Every quarter the UCLA Anderson School of Management hosts the highly reputable (and influential) UCLA Anderson Forecast, an economic forecast for the U.S. and California. As an Advisory Board member of UCLA Extension’s Credit Analysis and Management Certificate Program, I was invited to attend the September 2016 Economic Outlook, a live presentation by the economists and economics professors who contribute to the UCLA Anderson Forecast. You can read more about the event on the official UCLA Anderson Forecast blog, but here are some highlights.
The theme this quarter was the impact of the economy on the Presidential Election. David Shulman, Senior Economist for UCLA Anderson Forecast, opened the session with a non-partisan breakdown of the major economic policies of both major party candidates for President. For me, it was nice to see policy differences in black and white without the political spin of the candidates and their campaigns. Bottom line, Shulman concluded that no matter who wins, Hillary Clinton’s approach (increased taxes and increased government spending) and Trump’s approach (massive tax cuts, changes in trade policy, less regulation, and yes, increased government spending) would BOTH increase the deficit. The reason – both plans assume a national GDP growth rate north of 2%, but Shulman argued that without improvement in productivity (maybe) and significant growth in innovation (unlikely), GDP will remain on a growth path of 2%.
Jerry Nickelsburg, Adjunct Professor of Economics at the Anderson Business School, gave his forecast for California. While still one of the fastest growing states in the U.S., growth of California’s $2.5 trillion economy is slowing because the state is close to reaching full employment. Declining manufacturing coupled with historically slow population growth will continue to restrain economic growth. Nickelsburg also warned that a trade war would have a greater negative impact on California than most states.
Nickelsburg also presented some interesting stats on small business. I didn’t realize that the proportion of small businesses (defined as enterprises with 10 or fewer employees) in Los Angeles County is much greater than the proportion in the U.S. and 26% of employment is L.A. County. To me, that means that small business is (and has been) a significant part of our local economy which CMA has not been able to reach. Perhaps CMA’s strategic partnership with the local SBA will provide more opportunities to reach those business owners who may not fully understand how to leverage business credit for the benefit of their businesses.
Shifting from local to global trade, I learned more about the controversy surrounding the broad-ranging free trade agreement known as the Trans-Pacific Partnership (TPP). Given that much of California’s economy is dependent upon international business flowing through the Ports of Los Angeles (L.A. is the #1 export district in the U.S.), Long Beach and San Francisco, why wouldn’t a free trade agreement that represents 40% of the global market be good for our local and national economy? The panel of experts argued that intense opposition to TPP is grounded in a retreat into protectionism, a general reaction to insecurity and uncertainty. Most interestingly, they claim that TPP is not as much about free trade as it is about anti-free trade because of all the exceptions in the agreement for goods like drugs, intellectual property, and dairy, just to name a few. I suppose that’s the fine print.
Economist William Yu concluded the morning session with a presentation of an economic model that puts a weight of 51% on each state’s real median household growth to predict the outcome of Presidential elections. A 10% weight is put on economic performance factors, GDP growth, Misery index, and state median income growth; demography, religion, and “other” factors such as candidates’ character, leadership, trustworthiness, campaign messages and strategies are weighted 13%, 3%, and 20% respectively. Since the election in 1972, the model has correctly predicted the outcome of 8 out of the last 11 Presidential elections. The model incorrectly predicted the elections of 1976 (Carter v. Ford), 2000 (Bush v. Gore), and 2012 (Obama v. Romney). Yu stated that the model currently gives Hillary Clinton a very slight edge over Donald Trump, but he was quick to say that it is within the margin of error and with 20% of the prediction weighted on factors like character, leadership, and trustworthiness, there is no predicting the public’s taste.
So why am I writing about this? There are several reasons. For one, it proves that economic data can be used to predict a lot of things, including the outcome of a presidential election (or how liberal your company might be in assigning trade credit). It also nicely demonstrated the whole “cash to cash” cycle that was discussed at length at CreditScape and in various blogs throughout the year. Finally, in the glut of credit-related content that we’ve been talking about all year here, I’m interested to gauge member interest in hearing more about topics like this. As we’re putting our education calendar together for 2017, I’d love to know what topics you’re interested in learning more about, including economic forecasts like this one. Feel free to leave comments below.