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13 Attachment 3The leading independent economic forecast providing insight to decision makers in business, academia and government. December 2020 Economic Forecast ATTACHMENT 3 Platinum Sponsors Silver Sponsors Bronze Sponsors Bruin Sponsors Media Sponsor Principal Sponsor UCLA ANDERSON FORECAST December 2020 ECONOMIC OUTLOOK Gold Sponsors Academic Sponsor December 2020 Report THE UCLA ANDERSON FORECAST FOR THE NATION AND CALIFORNIA Forecast: 2020 - 2023 69th Year UCLA Anderson Forecast Director: Jerry Nickelsburg, Adjunct Professor of Economics, UCLA Anderson School The UCLA Anderson Forecast Staff: Edward Leamer, Distinguished Professor David Shulman, Senior Economist Emeritus Leo Feler, Senior Economist William Yu, Economist Leila Bengali, Economist George Lee, Publications and Marketing Manager Seth Katz, Director of Business Development Mina Prietto, Senior Admin Analyst The UCLA Anderson Forecast provides the following services: Membership in the California Seminar Membership in the Los Angeles and Regional Modeling Groups The UCLA Anderson Forecast for the Nation and California Quarterly Forecasting Conferences Special Studies California Seminar and Regional Modeling Groups members receive full annual forecast subscriptions, invitations to private quar- terly meetings of the Seminar and the right to access the U.S., California and Regional Econometric models. For information regarding membership in the California Seminar and the Los Angeles and Regional Modeling Groups or to make reservations for future Forecast Conferences, please call (310) 825-1623. The UCLA Anderson Forecast Sponsorships: · Logo/name recognition at each conference · Prominent placement on event materials, publications and on the official Forecast website · Priority admissions to conferences · Promotional tabling opportunities at events · Ability to interact with an audience that includes business, professional and government decision makers from all over California and the United States For information regarding sponsorship of the UCLA Anderson Forecast, please call (310) 825-1623 or visit www.uclaforecast.com This forecast was prepared based upon assumptions reflecting the Project’s judgements as of the date it bears. Actual results could vary materially from the forecast. Neither the UCLA Anderson Forecast nor The Regents of the University of California shall be held responsible as a consequence of any such variance. Unless approved by the UCLA Anderson Forecast, the publication or distribution of this forecast and the preparation, publication or distribution of any excerpts from this forecast are prohibited. Published quarterly by the UCLA Anderson Forecast, a unit of UCLA Anderson School of Management. Copyright 2020 by the Regents of the University of California. The Quarterly Forecast: “Climate Change | Business Change” Upcoming Events: Spring Quarterly Conference March 2021 Orange County Regional Economic Outlook TBA Summer Conference June 2021 Fall Quarterly Conference September 2021 UC Hastings/UCLA Anderson Forecast Joint Conference TBAWinter Quarterly Conference December 2021 December 2020 Report THE UCLA ANDERSON FORECAST FOR THE NATION AND CALIFORNIA Nation A Gloomy COVID Winter and an 13 Exuberant Vaccine Spring Leo Feler Trends in Solar Panel Adoption: 23 The Role of Costs, Benefits, Weather, and Peers Leila Bengali Uncertainty in the Post-Election 31 and COVID-19 World William Yu Jerry Nickelsburg Tables 43 Charts 59 The Economic/Pandemic Question: 65 To Close or Not to Close? Jerry Nickelsburg Leila Bengali Sea Level Rise and Its Impact on California 73 Housing Markets William Yu Tables 85 Summary Charts 87 California Acknowledgements Regional Modeling Group 97 Members 99 Sponsors 105 Speakers 113 DECEMBER 2020 REPORT THE UCLA ANDERSON FORECAST FOR THE NATION A Gloomy COVID Winter and an Exuberant Vaccine Spring Trends in Solar Panel Adoption: The Role of Costs, Benefits, Weather, and Peers Cathay Bank | UCLA Anderson Forecast | U.S.-China Economic Report - Fourth Quarter Update Uncertainty in the Post-Election and COVID-19 World UCLA Anderson Forecast, December 2020 Nation–13 A GLOOMY COVID WINTER AND AN EXUBERANT VACCINE SPRING A Gloomy COVID Winter and an Exuberant Vaccine Spring Leo Feler Senior Economist, UCLA Anderson Forecast December 2020 1. Mass vaccinations and a release of pent- up demand will lead to a boom in economic activity beginning in the second quarter of 2021, but until then, there will be a lot of unnecessary hardship Following a record 33.1% annualized growth rate of real GDP in Q3 2020, we are forecasting a weak 1.2% annualized growth rate in Q4 2020 and 1.8% in Q1 2021 (see Exhibit 1). This leaves the economy 3.3% and 2.8%, respectively, below its peak in Q4 2019. Based on recent vaccine news, we expect limited vaccinations to begin in mid-December for health care workers, frontline workers, and vulnerable populations and for vaccines to be widely available for the general population beginning early in Q2 2021. With mass vaccinations, we forecast robust growth in Q2 2021 of 6.0% at an annualized rate, and then consistent growth above 3% well into 2023. We expect the economy will reach its previous peak by the end of 2021. This, however, will still leave it 4.8% below the trend of where the economy likely would have been without the COVID shock (see Exhibit 2). • Because of rising COVID infections and increased social distancing, we’re forecasting slower growth in real GDP for Q4 2020 and Q1 2021, of 1.2% and 1.8% SAAR, respectively. • With mass vaccinations, we forecast robust growth in Q2 2021 of 6.0% SAAR, and then consistent growth above 3% well into 2023. We expect the economy will reach its previous peak by the end of 2021. • These headline numbers don’t capture the economic misery that so many are experiencing. Currently, 20.5 million Americans are receiving some form of unemployment insurance benefit. Nearly nine percent of Americans live in households that are not current on rent or mortgage, 12 percent live in households where there was either sometimes or often not enough food to eat, and about one-third live in households where it has been somewhat or very difficult to pay for usual household expenses during the pandemic. • We expect the housing market to remain hot through at least 2023, with housing starts at their highest levels since 2007. • Even with a strong recovery beginning in Q2 2021, we expect only modest core inflation, around 2.1–2.2% per year, and gradual improvement in unemployment. We forecast that unemployment will remain above 5% through 2021 and will only fall to 4% by 2023. 14–Nation UCLA Anderson Forecast, December 2020 A GLOOMY COVID WINTER AND AN EXUBERANT VACCINE SPRING Exhibit 1 Real GDP growth rate, SAAR Source: U.S. Department of Commerce, Bureau of Economic Analysis and UCLA Anderson Forecast Notes: Real GDP growth rate, seasonally adjusted annual rate. 2.9 1.5 2.6 2.4 -5.0 -31.4 33.1 1.2 1.8 6.0 3.2 3.2 3.1 3.2 3.4 3.1 3.0 3.0 2.9 3.1 Q3Q2Q1Q1 Q4Q3Q2 Q4 Q2Q1 Q3 Q4 Q1 Q2 Q4Q3 Q1 Q2 Q3 Q4 Forecast → 2019 2020 2021 2022 2023 Real GDP growth rate, SAAR Exhibit 2 Real GDP levels and trends, $ Billions SAAR 21,000 18,500 19,000 0 21,500 19,500 17,500 20,000 20,500 17,000 18,000 Q3Q3Q4Q1 Q2 Q4 Q1 Q1Q3 Q2 Q3 Q4 Q1 Q4Q2Q3Q2Q2Q1Q4 4.8% 2.8%3.3% Forecast 2019 2020 2021 2022 2023 Real GDP levels and trends, SAAR Trend 2019 peak Source: U.S. Department of Commerce, Bureau of Economic Analysis and UCLA Anderson Forecast Notes: Real GDP growth rate, seasonally adjusted annual rate. UCLA Anderson Forecast, December 2020 Nation–15 A GLOOMY COVID WINTER AND AN EXUBERANT VACCINE SPRING With COVID infections surging and people social distancing more—either by their own choosing or because of renewed government restrictions—consumption of services has fallen and we expect it will continue to fall through the end of the year and early into next year. Emergency social assistance programs, including extended unemployment insurance and eviction, foreclosure, and student loan moratoria, are set to expire at the end of the year. Without renewed fiscal relief, we expect households that are currently receiving these social assistance benefits will cut expenditures and forgo their usual holiday shopping in anticipation of more severe hardship once these programs end. Currently, 20.5 million Americans are receiving some form of unemployment insur- ance benefit, compared to 1.5 million this time last year.1 Nearly nine percent of Americans live in households that are not current on rent or mortgage, of which nearly one-third say that eviction or foreclosure in the next two months is either very likely or somewhat likely, and 12 percent live in households where there was either sometimes or often not enough food to eat.2 About one-third of Americans live in households where it has been somewhat or very difficult to pay for usual household expenses during the pandemic.3 On the other end of the spectrum are households that have seen their savings and asset values swell. For those fortunate to maintain employment and income during this pandemic, their financial situation is better than before. Home values have increased, equity values have increased, and limited consumption opportunities during the past nine months mean that these households have been able to accumulate at least an additional $1.6 trillion in savings. 2. Limited holiday celebrations, with more “stuff” and fewer “experiences” In aggregate, consumers have more spending power now heading into the holidays than they normally would. Credit card and revolving balances are down and personal savings are up (see Exhibit 3). But surging COVID cases and the need to social distance will limit consumers’ ability to spend Exhibit 3 Credit card and revolving balances are down, personal savings are up Source: U.S. Deparment of Commerce, Bureau of Economic Analysis and Federal Reserve BoardNotes: $ billions. Credit card and revolving balances are seasonally adjusted. Personal savings are seasonally adjusted and annualized. 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 JanJul850 760 0 840 NovSepMayNov820 810 830 860 MayAug790 FebAprJunAugDecFebAprJunSepOctDecFebAprJunAugOctJanNovJulSep800 JulJanMayMar770 780 MarOctMarConsumer Loans: Credit Cardsand Revolving Plans, $ Billions SA Personal Savings,$ Billions SAAR 2018 2019 2020 1. Department of Labor, Unemployment Insurance Weekly Claims, November 25, 2020, p. 4, available at: https://www.dol.gov/sites/dolgov/files/OPA/newsreleases/ui-claims/20202177.pdf.2. United States Census Bureau, Household Pulse Survey, “Housing Insecurity,” “Likelihood of Eviction or Foreclosure,” and “Food Scarcity,” Week 18, available at: https://www.census.gov/data-tools/demo/hhp/#/.3. United States Census Bureau, Household Pulse Survey, “Difficulty Paying for Usual Household Expenses,” Week 18, available at: https://www. census.gov/data-tools/demo/hhp/#/. 16–Nation UCLA Anderson Forecast, December 2020 A GLOOMY COVID WINTER AND AN EXUBERANT VACCINE SPRING on services such as restaurants, vacations, and entertainment, which are labor-intensive. Instead, gift-giving this year will be about buying more “stuff.”4 The only difference this year compared to past years is that Americans have been buying more “stuff” for the past nine months. A key ques- tion is whether there’s saturation in consumption of goods or whether it’s possible for goods consumption to increase even further, given that consumers can’t spend as easily on experiences. Our forecast is that services consumption will increase slightly from a low base and goods consumption will decline modestly from a high base between Q3 and Q4 (see Exhibit 4). Continued weak spending on services and a shift to online purchases of goods will dampen employment gains, espe- cially relative to the usual holiday increase in retail and services employment. In addition, sustained higher goods purchases mean more imports, but this doesn’t have as big of an effect on employment gains as services consumption. We expect only modest reductions in the unemployment rate for the remainder of the year and early into 2021, with unemployment averaging 6.8% in Q4 and 6.6% in Q1. 3. More fiscal relief and government spending on vaccine distribution will prop-up a weak economy in the first quarter of 2021 With rising COVID cases following holiday gatherings and continued social distancing, we expect the economy will limp into 2021, with unemployment remaining high. Our assumption is that Congress will pass an additional $1 trillion in fiscal relief in January or early February, with this money entering the economy in Q1 and Q2. Even with additional government support, our forecast is for anemic growth of 1.8% annualized in Q1 2021. We also assume $200 billion in federal transfers to state and local governments and $50 billion of direct federal spending to support mass vaccination campaigns. This is our estimate for the all-in cost for end-to-end distribution, storage, handling, administration, and outreach associated with mass vaccination. Both fiscal relief and government spending on vaccinations will help prop-up a weak economy early in 2021. Without additional fiscal relief, the economy may teeter into reces- Exhibit 4 Real consumption of goods and services, $ Billions SAAR Source: U.S. Department of Commerce, Bureau of Economic Analysis and UCLA Anderson ForecastNotes: Real consumer spending in 2012 $ billions, seasonally adjusted annual rate. Annualized % change shown between Q3 and Q4 2020. 9,500 6,000 5,000 8,500 0 4,500 5,500 9,000 6,500 8,000 7,000 7,500 Q1 Q1Q2Q3 Q4Q4Q1 Q3Q3 Goods Q2 Q1 Q4Q4Q1Q2 Q3 Q4 Q3 Services Q2 Q2 +1.3% -2.2% Forecast 2019 2020 2021 2022 2023 Real consumption of goods and services, SAAR 4. For a discussion on how the purchase of “stuff” represents “millions of dollars and countless jobs,” see https://youtu.be/Yj8mHwvFxMc. UCLA Anderson Forecast, December 2020 Nation–17 A GLOOMY COVID WINTER AND AN EXUBERANT VACCINE SPRING Exhibit 5 Higher spending on restaurants, recreation, travel, accomodation, and healthcare services Source: U.S. Department of Commerce, Bureau of Economic Analysis and UCLA Anderson Forecast Notes: Real consumer spending in 2012 $ billions, seasonally adjusted annual rate. Increase of 12% between 2020 Q4 and 2021 Q4. 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 Q3Q1 Q2 Accomodation Q3 Healthcare Q4 Q2Q1 Q3Q4 Q1 Q2 Q2Q4Q3Q1 Q2 Q4 Q1 Q3 Q4 Travel Recreation Restaurants +12% Forecast 2019 2020 2021 2022 2023 Real consumer spending,$ billions SAAR sion in Q1 2021 depending on the prevalence of COVID infections and on the need to continue social distancing. 4. With mass vaccinations by mid-2021, we expect a boom in services, led by leisure, hospitality, entertainment, and recreation Once vaccines are widely available, our assumption is that households will not only resume their consumption of services, but those that accumulated savings during the pan- demic will overcompensate for the past year by consuming more services than they normally would. We expect a sig- nificant surge in spending on restaurants, recreation, travel, and accommodation, as well as in healthcare services, as people resume non-urgent and elective healthcare visits (see Exhibit 5). We also expect an increase in clothing purchases as individuals adjust to going out once again. But follow- ing a year of higher goods purchases, we expect consumers will reduce consumption of recreational goods, household goods, electronics, and other durable goods (see Exhibit 6). These changes represent a return to our former habits, and in our forecast, it means a reversion to long-run trends. What we did less of during the pandemic, we’ll do more of once the pandemic is over. That includes activities involv- ing in-person interaction. What we did more of during the pandemic, we’ll do less of once it’s over. That includes the stay-at-home purchases of the past year. We may never fully return to pre-pandemic habits, but without the constraints imposed by the pandemic, we’ll adjust our consumption behaviors to be more like before. 18–Nation UCLA Anderson Forecast, December 2020 A GLOOMY COVID WINTER AND AN EXUBERANT VACCINE SPRING 5. Housing will likely remain red-hot well into 2023, mitigating weak construction investment in commercial, state and local, and mines and wells We forecast that housing will remain strong well into 2023. Home builder confidence is at a record, and permits and housing starts continue to increase (see Exhibit 7). Underlying this are five factors. First, interest rates are likely to remain low for an extended period of time, which will fuel demand for home purchases. Second, without COVID concerns, sellers who were reluctant to put their homes on the market this past year may enter the market and relieve current inventory constraints. This is likely to be a case where supply begets demand, and the increased options induce more people to become buyers. Third, we’ll have more clarity on whether working from home will be sustainable over the longer-term once pandemic constraints are no longer binding. This clarity is likely to induce addi- tional rounds of people relocating away from urban cores to suburbs and larger homes. Fourth, there’s a demographic bubble of millennials aging into their prime earning and home-buying years (see Exhibit 8).5 This demographic shift will continue to fuel higher demand for home purchases. And fifth, as unemployment begins to come down and there’s less economic uncertainty, buyers who were reluctant to enter the market this past year may be more likely to enter to take advantage of continued low mortgage rates. As for where housing markets will be red-hot, there are a lot of unknowns. Once the economy fully reopens, urban cores will regain some of the amenity value lost during the pandemic, but demographic shifts, with millennials starting families, and continued opportunities to work from home will make the suburbs more attractive. 5. See Tim Duy, Fed Watch, “Quick Note on Demographics,” December 1, 2020, available at: https://blogs.uoregon.edu/timduyfedwatch/2020/12/01/ quick-note-on-demographics/. Exhibit 6 Lower spending on recreational goods, household goods, electronics, and other durable goods Source: U.S. Department of Commerce, Bureau of Economic Analysis and UCLA Anderson ForecastNotes: Real consumer spending in 2012 $ billions, seasonally adjusted annual rate. Decrease of 1% between 2020 Q4 and 2021 Q4. 0 200 400 600 800 1,000 1,200 1,400 1,600 Q2Q2Q1 Q3 Q4Q1Q4 Q3 Q1 Q2 Q3Q3 Q4 Q1 Q2 Q4 Q1 Q2 Q3 Q4 Other durable goods Recreational goods Electronics Household goods -1% Forecast 2019 2020 2021 2022 2023 Real consumer spending,$ billions SAAR UCLA Anderson Forecast, December 2020 Nation–19 A GLOOMY COVID WINTER AND AN EXUBERANT VACCINE SPRING Exhibit 7 The housing market shows continued strength, propelled by record-low mortgage rates and working-from-home: NAHB/Wells Fargo Housing Market Index reaches new highs and housing starts expected to remain high through 2023 Source: U.S. Census Bureau, U.S. Department of Housing and Urban Development, National Association of Home Builders, and UCLA Anderson ForecastNotes: The NAHB/Wells Fargo Housing Market Index measures home builder confidence. Data are available through November 2020. Housing starts data are available through October 2020. Forecasts for housing starts are for November 2020 and onwards. 0 10 20 30 40 50 60 70 80 90 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 2,200 2,400 20082001199720022007199620032004199920051995200920062017202120112020200019982012201320191991201519922016201420181990202219932023199420242010Home Builder Confidence IndexHousing Starts (‘000s SAAR) F Exhibit 8 Demographic bubble: millennials are aging into their prime earning and home-buying years Source: Tim Duy, Fed Watch, “Quick Note on Demographics,” December 1, 2020, available at: https://blogs.uoregon.edu/timduyfedwatch/2020/12/01/ quick-note-on-demographics/. U.S. Population by Age 20–Nation UCLA Anderson Forecast, December 2020 A GLOOMY COVID WINTER AND AN EXUBERANT VACCINE SPRING The flip-side of strong residential investment will be weak investment in commercial, state and local, and oil wells. If the higher rates of working-from-home and online shopping persist to a moderate extent after the pandemic is over, we’ll be over-supplied on office and retail space, and there will be little demand for additional commercial investment, at least in urban cores. State and local construction is likely to take a hit as state and local governments reduce infra- structure budgets in response to lower tax revenues and the need to replenish rainy-day funds. Finally, the domestic oil industry has been decimated by low oil prices, and we foresee diminished ongoing investment in oil wells for the next several years. 6. Brick-and-mortar retail and commercial offices will need to adapt to survive and become more about providing experiences The pandemic has taught us that we can run many of our errands online and we can do much of our work productively from home. This requires rethinking how we use our retail and commercial spaces. In order to compete with online retailers, brick-and-mortar retailers will need to differentiate themselves and provide not just a means to fulfill necessities, but also a shopping experience. This requires providing the ability to sample products (e.g., Apple Stores, Ulta Beauty, Sephora, Costco), offering assistance and recommendations (e.g. Ace Hardware, BestBuy), and cultivating a sense of community engagement (e.g., Lululemon, independent book stores). With the accelerated adoption of e-commerce, brick-and-mortar retailers will need to innovate so that their physical locations focus more on providing experiences while their online marketplaces fulfill necessities. Similarly, commercial offices are likely to become spaces tailored for interaction and collaboration. Early in the pandemic, there was much discussion about how the open- office concept would revert to workers having dividers or individual offices to allow for separation.6 But the success of working-from-home has revealed that, for moments when workers require separation for health or individual productivity reasons, they can work effectively from home, and when they need to interact with colleagues, they can go into the office. This past year of working remotely has revealed that there’s most likely an optimal mix of in-office and at-home work. This will have important implications for all the businesses that support urban core workers as urban cores are unlikely to achieve the daily density and volume of workers they had before. 7. Even with a strong recovery beginning in Q2 2021, we expect only modest inflation and gradual improvement in unemployment and trade Our forecast is that core inflation will average 1.8% for 2020 and remain muted through 2023, hovering around 2.1–2.2% per year (see Exhibit 9). There is considerable excess capac- ity to absorb a surge in consumer demand without leading to an increase in prices. This also means there is little risk of the Federal Reserve needing to increase rates to contain inflation, and the Fed Funds Rate is likely to remain near zero at least through the end of 2023. We forecast that the unemployment rate will decline gradu- ally as the economy picks up and people re-enter the labor force (see Exhibit 10). Nearly 1 million women exited the labor force this fall because of home schooling and caregiv- ing necessities, and more than 2 million have left the labor force since the beginning of the year.7 Their re-entry into the labor force will mitigate how quickly the unemployment rate will decline. We don’t expect the economy will reach 4.0% unemployment until the end of 2023. 6. See, for example, Matt Richtel, “The Pandemic May Mean the End of the Open-Floor Office,” New York Times, May 4, 2020, available at: https:// www.nytimes.com/2020/05/04/health/coronavirus-office-makeover.html. 7. See Kathryn A. Edwards, “Sitting it Out? Or Pushed Out? Women Are Leaving the Labor Force in Record Numbers,” RAND, October 23, 2020, available at: https://www.rand.org/blog/2020/10/sitting-it-out-or-pushed-out-women-are-leaving-the.html. UCLA Anderson Forecast, December 2020 Nation–21 A GLOOMY COVID WINTER AND AN EXUBERANT VACCINE SPRING Exhibit 10 Unemployment rate Source: U.S. Bureau of Labor Statistics and UCLA Anderson Forecast 3.9 3.6 3.6 3.5 3.8 13.0 8.8 6.8 6.6 6.1 5.8 5.4 5.0 4.8 4.6 4.4 4.2 4.1 4.0 3.9 Q2 Q2Q1Q4Q3Q1Q1Q2Q4Q2 Q3 Q1 Q2 Q3 Q4 Q1 Q3 Q4 Q3 Q4 Forecast 2019 2020 2021 2022 2023 Unemployment rate Exhibit 9 Core inflation: CPI excluding food and energy Source: U.S. Bureau of Labor Statistics, Consumer Price Index for All Urban Consumers: All Items Less Food and Energy in U.S. City Average and UCLA Anderson ForecastNotes: % change, seasonally adjusted annual rate. 2.2 2.2 2.8 2.0 2.0 -1.6 4.4 2.5 1.6 2.1 2.4 2.4 2.2 2.1 2.0 2.1 2.2 2.1 2.1 2.2 Q3Q1 2.0 Q4Q3 Q4Q1Q2 Q4 Q1Q4Q1 Q2 Q1 Q2 Q3 Q4 Q2 Q3 Q2 Q3 Forecast 2019 2020 2021 2022 2023 Core inflation, CPI excluding food and energy 22–Nation UCLA Anderson Forecast, December 2020 A GLOOMY COVID WINTER AND AN EXUBERANT VACCINE SPRING The pandemic has also exacted a greater economic toll on the U.S.’s main trading partners and on the sectors where the U.S. has a comparative trading advantage. Our imports are recovering, but our exports are likely to remain suppressed for some time (see Exhibit 11). Even as the dollar loses value—in response to greater global economic stability and the reversal of the flight to safety—export growth is likely to remain weak. 8. The ‘20s will be roaring, but with several months of hardship first With a vaccine and the release of pent-up demand, the next few years will be roaring as the economy accelerates and returns to previous growth trends. We expect a surge in services consumption and continued strength in housing markets to propel the economy forward. There will be a few areas of weakness as the economy adjusts to a post-pandemic normal with more working-from-home and online commerce than we had before, and for better and worse, some parts of the economy will never be the same. For better, the pan- demic has accelerated technological disruptions that have made education and healthcare more accessible, through online courses and telehealth. For worse, it has permanently eliminated many service and retail sector jobs and made the economy more unequal. Right now, the key issue is how we will make it through to an exuberant spring. These next few months will be dire, with rising COVID infections, continued social distancing, and the expiration of social assistance programs. Additional timely fiscal relief would prevent unnecessary hardship and allow the economy to maintain the structural relationships that will help us recover more quickly once vaccines become widely available. Exhibit 11 Recovering imports, struggling exports Source: U.S. Department of Commerce, Bureau of Economic Analysis and UCLA Anderson ForecastNotes: 2012 $ billions, seasonally adjusted annual rate. 2,000 3,500 4,000 0 3,000 2,500 Q4 Q2Q4Q2 Q4Q2Q2Q4 Q3Q1 Q3 Q2Q3Q1Q3 Imports Q3Q1 Q1Q1 Exports Q4 -13.9% -5.4% Forecast 2019 2020 2021 2022 2023 Imports and Exports, SAAR UCLA Anderson Forecast, December 2020 Nation–23 TRENDS IN SOLAR PANEL ADOPTION Trends in Solar Panel Adoption: The Role of Costs, Benefits, Weather, and Peers Leila Bengali Economist, UCLA Anderson Forecast December 2020 Adaptation to, and actions to mitigate, climate change takes and will take many forms. One of those forms is turning to sources of renewable energy to generate electricity, as both an adaptation and mitigation strategy. Encouraging a transi- tion towards renewables is a policy objective at both the state and federal level. California has SB 100 passed in 2018 (the goal of which is to reach 100% of retail end-use electricity generation from renewable and zero-carbon sources) and president elect Biden has indicated that investing in renew- able energy will be one of his administration’s policy goals. The focus of this report is one form of renewable energy for generating electricity: solar energy generated by photo- voltaic panels. This report examines what factors, such as economic costs and benefits, weather patterns, and peers’ decisions, predict residential solar panel adoption in the U.S. Solar energy is a small, but growing source of electricity in the U.S. For some perspective, the most recent data from September 2020 indicate that about 3.3% of net electricity generation in the U.S. came from solar energy. California produces and consumes more solar energy than most other states. During the same month, about 19.8% of net electric- ity generated in the state came from solar.1 While solar still generates a relatively small fraction of the nation’s energy needs, installation costs have come down, panel efficiency has increased, and the ability to store solar energy for later using batteries has become more available. For these and other reasons, the number of solar panel systems has in- creased over time. Summary • Solar panel system installations have increased over time in the U.S. • Falling costs (such as lower installation costs and higher electricity prices) and rising benefits (such as gains in solar panel efficiency) can help explain the trends in system installations. • This report also assesses whether local weather patterns and information from peers influence installations. • While recent local weather patterns do not have a significant effect, there is evidence that peers do have an influence on installations. The magnitude of the estimated relationship rivals that of the relationship between installations and some of the monetary installation costs and benefits. 1. https://www.eia.gov/state/?sid=CA#tabs-4. U.S. Energy Information Administration Electric Power Monthly Tables 1.1, 1.3A, and 1.17A. 24–Nation UCLA Anderson Forecast, December 2020 TRENDS IN SOLAR PANEL ADOPTION The decision to install a solar panel system is at part an eco- nomic one, weighing the costs (such as installation prices) and benefits (such as electricity prices that panel owners can avoid paying). In addition to objective facts about costs and benefits, learning about the suitability of the local area for solar and gathering information from peers could also play a role. This report considers these components (costs, benefits, local weather, and peers), aiming to give some perspective about the role of each in a predictive model of solar panel adoption. Trends in Residential Solar Panel Installations The number of new solar panel systems installed each year in the U.S. has grown over time. Figure 1 shows an estimate of new annual installations in the residential segment (which represent the bulk of installations). These estimates are based on data compiled by the Lawrence Berkeley National Labo- ratory from state agencies and utility companies and cover about 80% of all installations in the U.S.2 These systems are not evenly spread around the U.S. California has more solar panel systems than any other state, about seven times more than Arizona (a distant second) and about 8 times more than New Jersey (third). The goal of this report is to understand the forces behind this increase in installations. Looking at aggregate trends in costs and benefits over time can help understand the motivations to install solar panel systems. Natural contenders are factors that affect the economic costs and benefits of solar panel systems, such as the cost of buying and installing panels, up-front subsidies or grants to offset these costs, the cost of electricity from the grid (a substitute for electricity produced by solar panels), panel efficiency, and how favorable the local area is to solar production. These factors are shown in Figures 2 and 3, Table 1, and in the map in Figure 4.3 0 50000 100000 150000 200000 250000 300000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Number of New Solar Panel System Installations, Residential Source: Lawrence Berkeley National Laboratory and author's calculations. Figure 1 New Installations 2. The data on solar panel systems in this report come from Lawrence Berkeley National Laboratory’s Tracking the Sun project. The data cover pho-tovoltaic solar installations that are connected to the electrical grid and exclude utility scale installations. This dataset is not a perfect record of every system installed in the U.S., but the data represent about 80% of solar installations. Most installations are in CA, AZ, MA, NJ, NY. See https://emp.lbl. gov/tracking-the-sun for more details.3. These are certainly not the only factors that matter. Others include compensation for electricity generated but not used through programs such as net metering and power purchase agreements. UCLA Anderson Forecast, December 2020 Nation–25 TRENDS IN SOLAR PANEL ADOPTION Based on comparisons of costs and benefits, as factors on the cost side of solar panel system installations fall and benefits rise, there should be more installations. Fitting this broad prediction, installation costs have fallen over time: the total installation cost per unit of electricity produced (measured as watts of direct-current the installed system could output in standard test conditions) went from about $9 in 2002 to just under $4 in 2018 (Figure 2). Moving against this pattern, up-front rebates and grants to individuals installing solar panel systems were high during the early 2000’s, but have fallen as large incentive programs like the California Solar Initiative wound down and ended in 2016 (Figure 3). This timing may help account for the drop in the number of new systems installed in 2017 and 2018 relative to the level in 2016 (see Figure 1). (Note that the federal Solar Investment Tax Credit which was in place through most of the time period shown, is not included in these tabulations because it offers a reduction in federal taxes owed, rather than an up-front rebate.) On the benefits side, some of the economic benefits of solar are on upward trajectories: panels continue to become more efficient (measured as the fraction of energy captured by the panels that is converted into usable electric- ity), and the price of electricity from the grid (a substitute) has increased, which should increase demand for solar panel systems (Table 1). Solar panel production potential is also an important factor in the cost-benefit analysis. Installing a solar panel system is more lucrative if you live in Arizona than if you live in Maine. Differences in solar potential are driven in large part by latitude and longitude, and thus do not change as much over time. Figure 4 shows global hori- zontal solar irradiance (the kind of energy that photovoltaic solar panels can convert to electricity) across the U.S. Both California and Arizona tend to get more global horizontal solar radiance and are also the two states with the most solar panel systems. Taking all of this information on costs and 0 1 2 3 4 5 6 7 8 9 10 2002 2004 2006 2008 2010 2012 2014 2016 2018$ per wattAverage Solar Panel System Installation Cost ($ per watt), Residential Source: Lawrence Berkeley National Laboratory and author's calculations. Figure 2 Installation Price 26–Nation UCLA Anderson Forecast, December 2020 TRENDS IN SOLAR PANEL ADOPTION $0 $2,000 $4,000 $6,000 $8,000 $10,000 $12,000 $14,000 $16,000 $18,000 $20,000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Average Solar Panel System Rebate or Grant, Residential Note: Average is over non-zero rebates.Source: Lawrence Berkeley National Laboratory and author's calculations. Figure 3 Up-Front Rebates and Grants Figure 4 Usable Solar Energy Source: https://www.nrel.gov/gis/solar.html. UCLA Anderson Forecast, December 2020 Nation–27 TRENDS IN SOLAR PANEL ADOPTION benefits together, the patterns over time and across space generally fit with predictions of how consumers would be expected to respond to changes in the costs and benefits of installing solar panel systems. Explaining Solar Panel System Installations To provide a more precise test of these predictions, we can use regression models that yield estimates of the relationship between each factor and the number of new installations. To start, consider a model that examines patterns in the U.S. over time at the county level to estimate the relationship between the number of new residential solar panel instal- lations each month and each of the factors in Figures 2 – 4 and Table 1: installation costs, rebates, efficiency, electricity prices, and solar radiation:4 new installations = B0 + B1 (average solar radiation) + B3 (electricity price) + B4 (system installation cost per watt) + B5 (panel efficiency) + B6 (grants and rebates) + (state*month fixed effects), Table 1 Average Solar Panel Efficiency and Electricity Prices (Residential) Solar Panel Energy Conversion Efficiency (Percent Converted to Usable Energy) Electricity Price (Cents per kWh) 2002 13.7% 8.4 2009 14.8% 11.5 2018 18.8% 12.9 Sources: Lawrence Berkeley National Laboratory, author's calculations, and U.S. Energy Information Administration. where the B’s are the coefficients, or relationships, to be estimated. The state*month fixed effects are variables in- cluded to control for seasonal variation in panel installations, where the seasonality is allowed to be different for each state – March in Arizona is different from March in New Jersey. The results are much in line with intuition (Table 2; statisti- cally significant coefficients are bold). Areas that are gener- ally more suitable for solar electricity production (areas that have more solar radiation) tend to have more installations each month, though this relationship is only significant at the 10% level, not quite reaching accepted levels of statistical significance. System installations also respond in predicted ways to prices: lower installation costs and higher electricity prices are associated with more installations. Improvements in panel efficiency, which increase the benefit a consumer gets from installing a panel system, are also associated with more installations. The regression model indicates that the relationship between up-front rebates and grants and instal- lations is far from being statistically significant, perhaps reflecting the phase-out of many rebates over time at the same time as installations were generally on an upward trend. Economic costs and benefits are not the only factors that influence choices. The actions of and ideas provided by a consumer’s peers, specifically their friends, and the consumer’s own learning about the suitability of solar in their area (by observing local weather trends, for example) could factor into the decision to install solar panels. Exist- ing research supports the idea that local weather patterns affect decisions about solar installations (Lamp 2018, Liao 2020), and another line of research finds that the actions and experiences of one’s friends affect consumers’ decisions in domains such as real estate (Bailey et al. 2018). Separating the influence of friends from the influence of personal ob- servations of the local weather is difficult because friends An increase in ... average solar radiation of one MJ per m2 electricity price of one cent per kWh system installation cost of one dollar per watt panel efficiency of one percentage point grants and rebates of one dollar ... is associatied with 36.6 more installations 17.7 more installations 11.7 fewer installations 3.8 more installations 0.0002 fewer installations Table 2 Results: Costs, Benefits, and Installations 4. See the data glossary at the end of the report for details about the variables used in the analysis. 28–Nation UCLA Anderson Forecast, December 2020 TRENDS IN SOLAR PANEL ADOPTION often live in the same area and experience the same local weather, thus a consumer’s actions could be attributed either to friends or the weather. One solution is to look at the actions of geographically distant friends. Doing so reduces the chance that the friends experienced the same recent local weather patterns. Follow- ing an idea from Bailey et al. (2018), I use published data from Facebook, the Social Connectedness Index, to calculate a friend-weighted-average of the number of solar panel sys- tem installations in counties connected by friendship links on Facebook. To reduce the chance that friends experience similar local weather, I only use friend connections to out- of-state counties. For example, say the Social Connectedness Index between county A (in state S) and county B (in state T) is 5, and between county A and county C (in state E) is 15. If there are 10 solar panel system installations in B and 4 in C in a given month, then the friend-weighted-average number of installations for someone in county A for that month is: [5 / (5 + 15)] * 10 + [15 / (5 + 15)] * 4 = 5.5.5 To account for recent trends in local weather, I include a measure of solar radiation in the current and six most recent months. I use the concept of a ‘weather anomaly’ for this measure. There are a number of different ways to measure the ‘weather anomaly.’ Following a suggestion in Liao (2020), I compare average solar radiation over all days in the current month (June 2015, for example) to the histori- cal average for that time of the year (all days in June from 1980 to 2000). I then transform the difference into standard deviations away from the historical average for that month of the year as a normalization. Broadly, the results indicate that friends matter more than weather. Current and recent solar radiation does not have a statistically significant effect on solar panel system installa- tions.6 On the other hand, the actions of consumers in coun- ties where more friends live do appear to make a difference. If the friend-weighted-average number of solar installations increases by one in the current month, the associated increase in own-county installations is about 18 (a relationship that is statistically significant). This estimated relationship is not a trivial size. For comparison, the relationship between a one cent increase in electricity prices and monthly installations is also about 18. The magnitude (and statistical signifi- cance) of the relationship between own and friend-county installations falls when comparing the current month’s own county installations to the friend-weighted-average number of installations in past months, going from about 18 (same month) to about seven (one month ago) to about one (two months ago, and no longer statistically significant). Since there is typically a delay between the time a consumer de- cides to install solar panels and the actual installation date, one interpretation of this pattern of magnitude decay is that a consumer’s actions are swayed more by friends’ plans and ideas than by friends’ actions. If actions mattered, then lags of friend-weighted-average installations rather than friend- weighted-average installations in the same month should have more predictive power. An important caveat is that this analysis cannot rule out an alternative interpretation in which friends independently and simultaneously decide to install solar panels at the same time. The story here is that people often choose friends that are similar to themselves in some ways, and those similarities, not the actions or in- formation exchanged between friends, drive the observed relationship between own-county panel installations and installations in counties connected by friendships. So, while the evidence is consistent with friends playing a role in installation decisions, the analysis cannot prove decisively that this is the case. Taken together, economic costs and benefits and perhaps the information and ideas provided by friends help predict solar panel system installations in the U.S. Since capturing solar energy is often cited as a way to increase society’s ability to use renewable energy, these results provide some evidence on how to encourage solar panel adoption in instances where doing so is a policy goal. In addition to focusing on technological advancements that increase efficiency and bring down prices, encouraging installations in one county or state could encourage installations in other areas through networks of friends and the spread of information. 5. The Social Connectedness Index between pairs of counties does not change over time, while the number of installations in connected counties does. The Index is based on data as of August 2020, and thus an implicit assumption in the analysis is that the friendship network is stable over time.6. Though other research finds that recent local weather does affect choices about residential solar installations, the findings in this report do not nec- essarily conflict with the existing research. The evidence in this report does not support the interpretation that there is an effect of recent local weather on installations, but also cannot rule out that there is no effect. UCLA Anderson Forecast, December 2020 Nation–29 TRENDS IN SOLAR PANEL ADOPTION Data appendix Lawrence Berkeley National Laboratory, Tracking the Sun Data project compiles data on solar panel system installations. The data run from 1998 through 2018. The original data were aggregated to be used in the analysis in this report as follows: »Panel system installations: the number of systems installed by county and month-year »Installation price per watt: the average system installation price per watt of power output, by state and month-year »Efficiency: the average percent of solar energy that panels convert into usable electricity, by state and month-year »Rebates and grants: the average pre-tax value of up-front grants or rebates for installing a solar panel system in dollars, by state and month-year Historical weather data are from the Oak Ridge National Laboratory (https://daymet.ornl.gov/). The latitude and longitude coordinates from the center of each county were used to extract the weather data. »Historical average daily solar radiation: the average of daily total solar radiation over all days between 1980 and 2000, by county »Solar radiation: the average of daily solar radiation over each day in a given month, by county and month-year Other variables: »Electricity price: residential electricity prices reported by the U.S. Energy Information Administration in cents per kWh, by state and month-year (https://www.eia.gov/electricity/data.php#sales, Monthly Form EIA-861M) »The Social Connectedness Index, provided by Facebook, gives a measure of the number of friendship links between pairs of counties in the U.S. References Bailey, M., Cao, R., Kuchler, T., and Stroebel, J. (2018). The economic effects of social networks: evidence from the hous- ing market. Journal of Political Economy, 126(6), 2224-2276. Bailey, M., Cao, R., Kuchler, T., Stroebel, J., and A. Wong. (2018). Social connectedness: Measurements, determinants, and effects. Journal of Economic Perspectives, 32(3):259–80 and the Facebook Data for Good Program, Social Connectedness Index (SCI). https://dataforgood.fb.com/, Accessed 23 October 2020. Lamp, S. (2018). Sunspots that matter. Toulouse School of Economics, Working Papers, 17-879. Lawrence Berkeley National Laboratory. 2020. “Tracking the Sun Data Viewer.” https://trackingthesun.lbl.gov. Liao, Y. (2020). Weather and the Decision to Go Solar: Evidence on Costly Cancellations. Journal of the Association of Environmental and Resource Economists, 7(1), 1-33. Thornton, P.E., M.M. Thornton, B.W. Mayer, Y. Wei, R. Devarakonda, R.S. Vose, and R.B. Cook. (2018). Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. https:// doi.org/10.3334/ORNLDAAC/1328. UCLA Anderson Forecast, December 2020 Nation-31 UNCERTAINTY IN THE POST-ELECTION AND COVID-19 WORLD The election is over with and there will be a change in administrations come January 20th. However, that does not eliminate all of the uncertainty with respect to U.S. economic policy. There could well be a divided Congress, and the outgoing administration may have some new direc- tives that were not previously forecast. However, over the past two years, in a country that has harbored divided views on domestic policy, a rare consensus on a fundamentally changed view of U.S. economic engagement with Beijing has developed. As we mentioned in previous reports, the U.S. and China are unlikely to go back to the past era of strategic engagement. What a Biden Administration would change with respect to economic policy towards China is in style and method, not substance. The U.S. is more likely to confront and contain China by leveraging more multilateral frameworks with its allies than unilateral ones. One example is that U.S. could re-enter the CPTPP (Comprehensive and Progressive Agree- ment for Trans-Pacific Partnership) to bolster its leadership and to expand its interests in Asia. The U.S. could also re-join the WHO and make the WTO more functional as well. And the President-Elect has stated that America would return to the Paris Accord and seek cooperation on climate change issues with China. Though China was the largest CO2 emit- ter in the world in 2020, it recently committed to carbon neutrality prior to 2060. This is a rare space for increased cooperation in alternative energy and propulsion. To be sure, there will be more dialogues and efforts between the U.S. and China to address further escalating tensions. In the presidential campaign, however, President-Elect Biden committed to bringing manufacturing, particularly with respect to technologically advanced goods and renewable energy equipment back to the U.S.1 Thus, a continuation of the intention, if not the method of the past four years of partial economic disengagement should be expected. In this update report, we will discuss trade relations and technology competition between the U.S. and China. 1. In an interview with the New York Times on December 2, 2020, Biden said:” I want to make sure we’re going to fight like hell by investing in America first.” Cathay Bank | UCLA Anderson Forecast | U.S.-China Economic Report - Fourth Quarter Update Uncertainty in the Post-Election and COVID-19 World William Yu Economist, UCLA Anderson Forecast Jerry Nickelsburg Director, UCLA Anderson Forecast December 2020 32–Nation UCLA Anderson Forecast, December 2020 UNCERTAINTY IN THE POST-ELECTION AND COVID-19 WORLD The Slowly Decoupling U.S.-China Trade and “Just-In-Case” Global Supply Chains Figure 1 (left) shows U.S. nominal goods trade with the rest of the world (imports plus exports). Figure 1 (right) shows U.S. nominal goods trade deficits with the rest of the world (imports minus exports). The numbers in 2020 are based on Anderson Forecast projections. Due to the global pandemic induced recession, it is not surprising to see that U.S. total international trade is estimated to decline by 12% in 2020. In the 2008/2009 recession, the comparable decline in total trade was 19.8%. In both recessions, the circumstances of the downturn interrupted trade flows. In the latest, imports from China to the U.S. plummeted as Chinese factories shutdown and did not pick up until both they and U.S. factories began to reopen. Consequently, the slight decline in the U.S. goods trade deficit should not be taken as an indicator of a trend. Indeed, the deficit widened in the third quarter of 2020. Note that trade data in Figure 1 only includes goods. We use it for convenience for Figures 1 through 4 because data from the U.S. Bureau of the Census on monthly trade flows by country is available through September 2020, while the net export component of GDP that includes trade in services is only available by country with a considerable lag. For a more comprehensive picture of international trade, we should, of course, also examine trade in services including travel, education, and intellectual property transactions. We do not expect this to show a qualitative difference, however, as the estimated total trade of goods and services should decline by 14% in 2020, similar to the goods only decline (Figure 1A). The trade deficit in goods and services is estimated to have increased by 9% in 2020 with the difference being largely the collapse of international travel and the restrictions on international students coming to the U.S. $1,000 $2,000 $3,000 $4,000 $5,000 04 06 08 10 12 14 16 18 20 U.S. Trade (Exports & Imports) to the World Goods Only -12%-12% $300 $400 $500 $600 $700 $800 $900 $1,000 04 06 08 10 12 14 16 18 20 U.S. Trade Deficits (Imports minus Exports) to the World Goods Only ($Billion)($Billion) Figure 1 U.S. Total Goods Trade and Deficits Sources: U.S. Census and UCLA Anderson Forecast Figure 1A U.S. Total Goods and Services Trade and Deficits Sources: U.S. Census and UCLA Anderson Forecast $2,000 $3,000 $4,000 $5,000 $6,000 04 06 08 10 12 14 16 18 20 U.S. Trade (Exports & Imports) to the World -14% Goods and Services -14% $300 $400 $500 $600 $700 $800 $900 $1,000 04 06 08 10 12 14 16 18 20 U.S. Trade (Imports minus Exports) to the World Goods and Services ($Billion)($Billion) UCLA Anderson Forecast, December 2020 Nation-33 UNCERTAINTY IN THE POST-ELECTION AND COVID-19 WORLD Figure 2 (left) shows total U.S. nominal goods trade with China and Figure 2 (right) shows the U.S. goods trade defi- cit with China. We can see a clear turn in 2019. U.S. total goods trade with China declined by 15% in 2019, and we estimate that it will decline by another 8% in 2020. U.S. trade deficits with China contracted by a greater amount (-18% in 2019 and an estimated -15% in 2020). The main driver of the differential from the decline in world trade in 2019 and to some extent in 2020 is in the reduction of U.S. imports from China due to tariffs, non-tariff restrictions on trade, and a shift of low-cost labor manufacturing out of the now higher cost China. U.S. imports from China peaked in 2018 at $538 billion, dropped to $452 billion in 2019 (-18%), and to an estimated $402 billion in 2020 (-15%). It should be noted that weak U.S. holiday spending could further the reduction in imports into 2021. While the total U.S. goods trade deficits decreased slightly (Figure 1), the deficit with China decreased significantly (Figure 2). In contrast to consecutive annual declines in the goods trade deficit with China, U.S. goods trade deficits increased with all other countries by growth rates of 12% in 2019 and 8% in 2020 (Figure 3). This is evidence of U.S.- China decoupling since 2019. Figure 2 U.S. Total Goods Trade and Deficits to China Sources: U.S. Census and UCLA Anderson Forecast Figure 3 U.S. Total Goods Trade and Deficits to the World (Except China) Sources: U.S. Census and UCLA Anderson Forecast $100 $200 $300 $400 $500 $600 $700 04 06 08 10 12 14 16 18 20 U.S. Trade (Exports and Imports) with China Goods Only -15%-8% Goods Only -15%-8% $100 $200 $300 $400 04 06 08 10 12 14 16 18 20 U.S. Trade Deficits (Imports minus Exports) with China Goods Only -18% -15% -18% -15% ($Billion)($Billion) $1,000 $1,500 $2,000 $2,500 $3,000 $3,500 $4,000 04 06 08 10 12 14 16 18 20 U.S. Trade with the World except China Goods Only $200 $300 $400 $500 $600 $700 04 06 08 10 12 14 16 18 20 U.S. Trade Deficits with the World except China Goods Only +12% +8% a +12% +8% ($Billion)($Billion) 34–Nation UCLA Anderson Forecast, December 2020 UNCERTAINTY IN THE POST-ELECTION AND COVID-19 WORLD Figure 4 presents U.S. import growth from its major trading partners in 2019 (yellow bar) and 2020 (blue bar, estimated). The two gaining the most are Vietnam and Taiwan, both of which have experienced positive export growth to the U.S., including during the current pandemic induced reces- sion year. Singapore, Thailand and Malaysia also have had some modest positive growth in 2020. Much has been said about India becoming the next China due to its reinforced strategic alliance with the U.S. and much lower labor and land costs.2 But U.S. imports from India declined in 2020. Fundamental change in supply chains take time, and we still expect a China to India shift as part of the decoupling. Globalization has been long praised by Wall Street, Cham- bers of Commerce, and economists with its “just-in-time” supply chains providing low inventory costs, maximizing shareholder value, and generating more affordable products.3 The global pandemic led many to realize that just-in-time global supply chains are fragile, and that they can potentially lead to national security and public health consequences. Though having a higher marginal cost, “just-in-case” supply chains are risk reducing with larger inventories and alterna- tive domestic sources of production inputs. It is then natural to expect both China and the U.S. to enact policy to make sure there will be sufficient products and capacity at home in case of crises, disasters, conflicts, and/or another pandemic. Technology Competition between the U.S. and China In late October 2020, the Chinese Government published the major economic development targets in the 14th Five-Year Plan (2021-2025). Among many goals is “technology self- reliance.” This is both in response to escalating U.S.-China rivalry, various U.S. sanctions on Chinese tech companies, and as part of China’s 2016 “Made in China 2025” initiative. The strategic goals of “Made in China 2025” and “China Standards 2035” have China making major public invest- ments in domestic technology and innovation including advanced technologies such as AI, quantum computing, semiconductor, life science, and aerospace. Figure 4 U.S. Import Growth from Major Trading Partners, 2018 and 2019 Source: U.S. Census and UCLA Anderson Forecast -30% -20% -10% 0% 10% 20% 30% 40%VietnamTaiwanSingaporeThailandIrelandMalaysiaSouth KoreaMexicoGermanyIndiaItalyFranceU.K.JapanCanadaChinaBrazilAfricaU.S. Imports Growth 2018 to 2019 U.S. Imports Growth 2019 to 2020 (Predicted) 2. For instance, Govindarajan and Bagla suggest that India would replace China, if China falls in attractiveness in “As Covid-19 Disrupts Global Supply Chains, Will Companies Turn to India?” Harvard Business Review (May 2020). 3. For example, see Fullerton, McWatters, and Fawson, “An Examination of the Relationships between JIT and Financial Performance,” Journal of Operations Management, (2003), 21:4, pp 383-404. Kannan and Tan, “Just In Time, Total Quality Management, and Supply Chain Management: Un- derstanding Their Linkages and Impact on Business Performance,” Omega, (2005), 33:2, pp 153-162. Thomas Friedman, “The World is Flat.” (2005), Farrar, Straus, and Giroux. In Jagdish Bhagwati ed., “In Defense of Globalization: With A New Afterword.” (2004), Oxford University Press. UCLA Anderson Forecast, December 2020 Nation-35 UNCERTAINTY IN THE POST-ELECTION AND COVID-19 WORLD Recently, President Trump issued an executive order ban- ning U.S. residents from investing in 31 Chinese companies that are purported to engage in “military-civil fusion” ac- tivity. The order is to take effect in January 2021. Existing American investment will need to be divested by November 2021. These 31 companies include Huawei, China Mobile, Hikvision, and Aviation and Industry Corporation of China (AVIC). Several of these companies are already in the Department of Commerce’s Entity List and 13 of them are publicly traded. In a report sponsored by the Hinrich Foundation4 entitled “Strategic U.S.-China Decoupling in the Tech Sector,” Alex Capri (2020)5 suggests six major trends that will emerge from the tech competition between the U.S. and China: 1) Certain strategic value chains will decouple, restructure and diversify out of China. 2) The U.S., EU, and other countries will focus more on countering Beijing’s economic nationalism with techno- nationalism initiatives of their own. 3) Re-shoring and ring-fencing of some critical manu- facturing. 4) New public-private partnerships, and alliances to com- pete with China. 5) Multinationals will adjust to a world of increasingly fragmented and localized value chains. 6) Businesses will adopt “in-China-for-China” business models in order to access the Chinese market. What is the early evidence on Points 1 and 3? About 500 of some 22,000 commodity classifications in U.S. merchandise trade are identified as advanced technology.6 Focusing on two specific sectors: (1) imports of information and com- munication products, and (2) exports of aerospace, have the largest trade values among all the advanced technology products. Figure 5 lists the top 10 trading regions for U.S. import sources of information and communication products in 2018, 2019, and 2020.7 The U.S.’ top source of informa- tion and communication products is China. The trade war, tariffs, and Great Powers competition in the past two years have started a U.S.-China decoupling, in which U.S. imports from China declined from $157 billion in 2018 to $124 billion in 2019, and to $113 billion in 2020. At the same time, U.S. imports from Vietnam, Taiwan, South Korea, and Thailand all increased. 4. An Asia-based philanthropic organization that works to advance mutually beneficial and sustainable global trade. 5. A global value chain and international trade scholar and visiting senior fellow at National University of Singapore.6. There are 10 major sectors: biotechnology, life science, opto-electronics, information & communications, electronics, flexible manufacturing, advanced materials, aerospace, weapons, and nuclear technology. 7. The annual number in 2020 is estimated based on the growth rate in the first nine months of 2020 compared to the first nine months of 2019, season-ally adjusted. $0 $40 $80 $120 $160 ChinaMexicoTaiwanVietnamSouth KoreaThailandEuropeMalaysiaJapanPhilippines2018 2019 2020 (estimated) ($Billion) Figure 5 U.S. Imports of Advanced Technology Products--Information & Communications from 10 Major Trading Regions from 2018 to 2020 Source: U.S. Census 36–Nation UCLA Anderson Forecast, December 2020 UNCERTAINTY IN THE POST-ELECTION AND COVID-19 WORLD Figure 6 illustrates the top 10 trading regions for U.S. exports of aerospace products in 2018, 2019, and 2020. In 2020, due to the pandemic and disruption of the airline industry, we see across-the-board decline of U.S. exports. China was a major purchaser of Boeing airplanes before the 737 MAX ground- ing and the COVID-19 pandemic. We can see a dramatic decline of exports to China from $18 billion in 2018 to $11 billion in 2019, and $3.7 billion in 2020. Though the 2020 decline is across all regions, the 2019 is more specifically a decline in exports to China. The State of Technology Competitiveness: Intellectual Property and R&D Although it is generally recognized that the U.S. is further along in technology development and innovation than China, that gap has been closing. The number of patents is one way to measure innovation and technology advances of a coun- try. Figure 7 lists the number of patents granted by the U.S. Patent and Trademark Office to individuals or companies by their country of origin. The U.S. is, of course, at the top with the most patents granted (186,000 in 2019), followed by Japan with 56,000, South Korea with 23,600, and China with 23,000. Although China’s number is low compared to the U.S., it has had historically high growth rates. From 2017 to 2019 filings at the U.S. Patent Office grew from 14,900 to 23,000: a 55% increase. Over the past two years, China surpassed Germany in the of number of patents issued in the U.S.. Beyond the U.S. market, through Patent Cooperation Treaty System (PCT) at World Intellectual Property Orga- nization (WIPO), China (58,990) has surpassed the U.S. (57,840) as the top country for international patent applica- tions in 2019. Recent moves to restrict Chinese technology exports to the U.S. is expected to reverse the trend in patents filed in the U.S. by Chinese companies, but not the trend in the number of patents issued beyond the world market. The reversal will be exacerbated if the implementation of “China Standards 2035” results different technology protocols than are used in the U.S. $0 $10 $20 $30 $40 $50 $60 EuropeCanadaJapanBrazilChinaSingaporeTurkeyMexicoIndiaTaiwan2018 2019 2020 (estimated) ($Billion) Figure 6 U.S. Exports of Advanced Technology Products--Aerospace to 10 Major Trading Regions from 2018 to 2020 Source: U.S. Census UCLA Anderson Forecast, December 2020 Nation-37 UNCERTAINTY IN THE POST-ELECTION AND COVID-19 WORLD One area where China is lagging the U.S. in technology development is in R&D expenditures. To be sure, local cost differentials make comparisons of R&D across countries only suggestive. However, the 14th Five Year Plan explic- itly recognizes the differential illustrated below. Figure 8 lists the top 20 companies in the U.S. with the most R&D expenditures in 2016. The top American companies were Alphabet (Google), Microsoft, Intel, and Apple. Figure 9 shows the top 20 Chinese companies with the most R&D expenditures in 2016. The top four are Huawei, Alibaba, ZTE, and Tencent. However, besides Huawei8 and PetroChina, the technology prowess in terms of R&D in these top 20 Chinese firms are still lagging far behind top 20 in the U.S.. Note that these numbers only reflect com- pany R&D, not reflective of government R&D. According to OCED, R&D expenditures in whole China in 2018 was about $468 billion, still lower than $582 billion of the U.S., but higher than $465 billion of whole 28 EU countries. How this will change in the coming years is well illustrated by the case of Huawei. Huawei is the leading tech company in China and the largest communication equipment maker in the world, and it has become a target of U.S. actions. Fol- lowing an accusation of Huawei stealing trade secrets from six American companies, the U.S. with its allies, Australia, U.K., Japan, India, and Brazil, have banned or restricted Huawei’s communication equipment because of security concerns. In addition, the U.S. expanded its export control requirements, the Foreign Direct Product Rule (FDPR) in May 2020. Now, foreign companies are required to get a license before selling finished products if the manufactur- ing process involves certain American software, designs, tooling and equipment. The action involves a crucial player in the tech/semiconduc- tor supply chain: Taiwan Semiconductor Manufacturing Company (TSMC), the world’s largest contract chipmaker. If TSMC is not allowed to sell to Chinese companies such as Huawei, there would be a big hole in China’s tech value supply chain and its technology ambitions. As yet there are no Chinese semiconductor companies that can produce the required high-quality microchips. HiSilicon, Huawei’s fabless chip designer for smartphones and 5G infrastruc- 0 40 80 120 160 200 U.S.JapanSouth KoreaChinaGermanyTaiwanU.K.CanadaFranceIndiaIsraelItalyNetherlandsSwedenSwitzerland2017 2018 2019 (Thous) Figure t Number of Total Patents Granted in the U.S. by Country of Origin Source: U.S. Patent and Trademark Office 8. Huawei’s R&D spending (US$15. 3 billion) in 2019 might have surpassed Apple, Intel, and Microsoft. 38–Nation UCLA Anderson Forecast, December 2020 UNCERTAINTY IN THE POST-ELECTION AND COVID-19 WORLD Figure 8 Top 20 U.S. Companies in terms of R&D Expenditures Source: OECD Figure 9 Top 20 Chinese Companies in terms of R&D Expenditures Source: OECD 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 AlphabetMicrosoftIntelAppleJ & JGMPfizerFordMerckOracleCiscoFacebookIBMQualcommBMSGEGileadCelgeneEli LillyBoeing($Million) 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 HuaweiAlibabaZTETencentPetroChinaChina State Construction EngineeringChina RailwayBaiduCRRC ChinaChina Railway ConstructionSAIC MotorLenovoChina Communications ConstructionCtrip.COM InternationalPower Construction Corporation of ChinaMidea GroupChina Petroleum & ChemicalsChina Merchants BankBYDShanghai Construction($Million) UCLA Anderson Forecast, December 2020 Nation-39 UNCERTAINTY IN THE POST-ELECTION AND COVID-19 WORLD ture, relies on TSMC for chips. According to Capri (2020), TSMC depends on U.S. semiconductor manufacturing technology from Applied Materials, LAM research, KLA Tenor, Synopsys, and Cadence Design Systems; companies that control a majority of the global market. With the U.S. imposed FDPR, TSMC cannot make cutting edge chips for HiSilicon and Huawei. Note that TSMC currently makes computer chips used in Lockheed Martin F-35 fighter jet and is a key supplier for Apple, AMD, Qualcomm, Broadcom, and Nvidia. Wash- ington has pressured TSMC to produce the chips that are used inside U.S. military hardware within the U.S. in order to ensure U.S. tech supply chains are free from any Chinese interference. TSMC has decided to invest $12 billion to set up a wholly-owned subsidiary in Arizona in 2021. This is a further example of manufacturers diversifying supply chains from “just-in-time” to include “just-in-case”, and though it involves a Taiwanese company, it has direct implications for China as well. Conclusions • The U.S. is expected to change its economic policy toward China in style but not in substance under the new Biden administration. • U.S./China decoupling of trade with China has begun, is ongoing, and is expected to continue. This decoupling will speed the development of self-sufficiency in con- tested sectors in both the U.S. and China. • Tech competition is the leading edge of both the U.S. and China decoupling strategy. As this can be justified by both countries as a strategic necessity, we expect technology related goods and services to bear the brunt of the decoupling, but consumer non-durable goods, to the extent that they can still be produced cost ef- fectively in China, to continue to be imported into the U.S., and machinery, aircraft and agricultural products, to the extent that they do not involve excluded sensi- tive technology, to continue to be imported into China. DECEMBER 2020 REPORT THE UCLA ANDERSON FORECAST FOR THE NATION Tables FORECAST TABLES - SUMMARY UCLA Anderson Forecast, December 2020 Nation–43 Table 1: Summary of the UCLA Anderson Forecast for the Nation 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 GDP and Monetary Aggregates (% Ch.) Real GDP 2.2 1.8 2.5 3.1 1.7 2.3 3.0 2.2 -3.7 3.6 3.4 3.1 GDP Price Index 1.9 1.8 1.8 1.0 1.0 1.9 2.4 1.8 1.2 1.9 2.1 2.1 Money Supply (M1) 12.9 8.3 9.9 5.7 9.1 8.1 3.0 6.1 42.0 -6.5 4.0 5.1 Money Supply (M2) 7.6 6.1 5.8 5.7 7.3 4.9 3.5 6.7 24.2 -9.0 -3.5 -0.8 Interest Rates (%) on: Federal Funds 0.1 0.1 0.1 0.1 0.4 1.0 1.8 2.2 0.4 0.1 0.1 0.1 90-day Treasury Bill 0.1 0.1 0.0 0.1 0.3 0.9 1.9 2.1 0.4 0.1 0.1 0.1 10-yr Treasury Bond 1.8 2.4 2.5 2.1 1.8 2.3 2.9 2.1 0.9 1.1 1.3 1.5 30-yr Treasury Bond 2.9 3.4 3.3 2.8 2.6 2.9 3.1 2.6 1.6 1.9 2.2 2.4 Moody's AAA Corp. Bond 3.7 4.2 4.2 3.9 3.7 3.7 3.9 3.4 2.5 2.1 2.1 2.3 30-yr Bond Less Inflation 1.0 2.1 1.9 2.6 1.6 1.1 1.0 1.1 0.4 0.1 0.3 0.5 Federal Fiscal Policy (% Ch.) Defense Purchases Current $ -2.4 -6.1 -2.7 -1.8 -0.1 2.5 6.3 7.3 3.5 3.5 2.4 -0.3 Constant $ -3.4 -6.7 -4.1 -2.1 -0.5 0.8 3.3 5.6 3.2 1.6 0.1 -2.4 Other Expenditures Transfers to Persons -1.1 1.9 4.4 5.2 3.2 2.9 4.6 5.4 45.5 -13.2 -6.9 3.2 Grants to S&L Govít -5.9 1.3 10.0 7.7 4.5 0.5 4.1 4.4 44.3 -8.6 -10.3 -0.1 Billions of Current Dollars, Unified Budget Basis, Fiscal Year Receipts 2509 2825 3093 3275 3242 3344 3330 3497 3424 3613 3792 4034 Outlays 3570 3384 3581 3750 3824 4025 4203 4520 6746 5295 4912 4948 Surplus or Deficit ( - ) -1061 -560 -487 -475 -582 -681 -873 -1022 -3321 -1682 -1120 -914 As Shares of GDP (%), NIPA Basis Revenues 16.7 18.7 18.8 18.9 18.5 18.0 17.3 17.3 17.5 17.5 17.4 17.5 Expenditures 23.3 22.5 22.2 22.0 22.0 21.7 21.8 22.2 32.2 25.2 22.4 21.6 Defense Purchases 5.0 4.6 4.2 4.0 3.9 3.8 3.9 4.0 4.2 4.1 4.0 3.8 Transfers to Persons 14.2 13.9 13.9 14.1 14.1 14.0 13.8 14.0 20.9 17.2 15.2 14.9 Surplus or Deficit ( - ) -6.6 -3.8 -3.4 -3.1 -3.6 -3.7 -4.5 -4.9 -14.7 -7.7 -5.0 -4.1 Details of Real GDP (% Ch.) Real GDP 2.2 1.8 2.5 3.1 1.7 2.3 3.0 2.2 -3.7 3.6 3.4 3.1 Final Sales 2.1 1.6 2.7 2.8 2.4 2.4 2.8 2.2 -3.0 2.7 3.3 3.1 Consumption 1.5 1.5 3.0 3.8 2.8 2.6 2.7 2.4 -4.1 3.9 3.8 3.3 Nonres. Fixed Investment 9.5 4.1 7.2 2.3 0.5 3.7 6.9 2.9 -4.9 1.9 5.8 5.4 Equipment 11.0 4.7 7.0 3.0 -1.7 3.2 8.0 2.1 -5.7 6.6 4.5 3.4 Intellectual Property 5.0 5.4 4.8 3.8 7.6 4.2 7.8 6.4 0.0 0.9 7.4 7.3 Structures 13.0 1.3 11.0 -0.9 -4.4 4.2 3.7 -0.6 -10.9 -6.0 5.8 6.3 Residential Construction 13.2 12.5 3.7 10.2 6.6 3.9 -0.6 -1.8 5.0 8.7 -1.9 -0.9 Exports 3.4 3.6 4.2 0.4 0.3 3.9 3.0 -0.1 -13.7 5.3 10.2 7.0 Imports 2.7 1.5 5.0 5.2 1.7 4.7 4.1 1.1 -10.5 8.5 6.8 4.9 Federal Purchases -1.9 -5.5 -2.6 -0.0 0.6 0.3 2.8 4.0 4.2 0.1 -0.4 -1.9 State & Local Purchases -2.2 -0.3 0.2 2.9 2.6 1.2 1.2 1.3 -0.9 0.8 -0.2 2.1 Billions of 2012 Dollars Real GDP 16197 16495 16912 17432 17731 18144 18688 19092 18384 19040 19683 20290 Final Sales 16126 16387 16826 17295 17706 18128 18634 19043 18467 18958 19580 20187 Inventory Change 71 109 86 138 25 16 53 49 -83 82 103 103 FORECAST TABLES - SUMMARY 44–Nation UCLA Anderson Forecast, December 2020 Table 2: Summary of the UCLA Anderson Forecast for the Nation 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Industrial Production and Resource Utilization Production (% Ch.) 3.0 2.0 3.1 -1.0 -2.0 2.3 3.9 0.9 -7.5 1.9 3.8 3.4 Capacity Util. Manuf. (%) 74.5 74.4 75.2 75.3 74.2 75.1 76.6 75.6 69.5 71.5 73.3 74.5 Real Bus. Invest. (% of GDP) 15.7 16.3 17.0 17.1 17.1 17.3 17.7 17.7 17.8 17.8 17.9 18.0 Nonfarm Employment (mil.) 134.2 136.4 138.9 141.8 144.3 146.6 148.9 150.9 142.4 147.2 151.9 155.1 Unemployment Rate (%) 8.1 7.4 6.2 5.3 4.9 4.4 3.9 3.7 8.1 6.0 4.7 4.1 Inflation (% Ch.) Consumer Price Index 2.1 1.5 1.6 0.1 1.3 2.1 2.4 1.8 1.3 2.2 2.6 2.2 CPI less Food & Energy 2.1 1.8 1.8 1.8 2.2 1.8 2.1 2.2 1.8 2.2 2.2 2.1 Consumption Chain Index 1.9 1.3 1.5 0.2 1.0 1.8 2.1 1.5 1.2 1.8 1.9 1.9 GDP Chain Index 1.9 1.8 1.8 1.0 1.0 1.9 2.4 1.8 1.2 1.9 2.1 2.1 Producers Price Index 0.5 0.6 0.9 -7.2 -2.7 4.4 4.3 -1.0 -3.0 4.8 2.4 3.0 Factors Related to Inflation (% Ch.) Nonfarm Business Sector Total Compensation 2.6 1.3 2.8 3.1 1.1 3.5 3.4 3.6 5.9 0.5 1.4 3.1 Productivity 0.9 0.5 0.9 1.6 0.3 1.2 1.4 1.7 2.4 -0.8 0.2 1.5 Unit Labor Costs 1.8 0.8 1.9 1.5 0.7 2.2 1.9 1.9 3.5 1.3 1.1 1.6 Farm Price Index 3.2 1.4 1.1 -11.9 -9.6 3.1 -0.6 0.4 -3.8 0.2 1.8 4.2 Crude Oil Price ($/barrel) 94.2 97.9 93.3 48.7 43.2 51.0 64.9 57.0 38.6 43.9 52.6 56.2 New Home Price ($1000) 242.1 265.1 283.2 293.7 306.5 321.6 323.1 319.3 339.0 359.4 369.4 383.7 Income, Consumption and Saving (% Ch.) Disposable Income 5.3 0.0 5.6 4.4 3.0 4.9 5.8 3.7 6.7 -0.9 2.7 4.9 Real Disposable Income 3.3 -1.3 4.1 4.2 2.0 3.1 3.6 2.2 5.4 -2.6 0.8 2.9 Real Consumption 1.5 1.5 3.0 3.8 2.8 2.6 2.7 2.4 -4.1 3.9 3.8 3.3 Savings Rate (%) 8.9 6.4 7.4 7.6 6.9 7.2 7.9 7.6 15.9 10.4 7.8 7.4 Housing and Automobiles (Millions of Units) Housing Starts 0.784 0.928 1.000 1.107 1.177 1.207 1.248 1.295 1.382 1.512 1.472 1.446 Auto & Light Truck Sales 14.4 15.5 16.5 17.4 17.5 17.1 17.2 17.0 14.3 15.8 16.2 16.1 International Trade (% Ch.) Nominal U.S. Dollar Industrial Countries 3.6 3.0 3.1 15.7 1.2 -0.5 -2.3 3.5 -1.1 -6.2 -2.6 -0.7 Developing Countries 2.6 -0.5 3.0 10.6 8.0 -0.1 0.7 3.1 5.5 -3.6 -3.4 -0.6 Exports 4.2 3.7 4.3 -4.5 -1.7 6.6 6.5 -0.6 -16.2 7.4 12.0 8.5 Imports 2.9 0.2 4.2 -3.0 -1.9 6.9 7.1 -0.4 -12.4 10.3 8.1 5.8 Net Exports (bil. $) -568.6 -490.8 -507.7 -526.6 -512.5 -555.5 -609.5 -610.5 -628.9 -756.3 -729.3 -702.2 Real U.S. Dollar Industrial Countries 7.6 5.1 5.1 18.6 2.6 0.1 -0.2 6.7 0.7 -7.0 -3.3 -0.8 Developing Countries 6.6 1.6 5.0 13.5 9.5 0.9 2.7 6.5 6.6 -3.5 -3.8 -1.6 Exports 3.4 3.6 4.2 0.4 0.3 3.9 3.0 -0.1 -13.7 5.3 10.2 7.0 Imports 2.7 1.5 5.0 5.2 1.7 4.7 4.1 1.1 -10.5 8.5 6.8 4.9 Net Exports (bil. í12$) -568.6 -532.8 -577.2 -719.5 -763.6 -816.8 -877.7 -917.6 -901.5 -1047.7 -1042.4 -1040.4 FORECAST TABLES - QUARTERLY SUMMARY UCLA Anderson Forecast, December 2020 Nation–45 Table 3: Summary of the UCLA Anderson Forecast for the Nation 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q1 2022Q2 2022Q3 2022Q4 2023Q1 2023Q2 2023Q3 2023Q4 GDP and Monetary Aggregates (Annualized % Ch.) Real GDP -5.0 -31.4 33.1 1.2 1.8 6.0 3.2 3.2 3.1 3.2 3.4 3.1 3.0 3.0 2.9 3.1 GDP Price Index 1.4 -1.8 3.6 2.0 1.7 2.5 1.8 1.6 2.3 2.4 1.9 2.0 2.1 2.0 2.1 2.1 Money Supply (M1) 13.8 128.9 35.1 15.5 -10.3 -7.7 -5.0 -2.9 2.2 4.5 4.5 4.8 5.8 4.3 5.5 5.0 Money Supply (M2) 10.3 64.6 18.7 10.5 -12.8 -7.5 -6.6 -9.1 -5.4 -3.9 -2.7 -1.8 -1.7 -1.1 -0.8 0.6 Interest Rates (%) on: Federal Funds 1.3 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 90-day Treasury Bill 1.1 0.1 0.1 0.1 -0.0 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 10-year Treasury Bond 1.4 0.7 0.7 0.9 0.9 1.0 1.1 1.2 1.2 1.3 1.4 1.4 1.5 1.5 1.5 1.6 30-year Treasury Bond 1.9 1.4 1.4 1.6 1.7 1.9 2.0 2.1 2.1 2.2 2.3 2.3 2.4 2.4 2.4 2.4 Moody's Corp. Aaa Bond 2.9 2.4 2.2 2.5 2.4 2.1 2.0 2.0 2.0 2.0 2.1 2.1 2.2 2.2 2.3 2.4 30-yr Bond Less Inflation 0.6 3.0 -2.3 -0.1 0.4 -0.2 -0.1 -0.1 0.4 0.4 0.5 0.2 0.4 0.6 0.5 0.5 Federal Fiscal Policy (Annualized % Ch.) Defense Purchases Current $ -0.4 1.2 5.0 2.2 4.9 3.4 3.4 2.4 2.6 2.1 2.5 -1.0 -1.0 -1.1 -1.1 0.6 Constant $ -0.3 3.8 3.0 -0.5 2.8 1.4 1.2 -0.0 0.2 -0.2 0.2 -3.1 -3.1 -3.1 -3.1 -1.5 Other Expenditures Transfers to Persons 12.4 1535.6 -77.8 -43.4 32.5 -3.2 -32.4 -6.3 -1.5 -1.2 -1.6 2.7 7.3 3.0 3.2 3.1 Grants to S&L Govít 8.3 2351.0 -92.6 17.7 15.3 35.4 -9.7 -31.7 0.0 -12.4 -14.3 2.5 3.9 4.4 2.6 2.1 Billions of Current Dollars, Unified Budget Basis, Fiscal Year Receipts 797 657 1160 811 790 1064 891 868 837 1107 929 920 895 1163 1000 976 Outlays 1184 2657 1548 1357 1406 1361 1270 1258 1272 1219 1220 1201 1249 1222 1251 1226 Surplus or Deficit ( - ) -387 -2001 -388 -546 -616 -297 -379 -390 -435 -112 -291 -281 -354 -59 -251 -249 As Shares of GDP (%), NIPA Basis Revenues 17.4 17.7 17.6 17.2 17.7 17.5 17.4 17.4 17.4 17.4 17.4 17.4 17.5 17.5 17.6 17.5 Expenditures 22.7 46.7 34.1 26.7 27.0 26.4 24.3 23.3 22.9 22.5 22.2 21.9 21.9 21.7 21.5 21.3 Defense Purchases 4.1 4.5 4.2 4.2 4.2 4.1 4.1 4.1 4.1 4.0 4.0 4.0 3.9 3.8 3.8 3.7 Transfers to Persons 14.5 32.2 20.4 17.6 18.7 18.2 16.3 15.8 15.5 15.3 15.0 14.9 15.0 14.9 14.9 14.8 Surplus or Deficit ( - ) -5.3 -28.9 -16.5 -9.5 -9.3 -8.8 -7.0 -6.0 -5.6 -5.2 -4.8 -4.5 -4.3 -4.2 -4.0 -3.8 Details of Real GDP (Annualized % Ch.) Real GDP -5.0 -31.4 33.1 1.2 1.8 6.0 3.2 3.2 3.1 3.2 3.4 3.1 3.0 3.0 2.9 3.1 Final Sales -3.3 -28.0 24.6 0.3 1.7 5.1 2.9 3.2 3.2 3.2 3.2 3.1 3.1 3.0 3.1 3.2 Consumption -6.9 -33.2 40.7 0.1 1.2 6.1 3.6 5.3 3.2 2.9 3.2 3.6 3.4 3.1 3.2 3.2 Nonres. Fixed Investment -6.7 -27.2 20.3 2.3 0.2 0.1 5.8 5.8 6.8 6.6 6.1 4.7 5.2 5.5 5.3 5.6 Equipment -15.2 -35.9 70.1 9.4 -1.2 -0.3 9.1 4.9 5.3 3.4 2.9 3.5 4.2 4.0 1.8 2.7 Intellectual Property 2.4 -11.4 -1.0 1.3 0.6 0.6 7.4 6.2 9.1 8.6 7.3 7.0 7.2 7.2 7.2 7.3 Structures -3.7 -33.6 -14.6 -10.3 2.7 0.1 -3.9 7.2 6.0 10.0 11.1 3.2 4.0 5.6 9.2 8.4 Residential Construction 19.3 -36.0 59.9 24.2 5.2 4.4 -3.9 -3.2 -3.5 -0.2 -0.3 -1.8 -1.0 -0.8 -0.3 -0.3 Exports -9.5 -64.4 59.7 6.7 6.2 7.2 10.5 14.6 11.3 8.0 7.1 8.1 7.3 6.3 5.7 5.8 Imports -15.0 -54.1 91.1 7.3 4.4 6.6 8.2 9.9 7.1 4.3 5.0 6.1 5.6 4.1 3.8 3.8 Federal Purchases 1.6 16.4 -6.2 -3.2 1.1 1.2 0.7 -0.7 -0.4 -0.9 -0.5 -2.2 -2.2 -2.4 -2.4 -1.4 State & Local Purchases 1.1 -5.4 -3.3 -3.0 5.1 8.3 -0.6 -8.8 1.7 2.1 1.8 2.0 2.1 2.4 2.2 2.3 Billions of 2012 Dollars (SAAR) Real GDP 19011 17303 18584 18639 18721 18996 19145 19298 19448 19600 19766 19918 20067 20215 20360 20517 Final Sales 19092 17590 18585 18600 18680 18911 19045 19197 19350 19504 19657 19808 19959 20107 20261 20421 Inventory Change -81 -287 -1 39 41 84 100 101 98 96 109 110 108 107 100 96 FORECAST TABLES - QUARTERLY SUMMARY 46–Nation UCLA Anderson Forecast, December 2020 Table 4: Summary of the UCLA Anderson Forecast for the Nation 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q1 2022Q2 2022Q3 2022Q4 2023Q1 2023Q2 2023Q3 2023Q4 Industrial Production and Resource Utilization Production (Ann. % Ch.) -6.8 -42.9 39.8 1.3 -2.1 6.2 2.5 2.6 4.1 4.5 4.5 3.4 3.5 3.0 2.5 3.0 Capacity Util. Manuf. (%) 73.9 63.1 70.3 70.6 70.0 71.7 72.0 72.2 72.5 73.0 73.5 73.9 74.2 74.5 74.6 74.7 Real Bus. Invest. (% GDP) 17.8 17.9 17.7 18.0 18.0 17.8 17.8 17.8 17.8 17.9 17.9 17.9 18.0 18.0 18.1 18.1 Nonfarm Emp. (mil.) 151.9 133.7 140.8 143.1 144.9 146.5 147.9 149.2 150.5 151.6 152.4 153.3 154.1 154.8 155.5 156.0 Unemployment Rate (%) 3.8 13.0 8.8 6.8 6.6 6.1 5.8 5.4 5.0 4.8 4.6 4.4 4.2 4.1 4.0 3.9 Inflation (Annualized % Ch.) Consumer Price Index 1.2 -3.5 5.2 2.3 1.4 2.4 3.0 3.0 2.4 2.5 2.3 2.4 2.2 1.8 2.1 2.1 Total less Food & Energy 2.0 -1.6 4.4 2.5 1.6 2.1 2.4 2.4 2.2 2.1 2.0 2.1 2.2 2.1 2.1 2.2 Consumption Chain Index 1.3 -1.6 3.7 1.7 1.3 2.0 2.1 2.3 1.7 1.8 1.8 2.1 2.0 1.8 1.9 1.9 GDP Chain Index 1.4 -1.8 3.6 2.0 1.7 2.5 1.8 1.6 2.3 2.4 1.9 2.0 2.1 2.0 2.1 2.1 Producers Price Index -4.5 -18.6 13.7 8.1 6.2 5.3 2.6 2.6 1.9 1.7 2.3 3.6 3.4 2.8 3.2 2.5 Factors Related to Inflation (Annualized % Ch.) Nonfarm Business Sector Total Compensation 9.2 20.0 -4.4 -0.1 -0.0 -0.4 0.1 0.9 1.7 1.9 2.1 2.7 3.3 3.6 3.8 4.0 Productivity -0.3 10.6 4.9 -7.8 -3.0 2.6 -1.0 -0.4 -0.5 0.8 1.6 1.0 1.5 1.6 1.8 2.3 Unit Labor Costs 9.6 8.5 -8.9 8.4 3.1 -2.9 1.2 1.3 2.2 1.1 0.4 1.7 1.8 1.9 2.0 1.7 Farm Price Index -14.0 -43.8 48.3 32.1 -28.3 11.8 -2.6 6.8 -1.7 2.3 0.8 1.9 13.4 0.7 1.0 2.0 Crude Oil Price ($/barrel) 45.8 27.8 40.9 39.8 40.1 41.4 45.4 48.6 49.9 51.7 53.5 55.2 56.3 55.8 56.3 56.6 New Home Price ($1000) 329.6 322.8 326.4 377.2 353.9 361.5 361.0 361.2 365.4 370.9 370.3 371.3 374.4 385.7 386.8 388.1 Income, Consumption and Saving (Annualized % Ch.) Disposable Income 3.9 44.3 -13.2 -12.5 5.6 0.5 -3.8 3.1 4.0 4.2 4.3 4.4 5.4 5.0 5.1 5.1 Real Disposable Income 2.6 46.6 -16.3 -14.0 4.2 -1.5 -5.8 0.8 2.3 2.4 2.4 2.3 3.3 3.2 3.2 3.1 Real Consumption -6.9 -33.2 40.7 0.1 1.2 6.1 3.6 5.3 3.2 2.9 3.2 3.6 3.4 3.1 3.2 3.2 Savings Rate (%) 9.6 25.7 15.8 12.4 12.9 11.2 9.2 8.2 8.0 7.9 7.7 7.5 7.4 7.4 7.4 7.4 Housing and Automobiles (Millions of Units, SAAR) Housing Starts 1.484 1.079 1.430 1.535 1.526 1.515 1.513 1.493 1.489 1.480 1.464 1.455 1.458 1.455 1.441 1.431 Auto & Light Truck Sales 15.0 11.3 15.3 15.4 15.6 15.7 15.8 16.0 16.2 16.2 16.2 16.3 16.4 16.2 16.1 15.9 International Trade (Annualized % Ch.) Nominal U.S. Dollar Industrial Countries 3.4 4.0 -17.1 -8.6 -4.9 -1.4 -6.1 -5.0 -1.6 -0.8 -0.2 -2.9 -0.1 0.1 -0.4 -0.1 Developing Countries 6.0 28.0 -10.8 -5.7 -6.1 -0.1 -5.7 -5.6 -5.2 -1.2 -0.5 -0.4 -0.8 -1.3 0.1 0.4 Exports -11.7 -71.1 80.2 11.9 8.4 8.4 12.3 17.1 13.3 9.6 8.6 9.7 8.8 7.8 7.2 7.2 Imports -16.2 -59.9 108.3 10.8 5.1 7.1 12.1 14.9 6.3 2.7 7.1 8.1 6.4 4.4 4.2 4.3 Net Exports (bil. $) -494 -545 -731 -745 -738 -744 -764 -780 -753 -717 -721 -726 -722 -708 -695 -683 Real U.S. Dollar Industrial Countries 6.1 7.4 -17.5 -9.9 -6.4 -3.3 -5.4 -5.2 -2.5 -2.1 -1.3 -3.6 -0.1 0.6 0.3 0.5 Developing Countries 6.2 29.5 -9.9 -9.0 -4.6 0.8 -5.4 -5.9 -5.8 -2.1 -1.2 -1.2 -1.8 -2.3 -1.0 -0.9 Exports -9.5 -64.4 59.7 6.7 6.2 7.2 10.5 14.6 11.3 8.0 7.1 8.1 7.3 6.3 5.7 5.8 Imports -15.0 -54.1 91.1 7.3 4.4 6.6 8.2 9.9 7.1 4.3 5.0 6.1 5.6 4.1 3.8 3.8 Net Exports (bil. í12$) -788 -775 -1011 -1032 -1034 -1047 -1056 -1055 -1050 -1039 -1039 -1042 -1045 -1042 -1039 -1036 FORECAST TABLES - DETAILED UCLA Anderson Forecast, December 2020 Nation–47 Table 5A: Gross Domestic Product 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Billions of Current Dollars Gross Domestic Product 16197 16785 17527 18238 18745 19543 20612 21433 20891 22053 23272 24488 Personal Consumption Expenditures 11007 11317 11823 12297 12770 13340 13993 14545 14124 14943 15805 16638 Durable Goods 1144 1189 1242 1308 1350 1411 1482 1534 1615 1688 1703 1740 Autos and Parts 397 418 442 475 486 504 523 522 535 577 587 598 Nondurable Goods 2494 2541 2621 2615 2648 2762 2890 2978 3032 3111 3239 3362 Services 7369 7587 7960 8374 8772 9168 9621 10032 9477 10143 10863 11536 Gross Private Domestic Investment 2622 2826 3044 3237 3188 3351 3633 3751 3566 3918 4165 4384 Residential 432 510 560 634 699 760 798 807 876 987 1000 1020 Nonres. Structures 479 493 578 584 560 599 631 650 586 563 615 672 Equipment 983 1027 1092 1119 1089 1122 1213 1241 1170 1254 1326 1378 Intellectual Property 656 692 730 763 812 853 932 1004 1012 1031 1116 1205 Change in Inv. 71 105 84 137 28 16 58 49 -78 82 107 108 Net Exports -569 -491 -508 -527 -513 -556 -609 -610 -629 -756 -729 -702 Exports 2191 2273 2372 2266 2227 2375 2529 2515 2107 2263 2535 2750 Imports 2760 2764 2879 2792 2740 2930 3138 3125 2736 3020 3264 3453 Government Purchases 3137 3132 3168 3230 3299 3407 3595 3748 3829 3948 4033 4168 Federal 1287 1227 1215 1221 1235 1264 1339 1419 1482 1522 1537 1541 Defense 814 764 743 730 729 747 794 852 882 914 935 932 Other 472 462 472 491 506 517 545 567 599 608 602 608 State and Local 1850 1906 1953 2009 2065 2143 2256 2329 2348 2427 2495 2627 Billions of 2012 Dollars Gross Domestic Product 16197 16495 16912 17432 17731 18144 18688 19092 18384 19040 19683 20290 Personal Consumption Expenditures 11007 11167 11497 11934 12265 12587 12928 13240 12704 13205 13703 14154 Durable Goods 1144 1214 1302 1401 1482 1585 1693 1775 1883 1976 2017 2098 Autos & Parts 397 415 439 473 489 513 535 532 534 556 557 564 Nondurable Goods 2494 2538 2605 2694 2762 2834 2910 3001 3072 3126 3176 3247 Services 7369 7415 7595 7849 8036 8195 8367 8521 7887 8238 8626 8928 Gross Private Domestic Investment 2622 2801 2959 3122 3075 3183 3385 3443 3227 3489 3639 3776 Residential 432 485 504 555 592 616 612 602 632 686 674 668 Nonres. Structures 479 485 539 534 510 532 551 548 488 459 485 516 Equipment 983 1029 1101 1135 1115 1150 1242 1268 1196 1275 1332 1378 Intellectual Property 656 691 725 752 810 844 910 968 968 977 1049 1125 Change in Inv. 71 109 86 138 25 16 53 49 -83 82 103 103 Net Exports -569 -533 -577 -720 -764 -817 -878 -918 -901 -1048 -1042 -1040 Exports 2191 2270 2365 2375 2382 2476 2550 2547 2198 2314 2549 2727 Imports 2760 2802 2942 3095 3146 3292 3427 3464 3099 3362 3592 3767 Government Purchases 3137 3061 3033 3088 3144 3172 3230 3304 3338 3354 3345 3365 Federal 1287 1215 1184 1184 1191 1194 1228 1277 1331 1331 1326 1300 Defense 814 760 728 713 710 715 739 780 805 818 818 798 Other 472 456 455 470 480 478 488 497 525 514 508 502 State and Local 1850 1845 1849 1903 1952 1976 2000 2026 2008 2023 2020 2063 FORECAST TABLES - DETAILED 48–Nation UCLA Anderson Forecast, December 2020 Table 5B: Gross Domestic Product 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Annual Rates of Change of Current Dollar GDP Components (%) Gross Domestic Product 4.2 3.6 4.4 4.1 2.8 4.3 5.5 4.0 -2.5 5.6 5.5 5.2 Personal Consumption Expenditures 3.4 2.8 4.5 4.0 3.8 4.5 4.9 3.9 -2.9 5.8 5.8 5.3 Durable Goods 4.6 3.9 4.4 5.3 3.3 4.5 5.0 3.6 5.3 4.5 0.8 2.2 Autos and Parts 8.6 5.3 5.9 7.5 2.1 3.7 3.9 -0.3 2.6 7.9 1.6 1.9 Nondurable Goods 2.8 1.9 3.2 -0.2 1.3 4.3 4.7 3.0 1.8 2.6 4.1 3.8 Services 3.5 3.0 4.9 5.2 4.7 4.5 4.9 4.3 -5.5 7.0 7.1 6.2 Gross Private Domestic Investment 12.4 7.8 7.7 6.3 -1.5 5.1 8.4 3.3 -4.9 9.9 6.3 5.3 Residential 14.0 18.0 9.8 13.2 10.4 8.7 5.0 1.1 8.5 12.7 1.3 2.0 Nonres. Structures 18.5 2.7 17.3 1.2 -4.1 6.9 5.4 3.0 -9.8 -4.0 9.2 9.2 Equipment 11.6 4.4 6.3 2.5 -2.8 3.1 8.1 2.3 -5.7 7.2 5.8 3.9 Intellectual Property 5.5 5.5 5.6 4.4 6.4 5.1 9.2 7.7 0.9 1.9 8.2 8.0 Exports 4.2 3.7 4.3 -4.5 -1.7 6.6 6.5 -0.6 -16.2 7.4 12.0 8.5 Imports 2.9 0.2 4.2 -3.0 -1.9 6.9 7.1 -0.4 -12.4 10.3 8.1 5.8 Government Purchases -0.4 -0.1 1.1 2.0 2.1 3.3 5.5 4.2 2.2 3.1 2.1 3.4 Federal -1.0 -4.7 -0.9 0.5 1.1 2.4 6.0 6.0 4.4 2.7 1.0 0.2 Defense -2.4 -6.1 -2.7 -1.8 -0.1 2.5 6.3 7.3 3.5 3.5 2.4 -0.3 Other 1.6 -2.1 2.0 4.1 3.1 2.1 5.5 4.0 5.7 1.5 -1.0 1.1 State and Local 0.1 3.0 2.5 2.9 2.7 3.8 5.3 3.2 0.8 3.4 2.8 5.3 Annual Rates of Change of Constant Dollar GDP Components (%) Gross Domestic Product 2.2 1.8 2.5 3.1 1.7 2.3 3.0 2.2 -3.7 3.6 3.4 3.1 Personal Consumption Expenditures 1.5 1.5 3.0 3.8 2.8 2.6 2.7 2.4 -4.1 3.9 3.8 3.3 Durable Goods 6.0 6.1 7.2 7.6 5.8 6.9 6.8 4.8 6.1 4.9 2.1 4.0 Autos & Parts 7.2 4.7 5.8 7.6 3.3 5.0 4.3 -0.5 0.2 4.2 0.1 1.3 Nondurable Goods 0.4 1.8 2.6 3.4 2.5 2.6 2.7 3.1 2.4 1.8 1.6 2.2 Services 1.2 0.6 2.4 3.3 2.4 2.0 2.1 1.8 -7.4 4.5 4.7 3.5 Gross Private Domestic Investment 11.0 6.9 5.6 5.5 -1.5 3.5 6.3 1.7 -6.3 8.1 4.3 3.8 Residential 13.0 12.4 3.8 10.2 6.6 4.0 -0.6 -1.7 5.1 8.6 -1.8 -0.8 Nonres. Structures 13.0 1.3 11.0 -0.9 -4.4 4.2 3.7 -0.6 -10.9 -6.0 5.8 6.3 Equipment 11.0 4.7 7.0 3.0 -1.7 3.2 8.0 2.1 -5.7 6.6 4.5 3.4 Intellectual Property 5.0 5.4 4.8 3.8 7.6 4.2 7.8 6.4 0.0 0.9 7.4 7.3 Exports 3.4 3.6 4.2 0.4 0.3 3.9 3.0 -0.1 -13.7 5.3 10.2 7.0 Imports 2.7 1.5 5.0 5.2 1.7 4.7 4.1 1.1 -10.5 8.5 6.8 4.9 Government Purchases -2.1 -2.4 -0.9 1.8 1.8 0.9 1.8 2.3 1.0 0.5 -0.3 0.6 Federal -1.9 -5.5 -2.6 -0.0 0.6 0.3 2.8 4.0 4.2 0.1 -0.4 -1.9 Defense -3.4 -6.7 -4.1 -2.1 -0.5 0.8 3.3 5.6 3.2 1.6 0.1 -2.4 Other 0.9 -3.5 -0.1 3.3 2.2 -0.5 2.1 1.8 5.7 -2.2 -1.2 -1.1 State and Local -2.2 -0.3 0.2 2.9 2.6 1.2 1.2 1.3 -0.9 0.8 -0.2 2.1 FORECAST TABLES - DETAILED UCLA Anderson Forecast, December 2020 Nation–49 Table 6: Employment 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Employment (Millions) Total 142.5 143.9 146.3 148.8 151.4 153.3 155.8 157.5 148.0 153.9 158.0 160.5 Nonagricultural 134.2 136.4 138.9 141.8 144.3 146.6 148.9 150.9 142.4 147.2 151.9 155.1 Natural Res. & Mining 0.8 0.9 0.9 0.8 0.7 0.7 0.7 0.7 0.6 0.6 0.6 0.6 Construction 5.6 5.9 6.2 6.5 6.7 7.0 7.3 7.5 7.3 7.3 7.5 7.6 Manufacturing 11.9 12.0 12.2 12.3 12.4 12.4 12.7 12.8 12.3 12.2 12.5 12.7 Trans. Warehous. Util 5.0 5.0 5.2 5.4 5.6 5.7 6.0 6.2 5.9 6.0 6.3 6.5 Trade 20.4 20.7 21.1 21.4 21.6 21.7 21.6 21.5 20.7 21.6 21.5 20.7 Financial Activities 7.8 7.9 8.0 8.1 8.3 8.4 8.6 8.7 8.7 8.8 9.3 9.5 Information 2.7 2.7 2.7 2.8 2.8 2.8 2.8 2.9 2.7 2.8 2.9 2.9 Professional & Bus. 18.0 18.6 19.1 19.7 20.1 20.5 21.0 21.3 20.4 21.4 23.2 24.2 Education & Health 20.8 21.1 21.4 22.0 22.6 23.2 23.6 24.2 23.2 24.1 24.4 24.9 Leisure & Hospitality 13.8 14.3 14.7 15.2 15.7 16.1 16.3 16.6 13.3 14.6 15.2 16.2 Other Services 5.4 5.5 5.6 5.6 5.7 5.8 5.8 5.9 5.4 5.7 6.1 6.3 Government 21.9 21.8 21.9 22.0 22.2 22.3 22.4 22.6 21.9 21.9 22.5 23.1 Federal 2.8 2.8 2.7 2.8 2.8 2.8 2.8 2.8 2.9 2.9 2.9 2.9 State & Local 19.1 19.1 19.1 19.3 19.4 19.5 19.6 19.8 18.9 19.0 19.6 20.2 Population and Labor Force (Millions) Population aged 16+ 249.2 251.4 253.7 255.9 258.3 260.4 262.2 264.0 265.9 268.1 270.3 272.6 Labor Force 155.0 155.4 155.9 157.1 159.2 160.3 162.1 163.5 161.0 163.6 165.8 167.3 Unemployment (%) 8.1 7.4 6.2 5.3 4.9 4.4 3.9 3.7 8.1 6.0 4.7 4.1 Table 7: Personal Income and Its Disposition 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Billions of Current Dollars Personal Income 14010 14181 14992 15724 16161 16949 17852 18552 19625 19583 20176 21210 Wages & Salaries 6927 7113 7475 7859 8089 8471 8894 9309 9289 9786 10278 10832 Other Labor Income 1126 1195 1227 1271 1294 1346 1431 1474 1453 1494 1550 1610 Nonfarm Income 1286 1315 1378 1367 1389 1467 1543 1608 1612 1558 1651 1777 Farm Income 61 88 70 56 36 42 43 50 54 41 47 61 Rental Income 518 557 605 649 683 722 759 787 805 841 898 963 Dividends 835 793 953 1033 1077 1161 1305 1290 1261 1236 1317 1415 Interest Income 1331 1273 1349 1439 1474 1578 1642 1677 1640 1597 1589 1590 Transfer Payments 2363 2424 2542 2685 2777 2855 2970 3125 4283 3833 3684 3843 Contributions for Soc. Ins. -437 -578 -607 -635 -658 -693 -735 -770 -771 -802 -839 -882 Pers. Tax & Nontax Payments 1510 1676 1785 1940 1958 2047 2085 2203 2188 2300 2423 2593 % of Pers. Income 10.8 11.8 11.9 12.3 12.1 12.1 11.7 11.9 11.1 11.7 12.0 12.2 Disposable Income 12501 12505 13207 13784 14203 14902 15767 16349 17437 17283 17753 18617 Consumption 11007 11317 11823 12297 12770 13340 13993 14545 14124 14943 15805 16638 Interest Payments 232 230 244 265 273 297 333 362 306 339 348 370 Transfers To Gov. & Foreigners 155 158 170 183 185 193 203 210 203 206 217 226 Personal Saving 1107 800 971 1039 975 1071 1237 1231 2804 1795 1383 1383 Personal Saving Rate (%) 8.9 6.4 7.4 7.6 6.9 7.2 7.9 7.6 15.9 10.4 7.8 7.4 FORECAST TABLES - DETAILED 50–Nation UCLA Anderson Forecast, December 2020 Table 8: Personal Consumption Expenditures By Major Types 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Billions of Current Dollars Personal Consumption 11007 11317 11823 12297 12770 13340 13993 14545 14124 14943 15805 16638 Durable Goods 1144 1189 1242 1308 1350 1411 1482 1534 1615 1688 1703 1740 Autos and Parts 397 418 442 475 486 504 523 522 535 577 587 598 Nondurable Goods 2494 2541 2621 2615 2648 2762 2890 2978 3032 3111 3239 3362 Services 7369 7587 7960 8374 8772 9168 9621 10032 9477 10143 10863 11536 Billions of 2012 Dollars Personal Consumption 11007 11168 11502 11943 12279 12614 12970 13297 12842 13340 13820 14273 Durable Goods 1144 1214 1302 1401 1482 1585 1693 1775 1883 1976 2017 2098 Autos and Parts 397 415 439 473 489 513 535 532 534 556 557 564 Nondurable Goods 2494 2538 2605 2694 2762 2834 2910 3001 3072 3126 3176 3247 Services 7369 7415 7595 7849 8036 8195 8367 8521 7887 8238 8626 8928 Annual Rates of Real Growth Personal Consumption 1.5 1.5 3.0 3.8 2.8 2.7 2.8 2.5 -3.4 3.9 3.6 3.3 Durable Goods 6.0 6.1 7.2 7.6 5.8 6.9 6.8 4.8 6.1 4.9 2.1 4.0 Autos and Parts 7.2 4.7 5.8 7.6 3.3 5.0 4.3 -0.5 0.2 4.2 0.1 1.3 Furniture 2.9 5.8 8.5 9.2 8.0 8.0 6.9 3.4 6.2 -0.6 1.2 6.4 Other Durables 4.1 4.9 8.2 7.8 2.0 3.4 5.7 5.1 -1.6 15.6 2.8 2.1 Nondurable Goods 0.4 1.8 2.6 3.4 2.5 2.6 2.7 3.1 2.4 1.8 1.6 2.2 Food and Beverages 0.9 1.1 1.9 1.5 3.1 3.4 2.4 1.7 6.9 -1.4 -0.5 0.8 Gasoline and Oil -0.6 1.6 -0.3 4.6 0.7 -0.4 -0.3 -0.3 -12.7 8.5 3.2 1.7 Fuel -11.9 5.9 4.9 5.3 -2.9 -3.4 -4.0 -2.9 11.1 6.4 0.8 -0.1 Clothing and Shoes 0.2 0.5 2.6 3.5 2.3 1.6 3.7 3.7 -8.7 9.7 5.4 4.1 Other Nondurables 1.0 3.0 4.6 4.6 2.7 3.2 3.6 5.3 5.9 1.3 2.1 3.0 Services 1.2 0.6 2.4 3.3 2.4 2.0 2.1 1.8 -7.4 4.5 4.7 3.5 Housing 0.6 0.2 1.9 3.1 1.9 1.0 0.8 1.3 1.2 1.3 1.5 1.3 Transportation Serv. 1.9 4.5 5.0 3.7 4.3 3.7 3.8 1.6 -22.3 10.6 9.3 9.5 Health Care 1.8 0.6 3.3 5.4 4.0 2.3 2.4 2.7 -8.5 7.9 3.4 2.4 Recreational Service 2.5 2.0 2.5 3.7 2.9 1.1 2.2 1.3 -31.0 17.5 23.4 3.3 Food Svcs. Accom. 2.4 1.7 3.4 4.3 2.2 2.5 2.8 1.2 -21.4 12.7 14.2 7.7 Financial Services -1.4 -0.6 0.3 2.4 -2.0 2.1 0.3 2.1 0.7 -2.4 -0.3 3.2 Other Services 0.5 -1.4 2.4 2.7 2.9 3.3 2.9 3.4 -13.2 5.3 6.4 6.1 Table 9: Residential Construction and Housing Starts 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Housing Starts (Millions of Units) Housing Starts 0.784 0.928 1.000 1.107 1.177 1.207 1.248 1.295 1.382 1.512 1.472 1.446 Single-family 0.537 0.619 0.646 0.712 0.785 0.851 0.872 0.893 0.993 1.169 1.099 1.060 Multi-family 0.247 0.309 0.354 0.394 0.392 0.356 0.376 0.403 0.389 0.343 0.373 0.386 Residential Construction Expenditures (Billions of Dollars) Current Dollars 432.0 510.0 560.2 633.8 699.5 760.3 798.5 807.1 875.7 987.2 1000.4 1020.2 2012 Dollars 432.0 485.5 504.1 555.4 592.1 615.7 612.0 601.5 632.1 686.2 673.7 668.4 % Change 13.0 12.4 3.8 10.2 6.6 4.0 -0.6 -1.7 5.1 8.6 -1.8 -0.8 Related Concepts Treas. Bill Rate 0.1 0.1 0.0 0.1 0.3 0.9 1.9 2.1 0.4 0.1 0.1 0.1 Mortgage Rate Conv. 30-Yr. 3.7 4.0 4.2 3.9 3.6 4.0 4.5 3.9 3.1 3.1 3.2 3.3 New Home Price ($1000) 242.1 265.1 283.2 293.7 306.5 321.6 323.1 319.3 339.0 359.4 369.4 383.7 % Change 7.9 9.5 6.8 3.7 4.3 4.9 0.5 -1.2 6.2 6.0 2.8 3.9 Real Disp. Income 12500.6 12504.7 13207.1 13784.3 14202.8 14901.9 15766.5 16348.6 17437.0 17283.3 17752.9 18617.1 % Change 3.3 -1.3 4.1 4.2 2.0 3.1 3.6 2.2 5.4 -2.6 0.8 2.9 FORECAST TABLES - DETAILED UCLA Anderson Forecast, December 2020 Nation–51 Table 10: Nonresidential Fixed Investment and Inventories 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Billions of Current Dollars Nonres. Fixed Investment 2119 2211 2400 2467 2460 2574 2777 2895 2769 2848 3057 3255 Equipment 983 1027 1092 1119 1089 1122 1213 1241 1170 1254 1326 1378 Intellectual Property 656 692 730 763 812 853 932 1004 1012 1031 1116 1205 Nonresidential Structures 479 493 578 584 560 599 631 650 586 563 615 672 Buildings 192 204 235 285 321 329 347 359 336 301 348 407 Commercial 76 84 103 119 145 154 164 167 162 137 161 189 Industrial 47 50 58 80 76 68 70 77 70 69 84 96 Other Buildings 69 70 74 86 99 107 113 115 104 95 104 121 Utilities 112 109 126 133 135 132 132 141 151 163 149 138 Mining Exploration 153 156 188 137 76 108 125 121 71 68 84 92 Other 23 24 28 29 29 30 28 30 29 30 34 36 Billions of 2012 Dollars Nonres. Fixed Investment 2119 2206 2365 2420 2433 2524 2699 2777 2641 2690 2846 3000 Equipment 983 1029 1101 1135 1115 1150 1242 1268 1196 1275 1332 1378 Intellectual Property 656 691 725 752 810 844 910 968 968 977 1049 1125 Nonresidential Structures 479 485 539 534 510 532 551 548 488 459 485 516 Buildings 192 199 222 264 291 290 292 288 263 237 268 299 Commercial 76 82 98 111 134 139 142 138 131 114 132 148 Industrial 47 49 55 74 70 61 60 63 55 51 60 67 Other Buildings 69 68 69 79 87 90 91 88 77 72 76 85 Utilities 112 108 123 128 129 124 119 121 128 132 116 108 Mining Exploration 153 155 168 120 69 96 121 118 72 64 76 82 Other 23 23 26 26 25 25 23 23 22 22 23 23 Percent Change in Real Nonresidential Fixed Investment Nonres. Fixed Investment 9.5 4.1 7.2 2.3 0.5 3.7 6.9 2.9 -4.9 1.9 5.8 5.4 Equipment 11.0 4.7 7.0 3.0 -1.7 3.2 8.0 2.1 -5.7 6.6 4.5 3.4 Intellectual Property 5.0 5.4 4.8 3.8 7.6 4.2 7.8 6.4 0.0 0.9 7.4 7.3 Nonresidential Stuctures 13.0 1.3 11.0 -0.9 -4.4 4.2 3.7 -0.6 -10.9 -6.0 5.8 6.3 Buildings 9.6 3.9 11.4 18.8 10.5 -0.6 1.0 -1.5 -8.8 -9.7 12.9 11.7 Commercial 9.6 9.1 19.0 12.8 20.7 3.6 2.2 -2.6 -5.2 -12.9 15.8 12.2 Industrial 15.2 4.2 12.9 34.4 -4.9 -13.4 -1.8 4.6 -11.9 -6.8 16.4 11.2 Other Buildings 6.1 -1.9 1.2 14.8 10.6 2.9 1.0 -3.7 -11.9 -6.6 6.0 11.1 Utilities 19.8 -4.1 14.4 4.1 0.7 -4.3 -3.8 2.1 5.8 2.7 -11.8 -6.7 Mining Exploration 11.9 1.6 8.0 -28.6 -42.1 38.8 25.2 -2.1 -39.1 -10.8 17.9 8.9 Other 17.7 3.3 12.5 -1.2 -4.3 1.5 -10.1 3.3 -6.8 0.6 4.0 1.4 Related Concepts Annual Growth-Price Deflator: Producers Dur. Equip. 0.6 -0.2 -0.6 -0.5 -1.1 -0.1 0.1 0.2 -0.0 0.5 1.2 0.5 Structures 4.9 1.5 5.7 2.1 0.3 2.6 1.7 3.6 1.2 2.1 3.3 2.7 Moody's AAA Corp. Rate (%) 3.7 4.2 4.2 3.9 3.7 3.7 3.9 3.4 2.5 2.1 2.1 2.3 Cap. Util. in Manufacturing (%) 74.5 74.4 75.2 75.3 74.2 75.1 76.6 75.6 69.5 71.5 73.3 74.5 Final Sales (bil. í12$) 16126 16387 16826 17295 17706 18128 18634 19043 18467 18958 19580 20187 Change in Business Inventories (Billions $) Current Dollars 71.2 104.5 84.0 136.8 28.4 16.3 57.7 49.1 -78.0 82.4 107.1 108.4 2012 Dollars 71.2 108.7 86.3 137.6 24.5 15.8 53.4 48.5 -82.6 81.5 103.0 102.7 FORECAST TABLES - DETAILED 52–Nation UCLA Anderson Forecast, December 2020 Table 12: State and Local Government Receipts and Expenditures 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Billions of Current Dollars Current Receipts 2056 2146 2258 2373 2432 2515 2643 2743 3011 3029 3080 3194 Tax Receipts 1415 1491 1542 1598 1639 1719 1810 1877 1883 1970 2085 2185 As % of GDP 8.7 8.9 8.8 8.8 8.7 8.8 8.8 8.8 9.0 8.9 9.0 8.9 Pers. Current Taxes 343 374 381 407 410 432 468 490 502 526 558 593 Corp. Income Taxes 51 54 57 56 53 54 60 70 64 70 82 83 Prod. & Import Taxes 1021 1063 1105 1135 1175 1233 1282 1318 1316 1374 1445 1509 Contributions For Social Ins. 17 18 19 19 20 20 21 22 20 20 21 22 Income on Assets 82 82 84 82 85 91 95 97 98 102 107 111 Transfer Receipts 550 561 616 675 690 691 723 753 1024 951 875 881 Federal Grants-in-Aid 444 450 495 533 557 560 583 608 877 802 720 719 From Persons 65 66 70 76 77 80 84 88 90 92 96 100 From Business 40 45 46 66 56 51 54 56 56 57 59 62 From Rest of the World 0 0 5 1 0 0 1 1 0 0 0 0 Surplus of S&L Gov't Enterprises -8 -6 -4 -2 -3 -6 -5 -6 -14 -15 -8 -5 Expenditures 2339 2411 2495 2589 2671 2754 2857 2951 2999 3118 3218 3388 As % of GDP 14.4 14.4 14.2 14.2 14.2 14.1 13.9 13.8 14.4 14.1 13.8 13.8 Purchases 1517 1575 1614 1653 1694 1758 1848 1898 1896 1972 2033 2148 Transfer Payments 541 564 618 665 693 707 727 755 818 862 890 934 Interest Payments 281 271 263 270 283 288 281 298 285 283 294 306 Subsidies 0 0 0 1 1 1 1 1 1 1 1 1 Surplus or Deficit ( - ) -283 -265 -238 -216 -239 -239 -214 -208 11 -89 -138 -194 Table 11: Federal Government Receipts and Expenditures 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Billions of Current Dollars Unified Budget Basis (FY) Receipts 2509 2825 3093 3275 3242 3344 3330 3497 3424 3613 3792 4034 Outlays 3570 3384 3581 3750 3824 4025 4203 4520 6746 5295 4912 4948 Surplus or Deficit ( - ) -1061 -560 -487 -475 -582 -681 -873 -1022 -3321 -1682 -1120 -914 National Income & Products Accounts Basis, Calendar Year Current Receipts 2700 3139 3292 3448 3463 3524 3568 3711 3655 3854 4049 4296 Current Tax Receipts 1573 1745 1900 2024 2020 2015 2017 2132 2065 2170 2292 2456 Pers. Current Taxes 1166 1303 1404 1533 1548 1615 1618 1713 1686 1774 1865 2000 Corp. Income Taxes 275 298 340 329 312 245 211 217 200 199 222 242 Prod. & Import Taxes 115 125 136 140 136 131 163 174 151 167 175 182 From Rest of the World 17 18 20 22 24 25 26 28 28 30 31 32 Contributions for Soc. Ins. 938 1092 1140 1191 1225 1284 1345 1402 1414 1474 1542 1621 Income Receipts on Assets 141 243 172 160 140 139 123 111 119 151 154 156 Current Transfer Receipts 56 69 87 76 80 85 84 68 57 59 61 64 Surplus of Govít. Enterprises -8 -10 -7 -3 -1 1 -1 -2 -0 0 0 0 Current Expenditures 3773 3771 3889 4008 4132 4247 4499 4758 6731 5563 5210 5291 Consumption Expenditures 999 957 951 954 967 985 1044 1097 1140 1166 1178 1186 Defense 650 611 599 587 590 602 636 677 695 720 738 739 Nondefense 349 346 353 367 377 383 407 421 445 447 440 447 Transfer Payments 2294 2338 2441 2568 2651 2726 2853 3006 4373 3796 3536 3648 Gov't Social Benefits 1782 1822 1881 1970 2025 2099 2196 2323 3418 2924 2744 2856 To Rest of the World 18 19 20 20 21 22 23 24 28 27 28 30 Grants-in-Aid 494 498 541 578 605 606 634 658 927 845 763 763 To S&L Gov't 444 450 495 533 557 560 583 608 877 802 720 719 To Rest of the World 49 48 46 45 48 46 51 50 49 43 44 44 Interest Payments 423 416 439 429 454 476 541 582 552 474 426 394 Subsidies 58 59 58 57 61 59 63 73 665 127 70 62 Surplus or Deficit ( - ) -1073 -632 -597 -560 -669 -722 -932 -1047 -3076 -1709 -1161 -994 FORECAST TABLES - DETAILED UCLA Anderson Forecast, December 2020 Nation–53 Table 13: U.S. Exports and Imports of Goods and Services 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Billions of Current Dollars Net Exports -- Goods & Serv. -569 -491 -508 -527 -513 -556 -609 -610 -629 -756 -729 -702 Current Account Balance -418 -337 -368 -407 -395 -365 -450 -480 -683 -763 -691 -623 Merchandise Balance -780 -737 -777 -793 -777 -835 -902 -889 -875 -974 -985 -985 Exports -- Goods & Serv. 2191 2273 2372 2266 2227 2375 2529 2515 2107 2263 2535 2750 Merchandise 1522 1559 1615 1495 1444 1542 1664 1637 1399 1535 1703 1833 Food, Feeds & Beverages 133 136 144 128 131 133 133 131 136 131 137 147 Industrial Supplies 483 492 501 418 388 459 537 527 442 465 541 596 Motor Vehicles & Parts 146 153 160 152 150 158 159 162 128 159 183 189 Capital Goods Ex. MVP 527 535 552 540 520 534 563 548 470 559 607 643 Computer Equipment 49 48 49 47 45 46 50 47 41 48 46 46 Aircraft 94 105 113 119 121 121 131 126 71 99 134 152 Other 384 382 390 373 354 367 383 375 357 412 426 445 Consumer Goods Ex. MVP 181 188 198 197 193 197 206 205 168 167 174 189 Other 51 55 61 60 62 61 66 63 56 54 61 69 Services 670 714 757 771 783 833 865 878 708 728 832 917 Imports -- Goods and Serv. 2760 2764 2879 2792 2740 2930 3138 3125 2736 3020 3264 3453 Merchandise 2301 2296 2392 2288 2221 2377 2566 2526 2274 2509 2688 2818 Food, Feeds & Beverages 111 116 127 129 131 139 148 152 153 164 170 173 Petroleum & Products 434 388 354 197 160 197 239 207 125 160 220 238 Indus. Supplies Ex. Petr 289 291 316 291 278 306 336 312 283 274 279 232 Motor Vehicles & Parts 298 310 329 350 351 359 372 377 316 440 433 421 Capital Goods Ex. MVP 552 559 599 607 594 643 695 681 638 682 701 709 Computer Equipment 122 121 122 120 115 128 142 131 141 145 142 136 Aircraft 40 47 53 55 50 51 55 63 47 54 63 70 Other 389 391 423 432 429 463 497 487 449 483 496 503 Consumer Goods. Ex. MVP 519 533 559 596 585 603 648 656 627 640 732 886 Other 98 100 108 118 123 129 127 141 131 148 153 160 Services 458 468 488 504 519 553 573 600 462 511 577 634 Billions of 2012 Dollars Net Exports -- Goods & Serv. -569 -533 -577 -720 -764 -817 -878 -918 -901 -1048 -1042 -1040 Exports -- Goods & Serv. 2191 2270 2365 2375 2382 2476 2550 2547 2198 2314 2549 2727 Imports -- Goods & Serv. 2760 2802 2942 3095 3146 3292 3427 3464 3099 3362 3592 3767 Exports and Imports -- % Change Current Dollars Exports 4.2 3.7 4.3 -4.5 -1.7 6.6 6.5 -0.6 -16.2 7.4 12.0 8.5 Imports 2.9 0.2 4.2 -3.0 -1.9 6.9 7.1 -0.4 -12.4 10.3 8.1 5.8 Constant Dollars Exports 3.4 3.6 4.2 0.4 0.3 3.9 3.0 -0.1 -13.7 5.3 10.2 7.0 Imports 2.7 1.5 5.0 5.2 1.7 4.7 4.1 1.1 -10.5 8.5 6.8 4.9 Production Indicators -- % Change U.S. Industrial Production 3.0 2.0 3.1 -1.0 -2.0 2.3 3.9 0.9 -7.5 1.9 3.8 3.4 Real GDP-Industrial Countries 1.2 1.6 2.2 1.6 1.4 2.8 1.8 1.4 -6.7 3.8 3.2 2.0 Real GDP-Developing Countries 4.2 3.8 3.7 3.4 2.9 3.6 3.2 1.6 -5.6 5.0 3.3 3.0 Price Indicators Price Deflators (% Ch.) Exports 0.8 0.2 0.1 -4.9 -2.0 2.6 3.4 -0.4 -3.1 2.2 1.7 1.4 Imports 0.2 -1.4 -0.8 -8.0 -3.5 2.2 2.9 -1.5 -2.2 2.0 1.2 0.8 Crude Oil Prices ($/barrel) 94.2 97.9 93.3 48.7 43.2 51.0 64.9 57.0 38.6 43.9 52.6 56.2 Real U.S. Dollar Ex. Rate-Indust. Countries 1.00 1.05 1.10 1.31 1.34 1.35 1.34 1.43 1.44 1.34 1.30 1.29 % Change 7.6 5.1 5.1 18.6 2.6 0.1 -0.2 6.7 0.7 -7.0 -3.3 -0.8 Ex. Rate-Dev. Countries 1.00 1.02 1.07 1.21 1.32 1.34 1.37 1.46 1.56 1.50 1.44 1.42 % Change 6.6 1.6 5.0 13.5 9.5 0.9 2.7 6.5 6.6 -3.5 -3.8 -1.6 FORECAST TABLES - DETAILED 54–Nation UCLA Anderson Forecast, December 2020 Table 14: Price Indices for GDP and Other Inflation Indicators (Percent Change) 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Implicit Price Deflators GDP 1.9 1.8 1.8 1.0 1.0 1.9 2.4 1.8 1.2 1.9 2.1 2.1 Consumption 1.9 1.3 1.5 0.2 1.0 1.8 2.1 1.5 1.2 1.8 1.9 1.9 Durables -1.3 -2.0 -2.6 -2.2 -2.4 -2.3 -1.7 -1.2 -0.8 -0.3 -1.2 -1.7 Motor Vehicles 1.4 0.5 0.0 -0.1 -1.2 -1.2 -0.4 0.2 2.2 3.7 1.5 0.6 Furniture 0.0 -1.9 -3.4 -2.4 -2.6 -2.8 -1.1 0.7 0.4 -2.2 -2.6 -2.0 Other Durables -0.8 -2.1 -4.0 -3.6 -0.8 -1.3 -1.8 -2.3 -2.5 -2.3 -0.7 -1.4 Nondurables 2.4 0.1 0.5 -3.5 -1.3 1.6 1.9 -0.1 -0.5 0.9 2.5 1.5 Food 2.4 1.0 1.9 1.1 -1.0 -0.1 0.5 1.0 3.4 1.3 1.9 1.5 Clothing & Shoes 3.6 1.0 0.3 -1.2 -0.3 -0.6 0.1 -1.4 -5.2 -1.9 -0.1 -0.0 Gasoline 3.4 -2.7 -3.6 -26.7 -11.5 13.0 13.7 -3.5 -16.2 2.3 12.0 4.0 Fuel 1.4 -1.2 -0.5 -28.8 -17.1 15.3 20.9 -4.5 -22.6 0.5 10.6 4.8 Motor Vehicle Fuel 3.5 -2.8 -3.8 -26.5 -11.2 12.8 13.2 -3.4 -15.8 2.4 12.1 3.9 Services 2.2 2.3 2.4 1.8 2.3 2.5 2.8 2.4 2.1 2.5 2.3 2.6 Housing 2.3 2.3 2.7 3.1 3.3 3.4 3.4 3.4 2.9 2.6 2.8 2.8 Utilities -0.2 3.2 4.2 -0.5 0.0 3.3 1.4 0.8 1.1 2.0 1.5 3.2 Electricity -0.0 2.1 3.6 0.6 -1.1 2.2 0.7 0.2 0.2 1.4 1.3 2.2 Natural Gas -9.7 4.8 7.1 -11.9 -2.4 8.0 0.1 -1.5 -0.1 2.1 -2.0 4.3 Water & Sanit. 5.3 4.4 3.6 4.3 3.6 3.3 3.5 3.2 3.1 3.0 3.3 4.4 Health Care 1.8 1.4 1.1 0.6 1.2 1.5 1.9 1.8 2.5 2.0 1.7 2.2 Transportation 2.0 1.0 1.3 0.4 0.8 1.2 2.1 2.0 -1.5 1.8 3.7 2.5 Recreation 2.8 1.7 1.8 1.6 2.4 2.8 2.1 2.0 2.5 2.8 2.3 2.5 Food & Accomm. 2.8 2.1 2.6 2.8 2.6 2.1 2.3 2.8 2.0 4.6 3.9 3.2 Financial & Insur. 4.2 5.3 5.4 2.9 4.9 4.8 6.1 2.9 1.5 2.7 1.3 2.8 Other Services 2.6 2.9 2.5 1.9 2.0 2.2 2.8 2.4 2.2 2.2 1.8 2.4 Investment Deflators: Nonresidential 1.5 0.3 1.2 0.4 -0.8 0.9 0.9 1.3 0.6 1.0 1.4 1.0 Structures 4.9 1.5 5.7 2.1 0.3 2.6 1.7 3.6 1.2 2.1 3.3 2.7 Equipment 0.6 -0.2 -0.6 -0.5 -1.1 -0.1 0.1 0.2 -0.0 0.5 1.2 0.5 Intellectual Prop. 0.5 0.1 0.7 0.6 -1.1 0.8 1.3 1.3 0.8 0.9 0.8 0.7 Residential 1.0 5.1 5.8 2.7 3.5 4.5 5.6 2.8 3.2 3.9 3.2 2.8 Government Purchases 1.7 2.3 2.1 0.2 0.3 2.4 3.6 1.9 1.1 2.6 2.4 2.8 Federal 0.9 0.9 1.7 0.5 0.6 2.1 3.1 1.9 0.2 2.6 1.5 2.2 State and Local 2.3 3.3 2.3 -0.0 0.2 2.5 4.0 1.9 1.7 2.6 3.0 3.1 Exports 0.8 0.2 0.1 -4.9 -2.0 2.6 3.4 -0.4 -3.1 2.2 1.7 1.4 Imports 0.2 -1.4 -0.8 -8.0 -3.5 2.2 2.9 -1.5 -2.2 2.0 1.2 0.8 Other Inflation Related Indicators Cons. Price Index - All Urban 2.1 1.5 1.6 0.1 1.3 2.1 2.4 1.8 1.3 2.2 2.6 2.2 Producers Price Index 0.5 0.6 0.9 -7.2 -2.7 4.4 4.3 -1.0 -3.0 4.8 2.4 3.0 Nonfarm Sector Indicators Total Compensation 2.6 1.3 2.8 3.1 1.1 3.5 3.4 3.6 5.9 0.5 1.4 3.1 Productivity 0.9 0.5 0.9 1.6 0.3 1.2 1.4 1.7 2.4 -0.8 0.2 1.5 Unit Labor Costs 1.8 0.8 1.9 1.5 0.7 2.2 1.9 1.9 3.5 1.3 1.1 1.6 Crude Oil Prices ($/barrel) West Texas Intermediate 94.2 97.9 93.3 48.7 43.2 51.0 64.9 57.0 38.6 43.9 52.6 56.2 FORECAST TABLES - DETAILED UCLA Anderson Forecast, December 2020 Nation–55 Table 15: Producer Price Indexes 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Annual Percent Change All Commodities 0.5 0.6 0.9 -7.2 -2.7 4.4 4.3 -1.0 -3.0 4.8 2.4 3.0 Industrial Commodities 0.0 0.4 0.6 -7.5 -2.3 5.0 5.2 -1.5 -3.6 5.3 2.5 3.1 Textiles & Apparel 0.3 0.8 1.5 -1.0 -0.5 1.2 2.9 0.9 -0.6 3.6 1.1 1.2 Fuels -1.8 -0.2 -0.9 -23.5 -9.1 12.3 10.9 -7.1 -15.6 11.3 4.2 5.8 Chemicals 0.5 0.9 0.6 -5.3 -0.3 5.9 5.1 -2.0 -3.1 5.7 3.3 3.4 Rubber & Plastics 2.3 1.1 0.6 -1.7 -1.3 2.4 3.2 0.5 -0.8 2.9 1.4 2.2 Lumber & Wood 3.5 6.5 4.3 -1.0 0.4 3.5 5.8 -2.9 6.6 6.3 -0.1 1.5 Pulp & Paper -0.4 1.9 0.7 -0.7 -0.4 2.8 2.1 -0.2 0.1 5.3 2.5 1.9 Metals & Products -2.7 -2.9 0.7 -6.9 -2.9 6.9 7.6 -1.1 -0.6 6.4 1.4 2.8 Equipment 1.1 0.7 0.8 0.5 -0.1 0.7 1.8 2.2 1.2 2.1 1.2 1.6 Trans. Equipment 2.2 1.2 1.4 1.4 0.4 0.9 1.3 0.9 0.6 2.1 3.0 2.5 Farm 3.2 1.4 1.1 -11.9 -9.6 3.1 -0.6 0.4 -3.8 0.2 1.8 4.2 Processed Foods & Feeds 3.9 1.5 3.9 -3.4 -2.7 1.0 0.3 1.4 1.4 3.2 2.1 1.7 By Stage of Processing Crude Materials -3.2 2.1 1.1 -24.2 -8.3 10.0 4.9 -7.1 -11.5 10.2 1.8 4.4 Intermediate Materials 0.5 0.1 0.5 -6.9 -3.1 4.7 5.3 -1.4 -3.2 4.1 1.4 2.2 Finished Goods 1.9 1.2 1.9 -3.3 -1.0 3.2 3.0 0.8 -1.4 2.2 2.9 2.1 Finished Consumer Goods 1.9 1.4 2.1 -4.8 -1.5 4.0 3.6 0.3 -2.2 4.8 2.7 2.9 Finsihed Producer Goods 1.9 0.9 1.4 1.2 0.4 1.0 1.6 2.1 1.3 2.1 1.9 2.0 Table 16: Money and Interest Rates 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Billions of Dollars Money Supply (M1) 2436 2637 2898 3064 3342 3613 3721 3949 5606 5241 5450 5731 Money Supply (M2) 10342 10971 11603 12263 13156 13800 14278 15236 18923 17217 16622 16493 Percent Change Money Supply (M1) 12.9 8.3 9.9 5.7 9.1 8.1 3.0 6.1 42.0 -6.5 4.0 5.1 Money Supply (M2) 7.6 6.1 5.8 5.7 7.3 4.9 3.5 6.7 24.2 -9.0 -3.5 -0.8 Interest Rates (Percent) Short-Term Rates 3-Month Treas. Bill 0.1 0.1 0.0 0.1 0.3 0.9 1.9 2.1 0.4 0.1 0.1 0.1 Prime Bank Loans 3.3 3.3 3.3 3.3 3.5 4.1 4.9 5.3 3.5 3.3 3.3 3.3 U.S. Government Bond Yields 5-Year Maturity 0.8 1.2 1.6 1.5 1.3 1.9 2.7 2.0 0.5 0.5 0.7 0.9 10-Year Maturity 1.8 2.4 2.5 2.1 1.8 2.3 2.9 2.1 0.9 1.1 1.3 1.5 30-Year Maturity 2.9 3.4 3.3 2.8 2.6 2.9 3.1 2.6 1.6 1.9 2.2 2.4 State and Local Government Bond Yields Domestic Municipal Bond 3.7 4.3 4.2 3.7 3.3 3.7 4.0 3.6 2.7 2.3 2.4 2.5 Corporate Bond Yields Moody's AAA Corp. Bond 3.7 4.2 4.2 3.9 3.7 3.7 3.9 3.4 2.5 2.1 2.1 2.3 Mortgage Rate Conventional 30-Year 3.7 4.0 4.2 3.9 3.6 4.0 4.5 3.9 3.1 3.1 3.2 3.3 DECEMBER 2020 REPORT THE UCLA ANDERSON FORECAST FOR THE NATION Charts CHARTS – FORECAST UCLA Anderson Forecast, December 2020 Nation–59 CHARTS – FORECAST 60–Nation UCLA Anderson Forecast, December 2020 CHARTS – FORECAST UCLA Anderson Forecast, December 2020 Nation–61 CHARTS – FORECAST 62–Nation UCLA Anderson Forecast, December 2020 DECEMBER 2020 REPORT THE UCLA ANDERSON FORECAST FOR CALIFORNIA The Economic/Pandemic Question: To Close or Not to Close? Sea Level Rise and Its Impact on California Housing Markets UCLA Anderson Forecast, December 2020 California–65 THE ECONOMIC/PANDEMIC QUESTION: TO CLOSE OR NOT TO CLOSE? The Economic/Pandemic Question: To Close or Not to Close? Jerry Nickelsburg Director, UCLA Anderson Forecast Leila Bengali Economist, UCLA Anderson Forecast December 2020 Introduction Since the pandemic-induced recession began last March, we have said that the course of the pandemic, and the pub- lic health policy response to it, is critical to the economic forecast. As well, we have pointed out that we do not know what the future will bring with respect to the pandemic. What we do know is that the pandemic is raging across the country once again. California has responded, as before, with more restrictive non-pharmaceutical interventions (NPI) via mask mandates, closures, and gathering restric- tions. We expect that to continue, particularly through the holiday season as significant traveling by Americans has thus far presaged further increases in COVID cases.1 We also know that at least three vaccines are in the latter stages of testing and approval. Does this mean that we are out of 1. Though total domestic and foreign air travel remained significantly below a year ago, from the last week in October to the last week in November, the total number of passengers processed by TSA increased by 16%. A year previous the increase was 8%. https://www.tsa.gov/coronavirus/passenger- throughput Summary • As 2020 draws to a close, labor markets in California are weaker than those in the U.S. overall. • Non-pharmaceutical interventions (such as mask mandates and restrictions on business operations) tend to be more restrictive in CA than elsewhere. • Across the U.S. in October 2020, states with more restrictive non-pharmaceutical interventions tended to have higher unemployment rates, though historical evidence suggests that more restrictive non-pharmaceutical interventions may not significantly affect economic activity in the near term and may help in the long term. • Looking to the future, the forecast for the state is for the technology sectors, residential construction, and logistics to lead the recovery, and for California post-pandemic to grow faster than the U.S. 66–California UCLA Anderson Forecast, December 2020 THE ECONOMIC/PANDEMIC QUESTION: TO CLOSE OR NOT TO CLOSE? -600 -500 -400 -300 -200 -100 0 100 Federal Gov't.Mining & LoggingFinanceProf. Sci. & Tech.Mgmt of CompaniesConstructionState & Local (excl Ed.)Tsp. Whs. & Util.Wholesale TradeInformationDurable GoodsNon-Durable GoodsHealth Care & Soc. Svc.Administ. SvcRetail TradeOther Svc.Education (pvt + public)Leisure & HospitalityThousandsCHANGE IN NO. OF JOBS BY SECTOR (Oct. 2019 to Oct. 2020) Figure 1 Change in Number of Jobs by Sector the woods soon? The answer is maybe. There is still much that is unknown, however for purposes of our forecast, we are assuming that by summer a large number of people will have received one of the vaccines. In this California report we ask two questions: where are we now? And what are the likely future effects of the more restrictive NPIs on the state’s economy? The short answer is that the state has higher unemployment than in the U.S. overall, and the state is due to grow faster than the U.S. once restrictions are lifted and the pandemic is in the rear view mirror. Sectoral employment retrospective The near-term recovery in employment in the state depends critically on the course of the pandemic. As we move through Thanksgiving to New Year’s Eve and usher 2020 out, we are confronting new highs in COVID cases and changing restrictions on economic activity. How this plays out is an open question, however, to make our forecast we must first make an assumption about the pandemic and the policy re- sponse. Our assumption is that the elevated number of cases will remain for the balance of the year, and households will remain cautious when it comes to holiday activities includ- ing in-store shopping. This will mean a weak growth rate through the balance of the year and into early 2021. With at least three vaccines in the latter stages of testing and approval, for the purposes of our forecast we also assume that a large number of people will have received one of the vaccines by summer, ushering in the beginning of a return to normalcy. In the 2020 recession a few sectors have been shouldering the brunt of the job loss. On a year over year basis, including the recovery of some of the lost employment occurring between April and Oc- tober, leisure and hospitality, retail, and education remain the weakest (Figure 1). Since October 2019, 1.37 million non-farm payroll jobs in California have been lost. Leisure and hospitality and education account for 55% of the job loss, with almost 80% of the education employment decline in the public sector. Another 15% of the job loss is in retail and other services for a total of 70% of all unemployment in the state. These sectors will also be impacted by the rate of recovery as they each involve a higher level of human contact than other economic activity. Source: California EDD UCLA Anderson Forecast, December 2020 California–67 THE ECONOMIC/PANDEMIC QUESTION: TO CLOSE OR NOT TO CLOSE? -14.0% -12.0% -10.0% -8.0% -6.0% -4.0% -2.0% 0.0%Silicon ValleyU.S.Inland EmpireSac. DeltaSan DiegoLos AngelesOrange CountyNorth BayJeffersonSJ ValleySan FranciscoEast BayCentral CoastCalifornia Regional Job Loss (Oct. 2019 to Oct. 2020, SA) Regionally the recession has been uneven as well (Figure 2). However, unlike the great recession, there is not the bifurcated impact of inland vs coastal California. San Fran- cisco, the North and East Bay, the Great State of Jefferson, and the San Joaquin Valley have all contracted by about the same percentage. The Inland Empire, Silicon Valley, San Diego, Sacramento and the Delta have fared better and contracted less. Some of this is due to the impact of a shut- down in tourism. San Francisco is a major destination for international tourists, and Napa and Sonoma for domestic tourists. The Inland Empire has been rebounding with resi- dential construction and logistics, and Silicon Valley with the demand for new software technologies for the new way in which business and socializing are being conducted today. Also important in understanding regional differences is the way in which commuters appear in the data. The data on unemployment are from the CPS (Current Population Sur- vey also known as the Survey of Households). This survey polls individuals by their domicile. The payroll employment data shown here in Charts 1 and 2 are from the Current Employment Statistics survey which collects data on payroll jobs by the employer’s location. For example, the Inland Empire lost 6.9% of its payroll jobs from October 2019 to October 2020 while Orange County lost 8.39%. However the unemployment rate in both places rose about the same amount, about 5 percentage points (3.9% to 9.0% in the IE and 2.6% to 7.5% in Orange County). The differential stems from the fact that commuters into Orange County from the less expensive communities in the Inland Empire, particu- larly those working in the northern parts of the county’s leisure and hospitality industry, are counted as unemployed in Riverside County and not in Orange County. We find the same pattern with San Joaquin and the East Bay relative to Silicon Valley and San Francisco in Northern California. Since lower income sectors are projected to grow slower than higher income sectors, and commuters from inland counties are more likely to be lower income, the spillover effects of the growth of technology, advanced manufactur- ing, and professional services in the coastal cities may be less pronounced than in previous recessions. Figure 2 California Regional Jobs Loss Source: California EDD 68–California UCLA Anderson Forecast, December 2020 THE ECONOMIC/PANDEMIC QUESTION: TO CLOSE OR NOT TO CLOSE? 35,000,000 40,000,000 45,000,000 50,000,000 55,000,000 60,000,000 65,000,000 Jan-2000May-2000Sep-2000Jan-2001May-2001Sep-2001Jan-2002May-2002Sep-2002Jan-2003May-2003Sep-2003Jan-2004May-2004Sep-2004US Airline Revenue Passenger Miles (Seasonally Adjusted, 000) Figure 3 U.S. Airline Revenue Passenger Miles in traffic and the return to the previous peak. There is a 31 month recovery in commercial airline domestic travel as measured by revenue-passenger-miles. However, the decline and recovery, then as now, is confounded with a recession. Beginning in March of 2001 and extending through Novem- ber of the same year the economy contracted. It was a mild recession, however that loss of income affected the demand for passenger traffic as well. In a 2004 study by Ito and Lee,2 these and other factors af- fecting the demand for air traffic were separated out. They found that while there was a 30% instantaneous decline in demand right after 9/11, there was a relatively rapid recov- ery of all but 7.5% of that decline. That residual persisted through the extent their data. This result is consistent with other studies of the economic impact of accidents on air traf- fic (see for example Barnett and LoFazo (1983) and Squalli (2005)3 ). Applying their model to the leisure and hospitality demand in California presents a somewhat gloomy picture. Specifically, the sector remains at 20% below its previous peak at the end of our forecast horizon (2023) due to both the safety and income effects. That translates to 200,000 relatively low-income Californians with long-term unem- ployment for 30 months following the end of the pandemic. Human contact sectors: How long until recovery In previous California reports we wrote about our analysis of fear-of-flying data and how that informs our forecast for the current downturn. It bears repeating as it is an important element of the forecast. What is different now from last June when we did this analysis is the new, more acute, wave of infections. It is possible that we are in for a long winter and that the pandemic will not cease to have a major impact on the leisure and hospitality, retail, other services, and edu- cation sectors until widespread vaccination occurs. In our national forecast we assume that this is late spring to early summer 2021. What that means for the recovery of the hu- man contact intensive sectors is that their recovery, which began in June, will experience a hiatus until the coming June. To understand how long it will take, we turned to an analysis of the loss in passengers from the 9/11 attacks on American aviation. Though quite different than a pandemic, it is similar in two respects. First, the demand for domestic air travel is discretionary, and second, the decline in demand was a consequence of safety concerns. Figure 3 shows the decline Source: U.S. Department of Transportation 2. Harumi Ito and Darin Lee. 2005. “Assessing the Impact of the September 11 terrorist attacks on US airline demand.” Journal of Economics and Business. Vol:57 (1). Pp:75-95.3. Barnett, A. and LoFaso, A. J. 1983. “After the Crash: The Passenger Response to the DC-10 Disaster.” Management Science. Vol:29. Pp:1225–1236. Squalli, J. 2005. “Do Consumers Have Imperfect Recollection about Airline Safety?” Applied Economics Letters. Vol:12. Pp:169–176. UCLA Anderson Forecast, December 2020 California–69 THE ECONOMIC/PANDEMIC QUESTION: TO CLOSE OR NOT TO CLOSE? To be sure, some will find employment in other sectors, but in an economy that is demanding technical skills, it will be challenging. There is one important caveat. Our shelter-in- place and zoom-fatigue has been said to create an enormous pent-up demand for human interaction. That being the case, we can expect a little more rapid recovery than suggested by this fear-of-flying analysis. Nevertheless, 2024 remains the most likely return-to-previous peak employment in these sectors. Is California Falling Behind? Through the initial phase of the recession, March/April 2020, the contraction in employment in California looked much like the contraction nationwide (Figure 4). One would expect California to recover pari passu with the national economy based on these data. The differences would be in the faster growth from the tech sectors and the slower growth from the sectors serving international tourists. Otherwise, for a -60 -50 -40 -30 -20 -10 0 Payroll Jobs Leisure & Hospitality Retail Trade Healthcare & Social Services Other Services PERCENT PAYROLL JOB LOSS FEB.–APRIL 2020 US CA Figure 4 Percent Payroll Job Loss Feb–April 2020 Source: EDD.ca.gov, BLS.gov 0 2 4 6 8 10 12 14 16 NBIWUTSCMNNDINVAIDWYOHALWAOKNCWVILTXPAMATNAZDCCANYHWUnemployment Rate by State(October 2020)% Figure 5 Unemployment Rate by State Source: BLS.gov 70–California UCLA Anderson Forecast, December 2020 THE ECONOMIC/PANDEMIC QUESTION: TO CLOSE OR NOT TO CLOSE? change, California looked to be quite average in the reces- sions impact. However, the expansions in the state and in the U.S. overall look a bit different (Figure 5). California has one of the high- est unemployment rates in the U.S. Tourism is one reason. Another is that the extent of the government intervention in California via NPI compared to other states is somewhat different, and that raises the question, what are the near term and long term economic impacts of the NPI policies in California? Economic implications of closures To begin to answer the question we look at the relationship between non-pharmaceutical interventions (NPI), a fancy way of saying shutdowns, gathering restrictions and mask mandates, and indicators of the labor market (the unemploy- ment rate and employment growth rates). To analyze the relationship between labor markets and NPIs, we culled data gathered by the University of Oxford and aggregated by the New York Times.4 From these data we assigned each state a value with 0 indicating the least restrictive NPIs, 1 moderate, and 2 most restrictive during the month of October 2020. In a regression of unemployment rates on this measure of public health policy, policy variation explained just under a quarter of the unemployment rate differences between states (as measured by the regression’s R-squared). Using this model, we derived an unemployment for each state as if all states were at the least restrictive NPI level (Figure 6). While California is not in the middle of the pack, it is not far off, about 1.3 percentage points higher than the average. A higher implied unemployment rate in the state is due, at least in part, to the fact that California is host to over 20% of all foreign tourists coming to the U.S.; tourists who are no longer making the journey. If we repeat this exercise using a model that includes an indicator control for states with significant international tourism (California, Nevada, Hawaii, New York and Florida), California’s implied unem- ployment rate is lower than the average for all other states. We can also look at the relationship between payroll employ- ment and NPIs. Using the same NPI variable as before in a regression to explain the change in total non-farm payroll employment by state from October 2019 to October 2020, we find similar results (Figure 7). The NPI variable explains a third of the variation in growth rates in employment across states. Moreover, in this regression, the counterfactual growth of employment in California with all states set to have the least restrictive level of NPIs rests squarely in the middle of the pack. From these simple regressions we learn two things about the forecast. First, since California, as a matter of public health policy, tends towards more restrictive NPIs than many other states, so long as the pandemic rages, employment growth will be slower and the unemployment rate higher than in the rest of the nation. Second, the underlying economy is not necessarily weaker than other states in the U.S., though each state has its own labor market idiosyncrasies. Will more restrictive NPIs have longer term adverse effects on the California economy? There is not a lot of evidence to work with, but recent studies of the 1918/1919 Influenza Pandemic suggest the opposite. For example, a research project by economists at the Federal Reserve and MIT found that over the course of the influenza pandemic, NPIs had no statistically significant impact on economic activity.5 The reason for this was twofold. First, in cities with less restrictive NPIs, more employees were sick and therefore produced less output. Second, because health outcomes were worse, consumers were more reticent to purchase goods and services involving higher degrees of human contact. Thus there was both a demand and supply consequence for those cities with less restrictive NPIs. Subsequent to the pandemic, and adjusting for population size and migration, they found that cities with more restrictive NPIs experienced faster post-pandemic growth. To be sure, the economy of 2020 is quite different than that of 1918. It is less rural, more urbanized, more globalized, and more mobile between re- gions. Nevertheless, the results are informative. Thus, with the expectation that the tech sectors along with residential construction and logistics will be leading the recovery, our forecast has California, post-pandemic, once again growing faster than the U.S. 4. https://covidtracker.bsg.ox.ac.uk , https://www.nytimes.com/interactive/2020/11/18/us/covid-state-restrictions.html?name=styln-coronavirus&region=TOP_BANNER&block=storyline_menu_recirc&action=click&pgtype=Interactive&impression_id=6b50d752-2b45-11eb-be08- 77c2b2e224fa&variant=1_Show 5. Correia, Sergio and Luck, Stephan and Verner, Emil, Pandemics Depress the Economy, Public Health Interventions Do Not: Evidence from the 1918 Flu (June 5, 2020). http://dx.doi.org/10.2139/ssrn.3561560 UCLA Anderson Forecast, December 2020 California–71 THE ECONOMIC/PANDEMIC QUESTION: TO CLOSE OR NOT TO CLOSE? The Forecast Although the timing may be offset with California beginning a significant recovery later than some other states, we expect the California recovery to ultimately look very much like the U.S.6 The recovery in CA will be slower in the leisure and hospitality and retail sectors due to the disproportionate reliance on international tourism7, and mixed in transporta- tion and warehousing due to the shift to online shopping on the one hand and the expected continuation of the trade war with China in a Biden administration on the other8, but faster in business, scientific and technical services and in the information sector due to the demand for new technologies for the new way we are working and socializing, and faster in residential construction as California’s shortage of housing relative to demand drives new developments. The unemployment rate for the 4th quarter of this year is expected to be 8.9%, and for the entire years 2021, 2022 and 2023 we expect average unemployment rates of 6.9%, 5.2% and 4.4% respectively. Our forecast for 2021, 2022 and 2023 is for total employment growth rates to be 6.1%, 3.4% and 2.2%. Non-farm payroll jobs are expected to grow 3.6%, 3.8% and 2.5% during the same three years. Real personal income is forecast to fall by -1.0% in 2021 as transfers from the stimulus packages expire and grow by 2.1% and 3.4% in 2022 and 2023. In spite of the recession, the continued demand for a limited housing stock coupled with low interest rates leads to a forecast of a relatively rapid return of homebuilding. Our expectation is for 123K net new units in 2021; a 16.2% increase from 2020 and continuing to grow to 132K for 2023. Needless to say, this level of home building means that the prospect for the private sector building out of the housing affordability problem over the next three years is nil. 6. Leo Feler, “A gloomy COVID winter and an exuberant vaccine spring” UCLA Anderson Forecast. December 2020.7. California’s share of international tourists to the United States in 2018 was 21.39%. U.S. National Travel and Tourism Office. https://travel.trade.gov/outreachpages/inbound.general_information.inbound_overview.asp 8. William Yu and Jerry Nickelsburg. “The Pandemic and the Trade Agreement.” Cathay Bank. March 2020. And “The Economic implications of the National Security Law” Cathay Bank. May 2020. 0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0% CA HI NV Figure 6 Implied October 2020 U-Rate With Less Restrictive NPI for All States Sources: New York Times, Oxford University, UCLA Anderson Forecast -14.0 -12.0 -10.0 -8.0 -6.0 -4.0 -2.0 0.0 2.0 CA HI % Figure 7 Impled Oct 2019 to Oct 2020 Growth Rate With Less Restrictive NPI, Non-Farm Payroll Jobs Sources: New York Times, Oxford University, UCLA Anderson Forecast UCLA Anderson Forecast, December 2020 California–73 SEA LEVEL RISE AND ITS IMPACT ON CALIFORNIA HOUSING MARKETS Sea Level Rise and Its Impact on California Housing Markets William Yu Economist, UCLA Anderson Forecast December 2020 Summary • The impact of sea level rise (SLR) on coastal California housing markets are estimated as follows: • Number of homes affected -- 1 foot: 10,900, 2 feet: 19,000, 4 feet: 66,600 • Number of people affected -- 1 foot: 27,000, 2 feet: 46,000, 4 feet: 155,600 • Property value loss -- 1 foot: $11 billion, 2 feet: $20 billion, 4 feet: $68 billion • Coastal California zip codes are divided into three zones by the percentage of housing units impacted by SLR of 4 feet: Green Zone (0%, 196 zip codes), Yellow Zone (below 4%, 81 zip codes), and Red Zone (above 4%, 30 zip codes). • We do not find evidence that homebuyers have seriously factored SLR risk into their investment decisions in California. Red Zone houses are still in high demand by high-income and high-education households. The latest report from the United Nations’ Intergovernmental Panel on Climate Change (IPCC) predicts that global mean sea levels will mostly likely rise between 0.95 feet and 3.6 feet by the end of the century.1 Their forecasted range of sea level rise (SLR) is based on two assumptions from Representative Concentration Pathways (RCP): 1) Low scenario (RCP2.6) represents a low greenhouse gas emissions and high mitigation future with projected global mean surface temperature increased by 1.6 de- grees Celsius by 2100, causing SLR of 0.95 feet; 2) High scenario (RCP8.5) represents high greenhouse gas emissions in the absence of policies to combat climate change leading to a temperature increase of 4.3 degrees Celsius by 2100, causing SLR of 3.6 feet.2 Based on IPCC’s forecasts and assumptions, this report will analyze how and where SLR would impact California coastal housing markets. The Direct Impact on California Housing Markets To measure how many houses would be affected and where they would be exposed to SLR, we use the data from the Union of Concerned Scientists (UCS)3 based on Zillow Transaction and Assessment Dataset (ZTRAX). They pro- vide data to project how many homes and people will be at risk of chronic inundation due to SLR by zip code in the U.S. by the year 2100. Figure 1 shows the number of people and homes in California and Figure 2 shows their estimated 1. See IPCC’s Special Report on the Ocean and Cryosphere in a Changing Climate. https://www.ipcc.ch/srocc/ 2. Alternatively, National Oceanic and Atmospheric Administration (NOAA) develops three scenarios: (1) Low scenario: SLR 1.6 feet by 2100; (2) Intermediate scenario: SLR 1 foot by 2035 and 4 feet by 2100; (3) High scenario: SLR 2 feet by 2045 and 6.5 feet by 2100. 3. See its report “Underwater—Rising Seas, Chronic Floods, and the Implications for US Coastal Real Estate.” And https://ucsusa.maps.arcgis.com/apps/MapJournal/index.html?appid=0befd6dac46f4e0dbee2c3d8f539ab1a# 74–California UCLA Anderson Forecast, December 2020 SEA LEVEL RISE AND ITS IMPACT ON CALIFORNIA HOUSING MARKETS total home values at risk of SLR by three scenarios: 1 foot (low scenario by IPCC and intermediate scenario by 2035 by NOAA), 2 feet (high scenario by 2045 by NOAA), and 4 feet (high scenario by IPCC and intermediate scenario by 2100 by NOAA). With an SLR of 1 foot, 10,900 homes on the California coastline would face chronic inundation4; 27,000 people would be impacted directly; and the loss of total home value would amount to $11 billion. The number of homes and people impacted by SLR are from UCS, and the total estimated loss of property values are calculated from the percentage of homes impacted by SLR multiplied by the median home value in each zip code provided by American Community Survey (ACS) in 2018.5 If we use Zillow’s median home value in October 2020, the total loss will rise to $15.6 billion. If the SLR reaches 2 feet, 19,000 homes in California will be at risk; 46,000 people will be impacted directly; and the loss of total home value will climb to $20 billion ($27 billion from Zillow’s median home value in Oct. 2020). If the SLR goes to 4 feet, 66,600 homes in California will be at risk; 155,000 people will be impacted directly; and the loss of total home value will surge to $68 billion ($93 billion from Zillow in Oct. 2020). Note that the economic loss of SLR on the local economy is not limited to loss of residential properties. Additional loss includes damage on commercial properties, foregone property tax revenues and foregone local consumption and business by residents. Based on 2018 ACS numbers, there were 136 million housing units in the U.S. and 14 million in California. The percentage of homes impacted by SLR in the U.S. and Cali- fornia are 0.1% in the U.S. and 0.08% in California with SLR of 1 foot; 0.22% in the U.S. and 0.13% in California with SLR of 2 feet; and 0.9% in the U.S. and 0.5% in California with SLR of 4 feet. That said, SLR risk on California real estate is milder than the national average. The real estate in Florida, on the East Coast, and in the Gulf Coast regions will face more severe damage than in California should SLR 0 25 50 75 100 SLR 1ft SLR 2ft SLR 4ft Home (Thous) 10.9 19 66.6 Home 10.9 19 66.6 0 50 100 150 200 SLR 1ft SLR 2ft SLR 4ft People (Thous) 27 46 155 People 27 46 155 Figure 1 Impact of Sea Level Rise on Number of Homes, People in California Sources: Union of Concerned Scientists, American Community Survey and Author’s Calculation $0 $20 $40 $60 $80 $100 SLR 1ft SLR 2ft SLR 4ft 5-Year ACS 2018 Zillow in Oct. 2020 ($Billion)($Billion) Figure 2 Impact of Sea Level Rise on Total Home Value in California by Two Measures Sources: Union of Concerned Scientists, American Community Survey and Author’s Calculation 4. Chronic inundation refers to any area where high tide floods usable, non-wetland area at least 26 times per year.5. It is a 5-year ACS, for the period of 2014 to 2018. So the median home value might reflect the value prior to 2018. 6. “Is the Risk of Sea Level Rise Capitalized in Residential Real Estate?” Review of Financial Studies, (2019), 33:3, pp 1217-1255 meet predictions. Murfin and Spiegel (2020)6 estimate that Florida, New York, and New Jersey will encounter more loss of total home value than California due to SLR. In particular, Florida’s loss is estimated at around 5 times as California. UCLA Anderson Forecast, December 2020 California–75 SEA LEVEL RISE AND ITS IMPACT ON CALIFORNIA HOUSING MARKETS The Three Zones of Coastal California To simply the analysis, we use the intermediate scenario of SLR of 4 feet for the rest of the report. To show the degree of risk in California coastal zip codes impacted by SLR of 4 feet, we calculate the percentage of homes at risk of SLR over the total housing units for each zip code. There are about 111 zip codes facing risk from SLR of 4 feet with a varying degree of percentages of impacted housing units. For instance, the zip code with the highest percentage (77%) of housing units facing SLR risk is 94065 in Redwood City, followed by 94404 (64%) in Foster City and 92661 (46%) in Newport Beach. We arbitrarily categorize the zip codes with more than 4% of homes impacted by SLR of 4 feet as the Red Zone and the rest of the zip codes (below 4%) as the Yellow Zone. As shown in Figure 3, there are 30 zip codes in the Red Zone and 81 zip codes in the Yellow Zone. The details of zip codes in the Red and Yellow Zones are shown in the Appendix. Figure 4 uses the size of circle to display the number of residents that will be directly impacted by SLR of 4 feet: the larger the circle, the more people will be affected. Similar to Figure 2, it is clear that the Bay Area would be the most impacted by SLR. For example, the zip code with the most people being impacted by SLR is 94404 in Foster City, in which there will be 23,000 people directly affected by SLR of 4 feet, followed by 94303 in Palo Alto with 16,000 people and 94403 in San Mateo with 11,400 people. Figure 3 Zip Codes in California Impacted by Sea Level Rise of 4 Feet for Selected Regions in California Sources: Union of Concerned Scientists, American Community Survey and Author’s Calculation Figure 4 Number of People by Zip Code in California Impacted by SLR of 4 Feet Sources: Union of Concerned Scientists, American Community Survey and Author’s Calculation 76–California UCLA Anderson Forecast, December 2020 SEA LEVEL RISE AND ITS IMPACT ON CALIFORNIA HOUSING MARKETS The Characteristics of the Three Zones Now let’s take a look at the characteristics of housing mar- kets in coastal California. It is worth noting that although for years we have heard of climate change and SLR risks on the coastline, coastal real estate is still in high demand in the U.S., whether in California or on any other coast. An ocean view and proximity to the beach continue to make these properties more expensive and attractive to buyers despite warnings of danger. This means the loss on homes due to SLR will be higher on coastal real estate than on an average house in the U.S. According to Zillow, total housing values in the U.S. amount to $33 trillion (median home value: $205,000). Total home values in California are about $7.3 trillion (median home value: $476,000). Among 14 million housing units in Cali- fornia, 3.1 million units are in the coastal zip codes (within 5 miles of shoreline). Among these zip codes, there are 30 in the Red Zone (with a total of 320,000 housing units) and 81 in the Yellow Zone (with a total of 846,000 units) as shown in Figure 2. The rest of the zip codes on the coastline (total- ing 2 million housing units) are in the Green Zone, which is not at risk with SLR up to 4 feet. Figure 5 presents the percentage change of home values7 since 1996 for coastal California zip codes, in which the Red Zone is at high risk to SLR of 4 feet, the Yellow Zone is at medium risk, and the Green Zone is at low risk, as well as the average of California homes. If home buyers and investors are rational, aware of climate change and SLR risks, and consider it when making home purchase decisions, we might expect to see the price growth in the Red Zone slower than in the Yellow Zone, and the Yellow Zone’s slower than in the Green Zone’s, and the Green Zone’s slower than California’s average. This did not quite happen. Rather, the Red Zone had the highest growth rate of home value, and the Yellow Zone had higher growth than the California average. 0% 50% 100% 150% 200% 250% 300% 350% 400% 96 98 00 02 04 06 08 10 12 14 16 18 20 Red Zone Yellow Zone Green Zone California Average Figure 5 Percentage Change of Median Home Values in Coastal California Zip Codes and All of California Since 1996 Sources: Zillow and Author’s Calculation 7. Based on Zillow’s home values index for all houses (SFR and Condo), smoothed and seasonally adjusted. UCLA Anderson Forecast, December 2020 California–77 SEA LEVEL RISE AND ITS IMPACT ON CALIFORNIA HOUSING MARKETS Figure 6 shows the correlation between percentage of homes exposed to SLR of 4 feet and home value growth from Janu- ary 2011 to October 2020 by coastal zip codes in California. There is no clear correlation. If homebuyers and investors are concerned with SLR risk, we should see a negative cor- relation. But in fact, if we run a regression in which home value growth is the dependent variable with two explanatory variables – (1) the percentage of homes exposed to SLR and (2) whole zip-code population – we will get a significant and positive correlation. That means zip codes with more SLR risk have seen more home value growth after controlling for population. That is consistent to the outperforming Red Zone line in Figure 5. Figure 7 (left) illustrates the median home values from Zil- low in October 2020 by three zones in coastal California and California as a whole. The median home value in the Red Zone is $1,341,000 for two possible reasons: (1) superior amenities as mentioned before and (2) many zip codes are located in the heart of Silicon Valley, which has experienced a robust tech boom over the past several years. The median home value in the Yellow and Green Zones are both around $1 million. If we assume that natural amenities are similar in these three zones, then there is no evidence of a price discount due to SLR exposure. Note that the median rent could be more likely to reveal amenity value free of SLR concern. In other words, in terms of reacting to future SLR risk, price discount is more likely to be reflected in current home values than in the current rents by controlling the same amenity in the same zip code. So if homebuyers in Califor- nia are rational, we should see that the ratio of home value to rent to be inversely correlated to % of home exposed to SLR. Figure 8 is the correlation of these two variables but we cannot see a significantly negative correlation. Bernstein et al. (2019)8 suggest that homes exposed to SLR sell for approximately 7% less than equivalent properties without exposure. Why did we not find it in California? There are two possible reasons: (1) They used individual property data while we use weighted average zip code data, or (2) They analyzed all coastal property in the U.S. It is likely that home price discount due to SLR is mostly driven in Florida and on the East and Gulf Coasts.9 Figure 7 (right) illustrates the home supply growth since 2000. The three zones in coastal California had lower hous- ing supply growth than the whole of California for three possible reasons: (1) there is less space available on the coast, (2) it is more difficult to build on the coast, and (3) home builders, lenders, and local governments did factor the SLR risk into their decisions. Note that the Red Zone had 0% 50% 100% 150% 200% 250% 300% 350% 0% 10%20%30%40%50%60%70%80% % of Homes Exposed to SLR of 4 FeetHome Value Growth from Jan 2011 to Oct 202094303 94065 94404Foster City 92661Balboa Peninsula 94502 90266Manhattan Beach 90402Santa Monica % of Homes Exposed to SLR of 4 FeetHome Value Growth from Jan 2011 to Oct 202094303 Palo Alto Redwood Shores 94404 92661Balboa Peninsula 94502Bay Farm Island 90266 90402Santa Monica Figure 6 Correlation between % of Homes Exposed to SLR of 4 Feet and Home Value Growth from January 2011 to October 2020 by Coastal Zip Codes in California Sources: Union of Concerned Scientists, American Community Survey, Zillow and Author’s Calculation $400 $600 $800 $1,000 $1,200 $1,400 $1,600 Red ZoneYellow ZoneGreen ZoneCA AverageMedian Home Value (Oct. 2020)Median Home Value (Oct. 2020) 8% 10% 12% 14% 16% 18%Red ZoneYellow ZoneGreen ZoneCA AverageHome Supply Growth Since 2000Home Supply Growth Since 2000 Figure 7 Median Home Values in October 2020 and Home Supply Growth Since 2000 in Coastal California Zip Codes and All of California Sources: Zillow and American Community Survey 8. See Bernstein, Gustafson, and Lewis, “Disaster on the Horizon: The Price Effect of Sea Level Rise,” Journal of Financial Economics, (2019), 134, pp 253-272.9. See Figure 1 in their article (P257). 78–California UCLA Anderson Forecast, December 2020 SEA LEVEL RISE AND ITS IMPACT ON CALIFORNIA HOUSING MARKETS lower housing supply growth than the Yellow and Green Zones. That could suggest relatively risk-averse behavior, but a 10% growth might still be too high to indicate serious consideration of risk. Figure 9 shows percentages of households (for both home- owners and renters) moved in by zone in three periods: before 2000, during the 2000s, and during the 2010s. It is interesting to see that around 50% of residents have moved into their residence since 2010. We see a similar pattern across these three zones and in California as a whole. This could imply that SLR risk has not yet discouraged home purchases in the Red and Yellow Zones. Would banks lend money to homebuyers when the collat- eral property might be at risk with SLR during its 30-year mortgage period? So far, the answer seems to be yes. Figure 10 presents the percentage of homes with mortgages by the three zones and in all of California. We do not see significant evidence that the Yellow Zone has less access to mortgages compared to the Green Zone, even though the Red Zone does have a slightly lower percentage of mortgages. Red Zone homeowners have higher mortgage costs compared to the Green Zone. It is unclear why the banks have not priced the SLR risk into their decisions. The first possible reason could be that the average effective mortgage holding period is less than 30 years. In fact, Figure 9 suggests that the median duration of a mortgage holder staying in a house is around 10 to 15 years in California. That is, starting from 2020, the median mortgage will end by 2035 when the current homeowners move on. The sec- ond possible reason is that all these mortgages will be sold to Fannie Mae and Freddie Mac, two federal agencies who have a mandate to provide liquidity to homebuyers, and be turned into mortgage-back securities for investors. There might be some political reasons for Fannie and Freddie to not raise the price of mortgage on properties with high SLR risk. Note that the high-risk flood insurance provided by the National Flood Insurance Program (NFIP) can only secure 0 20 40 60 80 100 0% 10%20%30%40%50%60%70%80% % of Homes Exposed to SLR of 4 FeetHome Value to Rent Rati% of Homes Exposed to SLR of 4 FeetHome Value to Rent RatioFigure 8 Correlation between % of Homes Exposed to SLR of 4 Feet and Ratio of Home Value to Annual Rents by Coastal Zip Codes in California Sources: Union of Concerned Scientists, American Community Survey, Zillow and Author’s Calculation 0% 10% 20% 30% 40% 50% 60% Red Zone Yellow Zone Green Zone CA Average Before 2000In the 2000sIn the 2010s Figure 9 Percentages of Households Moved in by Three Periods in Coastal California Zip Codes and All of California Source: American Community Survey UCLA Anderson Forecast, December 2020 California–79 SEA LEVEL RISE AND ITS IMPACT ON CALIFORNIA HOUSING MARKETS coverage of up to $250,000 for a residential building. That amount is significantly lower than a median home value in coastal California, making it less relevant when facing SLR risk. Will the insurance industry be able to provide some sort of market-rate climate insurance in the future to protect homeowners from SLR risks? It is likely, but we suggest the insurance premium will be extremely expensive because any SLR will hit all of the coastal U.S. at once. Unlike most other natural disasters, it will be difficult for insurers to diversify the SLR risk across the nation or the globe. Figure 11 (left) displays the median household income in the three zones and all of California. It is not surprising to see the highest household income in the Red Zone, followed by the Green/Yellow Zone, which is consistent to the home values as shown in Figure 7. Figure 11 (right) shows that education level is consistent with homeowners’ income level. That said, those who live in the Red Zone and are facing the highest risk of SLR in the future are also more educated and have the highest earning power. It is comforting to know they are more capable than middle-income or low-income households to navigate financial damage if faced with SLR in the future. California vs. Florida Using the same data source from UCS, Keys and Mulder (2020)10 suggest that since 2013 homebuyers started to fac- tor in SLR, resulting in lower home sales volume (by 20%) in most SLR-exposed communities (similar to the Red Zone in this report) than in less SLR-exposed areas (Green Zone) in coastal Florida. And since 2018, home prices in the Red Zone started to grow more slowly than in the Green Zone in Florida. That article suggests that homebuyers became more aware of climate change and SLR risk partially because of events including severe damage on the East Coast caused by Hurricane Sandy in October 2012. Why do we not see the same pattern in California? The first possible reason could be hurricanes do not strike the West Coast. Residents in California are less likely to imagine SLR risk compared to their Florida counterparts who have experienced horrific hurricane damage periodically.11 The second possible reason is that SLR will affect homes in California later than in Florida as homes in California are in higher elevation than those in Florida. For example, ac- cording to California’s Legislative Analyst’s Office (LAO)’s $50 $60 $70 $80 $90 $100 $110 $120 Red ZoneYellow ZoneGreen ZoneCA AverageMedian Household Income 30% 40% 50% 60%Red ZoneYellow ZoneGreen ZoneCA Average% of Residents with College Degree or Higher 60.0% 62.5% 65.0% 67.5% 70.0% 72.5% 75.0%Red ZoneYellow ZoneGreen ZoneCA Average% of Homes with Mortgage $2,000 $2,400 $2,800 $3,200 $3,600 Red ZoneYellow ZoneGreen ZoneCA AverageMedian Mortgage CostMedian Mortgage Cost Figure 10 Percentage of Homes with Mortgage and Median Mortgage Cost in Coastal California Zip Codes and All of California Source: American Community Survey Figure 11 Median Household Income and Percentage of Residents with College Degree or Higher in Coastal California Zip Codes and All of California Source: American Community Survey 10. See their paper, “Neglected No More: Housing Markets, Mortgage Lending, and Sea Level Rise,” NBER Working Paper 27930.11. One example is the following quote by Clifford Rossi, a former risk officer at both Fannie Mae and Freddie Mac, “It never reaches the point of people really kind of being forward-thinking about this until the crisis is upon you or about to hit you in the face.” November 30, 2020, Politico. https://www.politico.com/news/2020/11/30/climate-change-mortgage-housing-environment-433721 80–California UCLA Anderson Forecast, December 2020 SEA LEVEL RISE AND ITS IMPACT ON CALIFORNIA HOUSING MARKETS report,12 SLR will reach 1 foot in 2035 and 2 feet in 2060 in California coastline. Using a comprehensive database of all of U.S. coastal home sales until 2017 merged with data on elevation relative to local cities, Murfin and Spiegel (2020)13 suggest there is no evidence of a price discount for those homes subject to SLR risk. This implies there might be variation of perception, experiences, and reaction in response to SLR risk across coastal communities in the U.S. Californians for sure are now more aware of wildfire risks than residents in other states. Conclusions The take-aways of the report are as follows: • The projected impact of sea level rise (SLR) on coastal California housing markets are as follows: • Number of homes affected -- 1 foot: 10,900, 2 feet: 19,000, 4 feet: 66,600 • Number of people affected -- 1 foot: 27,000, 2 feet: 46,000, 4 feet: 155,600 • Property value loss -- 1 foot: $11 billion, 2 feet: $20 billion, 4 feet: $68 billion • We divide coastal California zip codes into three zones by the percentage of housing units impacted by SLR of 4 feet: Green Zone (0%, 196 zip codes), Yellow Zone (below 4%, 81 zip codes), and Red Zone (above 4%, 30 zip codes). • We do not find evidence that homebuyers have seriously factored SLR risk into their investment decisions in California. Red Zone houses are still in high demand by high-income and high-education households. 12. “What Threat Does Sea-level Rise Pose to California?” August, 2020. https://lao.ca.gov/Publications/Report/4261 13. Murfin and Spiegel, “Is the Risk of Sea Level Rise Capitalized in Residential Real Estate?” (2020) Review of Financial Studies, 33:3, 1217-1255. UCLA Anderson Forecast, December 2020 California–81 SEA LEVEL RISE AND ITS IMPACT ON CALIFORNIA HOUSING MARKETS Appendix. Zip Codes in Red Zone and Yellow Zone of Coastal California Zone Zip Code % of Home to SLR Risk Total # of homes Total population Zone Zip Code % of Home to SLR Risk Total # of homes Total population Red 94065 77.2% 5,275 12,579 Yellow 95076 1.5%25,359 86,703 Red 94404 64.2%15,149 36,905 Yellow 93035 1.4%12,158 29,404 Red 92661 46.1% 2,568 3,225 Yellow 94111 1.3% 2,624 3,620 Red 94502 45.7% 5,262 14,619 Yellow 94559 1.2%11,070 27,523 Red 94303 34.9%14,699 48,039 Yellow 94070 1.2%12,154 31,049 Red 94158 31.5% 4,265 7,291 Yellow 94603 1.1%10,434 34,593 Red 94403 27.8%17,241 44,300 Yellow 92101 1.1%27,236 39,313 Red 94401 27.4%13,511 35,414 Yellow 92106 1.0% 8,074 19,080 Red 94925 26.3% 4,053 9,866 Yellow 94801 0.9%10,311 29,958 Red 95564 26.2%202 432 Yellow 95501 0.8%11,107 23,467 Red 90803 24.4%18,166 32,389 Yellow 92104 0.8%23,304 45,202 Red 94940 23.9%138 234 Yellow 95039 0.7%424 1,195 Red 94949 16.0% 7,721 17,452 Yellow 94945 0.7% 7,503 19,035 Red 94939 15.8% 3,520 7,108 Yellow 95551 0.6%665 1,374 Red 92649 15.5%15,082 34,406 Yellow 92660 0.6%16,942 36,906 Red 94063 13.7%10,598 34,503 Yellow 94010 0.5%17,378 42,730 Red 94970 12.9%874 689 Yellow 94589 0.5%10,097 30,668 Red 94901 11.6%16,336 42,482 Yellow 94565 0.5%29,369 96,081 Red 92663 11.1%12,246 21,572 Yellow 90815 0.5%14,883 41,026 Red 94402 11.0%10,225 25,764 Yellow 95555 0.4%224 337 Red 94089 10.5% 8,474 22,313 Yellow 94965 0.4% 6,459 11,408 Red 94920 8.5% 5,954 12,797 Yellow 92107 0.3%14,706 31,148 Red 94903 8.3%12,587 30,048 Yellow 94710 0.3% 3,231 7,461 Red 94585 7.4% 9,572 29,599 Yellow 92008 0.3%13,051 27,330 Red 94501 7.0%26,889 63,843 Yellow 94608 0.3%15,194 30,289 Red 94577 6.4%17,922 48,088 Yellow 92625 0.2% 6,804 12,148 Red 92109 5.7%26,213 48,417 Yellow 94555 0.2%11,941 38,388 Red 92118 5.6%10,884 22,484 Yellow 95012 0.2% 2,739 10,792 Red 90740 5.1%13,714 24,494 Yellow 94956 0.2%916 1,224 Red 94002 5.0%11,015 27,202 Yellow 94956 0.2%916 1,224 Yellow 94904 3.9% 5,665 12,590 Yellow 95548 0.2%582 1,224 Yellow 94587 3.6%22,455 74,601 Yellow 94043 0.2%13,777 31,488 Yellow 93013 3.4% 7,565 16,644 Yellow 95410 0.2%629 1,159 Yellow 94030 3.1% 8,591 22,710 Yellow 95536 0.2% 1,270 2,898 Yellow 94941 3.1%14,226 32,013 Yellow 90293 0.2% 7,059 12,694 Yellow 94025 3.0%16,036 42,788 Yellow 94130 0.1%708 3,064 Yellow 92647 2.7%22,068 62,718 Yellow 95010 0.1% 4,847 9,030 Yellow 94937 2.6%732 816 Yellow 94601 0.1%16,489 52,299 Yellow 94607 2.6%12,397 26,254 Yellow 94503 0.1% 5,639 20,306 Yellow 94107 2.5%15,981 29,689 Yellow 93402 0.1% 6,850 16,350 Yellow 95503 2.5%10,799 25,503 Yellow 94553 0.1%19,612 49,699 Yellow 94066 2.5%15,238 43,124 Yellow 92121 0.1% 1,883 4,655 Yellow 90265 2.2% 9,818 18,389 Yellow 94804 0.0%15,303 41,510 Yellow 91932 2.1%10,488 26,701 Yellow 92054 0.0%17,787 42,173 Yellow 94924 2.1%858 1,134 Yellow 92054 0.0%17,787 42,173 Yellow 94590 2.0%16,069 37,377 Yellow 94606 0.0%16,245 38,303 Yellow 92648 1.9%21,180 46,890 Yellow 95062 0.0%16,798 38,028 Yellow 94579 1.8% 7,310 22,040 Yellow 94123 0.0%15,200 25,941 Yellow 94572 1.7% 3,395 10,411 Yellow 95476 0.0%17,420 36,792 Yellow 93041 1.7% 8,463 24,506 Yellow 92007 0.0% 4,838 11,234 Yellow 95002 1.7%594 2,146 Yellow 94105 0.0% 6,403 9,155 Yellow 95521 1.6% 9,470 21,462 Yellow 93442 0.0% 6,505 10,976 Yellow 94923 1.6% 1,286 846 Yellow 95437 0.0% 7,072 14,632 Yellow 94545 1.6% 9,675 32,525 Yellow 95531 0.0% 9,535 23,470 Yellow 94510 1.5%11,698 28,262 Yellow 95003 0.0%11,883 24,837 Yellow 94954 0.0%14,138 38,414 THE UCLA ANDERSON FORECAST FOR CALIFORNIA DECEMBER 2020 REPORT Tables FORECAST TABLES - SUMMARY UCLA Anderson Forecast, December 2020 California–85 Summary of the UCLA Anderson Forecast for California by Calendar Year 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Personal Income and Taxable Sales Personal Income (Bil. $) 1886.4 2021.0 2172.9 2273.6 2383.1 2514.5 2632.3 2777.2 2813.0 2952.5 3126.8 (% Ch.) 1.8 7.1 7.5 4.6 4.8 5.5 4.7 5.5 1.3 5.0 5.9 Real Personal Income (Bil. 2012 $) 1858.0 1956.1 2073.6 2120.8 2159.0 2196.5 2234.9 2313.9 2291.0 2338.3 2418.5 (% Ch.) 0.3 5.3 6.0 2.3 1.8 1.7 1.7 3.5 -1.0 2.1 3.4 Taxable Sales (Bil. $) 586.4 615.4 638.3 654.1 678.7 708.0 733.8 670.6 725.1 735.9 754.4 (% Ch.) 5.1 4.9 3.7 2.5 3.8 4.3 3.6 -8.6 8.1 1.5 2.5 Real Taxable Sales (Bil. 2012 $) 577.6 595.7 609.2 610.1 614.8 618.4 622.9 558.5 590.5 582.9 583.5 (% Ch.) 3.5 3.1 2.3 0.2 0.8 0.6 0.7 -10.3 5.7 -1.3 0.1 Price Inflation (% Change) Consumer Prices 1.5 1.8 1.4 2.3 3.0 3.7 2.9 1.9 2.3 2.8 2.4 Employment and Labor Force (Household Survey) Employment (% Ch.) 2.1 2.1 2.0 1.8 1.5 1.1 0.9 -8.3 6.1 3.4 2.2 Labor Force (% Ch.) 0.5 0.5 0.6 1.0 0.8 0.6 0.6 -2.0 2.3 1.5 1.4 Unemployment Rate (%) 8.9 7.5 6.2 5.5 4.8 4.3 4.1 10.3 6.9 5.2 4.4 Nonfarm Employment (Payroll Survey, % Change) Total Nonfarm 2.6 2.8 3.0 2.7 2.1 2.1 1.5 -6.8 3.6 3.8 2.5 Natural Resources & Min. -0.1 3.3 -9.6 -15.6 -1.9 2.6 0.4 -1.6 -2.1 -0.3 1.4 Construction 8.0 5.8 8.5 6.0 4.5 6.2 2.7 -3.7 3.6 1.1 2.2 Manufacturing 0.2 1.4 1.8 0.5 0.2 0.9 0.0 -6.2 0.8 2.7 3.1 Nondurable Goods 0.5 1.2 1.3 0.9 -0.6 -1.2 -0.8 -9.2 1.7 3.3 3.5 Durable Goods -0.0 1.6 2.1 0.3 0.6 2.0 0.5 -4.5 0.2 2.4 2.8 Tran., Warehousing & Utility. 3.2 4.1 6.2 6.7 6.3 5.2 5.4 -1.7 2.8 3.3 3.4 Trade 1.8 2.1 1.7 0.9 0.5 -0.1 -1.3 -6.5 3.7 -0.4 -0.8 Information 3.1 2.9 5.3 7.9 0.6 2.6 3.5 -4.9 5.2 4.7 5.0 Financial Activities 1.2 -0.0 2.5 2.6 1.2 0.6 0.4 0.1 2.1 1.1 1.6 Professional & Bus. Servs. 4.4 3.4 2.6 1.6 2.0 3.4 2.0 -4.2 4.8 6.8 3.3 Educational & Health Servs. 3.4 3.0 3.6 3.6 3.8 2.7 3.0 -3.6 3.0 2.6 1.5 Leisure & Hospitality 4.9 4.9 4.1 4.1 2.7 2.0 2.0 -23.2 12.7 7.6 4.7 Other Services 2.4 3.7 1.6 1.8 1.9 1.4 0.8 -16.1 5.4 9.4 4.1 Federal Government -1.9 -1.3 0.8 1.3 0.2 -0.8 0.9 5.2 -1.8 -0.0 0.3 State and Local Government 0.1 2.0 2.2 2.3 1.7 1.2 1.0 -4.9 -1.1 4.7 3.2 Nonfarm Employment (Payroll Survey, Thousands) Total Nonfarm 15150.8 15575.0 16048.6 16479.3 16827.1 17173.1 17430.4 16239.9 16829.4 17466.6 17900.8 Natural Resources & Min. 28.3 29.2 26.4 22.3 21.9 22.4 22.5 22.2 21.7 21.6 21.9 Construction 637.7 674.6 731.8 775.4 810.2 860.3 883.8 850.7 881.7 891.5 911.1 Manufacturing 1261.7 1279.7 1302.3 1309.1 1311.7 1323.0 1323.0 1240.9 1250.3 1283.8 1323.2 Nondurable Goods 470.1 475.7 481.6 486.1 483.4 477.7 473.8 430.0 437.4 451.8 467.8 Durable Goods 791.6 804.0 820.7 823.0 828.3 845.3 849.3 810.9 812.9 832.0 855.5 Tran., Warehousing & Utility 503.7 524.5 557.2 594.5 632.0 664.6 700.6 688.6 707.6 730.6 755.5 Trade 2264.5 2311.0 2351.1 2372.9 2384.4 2382.7 2351.2 2198.4 2280.6 2270.4 2252.0 Information 450.2 463.5 488.2 526.6 529.9 543.5 562.5 534.8 562.8 589.5 619.0 Financial Activities 783.1 782.8 802.4 823.0 832.8 838.2 841.4 842.1 859.5 869.1 883.2 Professional & Bus. Servs. 2348.0 2427.2 2490.4 2531.4 2581.7 2669.4 2723.9 2610.8 2736.3 2922.9 3018.6 Educational & Health Servs. 2308.7 2378.1 2464.4 2552.3 2650.5 2722.3 2805.0 2702.7 2784.5 2855.9 2898.3 Leisure & Hospitality 1675.3 1756.7 1828.6 1902.9 1954.1 1993.7 2032.7 1560.6 1758.8 1892.6 1981.2 Other Services 515.7 534.8 543.4 553.5 563.8 571.8 576.4 483.4 509.7 557.7 580.3 Federal Government 245.6 242.5 244.4 247.5 248.1 246.2 248.5 261.5 256.7 256.7 257.5 State and Local Government 2128.4 2170.4 2217.9 2268.0 2306.3 2335.0 2358.9 2243.3 2219.2 2324.2 2399.1 Construction Activity, Auto Registrations, and Population Residential Building Permits (Thous. Units) 85.4 86.5 98.5 101.3 114.1 117.2 112.7 106.2 123.4 128.6 131.6 Nonresidential Construction Value (Mil. 2012 $) 22280.6 21977.6 24081.1 24940.8 25578.6 29216.6 27104.7 19311.1 19453.7 21094.8 23028.6 Value (Mil. $) 22617.7 23571.9 26347.4 27369.6 28821.5 33464.5 32168.0 23202.8 23864.5 26726.2 29976.9 Auto Registrations (Mil.) 1.7 1.8 2.0 2.0 1.9 1.9 1.8 1.4 1.5 1.5 1.5 Net Immigration (Thous., Past Year) 69.0 73.4 66.4 34.6 55.6 37.4 -11.7 -9.2 -25.2 -33.0 -40.8 Population (Thous.) 38372.9 38700.1 39012.4 39279.0 39552.3 39784.3 39936.6 40092.8 40256.3 40403.5 40534.4 (% Ch.) 0.8 0.9 0.8 0.7 0.7 0.6 0.4 0.4 0.4 0.4 0.3 FORECAST TABLES - QUARTERLY SUMMARY 86–California UCLA Anderson Forecast, December 2020 Summary of the UCLA Anderson Forecast for California by Quarter 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q1 2022Q2 2022Q3 2022Q4 2023Q1 Personal Income and Taxable Sales Personal Income (Bil. $, S.A. Annualized) 2792.4 2734.7 2788.1 2808.6 2808.2 2847.0 2888.7 2932.5 2973.2 3015.6 3062.5 (% Ch. A. R.) -11.5 -8.0 8.0 3.0 -0.0 5.6 6.0 6.2 5.7 5.8 6.4 Real Personal Income (Bil. 2012 $, S.A. Annualized) 2321.6 2260.8 2295.9 2297.6 2278.8 2291.8 2310.3 2329.8 2347.7 2365.4 2388.0 (% Ch. A. R.) -14.5 -10.1 6.4 0.3 -3.2 2.3 3.3 3.4 3.1 3.0 3.9 Taxable Sales (Bil. $, S.A. Annualized) 694.8 715.8 719.8 723.0 726.4 731.1 732.4 734.5 736.1 740.7 746.3 (% Ch. A. R.) 95.7 12.7 2.2 1.8 1.9 2.6 0.7 1.2 0.9 2.6 3.0 Real Taxable Sales (Bil. 2012 $, S.A. Annualized) 577.7 591.8 592.7 591.5 589.4 588.5 585.7 583.5 581.2 581.0 581.9 (% Ch. A. R.) 89.0 10.1 0.6 -0.8 -1.4 -0.7 -1.9 -1.5 -1.6 -0.1 0.6 Price Inflation (% Change Annualized Rate) Consumer Prices 3.5 2.3 1.6 2.7 3.3 3.3 2.6 2.7 2.5 2.7 2.4 Employment and Labor Force (Household Survey) Employment (% Ch. A. R.) 21.1 29.4 5.8 4.6 4.2 3.8 3.7 3.1 2.2 2.5 2.4 Labor Force (% Ch. A. R.) 0.7 13.0 1.0 1.7 1.5 1.6 1.6 0.5 1.7 2.5 1.7 Unemployment Rate (%, S.A.) 11.9 8.9 7.8 7.2 6.6 6.1 5.7 5.1 5.0 5.0 4.8 Nonfarm Employment (Payroll Survey, % Change Annualized Rate) Total Nonfarm 17.0 10.3 6.7 4.7 4.8 4.2 3.9 3.6 2.5 2.6 2.6 Natural Resources & Min. -10.4 8.6 -6.0 0.1 -1.1 -0.9 -0.4 -0.7 1.8 0.1 2.4 Construction 17.2 16.6 2.7 1.8 0.3 2.9 0.3 0.8 0.9 1.4 3.1 Manufacturing 7.5 4.6 1.5 1.7 4.3 2.3 3.4 1.0 2.4 4.2 3.9 Nondurable Goods 11.5 8.4 2.5 3.1 6.5 0.1 5.5 2.1 2.4 3.5 4.7 Durable Goods 5.5 2.6 1.0 1.0 3.2 3.5 2.3 0.4 2.4 4.6 3.4 Tran., Warehousing & Utility 10.8 8.4 2.7 3.9 2.3 4.7 5.2 1.1 0.3 5.2 4.3 Trade 25.6 13.8 10.9 -3.4 0.2 -1.1 1.5 -1.8 -0.9 0.3 -0.6 Information 6.1 9.3 9.1 17.9 6.9 2.8 4.3 2.9 2.9 5.7 4.9 Financial Activities 4.8 6.5 2.6 1.0 0.2 1.5 0.1 1.1 3.5 1.3 1.9 Professional & Bus. Servs. 10.3 8.7 5.0 7.5 7.1 10.8 6.8 7.1 1.9 3.5 3.5 Educational & Health Servs. 17.0 3.1 4.4 8.2 1.9 2.6 1.5 5.0 0.0 0.7 2.9 Leisure & Hospitality 97.4 54.2 23.2 11.5 23.6 2.5 5.7 5.0 8.2 4.8 1.7 Other Services 38.7 24.7 12.5 6.4 3.5 15.2 9.5 9.4 12.3 2.5 4.7 Federal Government 43.6 -18.7 -9.6 -0.2 -0.2 0.0 -0.0 0.0 -0.0 0.4 0.4 State and Local Government -8.0 -2.0 3.7 2.3 2.3 6.3 6.0 5.2 3.9 3.0 2.9 Nonfarm Employment (Payroll Survey, Thousands, S.A.) Total Nonfarm 15882.5 16278.1 16543.4 16733.5 16932.7 17108.1 17271.6 17423.0 17529.5 17642.2 17755.2 Natural Resources & Min. 21.6 22.1 21.7 21.8 21.7 21.6 21.6 21.6 21.7 21.7 21.8 Construction 838.2 871.0 876.8 880.8 881.4 887.8 888.4 890.2 892.1 895.2 902.1 Manufacturing 1219.6 1233.3 1237.9 1243.2 1256.4 1263.6 1274.3 1277.5 1285.1 1298.5 1310.9 Nondurable Goods 420.3 428.8 431.5 434.7 441.7 441.7 447.7 450.1 452.8 456.7 462.0 Durable Goods 799.3 804.4 806.4 808.5 814.8 821.9 826.5 827.4 832.3 841.8 848.9 Tran., Warehousing & Utility 680.0 693.9 698.6 705.3 709.2 717.4 726.5 728.5 729.1 738.4 746.3 Trade 2166.5 2237.6 2296.2 2276.6 2278.0 2271.8 2280.3 2270.0 2264.8 2266.6 2263.3 Information 516.8 528.4 540.0 562.8 572.3 576.2 582.3 586.5 590.6 598.8 606.0 Financial Activities 838.2 851.6 857.0 859.0 859.4 862.7 862.8 865.3 872.8 875.5 879.6 Professional & Bus.Servs. 2572.0 2626.3 2658.9 2707.1 2753.9 2825.2 2872.3 2921.8 2935.9 2961.5 2987.0 Educational & Health Servs. 2683.0 2703.4 2732.9 2787.1 2800.1 2817.9 2828.6 2863.1 2863.3 2868.5 2888.7 Leisure & Hospitality 1426.2 1589.3 1674.5 1720.8 1814.4 1825.6 1851.3 1874.0 1911.4 1933.7 1941.8 Other Services 456.8 482.7 497.1 504.9 509.2 527.5 539.6 551.8 568.0 571.5 578.1 Federal Government 277.4 263.4 256.8 256.7 256.6 256.6 256.6 256.6 256.6 256.9 257.2 State and Local Government 2186.2 2175.1 2195.0 2207.6 2220.0 2254.3 2287.1 2316.1 2338.2 2355.5 2372.5 Construction Activity, Auto Registrations, and Population Residential Building Permits (Thous. Units, S.A. Annualized) 114.3 125.2 122.2 122.9 122.9 125.6 126.2 127.5 129.8 130.8 130.7 Nonresidential Construction Value (Mil. 2012 $, S.A. Annualized) 19497.5 19250.4 19440.5 19448.0 19261.3 19665.1 20093.9 20806.7 21614.6 21864.0 22175.7 Value (Mil. $, S.A. Annualized) 23402.8 23220.4 23567.5 23744.4 23710.7 24435.7 25173.3 26261.7 27476.8 27993.0 28585.0 Auto Registrations (Mil., S.A. Annualized) 1.4 1.5 1.6 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 Net Immigration (Thous., Past 4 Qtrs.) -18.4 -20.3 -22.3 -24.2 -26.2 -28.1 -30.1 -32.0 -34.0 -35.9 -37.9 Population (Thous.) 40114.9 40156.7 40197.6 40237.4 40276.2 40314.1 40350.9 40386.6 40421.4 40455.1 40487.9 (% Ch. A. R.) 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 DECEMBER 2020 REPORT THE UCLA ANDERSON FORECAST FOR CALIFORNIA Charts CHARTS – RECENT EVIDENCE UCLA Anderson Forecast, December 2020 California–89 14,000 15,000 16,000 17,000 18,000 19,000 20,000 2012 2014 2016 2018 2020 2022 Payroll Survey Emp. Household Survey Emp. California Employment (Thous., S.A.) 2 4 6 8 10 12 14 16 2012 2014 2016 2018 2020 2022 California Unemployment Rate (Percent, S.A.) 500 550 600 650 700 750 800 2012 2014 2016 2018 2020 2022 California Taxable Sales (Bil. $, S.A. Annualized) 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2012 2014 2016 2018 2020 2022 California New Car Registrations (Mil., S.A. Annualized) CHARTS – RECENT EVIDENCE 90–California UCLA Anderson Forecast, December 2020 0 20 40 60 80 100 120 140 2012 2014 2016 2018 2020 2022 Single-Family Multi-Family California New Residential Units Authorized By Building Permits (Thous., S.A. Annualized) 10,000 15,000 20,000 25,000 30,000 35,000 2012 2014 2016 2018 2020 2022 California Nonresidential Construction Authorized By Building Permits, Value (3-qtr. moving avg.) (Mil. $, S.A. Annualized) 550 600 650 700 750 800 850 900 950 2012 2014 2016 2018 2020 2022 California Construction Employment (Thous., S.A.) 1,800 1,900 2,000 2,100 2,200 2,300 12,000 13,000 14,000 15,000 16,000 2012 2014 2016 2018 2020 2022 Goods Producing (Left) Services (Right) California Employment by Sector (Thous., S.A.) CHARTS – RECENT EVIDENCE UCLA Anderson Forecast, December 2020 California–91 -6 -4 -2 0 2 4 6 8 10 2012 2014 2016 2018 2020 2022 California U.S. Real Personal Income California versus U.S. (% Ch. Year Ago) -15 -10 -5 0 5 10 2012 2014 2016 2018 2020 2022 California U.S. Nonfarm Employment California versus U.S. (% Ch. Year Ago) 2 4 6 8 10 12 14 16 2012 2014 2016 2018 2020 2022 California U.S. Unemployment Rate California versus U.S. (Percent, S.A.) -15 -10 -5 0 5 10 2012 2014 2016 2018 2020 2022 Nonfarm Emp. Real Personal Income California Employment and Real Personal Income (% Ch. Year Ago) CHARTS – FORECAST 92–California UCLA Anderson Forecast, December 2020 -20 -10 0 10 20 30 2012 2014 2016 2018 2020 2022 California Real Taxable Sales (% Ch. Year Ago) -1 0 1 2 3 4 2012 2014 2016 2018 2020 2022 California U.S. Consumer Price Index California versus U.S. (% Ch. Year Ago) 10.8 11.0 11.2 11.4 11.6 11.8 12.0 12.2 2012 2014 2016 2018 2020 2022 Employment Population California Share of U.S. Employment and Population (Percent) 14,000 14,500 15,000 15,500 16,000 16,500 17,000 17,500 18,000 18,500 2012 2014 2016 2018 2020 2022 History & Forecast Trend California Nonfarm Employment History & Forecast versus Trend (Thous., S.A.) CHARTS – FORECAST UCLA Anderson Forecast, December 2020 California–93 0.0 0.4 0.8 1.2 1.6 2.0 2000 2005 2010 2015 2020 California Population Growth (% Ch. A. R.) -50 0 50 100 150 200 250 300 2012 2014 2016 2018 2020 2022 Net Migration Natural Increase California Net Natural Population Increase and Net Migration (Thous., Over Past 4 Qtrs.) 30 32 34 36 38 40 42 2012 2014 2016 2018 2020 2022 California U.S. Population of California versus U.S. (CA. Mil.; U.S. 10 Mil.) 46.5 47.0 47.5 48.0 48.5 49.0 49.5 50.0 2012 2014 2016 2018 2020 2022 California U.S. Gross Labor Force Participation Rate (Labor Force/Total Population) California versus U.S. (Percent, S.A.) CHARTS – FORECAST 94–California UCLA Anderson Forecast, December 2020 12 16 20 24 28 32 36 2012 2014 2016 2018 2020 2022 California Nonresidential Construction Authorized By Building Permits, Real Value (Bil. 2012 $, S.A. Annualized) 200 240 280 320 360 400 2012 2014 2016 2018 2020 2022 U.S. Median Price of New Single-Family Homes (Thous. $, S.A.) 0 20 40 60 80 100 120 140 2012 2014 2016 2018 2020 2022 Single-Family Multi-Family California New Residential Units Authorized By Building Permits (Thous., S.A. Annualized) 550 600 650 700 750 800 850 900 950 2012 2014 2016 2018 2020 2022 California Construction Employment (Thous., S.A.) CHARTS – FORECAST UCLA Anderson Forecast, December 2020 California–95 2,100 2,200 2,300 2,400 2,500 2,600 2,700 2,800 2,900 3,000 2012 2014 2016 2018 2020 2022 California Employment in Education and Health Services (Thous., S.A.) 1,180 1,200 1,220 1,240 1,260 1,280 1,300 1,320 1,340 2012 2014 2016 2018 2020 2022 California Employment in Manufacturing (Thous., S.A.) 400 440 480 520 560 600 640 2012 2014 2016 2018 2020 2022 California Employment in Information (Thous., S.A.) 2,040 2,080 2,120 2,160 2,200 2,240 2,280 2,320 2,360 2,400 2012 2014 2016 2018 2020 2022 California Employment in Trade (Thous., S.A.) CHARTS – FORECAST 96–California UCLA Anderson Forecast, December 2020 740 760 780 800 820 840 860 880 900 2012 2014 2016 2018 2020 2022 California Employment in Financial Activities (Thous., S.A.) 2,100 2,150 2,200 2,250 2,300 2,350 2,400 2,450 2012 2014 2016 2018 2020 2022 California Employment in State and Local Government (Thous., S.A.) 2,000 2,200 2,400 2,600 2,800 3,000 3,200 2012 2014 2016 2018 2020 2022 California Employment in Professional & Business Services (Thous., S.A.) 240 245 250 255 260 265 270 275 280 2012 2014 2016 2018 2020 2022 California Employment in Federal Government (Thous., S.A.) REGIONAL MODELING GROUP UCLA Anderson Forecast, December 2020 Regional Modeling Group–97 The Los Angeles Department of Water and Power (DWP), established at the beginning of the century is the largest municipally-owned utility in the nation. It exists under and by virtue of the Charter of the City of Los Angeles enacted in 1925. With a work force in excess of 9,000, the DWP provides water and electricity to some 3.5 million residents and businesses in a 464-square-mile area. DWP’s operations are financed solely by the sale of water and electric services. Capital funds are raised through the sale of bonds. No tax support is received. A five-member Board of Water and Power Commissioners establishes policy for the DWP. The Board members are appointed by the Mayor and confirmed by the City Council for five-year terms. Regional Modeling Group REGIONAL MODELING GROUP 98–Regional Modeling Group UCLA Anderson Forecast, December 2020 The Los Angeles County Metropolitan Transportation Authority (Metro) is unique among the nation’s transportation agencies. It serves as transportation planner and coordinator, designer, builder and operator for one of the country’s largest, most populous counties. More than 9 million people – one-third of California’s residents – live, work, and play within its 1,433-square-mile service area. Besides operating over 2,000 coaches in the Metro Bus fleet, Metro also designed, built and now operates over 73 miles of Metro Rail service. The Metro Rail system currently consists of 62 stations and several more are in the planning and/or design stage. In addition to operating its own services Metro funds 16 municipal bus operators and funds a wide array of transportation projects including bikeways and pedestrian facilities, local road and highway improvements, goods movement, and the popular Freeway Patrol and Call Boxes. Recognizing that no one form of transit can solve urban congestion problems, Metro’s multimodal approach uses a variety of transportation alternatives to meet the needs of the highly diverse population in the region. Metro’s Mission is to insure the continuous improvement of an efficient and effective transportation system for Los Angeles County. In support of this mission, our team members provide expertise and leadership based on their distinct roles: operating transit system elements for which the agency has delivery responsibility, planning the countywide transportation system in cooperation with other agencies, managing the construction and engineering of transportation system components and delivering timely support services to the Metro organization. Metro was created in the state legislature by Assembly Bill 152 in May 1992. This bill merged the Los Angeles County Transportation Commission (LACTC) and the Southern California Rapid Transit District (RTD) to become the Los Angeles County Metropolitan Transportation Authority. The merger became effective on April 1, 1993. Metro is governed by a 13-member Board of Directors comprised of: the five Los Angeles County Supervisors, the Mayor of Los Angeles, three Los Angeles mayor-appointed members, four city council members representing the other 87 cities in Los Angeles County and one non-voting member is appointed by the Governor of California. SEMINAR MEMBERS UCLA Anderson Forecast, December 2020 Seminar Members–99 Inland Empire Center for Economics and Public Policy Mission Statement The mission of the Inland Empire Center for Economics and Public Policy (IEC) at Claremont McKenna College is to provide Inland Empire leaders with expert analysis of the region’s unique political and economic landscape. Background The IEC was founded in 2010 as a collaborative effort by the Rose Institute of State and Local Government and the Lowe Institute for Political Economy, both based at Claremont McKenna College. While the Inland Empire is one of California’s fast growing areas, there was little political and economic analysis specific to the region. Recognizing this void and the increasing importance of the area to California’s economy, the two research institutes saw the need for an organization that could deliver analysis on current issues impacting the Inland Empire. The Rose Institute and the Lowe Institute were uniquely positioned to create the IEC because their staffs both specialized in political and economic analysis and were familiar with the Inland Empire. The IEC brings together experts from both founding institutions. Marc Weidenmier, Ph.D., director of the Lowe Institute, is a Research Associate of the National Bureau of Economic Research and a member of the Editorial Board of the Journal of Economic History. Andrew Busch, Ph.D., director of the Rose Institute, is an expert in American government and politics. Manfred Keil, Ph.D., an expert in comparative economics, has extensive knowledge on economic conditions in the Inland Empire. Kenneth P. Miller, J.D., Ph.D., is an expert in California politics and policy who studies political developments in the Inland Empire. The primary ways that the IEC presents its analysis is through publications and conferences. The Inland Empire Outlook, which provides analysis on the Inland Empire’s political and economic developments, is the IEC’s predominant recurring publication. Its inaugural issue was published in Winter 2010. Besides publications, the IEC also hosts conferences throughout the Inland Empire. The conferences bring together panels of experts and business and political leaders in the Inland Empire to address current topics affecting the region. The annual economic forecast conference held at the Citizens Business Bank Arena in Ontario is in cooperation with the UCLA Anderson Forecast. Members SEMINAR MEMBERS 100–Seminar Members UCLA Anderson Forecast, December 2020 As the state's primary energy policy and planning agency, the California Energy Commission is committed to reducing energy costs and environmental impacts of energy use - such as greenhouse gas emissions - while ensuring a safe, resilient, and reliable supply of energy. The nonpartisan Legislative Analyst's Office (LAO) has been providing fiscal and policy advice to the California Legislature for more than 65 years. It is particularly well known for its fiscal and programmatic expertise and nonpartisan analyses relating to the state budget, including making recommendations for operating programs in the most effective and cost-efficient manner possible. Its responsibilities also include making economic and demographic forecasts for California, and fiscal forecasts for state government revenues and expenditures. It also prepares fiscal analyses for all propositions that appear on the California statewide ballot, including bond measures. For more information about the LAO, please visit our website at www.lao.ca.gov or call us at 916-445-4656. SEMINAR MEMBERS UCLA Anderson Forecast, December 2020 Seminar Members–101 The State of California’s Department of Finance is responsible for submitting to the State’s fiscal year budget to the Governor in January of each year. The Department is part of the State’s Executive Branch and part of the Governor’s Administration. The Director of Finance is appointed by the Governor and is his chief fiscal advisor. The Director sits as a member of the Governor’s cabinet and senior staff. Principal functions include: Establish appropriate fiscal policies to carry out the Administration’s Programs. Prepare, enact and administer the State’s Annual Financial Plan. Analyze legislation which has a fiscal impact. Develop and maintain the California State Accounting and Reporting System (CALSTARS). Monitor/audit expenditures by State departments to ensure compliance with approved standards and policies. Develop economic forecasts and revenue estimates. Develop population and enrollment estimates and projections. Review expenditures on data processing activities of departments. In addition, the Department of Finance interacts with the Legislature through various reporting requirements, by presenting and defending the Governor’s Budget and in the legislature. The Department interacts with other State departments on a daily basis on terms of administering the budget, reviewing fiscal proposals, establishing accounting systems, auditing department expenditures and communicating the Governor’s fiscal policy to departments. The energy industry is changing rapidly and dramatically. As global competition transforms the way companies do business, energy issues are no longer simply local, or even national. At the same time, its clear that the importance of providing reliable local service has never been more important. Our heritage at Southern California Edison is based on reliability. For more than 100 years we have provided high-quality, reliable electric service to more than 4.2 million business and residential customers over a 50,000 square mile service area in coastal, central, and southern California. Of course, recent changes in the California’s electric industry have affected us as well. In 1997, as part of the restructuring of the electric industry in our state, SCE sold its 12 fossil fuel generating stations and overhauled nearly every aspect of its business to prepare for the changing environment. While we still own and operate hydro and nuclear power facilities that serve our area, our main role is that of power transmission and distribution. The power needed for our customers is largely purchased from the California Power Exchange and provided by SCE to our customers without a price markup. At SCE we want you to know that even in times of change, we retain our proven commitment to service, reliability, innovation, and the community. SEMINAR MEMBERS 102–Seminar Members UCLA Anderson Forecast, December 2020 The Labor Market Information Division (LMID) of the Employment Development Department is the official source for California's labor market information. The LMID promotes California's economic health by providing information to help people understand California's economy and make informed labor market choices. We collect, analyze, and publish statistical data and reports on California's labor force, industries, occupations, employment projections, wages, and other important labor market and economic data. California’s vast labor market includes over 1.5 million employers covered by Unemployment Insurance and over 19 million people in its civilian labor force. For more information, visit our website at http://www.labormarketinfo.edd.ca.gov/ or call 916-262-2162. From its Los Angeles base, Allen Matkins has conquered California, opening up offices in San Francisco, San Diego, Century City, and Irvine. With approximately 200 lawyers, the firm is known as a top real estate practice in the Golden State. Grown in the City of Angels Allen Matkins has built its empire in the state where residents elect bodybuilders and shrug off earthquakes. Founded in Los Angeles in 1977, Allen Matkins has achieved notable success in corporate and hospitality work, as well as in the securities, employment, bankruptcy, and tax arenas. The firm has earned accolades from west coast publications like the Los Angeles Business Journal and the San Diego Business Journal. Its real strengths lie, however, in its real estate and litigation practices. The firm's litigation department has focuses in real estate, commercial, financial services, construction, environmental, and labor and employment litigation. The firm has not only worked with local clients-like representing a public-private partnership to modernize the Los Angeles Air Force Base-but has also secured nationally known clients including Wells Fargo Bank, Sares-Regis Group, AT&T, Black & Decker, Met Life, The Home Depot, Blackstone Real Estate Advisors, and Capmark Finance. Buying and Selling Up the California Coast Real estate is where the firm shines-Allen Matkins has ranked the No. 1 real estate law firm in California for a decade, according to Chambers & Partners. California Real Estate Journal has also placed Allen Matkins on the top of its real estate firm list, which was based on the number of real estate attorneys in each outfit. The firm's real estate practice handles all aspects of the real estate world, including litigation over construction, land use, landlord tenant, and condemnation issues. And handling the real estate transactions of the present is not enough for the firm; Allen Matkins seeks to predict the future. The firm has developed a partnership with UCLA Anderson Forecast, an organization of economists who attempt to posit unbiased forecasts for California's economy and the nation's. Allen Matkins and the Anderson Forecast put out commercial real estate forecasts, covering rental and vacancy rates. SEMINAR MEMBERS UCLA Anderson Forecast, December 2020 Seminar Members–103 State Controller Betty T. Yee was elected in November 2014, following two terms of service on the Board of Equalization. As Controller, she continues to serve the Board as its fifth voting member. The State Controller is the Chief Fiscal Officer of California, the sixth largest economy in the world. She helps administer two of the largest public pension funds in the nation and serves on 78 state boards and commissions. These are charged with duties ranging from protecting our coastline to helping build hospitals. The Controller is the state’s independent fiscal watchdog, providing sound fiscal control over more than $100 billion in receipts and disbursements of public funds a year, offering fiscal guidance to local governments, and uncovering fraud and abuse of taxpayer dollars. The State Controller's Functions • Account for and control disbursement of all state funds. • Determine legality and accuracy of every claim against the State. • Issue warrants in payment of the State’s bills including lottery prizes. • Administer the Uniform State Payroll System. • Audit and process all personnel and payroll transactions for state civil service employees, exempt employees and California State University employees. • Responsible for auditing various state and local government programs. • Inform the public of the State’s financial condition. • Administer the Unclaimed Property Law. • Inform the public of financial transactions of city, county and district governments. Accomplish more with new streams of revenue. Avenu Insights & Analytics works closely with government clients to help them realize their full revenue potential — without raising taxes. Supported by an experienced team of professionals who have driven results for thousands of government clients, Avenu’s proven solutions give officials the insight and support they need to protect and grow revenue for the communities they serve. We work with you to understand and project your jurisdiction’s tax and economic future, identify and resolve noncompliance issues, and secure the associated revenue. For the past 40 years, Avenu has helped over 3,000 jurisdictions benefit from our Revenue Enhancement and Tax Administration solutions. • Compliance Auditing ensures all expected revenue is accounted for and paid, in a wide variety of tax types, including Sales & Use, Alcohol, Lodging, Business License, Franchise Fee, and many more. • Data & Analytics uses advanced software to assist with Economic Development that aggregates and organizes jurisdictional data into intuitive, graphical views to identify the trends and causes of revenue shifts over any period. • Discovery & Recovery pinpoints and identifies non-filers and revenue shortfalls in license, permit and other taxes, and recovers payment with a budget-neutral approach. • Misallocation identifies tax revenues that have not been properly reported and distributed to the appropriate jurisdiction. • Tax & License Administration provides support across every local tax category and streamlines day-to-day operations, including data entry, billing & collections, funds distribution, compliance, taxpayer education & support services, and application / claims processing. Learn more by visiting www.AvenuInsights.com. MEMBERS 104 - Members UCLA Anderson Forecast, December 2020 Seminar Avenu Insights & AnalyticsCalifornia Economic ForecastCalifornia Energy CommissionCalifornia Legislative Analyst's OfficeClaremont McKenna CollegeCounty of Los Angeles CEOCSU, Dominguez HillsDepartment of FinanceDepartment of Water and PowerEmployment Development DepartmentLA Co Metropolitan Transportation AuthorityOrange County Transportation AuthoritySouthern California EdisonState Controller's Office Annual + ADPCitizens Business BankCity National Bank - CosciaCity of Los AngelesCity of Santa MonicaFirst Republic BankFive PointHanmi BankHomeStreet BankIS AssociatesLos Angeles Police Federal Credit UnionMcMaster-CarrMedPOINT Management, Inc.Metropolitan Water DistrictPacific Western BankPepperdine UniversityRPASouthern California Association of GovernmentsState Bank of India CaliforniaState Compensation Insurance FundSupervalu, Inc.WCIRB Annual ActNow StrategiesALG Inc.Alliance BernsteinAustrian Trade CommissionBank of HopeBoard of EqualizationCal RecycleCalifornia Air Resources BoardCalifornia Association Of RealtorsCalifornia Department of TransportationCalifornia Public Utilities Commission California State Polytechnic University, PomonaCalifornia State University, SacramentoCalifornia Steel Industries, IncCalifornia-Pacific ConferenceCathay BankChartwell Capital SolutionsChicago TitleChu & Waters, LLPCity of CarlsbadCity of La QuintaCity of San DiegoCity of San JoseCity of Santa ClaraCity of TorranceConsulate General of JapanCornerstone Community BankCounty of San DiegoDesmond, Marcello & AmsterEast West BankFDICFerradoGodshalkGranite Rock CompanyHarold Davidson & Associates Inc.Heritage Bank of CommerceHR and A Advisors, Inc.Kaiser PermanenteKPMG LLPLehigh Southwest Cement CompanyLos Angeles Public Library - Business Economics DeptMitsubishi Cement Corp.Neece AssociatesNewland Real Estate GroupNinth Circuit LibraryNorthern California Power AgencyOrange County Executive Office - BudgetOrange County Transportation AuthorityPreferred Employers Insurance CompanySan Diego Gas & Electric Co.SANDAGSchool Services of California Inc.Shorenstein PropertiesSMUDStanford UniversityState of Hawaii - Department of TaxationTC Metal Co.The Aerospace CorporationThe Olson CompanyUniversity of California Library, BerkeleyUniversity of California San DiegoUniversity of CincinnatiUniversity of RichmondWarland InvestmentsWescom Credit UnionYork University Libraries UCLA Anderson Forecast, December 2020 Sponsors-105 PUT OUR TAILORED INSIGHTS TO WORK FOR YOU. To make confident decisions about the future, middle market leaders need a different kind of advisor. One who starts by understanding where you want to go and then brings the ideas and insights of an experienced global team to help get you there. Experience the power of being understood Experience RSM. rsmus.com RSM US LLP is the U.S. member firm of RSM International, a global network of independent audit, tax and consulting firms. Visit rsmus.com /aboutus for more information regarding RSM US LLP and RSM International. Thinking about your business is a big part of ours. Sponsors 106-Sponsors UCLA Anderson Forecast, December 2020Winter/Spring 20194 |ECONOMIC HEADWINDS UNCHANGED EXPECTATIONS The Allen Matkins/UCLA Anderson Forecast California Commercial Real Estate Survey polls a panel of real estate professionals and projects a three-year-ahead outlook for California’s commercial real estate industry. Survey results help forecast potential opportunities and challenges affecting the office, multifamily, retail, and industrial sectors. The ongoing partnership between Allen Matkins and UCLA Anderson Forecast is now in its 12th year. Get your copy at: www.allenmatkins.com/ucla Winter/Spring 2019 Issue No. 24 COMMERCIALREAL ESTATESURVEY ALLEN MATKINS | UCLA ANDERSON FORECAST Office Space | Multi-Family | Industrial | Retail Stay on the Leading Edge of Commercial Real Estate UCLA Anderson Forecast, December 2020 Sponsors-107 Our University CheckingAccount is loaded withamazing features. Bank with your brain. • ucu.org • 800.UCU.4510 It’s easy — become a member today! • 1.00% APY* in dividends • Automatic ATM fee reimbursements up to $25 monthly1 • The potential to earn up to 5.00% APY when you have your loans with UCU Federally insured by NCUA. *APY = Annual Percentage Yield. Qualifying University Checking Accounts will earn 1.00% APY in dividends on balances up to $25,000. 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RH-0920-9021 Here for you In these unprecedented times, it is important that you know we are committed to providing you with the financial access, guidance, and support you need during this rapidly evolving situation. Through digital, mobile, and by phone, Wells Fargo Private Bank is here, and we continue to serve you and support our communities so that you can focus on what matters most—caring for your family’s health and safety. Wells Fargo Private Bank provides products and services through Wells Fargo Bank, N.A., the banking affiliate of Wells Fargo & Company, and its various affiliates and subsidiaries. Wells Fargo Bank, N.A. is the banking affiliate of Wells Fargo & Company. © 2020 Wells Fargo Bank N.A. Member FDIC. WCR-0420-00101 Helping you focus on what matters most Steven P. Mann Wealth Management Regional Managing Director 310-285-5929 manns@wellsfargo.com wellsfargoprivatebank.com Investment and Insurance Products: NOT FDIC Insured NO Bank Guarantee MAY Lose Value Proud sponsor Union Bank® is proud to support UCLA–Anderson Forecast. To learn more, contact: Stephen Sherline Private Wealth Management Executive 310-550-6439 stephen.sherline@unionbank.com or visit unionbank.com/theprivatebank ©2020 MUFG Union Bank, N.A. All rights reserved. Member FDIC.Union Bank is a registered trademark and brand name of MUFG Union Bank, N.A. To learn how we can help you sustain your operations and grow your business as economic growth resumes, visit bancofcal.com/UCLABusiness © 2020 Banc of California, N.A. All rights reserved. TOGETHER WE WINTM HELPING YOU MEET TODAY’S CHALLENGES AND TOMORROW’S OPPORTUNITIES. WE ARE THE BANC FOR BUSINESS Supporting every corner of our community. Proud to supportthe UCLA Anderson Economic Forecast A division of Zions Bancorporation, N.A. Member FDIC 1-800-CALIFORNIAcalbanktrust.com Vaco operates globally to serve you locally. With over 40 locations across the globe, Vaco is able to serve clients, candidates and consultants across a multitude of industries and areas of expertise. Our Los Angeles office has been named a Best Place to Work by the LABJ 12 consecutive years since its opening in 2006 and Vaco has been listed as one of Inc Magazine’s Fastest Growing Private Companies for 14 consecutive years since its launch in 2002. Free yourself and contact Vaco for consultative project resources, executive search, permanent placement, and strategic staffing needs today! info.losangeles@vaco.com | 310.693.0490 | vaco.com/losangeles UCLA Anderson Forecast, December 2020 Sponsors-111 Pacific Western Bank is a national financial institution with community focus. With over $27 billion in assets, we look to create opportunity for every client we serve. 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SPEAKERS UCLA Anderson Forecast, December 2020 Speakers–113 William Yu Economist William Yu joined the UCLA Anderson Forecast in 2011 as an economist where he focuses on the economic modeling, forecasting and Los Angeles economy. He also conducts research and forecasts on China’s economy, and its relationship with the U.S. economy. His research interests include a wide range of economic and financial issues, such as time series econometrics, data analytics, stock, bond, real estate, and commodity price dynamics, human capital, and innovation. Currently, he teaches business forecasting and data science courses at UCLA Anderson and UCLA Extension. He also serves as a faculty advisor for the Applied Management Research Program at UCLA Anderson. He has published over a dozen research articles in Journal of Forecasting, International Journal of Forecasting, Journal of International Money and Finance, etc. He also published op-ed articles in Los Angeles Times and other newspapers. He developed the City Human Capital Index and the Los Angeles City Employment Estimate. He has been cited in the local, national and overseas media frequently including Wall Street Journal, Los Angeles Times, Washington Post, Time, Bloomberg, CBS Money Watch, Al Jazeera, U-T San Diego, LA Daily News, LA Daily Breeze, Straits Times, NBC, ABC, CNBC, CNN, and NPR, as well as various Chinese and Korean media. Yu has been invited as a speaker for various events, including the annual Woo K. Greater China Business Conference and National Association for Business Economics. Yu received his bachelor’s degree in finance from National Taiwan University in 1995 and was an analyst in Fubon Financial Holding in Taipei from 1997 to 2000. In 2006, he received his Ph.D. degree in economics from the University of Washington where he was also an economics instructor and won two distinguished teaching awards. In 2006, he worked for the Frank Russell Investment Group for Treasury and corporate yields modeling and forecasting. From 2006 to 2011, he served as an assistant and an associate professor of economics at Winona State University where he taught courses including forecasting methods, managerial economics, international economics, and macroeconomics. Jerry Nickelsburg Director Jerry Nickelsburg joined the UCLA’s Anderson School of Management and The Anderson Forecast in 2006. Since 2017 he has been the Director of The Anderson Forecast. He teaches economics in the MBA program with a focus on Asian economies. As the Director of The Anderson Forecast he plays a key role in the economic modeling and forecasting of the National, and California economies. He has conducted research in the areas of labor economics, industrial organization, statistics, and international monetary economics, focusing on the development of new data and the application of economic theory and statistical methods to policy issues. His current academic research is on specific skills, structural unemployment, and on energy efficiency in transportation. He is a regular presenter at Economic Conferences and is cited in the national media including the Financial Times, Wall Street Journal, New York Times, Los Angeles Times, and Reuters. He received his Ph.D. in economics from the University of Minnesota in 1980 specializing in monetary economics and econometrics. He was formerly a professor of Economics at the University of Southern California and has held executive positions with McDonnell Douglas, FlightSafety International, and FlightSafety Boeing during a fifteen-year span in the aviation business. He also held a position with the Federal Reserve Board of Governors developing forecasting tools, and has advised banks, investors and financial institutions. From 2000 to 2006, he was the Managing Principal of Deep Blue Economics, a consulting firm he founded. He has been the recipient of the Korda Fellowship, USC Outstanding Teacher, India Chamber of Commerce Jubilee Lecturer, and he is a Fulbright Scholar. He has published over 100 scholarly and popular articles on monetary economics, economic forecasting and analysis, labor economics, and industrial organization and he is the author of two books on monetary economics and exchange rates. Speakers SPEAKERS 114–Speakers UCLA Anderson Forecast, December 2020 Edward Leamer Distinguished Professor Edward Leamer served as UCLA Anderson’s Chauncey J. Medberry Professor of Management and professor of economics, professor of statistics and director of the UCLA/Anderson Business Forecast Project. His philosophy on education is straightforward. After serving as assistant and associate professor at Harvard University, Leamer joined the UCLA faculty in 1975 as professor of economics. In 1990 he moved across campus to UCLA Anderson and was appointed to the Chauncey J. Medberry Chair. He is a fellow of the American Academy of Arts and Sciences, and a fellow of the Econometric Society. In 2014 he won the award for Outstanding Antitrust Litigation Achievement in Economics, awarded annually by the American Antitrust Institute. Leamer’s work has been impactful beyond the classroom and his academic research. As director of the UCLA Anderson Forecast, he influenced business practitioners in every field. For example, in his December 2000 forecast, the UCLA Anderson Forecast stood virtually alone in predicting the 2001 recession. In a special release on December 12, 2001, the Forecast correctly analyzed the likely unimportance of 9/11 for the evolution of the recession. In December 2002, Leamer began warning about a momentum-driven overheated housing market that was sure to cause problems for the economy in the future. Leamer is a research associate of the National Bureau of Economic Research and has been an occasional visiting scholar at the IMF and the Board of Governors of the Federal Reserve System. He has served on the Councils of Economic Advisors or Governor Wilson, Governor Schwarzenegger and Mayor Garcetti. He has been on the Advisory Board of the Bureau of Economic Analysis. He has published over 120 articles and five books and reminds those interested to hurry to Amazon.com to purchase his most recent books: either Macroeconomic Patterns and Stories, or The Craft of Economics. His research papers in econometrics have been collected in Sturdy Econometrics, published in the Edward Elgar Series of Economists of the 20th Century. His research in international economics and econometric methodology has been discussed in New Horizons in Economic Thought: Appraisals of Leading Economists. David Shulman Senior Economist Emeritus David Shulman is Distinguished Visiting Professor and a “Manag-ing Director” at the Financial Leadership Program at Baruch Col-lege where he mentors students seeking front-office careers on Wall Street, and a Visiting Scholar/Senior Economist at the UCLA Anderson Forecast where he is responsible for U.S. Macro. In addition, he is currently Managing Member of his LLC where he is engaged in investment and litigation consulting. He comments on his blog, http://shulmaven.blogspot.com. In December 2005, he retired from Lehman Brothers where he was Managing Director and Head REIT analyst. From 2001-04 he was voted on the Institutional Investor All Star Teams including First Team in 2002. Prior to joining Lehman Brothers in 2000 he was a Member and Senior Vice President at Ulysses Management LLC (1998-99) an investment manager of a private investment partnership and an offshore corporation whose total investment capital approximated $1 billion at the end of 1999. From 1986-1997, Mr. Shulman was employed by Salomon Brothers Inc in various capacities. He was Director of Real Estate Research from 1987-91 and Chief Equity Strategist from 1992-97. In the latter capacity he was responsible for developing the Firm’s overall equity market view and maintaining the Firm’s list of recommended stocks. Mr. Shulman was widely quoted in the print and electronic media and he coined the terms “Goldi-locks Economy” and “New Paradigm Economy”. In 1991, he was named a Managing Director and in 1990 he won the first annual Graaskamp Award for Excellence in Real Estate Research from the Pension Real Estate Association. Prior to joining Salomon Brothers Inc., he was Vice President and Director of Research Planning at TCW Realty Advisors in Los Angeles. Earlier in his career Mr. Shulman was an academic. He was an Associate Professor of Management and Economics at the University of California at Riverside and Financial Economist at the UCLA Business Forecasting Project. In 2017, the David Shulman Endowed Excellence in Teaching Award Fund was established by a former student of his. A graduate of Baruch College (1964), Mr. Shulman received his Ph.D. (1975) with a specialization in Finance and a M.B.A. (1966) from the UCLA Graduate School of Management. He is married and has three grown children. SPEAKERS UCLA Anderson Forecast, December 2020 Speakers–115 Leila Bengali Economist Leila Bengali is an economist at The Anderson Forecast. She joined in 2019. As an economist, and a native Californian, she focuses on modeling the California economy and on policy issues that are relevant to California. Having studied behavioral economics both in college and in graduate school, she brings insights from this field to her work at The Anderson Forecast. She received her Ph.D. in economics from Yale University in 2019 where she was selected for the Russell Sage Foundation Summer Institute in Behavioral Economics and awarded the Whitebox Advisors Doctoral Fellowship. Her fields of concentration were behavioral economics and public finance. After graduating from Swarthmore College in 2011 with a B.A. in economics (major) and psychology (minor), she worked as an analyst at Analysis Group in the San Francisco Bay Area. During her time in economic consulting, she worked with a team of economists and experts to provide litigation support and research for major national and international companies in industries ranging from manufacturing to information technology. After working in economic consulting, Leila joined Economic Research at the Federal Reserve Bank of San Francisco. Working with prominent economists on issues of employment, education, and economic mobility, Leila conducted research supporting U.S. monetary policy, writing reports for both internal and external audiences. Leila's research lies at the intersection of behavioral economics and public finance. Within these fields, she focuses on how and why individuals use or ignore information when making decisions and on the resulting implications for policy. Leila has also worked with local governments to design and implement policy evaluations and has published in the field of labor economics. Leo Feler joined the UCLA Anderson School of Management and the UCLA Anderson Forecast in 2020. He has conducted research and written articles in the areas of labor economics, urban economics, trade, banking and mergers and antitrust. He is responsible for the U.S. macroeconomic forecast. Prior to joining UCLA, Leo worked in management consulting at Cornerstone Research and Boston Consulting Group. At Cornerstone Research, he advised the U.S. government and corporations on antitrust litigation and economic disputes. At Boston Consulting Group, he advised clients in the consumer retail industry on revenue growth and supply chain optimization strategies. From 2010 to 2016, Leo was an assistant professor of international economics at Johns Hopkins University. He also worked at the World Bank, where he was an advisor to the country director for Brazil. Leo received his Ph.D. in economics from Brown University in 2010, specializing in urban and labor economics; his M.A. in international policy studies from Stanford University in 2002; and his B.A. in economics and international relations from Stanford University in 2002. Leo Feler Senior Economist