How Culture 500 Company Attrition Rates Compare Within Industriesĭuring the Great Resignation, companies within the same industry have experienced varying degrees of attrition. (See “How Culture 500 Company Attrition Rates Compare Within Industries.”) Workers are 3.8 times more likely to leave Tesla than Ford, for example, and more than twice as likely to quit JetBlue than Southwest Airlines. The figure below compares competitors with high and low attrition rates within their industries. Even within the same industry, we observed significant differences in attrition rates. Industry explains some of the variation in attrition rates across companies but not all of it. Enterprise software, which also suffered high churn, employs the highest percentage of engineering and technical employees. Management consulting, in contrast, had the second-highest attrition rate but also employs the largest percentage of white-collar professionals of any Culture 500 industry. Some of the hardest hit industries - apparel retail, fast food, and specialty retail - employ the highest percentage of blue-collar workers among all industries we studied. The Great Resignation is affecting blue-collar and white-collar sectors with equal force. The industries with the highest percentage of blue-collar workers are noted in light blue. This chart shows the average attrition rate across 38 industries from April through September 2021. Industry Average Attrition Rate in the Great Resignation 3 The data, from Revelio Labs, where one of us (Ben) is the CEO, enabled us to estimate company-level attrition rates for the Culture 500, a sample of large, mainly for-profit companies that together employ nearly one-quarter of the private-sector workforce in the United States. workers who left their employer for any reason (including quitting, retiring, or being laid off) between April and September 2021. To better understand the sources of the Great Resignation and help leaders respond effectively, we analyzed 34 million online employee profiles to identify U.S. More importantly, they are looking for ways to hold on to valued employees. 2 As the Great Resignation rolls on, business leaders are struggling to make sense of the factors driving the mass exodus. 1 Between April and September 2021, more than 24 million American employees left their jobs, an all-time record. This approach will return all possible synonyms, but some may not be very relevant.More than 40% of all employees were thinking about leaving their jobs at the beginning of 2021, and as the year went on, workers quit in unprecedented numbers. This approach tends to return the most relevant synonyms, but some words like "angry" won't return any synonyms. If (lemma_name != word and lemma_name not in synonyms): Lemma_name = lemma_name.lower().replace('_', ' ') Nltk.download('averaged_perceptron_tagger') Nltk.pos_tag(nltk.word_tokenize('foobar')) def download_nltk_dependencies_if_needed(): Here are some helper functions to make NLTK easier to use, and two examples of how those functions can be used. But that should be enough to get you started. and it's grouping them by parts of speech, and it's added in links to other things that you can follow, and so forth. Of course the website is also printing the part of speech ( sim.pos), list of lemmas ( sim.lemma_names), definition ( sim.definition), and examples ( sim.examples) for each synset at both levels. So, to show the same information as the website, start with something like this: for ss in wn.synsets('small'): For that, you call similar_tos() on each Synset. If you also want the "similar to" list, that's not the same thing as the synonyms. That's the same list of top-level entries that the web interface gave you. You might be interested in a Synset: > wn.synsets('small')
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