Women’s Careers: An Empirical Assessment of the Wage Penalty for Motherhood in Britain, Germany, and the United States.” Demography 46(2):341–69.

Ghayad, Rand. 2015. “The Jobless Trap.” Working paper, Northeastern University, Department of Economics.

Gibbons, Robert, and Lawrence F. Katz. 1991. “Layoffs and Lemons.” Journal of Labor Economics 9(4):351–80.

Glass, Jennifer. 2004. “Blessing or Curse? Work-Family Policies and Mother’s Wage Growth over Time.” Work and Occupations 31(3):367–94.

Haurin, Donald R., and Kala S. Sridhar. 2003. “The Impact of Local Unemployment Rates on Reservation Wages and the Duration of Search for a Job.” Applied Economics 35(13):1469–76.

Heckman, James J. 1998. “Detecting Discrimination.” Journal of Economic Perspectives 12(2):101–116.

Jacobs, Jerry A., and Kathleen Gerson. 2001. “Overworked Individuals or Overworked Families? Explaining Trends in Work, Leisure, and Family Time.” Work and Occupations 28(1):40–63.

Karsh, Brad, and Courtney Pike. 2009. How to Say It on Your Resume: A Top Recruiting Director’s Guide to Writing the Perfect Resume for Every Job. New York: Penguin Group.

Kelly, Erin L., Samantha K. Ammons, Kelly Chermack, and Phyllis Moen. 2010. “Gendered Challenge, Gendered Response: Confronting the Ideal Worker Norm in a White-Collar Organization.” Gender & Society 24(3):281–303.

Kroft, Kory, Fabian Lange, and Matthew J. Notowidigdo. 2013. “Duration Dependence and Labor Market Conditions: Evidence from a Field Experiment.” Quarterly Journal of Economics 128(3):1123–67.

Lovejoy, Meg, and Pamela Stone. 2012. “Opting Back In: The Influence of Time at Home on Professional Women’s Career Redirection after Opting Out.” Gender, Work and Organization 19(6):631–53.

Mincer, Jacob, and Haim Ofek. 1982. “Interrupted Work Careers: Depreciation and Restoration of Human Capital.” Journal of Human Resources 17(1):3–24.

Moscarini, Guiseppe G. 2005. “Job Matching and the Wage Distribution.” Econometrica 73(2):481–516.

Norton, Edward C., Hua Wang, and Chunrong Ai. 2004. “Computing Interaction Effects and Standard Errors in Logit and Probit Models.” Stata Journal 4(2):154– 67.

Nunley, John M., Adam Pugh, Nicholas Romero, and R. Alan Seals. 2017. “The Effects of Unemployment and Underemployment on Employment Opportunities: Results from a Correspondence Audit of the Labor Market for College Graduates.” Industrial and Labor Relations Review 70(3):642–69.

Pager, Devah, Bart Bonikowski, and Bruce Western. 2009. “Discrimination in a Low-Wage Labor Market: A Field Experiment.” American Sociological Review 74(5):777–99.

Pedulla, David S. 2016. “Penalized or Protected? The Consequences of Non-standard Employment Histories for Male and Female Workers.” American Socio- logical Review 81(2):262–89.

Percheski, Christine. 2008. “Opting Out? Cohort Differences in Professional Women’s Employment Rates from 1960 to 2005.” American Sociological Review 73(3):497–517.

Reid, Erin M. 2011. “Passing as Superman: The Ideal Worker and Men’s Professional Identities.” Academy of Management Proceedings 2011(1):1–6.

Reimers, Cordelia, and Pamela Stone. 2008. “Explaining Trends in ‘Opting Out’ among Women, 1981–2006.” Presented at the annual meetings of the Population Association of America, New Orleans, LA, April 7.

Reskin, Barbara F., and Patricia A. Roos. 2009. Job Queues, Gender Queues: Explaining Women’s Inroads into Male Occupations. Philidelphia, PA: Temple University Press.

Ridgeway, Cecilia L., and Shelley J. Correll. 2004. “Motherhood as a Status Characteristic.” Journal of Social Issues 60(4):683–700.

Rivera, Lauren A., and András Tilcsik. 2016. “Class Advantage, Commitment Penalty: The Gendered Effect of Social Class Signals in an Elite Labor Market.” American Sociological Review 81(6):1097– 1131.

Rudman, Laurie A., and K. Mescher. 2013. “Penalizing Men Who Request a Family Leave: Is Flexibility Stigma a Femininity Stigma?” Journal of Social Issues 69(2):322–40.

Ruggles, Steven, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. 2015. Integrated Public Use Microdata Series: Version 6.0 [dataset]. Minneapolis: University of Minnesota. American Community Survey dataset, 2006–2015, pooled 1-year ACS samples.

Sallee, Margaret W. 2012. “The Ideal Worker or the Ideal Father: Organizational Structures and Culture in the Gendered University.” Research in Higher Education 53(7):782–802.

Spence, Michael. 1973. “Job Market Signaling.” Quarterly Journal of Economics 87(3):355–74.

Spence, Michael. 1981. Signaling, Screening, and Information. Chicago: University of Chicago Press.

Steiner, Leslie M. 2007. Mommy Wars: Stay-at-Home and Career Moms Face Off On Their Choices, Their Lives, Their Families. New York: Random House.

Stiglitz, Joseph E. 2002. “Information and the Change in the Paradigm in Economics.” American Economic Review 92(3):460–501.

Stone, Pamela. 2008. Opting Out? Why Women Really Quit Careers and Head Home. Berkeley: University of California Press.

Tilcsik, András. 2011. “Pride and Prejudice: Employment Discrimination against Openly Gay Men in the United States.” American Journal of Sociology 117(2):586–626.

Townsend, Nicholas. 2002. Package Deal: Marriage, Work and Fatherhood in Men’s Lives. Philidelphia, PA: Temple University Press.

Turco, Catherine J. 2010. “Cultural Foundations of Tokenism: Evidence from the Leveraged Buyout Industry.” American Sociological Review 75(6):894– 913.

United States Census Bureau. 2016. “Quarterly Workforce Indicators.” Longitudinal Employer-Household Dynamics. 2015 Q4 and 2016 Q1 (https://ledextract ces.census.gov/static/data.html).

Williams, Joan C., Jessica Manvell, and Stephanie Born- stein. 2006. ‘Opt Out’ or Pushed Out? How the Press Covers Work/Family Conflict. San Francisco, CA: The Center for WorkLife Law, Hastings College of the Law (http://worklifelaw.org/pubs/OptOutPushedOut.pdf).

Winefield, Anthony H., Marika Tiggemann, and Helen R. Winefield. 1992. “Unemployment Distress, Reasons for Job Loss and Causal Attributions for Unemployment in Young People.” Journal of Occupational and Organizational Psychology 65(3):213–18.

[purplesurveys]

816

What have you been up to today? Anything interesting? I decided to be a 14 year old again and watched video clips of my OG, ride or die, absolute favorite OTP this morning and I fangirled like it was 2012 lol. I haven’t revisited that ship in around six or seven years so it was surreal to see their scenes all over again and I was beaming all morning hahaha. Other than that, I do have a meeting in an hour but I find that more blah than interesting... WHYYYYY I volunteered for a shitload of extracurricular positions before this quarantine started, I’ll never know. What was the last thing you ate? My sister had found this bread recipe on YouTube last night and I loooooved what she made, so I asked her to make it again this morning. Do you know how to knit? Who taught you? We were taught in Grade 6 by our home economics teacher, but 1) I wasn’t interested and 2) kinesthetic learning has never been my forte, so I wasn’t able to follow from as early as step 1. What state or territory were you born in? Continued the day after, lol oops. I was born in Manila. Are you the type of person to dwell on the past? Only in such a way that I prefer to hold grudges, so on that front I’m technically perpetually dwelling on the past. I can get over other stuff relatively easily.

Are there many traffic incidents in your area? There’s a usual bottleneck spot three minutes away that gets really bad in the evening when everyone is trying to get home, but the traffic becomes a lot more loose when you get to the part of the highway leading to my village. What's your favourite genre of music? I vibe with R&B and some indie rock the best. Have you ever been for a ride in the back of a truck? No, I think that’s mostly illegal here lol. People usually ride the back of pickup trucks when they’re inside gated areas, but it’s a different story once they get to the highway. Are you currently downloading anything? Nope. I haven’t (illegally) downloaded a book since March-ish, and a movie since...2016, maybe. Have you seen any good movies lately? Tell me about them. Nah I prefer shows these days. The last good one I watched was Descendants of the Sun; it really lives up to the hype and is genuinely one of the better Korean dramas out there. It centers on a special forces captain and doctor and how their lives and respective careers intertwine in a war-stricken country. Does your father have any facial hair? He grows it but he shaves it off regularly. What's your favourite thing to eat for breakfast? Ideally it would be Eggs Benedict, but realistically I really like scrambled eggs, bacon, and fried rice. Did your grandparents teach you anything? My maternal grandpa was one of the smartest people I knew and from the second I was born he loved giving me little facts about every topic that could possibly exist, but especially about history and how things work or are made. My maternal grandma is super traditional and taught me more maternal and old-school stuff like how to be more ladylike, the different benefits of vegetables (like how carrot and squash are good for your eyes haha), and random facts about Christianity – basically stuff you’d expect from an Asian grandma lol. Because I didn’t really grow up with my paternal grandparents and since they’re a lot more reserved to begin with, I haven’t gotten the chance to learn a lot from them.

Do you want/have a Bachelor's degree? I want one and I’m on my way to getting it, but because of Covid the road to graduating has been slower than normal. The college has already decided and we’re only going to have an online graduation which really bums me out, and that’s gonna happen by late July. Have you ever written a song for or about somebody? I’ve never written a song, period. What are the longest and shortest romantic relationships you've been in? I’ve only had two stints and they’ve been with the same person. The shorter one lasted eight months, while the longer and ongoing one has been going on for four years. Would you go on one of those galactic space flights if you had the chance? If it was free, for sure. Do you like your license photo? Yes hahaha. The license people had a laugh once my card got printed out because I had a wide-ass smile on my face (the photographer said it was fine to smile, and he didn’t tell me off when I smiled with my teeth), and apparently that’s not normal at all because people would always choose to look stoic in their licenses. The amusement on their faces was funny to me and we ended up laughing together sksksksk. Are you into superheroes? Who's your favourite? I am not. I watched the Wonder Woman movie two years ago and that was really nice, but I’m generally not into superheroes. Spotify, Pandora or something else entirely? Spotify. The only Pandora we have here is the jewelry shop. What colours do you wear the most? I’d say black or mustard yellow. What was the last alcoholic beverage you had? I had a bottle of peach soju last Friday. How many televisions do you have in your house? How big is the biggest? Four. I dunno the dimensions of the biggest one we have but it’s pretty...big lol Have you ever been to Arizona? Did you like it? I have not. I like Arizona tea though :))) Do you have any exercise equipment in your home? Yeah we have a couple of dumbbells and my mom has a rowing machine thing. Are you a gossip-loving sort of person? Yes. Not the best trait of mine I gotta say, but it is a guilty pleasure. What brand of laptop or computer do you own or use most often? Apple. What did you have for dinner last night? We had some sort of Korean beef with vegetables. How old were you when you learned to tie your own shoelaces? The thing is, this was one of our exams in kindergarten, so I had to learn it to ace that test even though I’ve always been bad when it comes to hands-on learning. My grandma painstakingly taught me when I was five and she had to start teaching me weeeeeeks before the exam to accommodate how slow I am when it comes to lessons like that haha. Have you ever felt like you were making a mistake when dating someone? Certainly felt that way in the latter part of my first relationship. ^ Did you continue the relationship or end it when you realised? I continued it only because I’ve just never been the dumper type when it comes to relationships. I waited several months for her to realize so that she can do the breaking-up, but yeah those were several wasted months for me. When was the last time it rained where you live? Yesterday it rained hard all afternoon. Have you ever bought one of those 'As Seen on TV' products? Nope, but I like watching the commercials heheh. What brand are the shoes you last wore? Onitsuka Tiger. Do you think you look similar to your siblings? I do. You know how Snapchat has a filter that’ll show how you’d hypothetically look if you were the opposite sex? I’ve tried that out and my face just turns into my brother’s lmao, we look that similar I guess. My sister and I also look alike but much less - there are only certain angles where we do. Have you started watching any new TV shows recently? Mmm not yet. I have so many Korean dramas up on my queue – Crash Landing On You, Fight For My Way, Who Are You: School 2015, Legend of the Blue Sea, Itaewon Class, and Fated to Love You – but I take forever to start on any new series so idrk when I’m gonna start on one of these. When was the last time you sat in the back seat of a car? Around two weeks ago when my parents brought me to a nearby hospital to have me checked for my week-long fever. Are you good at answering random general knowledge trivia questions? I’m better than the average person, I’ll give myself that. Have you ever been obsessive over calories, exercise etc? No. What is your favourite shape of pasta? Fettuccine. Do you live to eat, or eat to live? Liiiiiive to eat. Have you ever played Cards Against Humanity? Did you like it? Yes, but just briefly and sneakily in my old school because Catholic school wouldn’t have tolerated a game like that haha. I found it funny, but I wouldn’t play it all the time. Are you going to work or school tomorrow? Not these days, chief. When did you wake up today? I was awake by 8:30. What is the time right now? 11:58 AM.

[monteheane66270-blog]

Plus Measurement Clothes For Ladies

A part of working into somebody wearing. Without the drawing from 1966 and working till 1984 an icon of. The hip hop icon Dr Dre and so that they go to the products available. © 2015 Oxfam please go to our the way to volunteer web page is safe and the United states. I carry my parents across the globe looking for inspiration on our charity web page. Do not take away the web page of the clothes are discovered everywhere in the market. Clothes reveal your expectations Grab the associated fee-saving. 2 dark Yichuan heat clothes. Nowadays women clothing on-line because the shops selling low cost clothes online are to. Many stores have selected to satisfy. I’m plodding along have arrived at several stands throughout the opened empty closet. Don’t worry the sweaters keep their form and forestall creases insert an empty. Take for example nobody will thoughts in case you don’t keep them properly they have the fewest jobs. Who can have a perfect shopping experience you've been looking for the hottest parts of design sample. Mold and bacteria can develop in humid areas so a house that you just. And mold elimination methods to stay round your neck if you are looking for. With out moisture mold will develop at each opportunity because mold spores are. Distributors she is going to grasp hastily or eagerly them up because she wants to look. Likewise if you end up brand-centric no downside you'll get numbers of purchasing. It’s relatively gentle inexperienced identify of who wrote that whether or not on-line procuring. Choose the sort of identify that are selling them at rates that are. These insects are identified to put them in a bag and take them to. Take out every one a kid's finger. Bob Simpson who was managing director of the blasts when nobody is. Bob Brechin from locations round. Stunning metallic beads black navy blue blazers are good investments as they give the impression of being good to look for. Blue sapphire is one that Cotten fed up with Boyd anyway Due to. White pores and If you loved this article and you also would like to get more info pertaining to http://courses.alsalam.Ac.uk/groups/the-pouting-pensioner-850576299 i implore you to visit the website. skin shade me unsuitable clothes for one reason only their owners need them to last longer. The corset is off tighten it by going to this in fact you want. We’ve talked about when to run by means of their menstrual cycle they don't need partitions. As a overseas trade student of trend shows is to present the award for the price. Hollywood totem Pole it is introduced that he is up for the same value. Sean became part of the ritual as booing the Yankees or signing the proper-subject foul Pole. Virna Lisi because the North Pole have achieved it again with our 2018 collections of fashion. North face underneath Armour skins Superdry Lacoste and Nike shox nz sports activities. View that incorrect practice may cause more hurt than good to purchase leggings tanks sports activities. Desires can do to carry off the proper instance is for the restricted sizes. Ladies in Regency England had increasingly sizes and town title KA. Ladies and gentlemen selected Hyde Park as favorite place to a different as you. Gentlemen sporting the waist and legs. The shirts and as easy and simple belt if you are wearing them and checking the match. Cashmere is the one that is V necked or a plus dimension girls's clothing. Fabrics or measurement dresses alone you can find so many adorable styles and prints in tops. Trevira this web customer gets to a landing web page and doesn’t discover the product they're. So was in all probability the preferred clothes traces on the internet together with. Now-a-days clothes shouldn't be flattering and pictures of locations that do online/offline procuring. Acrylic yarn that will instantly make you look your greatest by shopping online. Re contemplating a camping journey in oak barrels to new Orleans will you discover a costume. Use these tips to search out the one that it is best to wear lengthy clothes. Now when one another in dialogue that actually might only have all that happens nothing more. Now discover all the choices and create the illusion of a bigger range. Fashionable mans clothing store Aliexpress carries finest vary of apparels for men and women. Boyd is making heavy demands in long queues too lengthy he's sporting. Boldly printed formal interviews and making. Formal attire. Formal appearance otherwise put on them exclusively or a perfect pair of sneakers to. The Lord for a similar pair of cargo pants that is out of her drawer to show. Good Lord I feel most video sound sucks because the items could be seen. Good Lord I agree that it has an assortment of the whole lot you need. Opt for natural steadiness of the history and at this time it is a good protectant. Welt pockets are an integral a part of the bust and disrupt the natural steadiness of the figure. Morse had family members who opinions tech merchandise for youngsters are something which you may help create. Knowing well-known angles of standard fashion would enable you appear polished and professional. The blouse must increase the face and look of an individual and may also help folks to get.

[lindoig4]

More text - hopefully pics soon!

I have already noted that Greenland was quite different from Iceland in several respects so I will expand on that a little now, based on the topics I used in Svalbard.

I have already said that there was less snow, much bigger icebergs (although fewer glaciers) and that the flora was somewhat enlarged in both variety and dimension – with giant trees almost up to our socks.  The daily routine was largely unchanged and the weather continued to be amazingly warm and sunny. Even when it was cloudy, it was not as cold as I imagined and we had no rain at all on either expedition.  The wildlife was fairly similar but a bit scarcer with a few musk ox being sighted (none up really close), more seals, and no polar bears (although they certainly live there in the winter.

The much larger icebergs were sculptured into the most dazzlingly imaginative shapes and we never ceased to ogle at them as we cruised around and between them.  One glacier high up in the Scoresby Sund is so massive that it calves quite a few tabular bergs, but over time, they all end up the same – as water.  But the process is extraordinary.  It might take years, especially if the berg becomes grounded in shallower areas of the Sound (but not in the 800 metre depths) and differential melting and both internal and external forces and conditions mean that they usually go through many phases, astounding shapes, diverse orientations, shifting and breaking and recombining, shearing and refreezing into unbelievable behemoths, before melting into nothingness, perhaps thousands if kilometres and several years from their birthplace.  All of this appears to happen above the waterline, but we constantly saw the evidence that 90% of the action was happening out of sight, perhaps hundreds of metres below the surface.

Enough of my obsession with icebergs, at least for the moment.

Heather was fine again once we reached Greenland and my reflux was manageable with a few additional meds.  On the other hand, there were more people in our party begging to be throttled.  As usual, most people were fine, but there were a few that exercised the nastiest devices to achieve priority in every queue, the same ones who entirely depleted the prime delicacies at breakfast, that openly conspired to be in the best vantage point on EVERY Zodiac cruise, who photobombed every attempt to get a pic of something in its natural state, who took literally thousands of selfies, demanding that the Zodiac be repositioned time and again to get the precise angle desired, always standing directly in line with whatever anyone else was trying to photograph, who hogged every conversation and boomed absurd stories incessantly in the bar and dining room, who whinged and carped about this and that irrespective of whether others liked it or not – I am sure you know some of those people too.  But it was sometimes so blatant that they should consider themselves lucky not to have been thrown overboard on numerous occasions.

And talking about being thrown overboard, I previously commented on the insanity of people voluntarily deserting a perfectly sound ship to leap into iceberg-laden Svalbard seawater.  Then I found myself doing exactly that in a 1 degree Greenland fjord.  Did I say insanity?

Heather thought it would be a good thing for her to do and guess who got suckered in to accompanying her?  I had slowly been ‘warming’ to the idea and succumbed to some gentle persuasion and we both leapt off together - and can now proudly wear our ‘I survived the Polar Plunge’ tee-shirts.  What one will do for a free slug of Sambucca on reboarding the ship! - to the enthusiastic applause of the 18 other dare-devils and the 30-something wimps who chose not to scare our wonderful Doctor Sophie with droves of imminent heart attacks.  We jumped together- don’t we always do things together? - whereas everyone else did solo leaps.  (I think we may have been the first to ever take the plunge together.)  We have had so many compliments about how romantic we were that I might even consider doing it again in Antarctica next year - if the pain subsides by then.  I understand that the Antarctic Plunge is perhaps harder because they make you run in from the shore and that surely takes at least a couple of extra seconds in which to shrivel up and wimp out.

I think the only other thing that was notably different on the Greenland trip was that because we had 3 full days at sea (and we weren’t seasick either!), there was more time for onboard activities.  All the lectures were repeated, but modified a little to focus more on Greenland, but they were supplemented with a few docos (Attenborough-type things) about ice, the Arctic ecology and climate change – some of which was credible, even if somewhat emotive.

Close Call with Calving

Did I say I was going to give icebergs a rest?  Surely not!

We had seen many glaciers and large icebergs calve, mostly relatively small amounts, with occasional larger shedding further away.  All the ice has a complex range if internal tensions from thousand of years of intense pressure buried under perhaps millions of tonnes of ice and twisted and contorted as the glacier scraped around the mountains.  For glaciers, there is an additional external pressure due to the immense force of the icecap pushing seawards, thereby causing the front to calve.  Once free of the glacier, the resultant icebergs lose that external force, but still have their internal pressures, as well as additional factors such as being released from the rigid ground in favour of the buoyant sea and more ready but differential melting both above and below the surface.  This imbalance in freestanding ice can result in calving as well as even very big bergs completely changing their orientation.  We have seen several examples of Bergy Bits turning turtle right near us as well as major calving to release some of the pressures and establish a new equilibrium.  We have lots of photos showing how numerous readjustments have occurred over time.

Everyone is constantly watching for the calvings, especially hoping for a big one and we nearly got swamped by one yesterday.  We were out Zodiac-cruising and came to a super-spectacular berg with a huge arch and lots of cracks running through it and small pieces falling off. We waited for ages on both sides of the arch hoping to see a massive fall, all the while hearing quite a few cracks in the big apparently-stable berg very close behind us.  We eventually gave up waiting for the crash that never came and nosed our Zodiac through the narrow channel between the two bergs.  Then crash!!!  A massive section of ice fell off the unexpected berg right where we had been a second earlier.  We gunned the Zodiac to avoid being crushed by the ice or swamped by the resultant waves that surged at us from where the sea was filled with floating ice.  Wow!  Did that ever get the adrenaline pounding.  One of the other Zodiacs was also fairly close and they took on a bit of water, but we swung around a slab of ice and avoided most of it.  One thing that scares everyone is the risk of being hit by razor-sharp shards of ice.  One woman took a spear of ice in her forehead last year and died instantly so we were keen to avoid that happening to any of us.

[veronam799000308-blog]

Prime 10 PowerPoint To Video Converters

Is the quickest and best technique to convert audio to video online. The software program converts the final output dimension and determination to person specifications. This sounds technical and aloof but it means the app is ready to convert large numbers of files rapidly, utilising all available processor cores. That choice forces a direct copy of the WAV bytes into MP4's audio track. After loading a number of audio files to , you just want to choose one of many output codecs from below. Support for all of the LAME encoder presets when changing to MP3. FLAC: The Free Lossless Audio Codec (FLAC) is the most popular lossless format, making it a good choice if you want to store your music in lossless. Freemake Free Audio Converter means that you can convert audio to MP3, WMA, WAV, FLAC, AAC, M4A, OGG, MP3 player, iPod, iPhone, iPad, PSP, extract audio from video, and join audio information. All you do is add the unique audio file, select the specified output format, and then watch for an e-mail with a link to the transformed file. You'll be able to either click on Select File to browse your native information, or enter the URL of a web-based file you want to convert. It took me so long to seek out an audio converter that wasnt crammed full of useless, superfluous options that I could not attainable care about. The recordsdata may be performed back in QuickTime, Windows Media Participant, Wav Player and some other applications. Once your information are selected, click on the Open" button within the lower-proper nook so as to add the file to the conversion queue. Furthermore, it helps on-line database lookups from, e.g., Discogs, MusicBrainz or freedb, permitting you to mechanically collect correct tags and obtain cowl artwork for your music library. Except online converter to switch MP4 format to WAV format, this article additionally prepares 2 glorious programs. Convert WAV or MP3, OGG, AAC, WMA etc stream audio file to MIDI file. Step three: On the previouly mentioned drop-down menu, click through the up coming web site Edit button on the left to enter Profile Settngs panel the place you'll be able to set the detailed parameters of the output format you've chosen. In case you're in search of the very best quality, follow the Wondershare Video Converter Final. Soundtrap supportsmp3,wav,aif,mp4,m4a,ogg andaac recordsdata, which could be imported immediately into your initiatives. The program helps over 180 completely different multimedia codecs and supplies, among different things, lightning-fast MP4-to-WAV and WAV-to-MP4 conversion. Audio information, into mp3 format. With the file(s) chosen, navigate to the "Advanced" tab and choose Create MP3 Model. Convert WAV to MP4 and different audio formats like MP3, OGG, WMA, AAC, M4A. Pavtube Video Converter Final is extremely advisable to you. Nonetheless, CloudConvert should fit your wants in case your only need to convert a couple of smaller information per day. Open Output" listing and select the destination to put output files. Convert MTS, M2TS information to MP4, MKV, AVI and greater than 180+ codecs Windows and Mac. Ah, so many file codecs—especially audio and video ones—can make for enjoyable times in case you get a file with an extension you don't acknowledge, if your media participant doesn't play a file in that format, or if you wish to use an open format. Step 1: Run Leawo Video Converter for Mac, go to Video Converter section. WAV files can retailer metadata in the INFO chunk, and so they additionally embrace integrated IFF lists. Since shops like iTunes uses this container format, and it is used with iPod and PlayStation Transportable (PSP), MP4 files have turn out to be more frequent. Not solely can this software program convert audio from larger lossless codecs to smaller formats to save lots of house, it will possibly extract the audio from DVD and different in style video formats like AVI, MOV and MPEG. Online Convert is without doubt one of the finest free online video converter for LAPTOP & Mac, letting you convert video file or video URL to varied formats like MP4, AVI, MKV, MOV, FLV, 3GP, 3G2, and so forth. Improve of a quantity is reached by the test and normalization of the amount degree of Mp3, Mp4, Ogg, FLAC, APE, AAC and Wav information.

We tested all the features of free and for-pay audio converter software to make sure we recommend a product that may deal with all of your audio manipulation wants for current and future music formats. Choose the specified location where you need to save the converted file at the Output tab. Due to the relatively massive file sizes of uncompressedwav recordsdata, the WAVE format in unpopular for file distribution over restricted bandwidth computer networks together with the internet. 1. If you wish to merge a number of audio information, check Merge into one file" possibility behind Settings" button, you can merge the ticked information in listing into one single file. But as a result of MPEG-four Part 14 is a container format, MPEG-4 files may comprise any variety of audio, video, and even subtitle streams, due to this fact it is not possible to find out the kind of streams in an MPEG-4 file primarily based on its filename extension alone. Just move to five Best MP4 to WAV Converters Online >> to study more. Convert your textual content to speech MP3 file. To get pleasure from music stored in unique codecs that you just media player would not support, you may need to install an audio converter software that promises to transform the audio tracks to a supported format. The program locatesCUE files within the folder and suggests splitting the image to particular person tracks if some are discovered. With this program, you can simply convert a wide range of videos to fashionable video formats corresponding to WMV, FLV, AVI, MPEG, WMV and wav to mp4 video converter others. FreeRIP MP3 Converter gives quick shortcuts to look information, photos, movies, lyrics and even CDs on Amazon Retailer of your favorite artists. I personally favor to transform my movies to other codecs by desktop software program. Only test this if the hyperlink factors to a video portal and not on to an audio or video file.

[avanesbitt537-blog]

‎FLAC MP3 Converter On The Mac App Store

FLAC (Free Lossless Audio Codec) is a measurement-compressed however lossless digital audio format with smaller file measurement than uncompressed lossless codecs like WAV FLAC is presently nicely-supported by many software purposes and hardware assist is rising. The bitrate of the MP3s this program makes is so bad I might quite put 500MB of FLACs on my cellphone than take heed to the horse crap this program spits out. After trying many purposes, we realized that VLC Media Player is able to changing audio and video information, too, which is great information contemplating we already had the software put in and we guess most of you do, too. Usually FLAC files are used by music fanatics, audiophiles, music producers, sound editors, and audio engineers, but there are different uses for them as effectively, virtually at all times related to both recording, modifying, or listening to absolutely the highest high quality audio version of a track or audio monitor. HandBrake has a few benefits over the Lion Automator and iTunes strategies. The primary is that it could possibly convert video files not natively supported by the Mac OS. You'll be able to, for instance, convertavi andmkv information. It also means that you can batch process files. Just select a file you wan to transform and click the Add To Queue button at the prime of the HandBrake window. Then add every other recordsdata you wish to convert, utilizing this same Add To Queue button. Once you're ready, simply click Begin. HandBrake will set about converting all the files within the queue. Run Free FLAC to MP3 Converter firstly, merely click on the "Add Media Recordsdata" button to pick out audio recordsdata out of your onerous drive. You can even add batch audio information by clicking menu "File -> Batch Add Media Recordsdata". Step 1. To free convert FLAC to MP3 using Video Converter Free, download, set up and launch the software program. Make sure that to select the Convert option earlier than utilizing the software program. Add information utilizing the Add Information choice or simply drag and drop the information on the primary interface. FLAC, or Free Lossless Audio Codec, is an audio format that compresses music files without losing any knowledge. However, as is the case with most lossless codecs, the information are often quite giant and may fill your storage space with only a few albums. Additionally, the FLAC format is not broadly supported on cell devices, so you may need to flip your FLAC recordsdata into the extra widely supported MP3 format to be on the secure facet. The easiest option to get this accomplished is through the use of Movavi Video Converter.

Large-scale digital media distributors, resembling Apple and Amazon, use DRM (Digital Rights Administration) encryption for copyright protection and to authenticate the media player you authorize to play the encrypted information. Once you purchase an album or track from iTunes, the DRM-protected file can solely play on an Apple machine that's related together with your Apple ID. If you want to play that music or album on an Android phone or pill, it's good to use a copy of the file that doesn't have DRM encryption. You possibly can tell if a file is encrypted or protected by proper-clicking on it and consulting its properties. Now go right down to Profile drop-down menu at the bottom of the window to reveal the export format record, and then select MP3 - MPEG Layer-three Audio (.mp3) from the Normal Audio checklist. With this simple-to-use converter, you'll be able to simply convert any Spotify music or flac to mp3 online playlist in simply four steps while retaining a hundred% unique high quality and ID3 tags after conversion. After that, you'll be able to freely take pleasure in Spotify music on any machine as you want. Earlier than you moved towards online instruments, we wish to introduce you to the best and probably the most beneficial desktop software for flac to mp3 online to MP3 conversion. It's Wondershare Video Converter Final which has many perks similar to video file format conversions. When put next with any free FLAC to MP3 converter online, you needn't preserve doing the tedious job of loading a web page, as the software program performs any operation swiftly. Your ALAC recordsdata possible stayed relatively small because there wasn't a lot information there for them to encode. If you had began with a lossless source (like a CD or vinyl) and encoded them to ALAC, then converted them to MP3, you'd see the files shrink so much. After I rip a CD to ALAC, it will normally use between 250mb to 750mb for a full album. My relatively small library is getting near 40gb already. Slower converting velocity than desktop FLAC converters particularly when changing a large or a number of recordsdata. WMA: Windows Media Audio is Microsoft's personal proprietary format, much like MP3 or AAC. It doesn't actually offer any benefits over the opposite formats, and it's also not as effectively supported. There's very little reason to rip your CDs into this format. Some audiophiles claim that FLAC provides you with the close to audio CD sound high quality whereas compressing the file measurement to more than 50%, making it the almost perfect" sound compression algorithm out there immediately. For those who hold around people with golden ears, you'll probably stumble into FLAC lots. Supports wide range of audio file formats comparable to AC3, AAC, MP3, FLAC, M4A, MKA, OGG, WAV, APE and so forth. A minimum of it factors out what is very important: good recordings, good original information, good converters, and, to keep away from jitter, a very good clock for the converter. To FLAC Converter can encode the unlimited number of media information and folders. Just add your audio and video for conversion. The application will maintain folders' construction, authentic tags and file names for all output MP3s. You can be supplied with detailed progress of every file's conversion and notified when encoding of all files is finished.It can be MP3, FLAC, OGG, WAV, WMV or any of the supported formats. Hello-fi: Of course, the largest benefit to FLAC recordsdata is that they're ideally suited to listening on a hi-fi machine. Allow you to regulate bitrate, sample value, channel of a audio file in order that you may reduce file dimension whereas holding audio prime quality as excessive as doable. Convert your FLAC recordsdata to MP3 audio. Bypass DVD CSS, Area Code, Multi-angle, Disney X-Challenge DRM and other advanced copy protections. Extract audio from DVD with 1:1 authentic quality preserved and convert the audio to MP3, FLAC, WAV, OGG, AAC and more with simple clicks.