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These are tech companies Americans want to work at most 

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Google is where Americans want to work the most in tech, receiving more than 487,000 searches a month Apple and Tesla take second and third, respectively

New research reveals that Google is the tech company Americans most want to work at. 

The new study from document management company SmallPDF analyzed monthly searches for openings at the biggest tech companies in the US to see which brand was getting the most interest in job opportunities. 

It found that Google comes out on top for searches, with ‘Google jobs’ receiving more than 339,000 searches a month on average in the US and the term ‘Google careers’ receiving more than 148,000 searches a month, adding up to a whopping total of 487,000 searches. This is more than 200,000 searches a month higher than second place. 

Apple comes in second place on the list, thanks to 180,000 searches every month for ‘Apple jobs’ and 99,000 searches a month on average for ‘Apple careers’, adding up to 279,000 searches a month. 

Coming in third place is a multinational automotive company, Tesla, with an average of 185,000 monthly searches for opportunities at the company. This is split down into 109,000 searches monthly for ‘Tesla jobs’ and 76,000 searches monthly for ‘Tesla careers.’ 

Facebook takes fourth place in the list, with 94,000 searches for ‘Facebook jobs’ and 49,000 searches a month for ‘Facebook careers’, which adds up to a total of 143,000 searches a month on average for Facebook work opportunities. 

Rounding out the top five is Microsoft, which receives more than 141,000 searches a month for openings at the company. ‘Microsoft jobs’ receives 66,000 searches a month, and ‘Microsoft careers’ receives 75,000 searches a month on average.

 

Company 

“Jobs” searches 

“Careers” searches 

Total 

 

Google 

339,000  

148,000  

487,000  

 

Apple 

180,000  

99,000  

279,000  

 

Tesla 

109,000  

76,000  

185,000  

 

Facebook 

94,000  

49,000  

143,000  

 

Microsoft 

66,000  

75,000  

141,000  

 

Salesforce 

52,000  

41,000  

93,000  

 

Verizon 

45,000  

41,000  

86,000  

 

Spectrum 

43,000  

38,000  

81,000  

 

Netflix 

45,000  

34,000  

79,000  

10  

AT&T 

37,000  

31,000  

68,000  

Commenting on the findings, a spokesperson from SmallPDF said: “While some of the US’s most well-known tech companies do indeed make their way into the top ten, many do not, indicating that the job searches for many people are varied and job seekers in the tech field are keeping their options open. The companies at the top of the list benefit from the prestige that their brand holds, which helps them attract the best talent, which helps them continue to lead the industry.” 

The study was conducted by SmallPDF, which offers easy PDF conversion tools, allowing you to be more productive and work smarter with documents.

 

Source: smallpdf.com

The Inland Empire Business Journal (IEBJ) is the official business news publication of Southern California’s Inland Empire region - covering San Bernardino & Riverside Counties.

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Career & Workplace

California’s Population Decline Continues to Hammer Labor Supply

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State’s Workforce Contracts Again In Latest Numbers; Unemployment Rate Ticks Up 

California’s labor market grew modestly in the latest numbers. Total nonfarm employment in the state expanded by 8,700 positions in September, according to an analysis released today by Beacon Economics. August’s gains were also revised down to 8,900, a 19,000 decrease from the preliminary estimate of 27,900.

As of September 2023, California has recovered all of the jobs that were lost in March and April 2020 (the beginning of the pandemic), and there are now 436,400 more people employed in the state compared to pre-pandemic February 2020. Since that time, total nonfarm employment in California has grown 2.5% compared to a 3.0% increase nationally. On an annual basis, California increased payrolls by 1.7% from September 2022 to September 2023, trailing the 2.1% increase at the national level over the same period.

California’s unemployment rate rose slightly to 4.7% in the latest numbers, up 0.1 percentage points from the previous month. The state’s unemployment rate remains elevated relative to the 3.8% rate in the United States overall. Moreover, California continues to struggle with its labor supply, which fell by 17,700 in September, a decrease of 0.1% on a month-over-month basis. Since February 2020, the state’s labor force has contracted by 216,300 workers, a 1.1% decline.

“Census figures released this week reveal the extent to which households continue to leave California,” said Taner Osman, Research Manager at Beacon Economics. “The state’s population has fallen by half a million people over the past three years and this is filtering through to the economy, where the labor force has shrunk and employers are struggling to find workers.”

Industry Profile  

  • At the industry level, job gains were mixed in the latest numbers. The Health Care sector led the way with payrolls expanding by 18,200, an increase of 0.7% on a month-over-month basis. With these gains, Health Care payrolls are now 9.6% above their pre-pandemic peak.
  • Leisure and Hospitality was the next best-performing sector, adding 11,300 jobs, a month-over-month increase of 0.5%. With these gains Leisure and Hospitality payrolls are now 0.4%, or 8,500 jobs, above their pre-pandemic peak.
  • Other sectors posting strong gains during the month were Retail Trade (3,100 or 0.2%), Construction (2,200 or 0.2%), Real Estate (600 or 0.2%), and Management (500 or 0.2%).
  • Payrolls decreased in a handful of sectors in September. Information experienced the largest declines, with payrolls falling by 7,300, a contraction of 1.3% on a month-over-month basis. However, this decline was driven by the strikes in the Motion Picture and Sound Recording sub-sector, which has shed 30,800 positions over the last year, a 18.2% decline.
  • Other sectors posting declines during the month were Professional, Scientific, and Technical Services (-5,900 or -0.4%), Administrative Support (-5,500 or -0.5%), Manufacturing (-4,600 or -0.3%), Finance and Insurance (-2,200 or -0.4%), Other Services (-1,100 or -0.2%), and Transportation, Warehousing, and Utilities (-500 or -0.1%).

Regional Profile

  • Regionally, job gains were led by Southern California in September. Los Angeles (MD) experienced the largest increase, with payrolls growing by 8,700 (0.2%) during the month. The Inland Empire (5,900 or 0.4%), Orange County (5,400 or 0.3%), San Diego (1,400 or 0.1%), and Ventura (800 or 0.3%) also enjoyed job gains. Over the past year, Orange County (2.1%) has seen the fastest job growth in the region, followed by Los Angeles (MD) (2.0%), El Centro (1.8%), Ventura (1.7%), San Diego (1.5%), and the Inland Empire (0.7%).
  • In the San Francisco Bay Area, growth was mixed. San Rafael (MD) (1,000 or 0.9%) and Santa Rosa (1,00 or 0.5%) enjoyed the largest increase during the month. Vallejo (600 or 0.4%) also saw payrolls expand. On the other hand, San Francisco (MD) (-4,100 or -0.3%), San Jose (-1,800 or -0.2%), the East Bay (-1,600 or -0.1%), and Napa (-300 or -0.4%) all experienced payroll declines during the month. Over the past 12 months, Santa Rosa (3.4%) has had the fastest job growth in the region, followed by San Rafael (MD) (3.0%), the East Bay (2.0%), Vallejo (1.9%), San Francisco (MD) (1.4%), San Jose (1.3%), and Napa (0.5%).
  • In the Central Valley, Sacramento experienced the largest monthly job gains with payrolls expanding by 2,200 (0.2%) positions in September. Payrolls in Bakersfield (700 or 0.2%), Modesto (700 or 0.4%), Redding (500 or 0.7%), Visalia (400 or 0.3%), Stockton (200 or 0.1%), and Chico (100 or 0.1%) also jumped during the month. On the other hand, Madera (-300 or -0.7%) and Merced (-100 or -0.1%) had payrolls decline. Over the past year, Yuba (2.6%) has enjoyed the fastest growth, followed by Hanford (2.4%), Fresno (2.3%), Sacramento (2.1%), Visalia (1.4%), Chico (1.3%), Bakersfield (1.0%), Madera (0.7%), Stockton (0.7%), Modesto (0.2%), Redding (0.0%), and Merced (-3.6%).
  • On California’s Central Coast, Santa Barbara (400 or 0.2%) added the largest number of jobs in September. Salinas (300 or 0.2%) and Santa Cruz (100 or 0.1%) also saw payrolls increase during the month. On the other hand, payrolls in San Luis Obispo declined (-300 or -0.2%). From September 2022 to September 2023, Salinas (4.2%) added jobs at the fastest rate, followed by San Luis Obispo (3.1%), Santa Barbara (2.9%), and Santa Cruz (1.7%).
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Bizz Buzz

Colton Resident Receives Free College Tuition and Books Through Walmart’s Education Program

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By Saul Martinez, Contributing Writer for IEBJ

#bizzbuzz

This year marks the five-year anniversary of Walmart’s Live Better U (LBU) education program. Over the past five years, the company has saved associates across the country nearly half a billion in education costs, reflecting the company’s commitment to creating a path for everyone to learn and grow. In California, we’ve seen 5,620 Walmart and Sam’s Club associates participate in Live Better U over the past five years.

One such success story is Robert Gay, who lives in Colton, CA, and earned his college degree – fully paid for by Walmart. Robert was stuck in a stagnant position at his previous company, hindered by the absence of a degree that prevented him from advancing further. However, upon discovering the Live Better U benefits offered by Walmart, he decided to take a leap of faith and join their team with the intention of completing his degree. After successfully graduating with a bachelor’s degree in October 2020, he now takes immense pride in his accomplishment of accepting a promotion to associate general manager. Throughout his journey, Robert received overwhelming support from his local team, who not only empathized with his workload challenges but also aided when needed.

Most individuals typically encounter Walmart through its retail outlets. The Inland Empire Business Journal had the opportunity to explore a consolidation center of Walmart situated in Colton, California. Our visit left us deeply impressed by the remarkable cleanliness and impeccable condition of the facility, almost reminiscent of a high-end showroom.

While on the tour, we observed the diligent measures taken by the leadership to maintain employee motivation and awareness regarding the daily, weekly, and monthly performance Key Performance Indicators (KPIs) of the facility. These KPIs were prominently displayed on digital monitors throughout the premises. The Colton leadership created a mascot and call their team the Colton Eagles.

We found ourselves deeply impressed by this aspect of Walmart, which is often hidden from public view. Walmart unquestionably stands out as a company that not only offers excellent career opportunities but also boasts a remarkable 100% tuition reimbursement program. If you are seeking a career in the Inland Empire, this proves to be an exceptional workplace choice.

Whether someone is chasing their first job or the opportunity that will define their career, Walmart is committed to creating pathways of opportunity for everyone.  

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Economy

The Recession That Didn’t Happen… And Why Most Forecasters Got It Wrong

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In arguing that there will be no near-term recession, Beacon Economics has been an outlier in the forecasting community.

Much to the chagrin of those who have been predicting otherwise, the U.S. economy has stubbornly continued to grow—and 2023 is shaping up to be a better year than 2022. Beacon Economics has argued all along that there is little reason to think we are heading for a near-term recession (outside of our worries about the potential impact of Fed policy). It seems as if our optimism is starting to spread. The Economist recently published an article titled Could America’s Economy Escape Recession?, the latest Wall Street Journal recession probability survey (which we contribute to) shows that economists’ expectations of a recession are starting to fade, and Bank of America became the first major forecast group to retract their recession call.

Beacon Economics recession probability rose only slightly in 2022, and our current estimate of a recession occurring in the next 12 months is at 5%, making us an outlier in the forecast community (take a look at the Journal’s survey and you’ll see what we mean). This isn’t to say that we don’t recognize signs of stress in the economy driven by higher interest rates and the recent bout of inflation. Rather, we’ve never viewed these issues as rising to the level of being systemic given that they were caused by the same thing that has kept consumer spending supercharged—the excessive stimulus thrown at the economy during the pandemic.

The greatest risk, as we have seen it, was always the undue tightening by the Federal Reserve, which was implemented in response their original sin of excessive loosening. But the nation has fared even better throughout the large interest rate increases than we thought it would. Now, with inflation cooling, the Fed seems likely to slow their credit tightening efforts, so even this concern is fading.

Admittedly, it is affirming to see our optimism playing out in the trends. But what should we make of this big miss by the broader forecasting community? Paul Krugman, in a recent New York Times column, had one answer—forecasters (at least in the aggregate) just aren’t very good at forecasting recessions. He notes that studies of the history of recession predictions show the forecasting community to be remarkably inaccurate—calling for recessions when they don’t occur, and largely failing to predict them when they do. So much for the wisdom of the crowds. But what Krugman never addresses is the ‘why’. Are forecasters just dumb? As John Kenneth Galbraith famously quipped “[t]he only function of economic forecasting is to make astrology look respectable.” Or is there something else going on?

It might seem surprising that forecasters haven’t learned how to predict recessions better, given the technical tools that have been developed over the past 50 years. The first macroeconomic computer model was built by U-Penn’s WEFA group back in the early 1970’s, winning the group’s leader, Lawrence Klein, a Nobel Prize. Today’s economists have far more computing power at their disposal, not to mention a broader set of quality data to play with. Yet, in the aggregate, forecasters still seem unable to see the arrival of the economic tempest until it is already upon us.

The issue with these big macro models is that they are primarily designed to calculate economic trends on the basis of a complex statistical estimate of covariances found within the historical data. Such models rely on each expansion being similar enough to the previous one that these covariances remain relevant. However, recessions are—by definition—a period when the economy deviates substantially from trend. As such, these sorts of forecast models simply don’t have the capacity to predict a recession, unless the forecaster specifically programs it in.

Those seeking to predict oncoming recessions often look for other sets of statistical leading indicators that can foretell when such a break from the trend could occur. In short, they look for historic patterns of data that seem to correlate with oncoming recessions. As it turns out, there are very few of these kinds of guideposts in the data—something that does not surprise us as we’ll explain in a moment. The one data point that does highly correlate with future recessions—and the one that is surely behind the so-far incorrect call of recession by the forecasting community at large—is the inverted yield curve (The yield curve is the difference between short and long run interest rates. In the past, when short run rates are higher than long run rates, we say the curve is inverted). This statistic does indeed have a good track record, with the five recessions prior to the COVID-19 pandemic all preceded by a negative yield curve. Hence, in July 2022 when the yield curve went negative, many forecasters viewed a recession as fait accompli. Yet, as always, conflating correlation with causation is liable to lead to bad calls.

Beacon Economics noted the inverted yield curve last year, but we did not view it as sufficient or even necessary evidence to predict an economic downturn (We can proudly state that the only two times Beacon Economics has predicted a recession was back in 2006 at the firm’s inception – I left the UCLA Anderson Forecast in 2006 to found Beacon in large part because I thought the real estate bubble would cause a recession upon its collapse, a point of view not welcomed by the UCLA Forecast’s director – and in March 2020 when it became obvious the pandemic had spread globally). More broadly, we do not believe that there is any recession-predicting “magic bullet” to be found in the data—yield curve or otherwise. To understand our view, start with the recognition that recessions are created by rapid changes in the structure of aggregate demand in an economy. The speed of change is faster than factors of production can be redeployed within that economy. The net result is an overall decline in output and an increase in slack resources—a recession. From this vantage point, predicting a recession means predicting the rapid change in aggregate demand. The key to understanding why there is no clear set of recession leading indicators is recognizing that the sources of recessions are highly varied.

Not unlike Tolstoy’s happy and unhappy families, while every expansion is similar to previous ones (this is the reason VAR models are good at predicting trends), every recession is liable to be significantly different from previous ones. There is a broad range of potential causes behind a rapid change in aggregate demand, from various forms of financial bubbles that will eventually pop, to bad government policy choices, to truly random events like global pandemics. Each type of recession driver has its own specific set of leading indicators compared to others. Add the additional facet that people are unlikely to make the same set of bad decisions that led to some economic calamity in the past, making it even less likely that two recessions will have similar leading statistical patterns (This is a version of the Lucas critique, which says once we make a big mistake we are unlikely to do it again, as we should have learned better. This implies that, statistically, the chance of back-to-back recessions looking the same is less than pure probability would suggest). Thus, relying on simplistic indicators will inevitably lead forecasters astray.

To appreciate this issue in the extreme, consider a situation where there can be no true leading indicators. In March 2020 when COVID-19 was spreading rapidly through the United States, it became clear that governments would be enacting strict public health measures to control the spread of the malady, and that these efforts were going to close a large portion of the service sector. It was pretty obvious that the U.S. economy was going to experience a recession, since this is exactly the type of rapid change in aggregate demand that drives recessions. But given the sheer randomness of the emergence of viral pandemics, there simply can be no economic leading indicator.

Of course, most recessions don’t begin so arbitrarily. In 2006 Beacon Economics was the first West Coast forecast to predict what eventually became known as the ‘Great Recession’, a destructive downturn that started in the 1st quarter of 2008. The roots of that recession were manmade in the form of a massive subprime consumer lending surge that started in 2003 and vastly overheated both the housing market and consumer spending. By 2006 it was clear that these imbalances had moved way past the point of no return and the economy would necessarily experience a recession—driven by rapid declines in the housing supply and consumer spending—once the sub-prime bubble inevitably collapsed in on itself. The imbalances were the leading indicators. Yet, we know that these imbalances were different than the ones that led to the tech downturn in 2000 (a stock market bubble combined with excessive business investment) or the 1991 downturn, which was driven by excesses in bank lending and commercial investments.

What all three of these recessions did have in common was the inverted yield curve, including in 2006 when Beacon Economics made its early call of the Great Recession. Ironically, at that time forecasters were more skeptical of this statistical bad omen. One article written at the time from U-Penn, home of the legendary WEFA model, stated that the inverted yield curve “… gave shudders to those who see the phenomenon as a harbinger of recession. And yet, the U.S. economy is strong, and surveys show most forecasters think it will stay that way.” In the first half of 2007 the Wall Street Journal recession probability survey was running 25%, as opposed to the 60% level during the first half of 2023.

Perhaps it was their bad call in 2006/7 that made more forecasters believe the yield curve indicator. Why hasn’t it worked for the current recession predictions? Inverted yield curves are primarily generated by the Fed’s choice to push up short-term interest rates. Back in 2006, short-run rates were pushed higher because the Fed was worried about consumer lending and the housing market. In the 2000 downturn in it was because the Fed was worried about the tech stock bubble. In these cases, the inverted yield curve can be thought of as nothing more than skid marks up to the edge of a cliff, created by a driver who realizes, belatedly, of the approaching danger. In contrast, in 2022, short-run rates were raised because the Fed was worried about inflation. But inflation by itself has never caused a recession. And as for the rest of the U.S. economy, there are no major imbalances as there were in 2006 or 2000. The link between the inverted yield curve and a true recession-causing imbalance in the economy wasn’t there this time.

But there is a deeper issue at play. The types of imbalances that ultimately end up collapsing, and cause recessions, are typically driven by narratives that, at least in hindsight, are clearly false. The tech bubble was driven by the “New Economy” narrative, while the Great Recession was driven by Wall Street’s magical alchemy that pretended to convert subprime debt into safe investments. Nobel Prize winning economist Robert Shiller notes in his book Irrational Exuberance “[h]ow errors of human judgment can infect even the smartest people, thanks to overconfidence, lack of attention to details, and excessive trust in the judgments of others, stemming from a failure to understand that others are not making independent judgments but are themselves following still others—the blind leading the blind.”

The fact is forecasters are human and just as likely as anyone to be swept up in a collective madness of broken narratives. William Bernstein, in his recent book The Delusions of Crowds, writes that the author of one of the earliest analyses of recession-causing bubbles, Charles Mackay, largely failed to recognize the bubble he was living in while writing his book. Mackay’s missive, Extraordinary Popular Delusions and The Madness of Crowds, was first issued in 1841 and examined the South Sea and Mississippi bubbles that had rocked the British and French two decades prior. Yet, he failed to see the crazed trading surrounding railroads that ended up causing massive damage to the British economy in the Panic of 1847.

One could suggest that if a narrative can actually cause a recession, it has to have the capacity to sway forecasters. But such a claim may be justifiably called self-aggrandizing, as it relies on forecasters actually having social and political clout. But, given what we have suggested above, this may well be by definition. In such a world, we have to rely on Warren Buffet’s famous line to be “fearful when others are greedy, and greedy when others are fearful.”

Ultimately, recession forecasts can only be created through a complex interaction of theory and data to identify when and where economic trends become so disengaged from normality as to ensure a recession when the process does eventually begin. Of course, such a determination is full of nuance and subtlety. Beacon Economics made the right call in 2006 because the signs of excess were, at least in our estimation, glaring. We made the right call in 2022 because there were few signs of such excess. We haven’t yet been tested by a less obvious situation. As the disclaimer goes, past results are no guarantee of future returns! Forecasting truly is an art. But I still believe we have a leg up by always keeping in mind not just what the data can tell us, but also what it can’t.

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