WEBVTT

1
00:00:00.100 --> 00:00:13.000
[upbeat music] Hey, friend, it's David Nebinsky, the community lead of the Chief of Staff Network.

2
00:00:13.640 --> 00:00:24.990
We help chiefs of staff through the three Cs: cohorts, curriculum, and community via Slack with over nine hundred people in it. Learn more about our membership, events, and programs at chiefofstaff.network.

3
00:00:25.360 --> 00:00:35.070
There you can also see and register for our upcoming fundamentals course for new chiefs of staff and people that are chief of staff curious that starts in January. The registration deadline is January 10th.

4
00:00:35.440 --> 00:00:43.260
On our website, you can also subscribe to our newsletter to stay in the loop as well. This episode of the Chief of Staff Podcast is with Rahul Desai with the Chief of Staff Network.

5
00:00:43.580 --> 00:00:51.980
In this episode, you'll learn about the details of our twenty twenty-four Chief of Staff Compensation Report, how to use it, the chief of staff job market, and so much more.

6
00:00:52.280 --> 00:01:03.700
Excited for you to level up as a chief of staff. Here we go with Rahul. Rahul. David. Getting together on the Chief of Staff Podcast, ayyy. When was that last time we had our last pod?

7
00:01:04.220 --> 00:01:13.320
That was- A few months ago, I think. Yeah. Yeah, been too long. We're, we're back. Um, we've been- We're back. Happy holidays. Exactly. The podcast has been going well.

8
00:01:13.400 --> 00:01:23.980
Thanks everybody for listening, for being part of it. We love getting your messages, comments. Uh, the Spotify Wrapped was super interesting as well just to see where, where y'all are listening from.

9
00:01:24.740 --> 00:01:42.640
Um, and, um, yeah, we-- Rahul has been spending a ton of time, um, creating these incredible in-depth research reports, and the text is there, uh, the reports are there, but we wanted to give you audio overview of them.

10
00:01:42.760 --> 00:01:56.560
And so, uh, the twenty twenty-four salary report, which we've been doing for a couple years, um, is out. You can download that on our website, and right now we're gonna share some, some takeaways from it.

11
00:01:57.240 --> 00:02:04.020
Um, Rahul, what's, what's the high level like, here's, here's what happened in the report? Yeah.

12
00:02:04.100 --> 00:02:15.920
I think the number one takeaway is that the average is pretty significantly higher than last year, and it goes kind of back to what we were seeing in twenty twenty-two.

13
00:02:16.420 --> 00:02:26.300
So to recap for people who don't have the report in front of them, in '22 we were seeing about a hundred and fifty-two K of average base compensation, um, so just cash.

14
00:02:26.940 --> 00:02:39.750
And then in '23 that took a hit, and some of that hit was associated to people from Asia, Africa, uh, you know, LatAm, where salaries are noticeably lower than the US and Europe.

15
00:02:40.400 --> 00:02:52.120
And so the average ended up being about a hundred and thirty-one K globally. And then if you took mainly English-speaking countries, uh, like what people colloquially call the West, it's about a hundred and forty-four.

16
00:02:52.280 --> 00:03:03.380
So still lower than '22, but not as low, um, compared to the global data set. And then this year we're really back to that hundred and fifties range. Average is around one fifty-four.

17
00:03:03.920 --> 00:03:18.799
Median and mode are sitting at one fifty. So after a dip last year in compensation, it seems like the market for chiefs of staff is back. Mm. Back, back, back. So what do you think...

18
00:03:19.860 --> 00:03:29.780
What do you think-- There's like two categories of, I think, people here. If you are in the role, and let's say you're making less than a hundred and fifty K, what do you think you should do?

19
00:03:29.860 --> 00:03:42.340
Absolutely negotiate for more. [laughs] Like, especially in startup land, most performance cycles are like every six months, every twelve months. You have that opportunity to advocate for yourself.

20
00:03:43.240 --> 00:03:52.620
Um, what I find that people struggle with in the chief of staff role is articulating the impact that they had in a numerical or metrical kind of way, right?

21
00:03:52.720 --> 00:04:05.800
You can say, "Oh, I helped out on the investor meetings and the board decks and the all-hands and OKRs," but it's hard to point to like, "I did X that resulted in Y percent increase of this metric or Z percent decrease of our costs."

22
00:04:05.820 --> 00:04:20.440
And I would say focus on some projects or something you can do that associates to those kinds of metrics, because then it's easier to say like, "Look, I did this, and what I'm asking for as a raise is not only in line with market, it's much less than the benefit I have generated for the company."

23
00:04:20.969 --> 00:04:33.390
And typically when you can make that argument, it's much, much easier to negotiate for, uh, a higher salary. And so this is around the time when people are having these kind of conversations?

24
00:04:33.440 --> 00:04:37.950
Or do, or should people bring it up like- No, I think this is, this is typically the time, right? Okay.

25
00:04:37.960 --> 00:04:48.740
Like end of the year, you often have the performance cycle happening in January, February, and then there's typically a summer cycle as well for most companies is what I've seen. Okay.

26
00:04:48.920 --> 00:05:03.920
And is there anything related to like bonus or equity that's, that's relevant and to that same like, call it negotiation or update or presentation or pitch that, uh, this is an in-role chief of staff.

27
00:05:03.980 --> 00:05:12.680
Like is there anything else there that is like noteworthy that people should know as they like make their case as to why they should get paid more? Yeah, absolutely.

28
00:05:12.780 --> 00:05:20.200
So for bonus, I'm just pulling up that data so that I can quote the right metrics to people and not lie about it.

29
00:05:20.320 --> 00:05:26.720
Uh, last year what we were seeing was like the average bonus was around twenty-nine K, and now it is thirty-three.

30
00:05:27.260 --> 00:05:36.840
So not a ton higher, but higher for sure, which I think gives more credibility to this story that the role is back compared to last year.

31
00:05:37.000 --> 00:05:44.780
And actually, we do have jobs data that shows that chief of staff hiring flowed relative to the historical average in twenty twenty-three.

32
00:05:45.480 --> 00:05:54.180
So now that that hiring is ticking back up, I think there's price competition to hire these people again. So you can see that in the bonus for sure, right? Like it's a ten percent increase.

33
00:05:54.260 --> 00:06:04.776
If you had thirty K last year, thirty-three, thirty-four K this year, uh, it's about a ten-ish percent increase.Um, but what we do see is actually the majority of chiefs of staff earn no bonus.

34
00:06:05.236 --> 00:06:17.976
Um, or the single largest group, right? Small or a large minority, uh, I would say. So in the past, we saw around forty-five percent didn't receive bonus. That was last year's data.

35
00:06:18.036 --> 00:06:28.756
This year it's around forty-two percent didn't receive bonus. But what I will say is interesting is like three percent of people earned above fifty percent of their base cash

36
00:06:29.836 --> 00:06:41.586
as a bonus, which is like pretty sizable significant amount. We're talking, you know, seventy-five K or more sometimes in bonus, uh, on top of that average pay, right? In the, in the hundreds of K- Yeah...

37
00:06:41.596 --> 00:06:56.496
that people are receiving. So some chiefs of staff are really making it. If you are, say hello. Um- Yeah, please. I'd like to be your friend. [laughs] Tell me your secrets. Um, uh, the...

38
00:06:56.556 --> 00:07:05.536
Is there anything related to like the s- like, the type of work that is really... Like a, obviously we have our leveling framework, which we also came out with a new version this year.

39
00:07:06.236 --> 00:07:20.696
Um, is there anything that, like, if people are listening to this and they're like, "I'm doing very strategic work, very impactful work. I have high impact, high influence, um, and

40
00:07:21.856 --> 00:07:33.386
high autonomy," is there anything about that that people should say, "You know what? Like, uh, I should be getting paid X more." I know you gave the example around like- Mm...

41
00:07:33.396 --> 00:07:41.996
if I'm m- making a big impact that's really driving the business, like, you know, then I should really think about it. But anything about the leveling framework and comp like... Yeah.

42
00:07:42.116 --> 00:07:55.376
Um, just flicking back up there so that I can tell you. It... I mean, pay definitely goes up with level going up. Uh, that's I think unsurprising to most people. Last year what we saw...

43
00:07:55.456 --> 00:08:04.306
And again, I'd like to note we switched from a five-step leveling framework to a six-step leveling framework in the past year so we could get slightly more granularity.

44
00:08:05.076 --> 00:08:17.676
Uh, but last year we had that five-step framework where level one was sort of that entry level and level five was that executive level. And what we saw was like a pretty obvious correlation between level and salary.

45
00:08:17.756 --> 00:08:30.236
And this year it's still there, but much less obvious. The steps, like what you get as an increase between levels, is relatively lower compared to last year.

46
00:08:30.876 --> 00:08:45.256
So in 2023 you might be seeing a jump of like 20K between each level. And now what we're seeing is actually on average level three, which is that manager level, is earning the least on average relative to all six levels.

47
00:08:46.036 --> 00:08:55.036
And really what we're looking at is more like a 10-ish K bump between levels on average. And some of that might just be skewness, right?

48
00:08:55.076 --> 00:09:02.516
Like in some of the levels we might have gotten some crazy outlier that skewed some of the data up or down relatively speaking.

49
00:09:02.576 --> 00:09:08.696
Like for example, the analyst data, so entry level, level one, was way higher than I anticipated.

50
00:09:09.005 --> 00:09:20.216
And when I looked back at that data, it was that almost all of those analyst type people came from really big companies where business intelligence is, you know, somewhat highly valued.

51
00:09:20.736 --> 00:09:34.696
And so they were citing something like in the 130, 140K range of cash. So I think that's something that's an interesting deviation from last year. It's like last year it was like clearly up and to the right.

52
00:09:34.776 --> 00:09:38.376
Level one's the lowest, level five was the highest, and it just goes up and to the right.

53
00:09:38.936 --> 00:09:50.576
And now what we see is like from level one to level three there's a drop, and then it kind of makes the, that hard V shape back up, up and to the right. Um, but I think some of that is, like,

54
00:09:51.596 --> 00:09:58.026
just the data we collected this year relative to what we collected last year. Um, and obviously this is...

55
00:09:58.056 --> 00:10:04.236
There's a lot of factors associated, like people's gender matters, people's education level matters, pe- the size of the company matters.

56
00:10:04.336 --> 00:10:13.396
Uh, but ultimately, like the conclusion I would draw is as your level goes up, your comp goes up, which remains true, and it has been true every year. And if someone's listening to this, how could they...

57
00:10:13.416 --> 00:10:21.716
Is there a way within like 5 to 10 seconds that they could realize what level they're at? Yeah, absolutely. So I would send people to the leveling framework, which is on our blog.

58
00:10:22.475 --> 00:10:32.976
Um, it's just chiefofstaff.network/leveling-framework. Um, and what that is is basically the decoder ring. Right? You can hold that up to any job description and say, "Okay.

59
00:10:33.076 --> 00:10:43.496
Well, I'm a level one on this scale, but a level three on this scale, and a level six on this scale." And at the end of it you can sort of figure out, actually, all of this averages out to about a level four.

60
00:10:44.296 --> 00:10:55.796
Um, and that's how I would go about it most likely. Do you think people could like... Like, it feels like, and you know, we've had these conversations around like, here's what you're working on next.

61
00:10:55.916 --> 00:11:01.536
Like, do you think people could like say, "Here's the stuff that I'm anticipating to work on.

62
00:11:01.796 --> 00:11:11.336
Like right now I'm probably a little bit more like a level three, but like based upon our conversations, it looks like I'm trending towards more of a level four, uh, therefore I should get paid as a level four."

63
00:11:11.396 --> 00:11:20.055
Like, do you think that's a reasonable argument to make? I think, yeah. I mean, people for sure have used this data to advocate for themselves in that way, right?

64
00:11:20.096 --> 00:11:34.016
The whole point of doing this work is to help chiefs of staff be valued fairly by the market. Um, and so this is like our fifth version of the report. We've been at this a while, and every year I hear people saying,

65
00:11:35.136 --> 00:11:41.226
"Hey, the report helped me earn 10K more, 20K more, X percent more." Um, so yeah.

66
00:11:41.416 --> 00:11:49.296
I would certainly say, like, if you've done the work and you feel confident going into that performance review, I would be advocating for an appropriate increase.

67
00:11:49.976 --> 00:12:05.116
Um, and you can hold this data up and say, "Hey, there is market data showing X, Y, and Z." Um, and in fact, like if you look at pave.com, which is a pretty well-known payment data provider, our data is actually-Lower.

68
00:12:05.606 --> 00:12:16.236
So chiefs of staff are quite fairly paid, I think, overall by the market. The reason why Pays data is skewed, I would say, is that they are mainly collecting American tech company data.

69
00:12:16.836 --> 00:12:29.816
And so if you start collecting global data that's not necessarily tech industry, uh, there will be some lower salaries relatively speaking. And what's the, the size of this data, the size of this, this report?

70
00:12:29.876 --> 00:12:40.526
Like, what, what's included in it? Like, what... Yeah. It's a great question. So we typically collect on the order of hundreds of data points. I think last year we had something like 500.

71
00:12:40.636 --> 00:12:51.176
This year it's, uh, in the neighborhood of 300. It's always enough to be statistically significant. Like, the worldwide audience of for-profit chiefs of staff is something like 40,000.

72
00:12:52.036 --> 00:13:05.636
So our data gets you well into the neighborhood of, like, 95% confidence. And w- what was, what was your ma- math score on the SATs? [laughs] What was my math score on the SATs? All this confidence, statistical.

73
00:13:05.976 --> 00:13:13.496
I love it, I love it, I love it. Eh, I don't know. [laughs] I did better on the English, honestly. Oh, interesting. Yeah. I've always done better on the English than the math. Um- Oh, interesting. Yeah.

74
00:13:13.876 --> 00:13:27.356
It is kind of interesting. Um, but they forced me to take stats in undergrad, so I tried. Um, and then y- you asked, you know, what did we collect? Yeah. And we collected so much data.

75
00:13:27.416 --> 00:13:32.106
I mean, some of this stuff doesn't even make it into the report. We talked... or, like, we gathered data around

76
00:13:33.676 --> 00:13:48.296
gender, years of experience, location, company size, company industry, uh, how people interact with different departments, people's likelihood to stay at their company, what they're looking for in their next job, uh, how happy they are.

77
00:13:48.356 --> 00:14:01.846
Like, all sorts of crazy stuff. It's, it's a lot of data. You talked about location and, and Heather Higgins, shout out Heather, um, asked about, like... I'd be curious about just, like, US specific data. Sure.

78
00:14:01.956 --> 00:14:12.585
Um, do you have any insights? Yeah, I do. What... Something that was actually very interesting this year, 'cause this is the first year that we actually collected on a city basis, not just a country basis. Mm.

79
00:14:12.616 --> 00:14:20.796
So you would think, you know, SF, New York, LA, those are the highest. No, it's Houston, and I was stunned to see it.

80
00:14:21.556 --> 00:14:34.356
Um, I think, or it appears to me that that has a lot to do with oil and gas, and some of those oil and gas people are, like, really well remunerated. So that's my assessment. And then we...

81
00:14:34.416 --> 00:14:45.016
I also saw that Brisbane, Australia was really high, and that was stunning. 'Cause typically the... with currency conversion and all that, Australians are paid worse on average compared to Americans.

82
00:14:45.076 --> 00:14:55.556
But apparently whoever's the chief of staff in Brisbane, they're doing okay. Yeah. And, um, the list that I, I shared in the report is all cities where we collected more than a handful of data points.

83
00:14:56.085 --> 00:15:04.305
So it's enough to say, okay, in this city there's, like, a critical mass here around this number. It's not just one person who's making that number... that amount of money.

84
00:15:05.376 --> 00:15:18.096
And compared to the average median and mode, New York and San Francisco are higher than that, right? Oh, yeah. Easily. So New York... SF is 191 and New York's 187. Although in...

85
00:15:18.116 --> 00:15:24.676
What's interesting, in SF the bonus is lower for people who earned a bonus, and the equity is much higher.

86
00:15:25.096 --> 00:15:38.856
So in New York, if you're optimizing for cash, you can probably fight for a little bit better of a bonus it looks like. Um, but there is something about just, like, the velocity of tech companies and their equity in SF.

87
00:15:39.556 --> 00:15:51.896
Like, scale of magnitude. Like, average equity in SF was 251K. Average equity in New York was, like, 75K. So quite, quite a lot different. And then when you said earlier, you said the, the role is back.

88
00:15:51.996 --> 00:16:06.276
Do you mean comp is back? I mean, the comp absolutely has rebounded, but I would also tell you that the number of job postings is up relative to last year. So last year I think there was something like

89
00:16:07.536 --> 00:16:23.816
between 6 and 800 open chief of staff roles. Uh, and now there's something like... This year, year to date up until Q3, there was something like 1,200. So- 1,200 and then 600 vers... 600 and 800.

90
00:16:23.856 --> 00:16:29.796
So, like, 1,200 and 1,400. Yeah. Like, we'll call it 700. Exactly. Yeah. 700 versus 1,200. So pretty significant jump up.

91
00:16:30.836 --> 00:16:42.396
Um, you know, this is nowhere near where we were at in 2021, which was the, the peak of the data set. We have five years of data. So in 2021 it was more like 2,400- Wow... open chief of staff jobs. Yeah. I don't know.

92
00:16:42.616 --> 00:16:53.516
Post-pandemic remote world was very kind to a lot of knowledge workers, I think. [laughs] Uh, okay. So that's, that's... it's not just the comp, it's open roles. Um- Yeah.

93
00:16:54.276 --> 00:17:03.596
It still is, it still is chal- challenging though. Oh, yeah. Like, it's challenging. I wouldn't, I wouldn't downplay how hard the job search is for people, right?

94
00:17:03.676 --> 00:17:13.756
I don't want someone to say, "Oh, you know, I listened to this pod and they said the, the cash is so good and there's so many open roles." No, I don't want you to take that away. Like,

95
00:17:15.216 --> 00:17:24.956
we're getting better, but we're not back to where we were in '21. I don't know if we'll ever get back to where we were in '21, 'cause that also was a market distortion, right? Money was cheap.

96
00:17:25.076 --> 00:17:34.616
Interest rates were really low. Like, the government was printing money and giving it away to people in the form of paycheck protection and stuff like that. So will we ever get back there? Not really.

97
00:17:34.716 --> 00:17:44.896
Um, but is this a sign of positive things to come? I sure hope so. And you've been, um... Like, you know how sometimes people have said, like,

98
00:17:46.476 --> 00:17:54.266
"Comp's the most important thing," or, um, you know, "Company logo is really important." Mm-hmm.

99
00:17:54.266 --> 00:18:05.216
Like, as you look at this data, as in all the conversations you've had with people related to roles, like, how do you think people should be thinking about, like, comp as a p- as... if they're looking for a new role?

100
00:18:05.436 --> 00:18:14.612
Like, given that you did this data, given that you've talked to so many people about... that are job seeking, like- Oh, yeahWhat do you, what do you... Like, how would you advise someone who's like, "Okay, like,

101
00:18:15.652 --> 00:18:29.792
they're offering me, like, 80K," which I know is below your number, Rahul, but, like, the role seems cool or, like, it seems like there's upside, or I like the company name. Like, how do you...

102
00:18:29.932 --> 00:18:36.592
H- how would you advise that person that's like the comp is below what your report says? Yeah. Yeah. So

103
00:18:37.872 --> 00:18:47.132
if there is an obvious path that the company's going to blow up and, like, you're gonna make it, right, your equity's gonna print you tons of money- In a good way, in a good way. [laughs] In a good way, right.

104
00:18:47.192 --> 00:18:53.912
Yeah, it's gonna like, it's gonna be a rocket ship. Um, I might say consider it, right? Like, if you think,

105
00:18:54.952 --> 00:19:04.232
oh, okay, like this company is YC backed or whatever indicators you wanna use of, like, this is a high signal business that's probably gonna have a good outcome, I might consider it.

106
00:19:04.792 --> 00:19:18.472
But what I would caution people is the money you're willing to accept does something in your own head of anchoring you, right? You start saying, "Oh, I'm worth 80K," or- Mm... "I'm worth X," or, "I'm worth Y." And

107
00:19:19.672 --> 00:19:33.552
if the market says, actually, like, this role is worth 150K, and you're only... You're saying to yourself, "I'm worth 80K," does that do things to your psychology that are adverse or deleterious? Maybe. Um,

108
00:19:34.452 --> 00:19:45.272
I think in some ways, like, the first job you ever get is pretty important because most people anchor, like, "Oh, I'm gonna get... I want a 20% increase on that when I get promoted," or, "When I go to...

109
00:19:45.292 --> 00:19:54.932
for my next job, I'm thinking about what I was earning before," right? People always have that in them, of I was thinking about, you know, what was my paycheck every month? What was I earning? So on and so forth.

110
00:19:55.992 --> 00:20:05.892
And so I think sometimes that this causes people to shortchange themselves if they get that large promotion or they make a lateral move at another company, and they say, "Well, you know, the comp I'm looking for is X."

111
00:20:06.612 --> 00:20:18.172
And if that's way lower than what the market standard was, but you've psychologically locked yourself into thinking like, "That's my rate," are you missing out on a lot more upside? Mm. I suspect probably.

112
00:20:18.202 --> 00:20:30.841
There's people like that out there, for sure. And so it sounds like you're saying comp is not the most important thing. Not necessarily. I would say, I would say, like, try to get market. Y- you deserve market rate. Mm.

113
00:20:30.852 --> 00:20:43.852
The market rate is the average. Like, it's there. Um, we have the data. We know this for a fact. I would say that, however, with that said, if you have a situation that is perfect for you, right?

114
00:20:43.932 --> 00:20:55.992
You love the principle, you guys have a preexisting relationship, you're... you have prior industry expertise. I don't know, things like that that matter to you that are not... you can't necessarily put a dollar sign on.

115
00:20:56.072 --> 00:21:03.612
I would go for it, right? I might give it a shot, depending on what that scenario is, and ultimately it depends on that individual and their life circumstances. Do they have a family?

116
00:21:03.692 --> 00:21:13.192
Does the job require them to relocate? There's all these things that go into that decision, that it's not just look at dollar sign and see is that right or not right, you know? Yeah.

117
00:21:14.392 --> 00:21:19.652
You mentioned that there was all these insights that you collected that didn't necessarily make it to the report.

118
00:21:19.992 --> 00:21:33.532
Is there anything that is really juicy or surprising that, like, didn't make it into the report that you wanna touch on? Um, let me look. I, [laughs] it's so much.

119
00:21:33.632 --> 00:21:38.252
Like, I don't even remember off the top of my head what was in some of this data. Um,

120
00:21:39.752 --> 00:21:52.812
uh, yeah, actually, I do have something good that didn't end up directly in there, which is that 20% of our respondents earned more than 200 grand. Wow. So the cash is out there, right? And that's, and that's cash.

121
00:21:52.852 --> 00:22:08.232
That's cash. That's, that's base cash. That's base cash. So people are doing okay. Um, what else stands out? I th- I always think it's really interesting when people get a PhD and then become a chief of staff, um,

122
00:22:09.302 --> 00:22:19.392
'cause it's a lot of time, and normally I don't think of PhDs as, like, going into the capitalist workforce, right? Typically, I think they become a professor or researcher or something like that.

123
00:22:19.452 --> 00:22:30.672
At least that's how the media talks about that career pathway. Um, but typically what we see is that these doctoral chiefs of staff are earning decently healthy money, right?

124
00:22:30.751 --> 00:22:42.132
Like, the mid-50% range of that is 145K to 170K. So, like, mostly above average, I would say. And then the max gets you up into, like, 300.

125
00:22:42.532 --> 00:22:50.932
So yeah, of those chiefs of staff who get doctorates, right, they're decently well m- remunerated. The people who have master's degrees by far do the best though.

126
00:22:50.992 --> 00:23:02.652
Like, the max salary of the master's degree people was, like, 420K, base cash again. So I don't know. I've never been someone who's like, "I'm so hungry to get an MBA," but- Yeah...

127
00:23:02.712 --> 00:23:15.352
in some cases, I think it makes a difference. The other thing that I think is just, like, important for people to consider is, um...

128
00:23:15.392 --> 00:23:27.332
Or I guess there was a point in the report that, that kind of touches on this, which was unlike 2023, the single biggest driver to leave a job has become bad management, um- Mm... from lack of advancement.

129
00:23:27.532 --> 00:23:40.512
Compensation remains in the third spot. Can you talk a little bit more about, uh, that stuff and how it relates to chief of staff role? As in why that's happening? Yeah. Or... Yeah, okay.

130
00:23:40.612 --> 00:23:49.192
So I'll talk a little bit about, like, what last year looked like in the data, and then I'll talk a little bit more about this year and why I would conjecture to see things have changed.

131
00:23:49.612 --> 00:23:57.992
And actually, I have the '22 data in front of me, so let me pull that up. Um, so in 2022, we asked what are the reasons you would leave your current job?

132
00:23:58.712 --> 00:24:10.992
And something like 27% of people said lack of growth opportunities as the number one issue, followed by too much work, bad culture, bad management, and, like, macroeconomy, in that order.

133
00:24:11.912 --> 00:24:21.808
Um, last year what people said was, again, growth opportunities-And then they said macroeconomy. So the macroeconomy came up. It jumped up from being the last place thing to the second place thing.

134
00:24:22.628 --> 00:24:31.868
And i-if you go back to this job state of chief of staff jobs data that we're talking about, that makes sense 'cause last year was, like, a pretty rough year for new open roles and things like that, layoffs.

135
00:24:32.088 --> 00:24:43.588
We've seen all these r-rolling layoffs happen, so, so on and so forth. And then, uh, basically tied were too much workload and bad culture, and then last was bad management.

136
00:24:44.148 --> 00:24:57.088
So people last year I would say were really starting to become quite concerned about the macroeconomy. Um, and then this year what we see is that actually bad management has come all the way to the front.

137
00:24:57.288 --> 00:25:09.048
When it, it jumped, right? It was the last thing. It was the, the lowest ranked item last year, and now it's the number one ranked item as why I would leave my company. Um, second is lack of advancement or growth.

138
00:25:09.508 --> 00:25:21.008
Uh, third is compensation. Fourth is... Tied for fourth are bad culture and work-life balance. And then close to last again is macroeconomy.

139
00:25:21.748 --> 00:25:32.328
Uh, and then some people cited, like, I'm not aligned to the company's vision, or I'm looking for something new, which are like... Those, those were kind of the grab bag answers. Um, but bad management, right?

140
00:25:32.368 --> 00:25:34.188
Kinda came out of nowhere.

141
00:25:34.288 --> 00:25:49.928
And I think it's wrapped up in this whole story that I, I'm starting to see in the media more and more of, like, Pew data and a bunch of other of these, like, social science data organizations are now showing that employees have the highest resentment levels they've ever had- Wow...

142
00:25:49.968 --> 00:26:01.908
at work. Um, I think that this is... It's gotta be tied into this, right? Of, like, people... You know, there's this adage that people say, right? People don't quit a job, they quit a manager. Yeah.

143
00:26:02.628 --> 00:26:16.468
And I think that idea and this notion of, like, this rising tide of resentment and people citing, "I would leave my job because of bad management," those all are tied together in some way. I would also cite that, um,

144
00:26:17.528 --> 00:26:26.628
some of this is probably, like, return to office mandates. And I think- Hmm... the conspiracy theory, right? W- how true or not true it is, I, I don't know how to tell you.

145
00:26:26.688 --> 00:26:33.038
But people are saying, like, "Oh, actually return to office is a way to do a layoff without doing a layoff." Right?

146
00:26:33.108 --> 00:26:48.547
If you tell, oh, half your staff who is remote that they have to come back or they're gonna lose their jobs because they're in, not in compliance with the policies of the company, it's basically doing a layoff without having to follow the WARN Act or whatever the European Commission says about doing that sort of stuff.

147
00:26:48.708 --> 00:27:02.268
Um, so that's sort of conspiracy theory du jour, but there's some plausible facts associated to that. Um, but yeah, I think it's this tension of, like, should I go back? I liked my life remote.

148
00:27:02.968 --> 00:27:10.198
Is my manager relationship good? And that's, and that's- And my work-... principal relationship, right? That's, like, manager as in, like- Yeah...

149
00:27:10.228 --> 00:27:19.047
in a chief of staff case that's, like, CEO or the leader that you're working with. Yeah. I mean, in some cases it could be, like, bleed to the rest of the senior leadership team.

150
00:27:19.088 --> 00:27:29.278
I've heard some cases where, like, a chief of staff has two principals. I strongly don't advise being in a situation like that. It's very hard. Uh, a person cannot easily serve two masters.

151
00:27:29.708 --> 00:27:43.188
Um, but with that said, yeah, it's typically direct manager. Okay. So bad management is related to that. Um- And that's so subjective, right? It's so hard to say, like, actually the thing underneath here is this.

152
00:27:44.068 --> 00:27:53.748
Uh, the notion of bad management is actually quite subjective, right? Like, what's bad management for one person could be great management for another person. It's a feeling. Except in the most egregious cases.

153
00:27:53.788 --> 00:28:01.928
It's a feeling. Yeah. Yeah, yeah. For people. It's a feeling. Right? Except... Yeah. Yes. Yeah. And is there anything about... You talked a little bit about return to office and stuff.

154
00:28:01.988 --> 00:28:13.908
Is there anything related to, like, in-person versus remote versus hybrid in this data at all that's interesting? So we did not collect it as part of this data set. We did collect it in our member Slack a long time ago

155
00:28:14.808 --> 00:28:30.088
and... Or not that long ago. It must... Maybe six months back if I recall correctly. And we had something like, um, probably 40% remote, 50 or s- uh, yeah, like 50% hybrid, and then the remainder fully in person.

156
00:28:30.548 --> 00:28:44.218
I would be curious to do that survey again and see how much the pendulum has swung back to be in person. Rahul just has got a thirst for these reports and surveys and number crunching for y'all.

157
00:28:44.248 --> 00:28:53.068
You learn a cool thing about the world, right? There's stuff in my head that no one on the planet knows, which is kind of fascinating. It's just fun, right? I'm a weird nerd in that regard.

158
00:28:53.828 --> 00:29:03.928
What else is in that head that no one knows that we could share on this pod? Um, well, I, you know... So I'm friends with the people who run Tech Ladies, right?

159
00:29:03.968 --> 00:29:13.838
And as, as you're obviously aware, like, most chiefs of staff are women as well. Um, and in that data set what was really fascinating... 'Cause they had published that report. Um,

160
00:29:14.908 --> 00:29:21.918
what was really fascinating was they did not only salary but also benefits, right? What benefits matter the most. Mm.

161
00:29:21.968 --> 00:29:33.988
And health insurance was number one 'cause, you know, their data set is mostly US skewness, so we don't get healthcare from the government in the same way that someone in Europe or some of the Asian countries or wherever would get that.

162
00:29:34.808 --> 00:29:44.108
Um, and so healthcare... Health insurance, number one. Number two and three were remote work and flexible hours. Hmm.

163
00:29:44.488 --> 00:29:56.988
And I think that is what somewhat influences how I'm talking about this story of bad management, return to office, resentment, all of those things that are kind of related but not...

164
00:29:57.048 --> 00:30:04.188
I can't draw, like, a statistical correlation between any of them 'cause I don't have good enough data on any of those things. Yeah.

165
00:30:05.228 --> 00:30:13.648
I w- I would've been okay if you shared anything else, some, some other random fun facts that were... [laughs] Well, I can... I mean, if you want a really dumb fun fact, uh,

166
00:30:15.328 --> 00:30:24.152
y- if you ever get stuck in the Arctic and you manage to kill a polar bear, don't eat its liver 'cause the vitamin A is so high it will kill youWhat?

167
00:30:24.652 --> 00:30:34.192
Yeah, you will overdose on vitamin A if you try to eat a polar bear's liver. Wow. Yeah. It's one of the wildest fun facts I've ever heard. [laughs] How'd you learn that?

168
00:30:35.132 --> 00:30:50.572
I used to read, like, fun facts apps for fun when I was bored in class in, like, middle school. [laughs] People thought they were coming here for, you know, compensation information. You're leaving here with life tips.

169
00:30:51.392 --> 00:31:03.132
Yeah. Life hacks. I mean, I don't know how frequently that would happen to somebody. Yeah. Um... But it's good to know. Um, okay. So we talked about in role, we talked about people that are seeking roles.

170
00:31:03.172 --> 00:31:08.832
We talked about some different, like, ways to, to spin and look at this data.

171
00:31:08.912 --> 00:31:19.172
I really like that part about, like, make sure that you are getting the market, um, or at least advocating for the market, or that you deserve the market. I thought that was super interesting. Mm-hmm. Um...

172
00:31:20.592 --> 00:31:29.932
Right, and bring data, right? The data is here for you- Mm... to be able to make this claim of, like, the market looks like X, they have hundreds of data points. It's... This is what it is, right?

173
00:31:29.972 --> 00:31:36.092
And that's how companies do this stuff. They go on Pave. Pave tells you what the sample size of the data is. They tell you what the different percentiles are.

174
00:31:36.972 --> 00:31:45.272
And that's how companies decide what their ladders and what their bands are. It's not rocket science, right? It's actually a pretty simple spreadsheet model typically.

175
00:31:46.092 --> 00:31:59.772
Um, so if you say, like, "Hey, I've got hundreds of data points for this chief of staff role, and market looks like X for someone of my level and my capacity in this region," there you go, right?

176
00:31:59.852 --> 00:32:11.972
And I, I'm looking at the Pave data right now. They have 960 responses collected ever, and that is... We actually have more. Uh, if you go from 2019 onwards- Yeah... we have more data than that by far.

177
00:32:12.592 --> 00:32:27.092
Check out all the reports, jobs report, salary report. Um, and yeah, Rahul said bring data, which comes up in our conversations, uh, about, about- Yes. Frequently. [laughs] That's right. That's right. I've got...

178
00:32:27.372 --> 00:32:38.542
I got a feeling, I got an intuition on this, and you're like, "What's the data, man?" [laughs] Yeah. [laughs] Um, cool. Right, 'cause then, then you can't... It's hard to refute, right? When you bring the numbers- Mm...

179
00:32:38.552 --> 00:32:50.152
and make your claim, the claim is, like, ironclad in a way that if you say, "I feel X," the claim is not ironclad, and then it's much more risky to go act on that claim. Right. And that's what...

180
00:32:50.192 --> 00:32:57.932
That's, like, the mode that people are in, right? Like, everyone's so profit minded these days. Mm. And I don't mean that in a bad way, right? I, I don't... It's fine.

181
00:32:58.632 --> 00:33:13.542
Um, just the market has turned in that direction to be, like, really profit driven versus growth driven as it used to be in '21, '22. Um, and based on that, people are, like, pretty risk averse- Mm... I would say, right?

182
00:33:13.632 --> 00:33:26.862
Like, if your only focus is getting that profit number up, anything that if you take a risk and that risk fails, and you make, draw that profit number down based on that is, like, pretty tough to sell anywhere, I think.

183
00:33:28.052 --> 00:33:43.752
Learn more at chiefofstaff.network. Mm. Slide into those DMs. Um, Rahul, any, any, any final thoughts? No. I just... I have fun talking to you. I'm glad we do this. And, uh, I was also pleased by the Spotify Wrapped.

184
00:33:43.982 --> 00:33:53.752
[laughs] To those few hundred people for whom we are the number one podcast, keep it up. Tell your friends. Yeah. And, uh, I hope people have happy holidays. Likewise. Likewise. Thank you so much, Rahul.

185
00:33:53.832 --> 00:33:55.411
Thanks everybody for listening. We'll talk to you soon.

186
00:34:03.752 --> 00:34:14.832
[outro music]