A review of Albany campaign spending in the 2020 election

Dear Albany residents,

Although I was termed-out from the Albany City council at the end of 2020, I said I would post the campaign spending results for the 2020 elections, as I have for previous elections. To keep this consistent with the previous postings, the expenditure dollar amounts here are from candidates’ and organizations’ last posted California Fair Political Practices Commission (FPPC) Form 460 page 3, line 11. These are available on the city website here and here.

Albany Unified School District

Measure B was a parcel tax for the Albany Unified School District. It passed in March 2020. I wrote about it here. The total expenditures of the Yes on Albany Schools Measure B committee were $17,575. The majority of that amount came in the form of three $5,000 donations from major school architectural or construction firms. At least one of these firms also contributed to the campaigns to pass the 2016 bond measures B and E to fund the rebuilding of Ocean View and Marin schools. I wrote about that here (you have to scroll down a bit).

Of the three firms that contributed $5,000 to the 2020 Measure B campaign, Dervi Castellanos Architects was the firm that also contributed $5,000 to the 2016 bond campaign. Another, Overaa Construction, is currently working on the Ocean View project. At least I have seen their sign at the Ocean View construction site, so I assume they have been working on that project. I am not sure what other firms are working on the project except for those whose signs are there.

Technically a school construction bond campaign committee must be completely legally separate from the school district itself. However, it still makes me a little nervous when a firm makes a $5,000 contribution to a bond campaign committee to fund a project and is later hired to work on the project. It doesn’t quite pass the “avoid even the appearance of a conflict of interest” test. But that is an issue for the school district to consider.

School board candidates

I’ll simply list the candidates in declining order of how much they spent:

Brian Beall, $4,849

Melissa Boyd, $2,968

Veronica Davidson, no reporting, expenditures below the reporting limit of $2,000.

Albany Teachers Association, $611, mostly for campaign materials in support of state Proposition 15, the “split roll” initiative that would have modified the way commercial property taxes are determined under Proposition 13 (1978).

Albany City Measures

Voter Choice Albany $54,685

Albany Care About Climate $9,808

These two committees supported Measures BB and DD respectively. Both passed.

Albany City Council

Albany has a voluntary campaign spending limit of $6,000. This does not include the Alameda County fee of approximately $1,000 for the 250-word statement that appears in both the sample ballot and the actual ballot. Two of the candidates agreed to the accept the voluntary spending limit–Tod Abbott and Preston Jordan. However, since all but $50 of Jordan’s campaign spending was funneled through a separate committee, Albany Forward, Abbott was the only candidate who spent less than the $6,000 limit.

Ge’Nell Gary $15,644

Albany Forward $12,136

Tod Abbott $5,125

Preston Jordan $50

Aaron Tiedemann $0

Albany Forward was a committee formed to support the election campaigns of candidates Preston Jordan and Aaron Tiedemann.


Albany’s geographic segregation

First, A quick survey of election results

With the elections over, it’s time to take a look at the results. In Albany, the good news is that both the well-qualified African-American female candidates won their elections—Ge’Nell Gary to the city council and Melissa Boyd to the school board. The new city council will consist of three women (two White, one African-American), and two White men. The school board will consist of four women (one African-American, one Latina, and two White), and one African-American man. This may be a first for AUSD—no White males on the school board.

The bad news is that Tod Abbott was not elected to the council. This is a real loss for the city. Tod was among the most qualified candidates to run for council in my memory. Fortunately for both Tod and the city, he is already very involved in civic activities, and I hope for our sake he stays engaged.

The tax measures were a mixed bag. The city passed two out of the three that were on the ballot. Measure CC will increase the transfer tax when a property is sold, and Measure DD adds a user utility tax to our water bills. Measure EE, the special tax for paramedic and ambulance services, failed to achieve the required super-majority and was defeated. The polling for the measures was conducted pre-Covid, so I was prepared for some disappointing results.

Measure BB, the ranked choice voting initiative, won with 73 percent of the votes. Meanwhile at the polls, RCV continued to have a minor effect. There were 23 RCV elections in the Bay area–six in San Francisco, three in San Leandro, nine in Oakland and five in Berkeley. In only one, the District 7 supervisor race in San Francisco, was the eventual winner the not the first-round winner. This is consistent with past experience on RCV–nationally it has only made a difference in about one election in 20. (For my take on RCV, please see my previous post.)

At the federal level, the role of RCV was odder still. RCV is used to elect senators in Maine, but it didn’t matter this time because Republican Susan Collins won a majority in the first round of voting. In Georgia, it’s a good thing that RCV was not used to pick that state’s two senators. If RCV had been used, it’s very likely we would now have two Republican senators from Georgia. Because runoff elections are required, the Democrats get another chance. According to the New York Times, runoff elections were created in the 1960s as racist barriers. But that’s not how it turned out this time.

Exploring Albany through American Community Survey data

When I first came to Albany I lived in University Village (1995-2000). Back then, what remained of the original WWII shipyard housing was still being used to house UC graduate students. Many of those shipyard workers had been African-American. With the help of a UC Berkeley reference librarian who was an expert on census data, I explored the history of University Village. My article still exists in a somewhat garbled form on the Albany Patch website.

A few months ago I began to wonder what census data could tell us about Albany today, and from an unexpected source I found a motherload of information. The United States Census Bureau conducts the national census every decade. It also collects data in between decades through the American Community Survey (ACS). A user-friendly version of the data comes in the form of the Narrative Profiles, 22-page reports which allow you to select national, state, county, city and census tract data. The 22 pages are consistently formatted, making it easy to compare different regions. If you already know what you are looking for, there are more direct ways to search the data. But the narrative profiles allow you to explore the data and make connections that you otherwise might miss.

If you are interested, I have created a download link that includes the narrative profiles for all six Albany census tracts, the cities of Albany and Berkeley, Alameda County, the state of California, the USA, and my spreadsheet summary of what I found. You can download the zip file here. The map below displays the six census tracts that make up the City of Albany, which is shown in pink. The six census tracts are numbered 4201-4206 as you move counter-clockwise from the NE corner.

Map 1: The six Albany census tracts in pink.

Here’s an easy way conceptualize how the census tracts are defined: Draw two vertical lines through the map, one along San Pablo Ave and one along the BART tracks. Next draw a horizontal line along Solano Ave from San Pablo Ave to the eastern city border. Finally, draw a curving line that follows Buchanan Ave from San Pablo Ave to the western border of Albany. These lines divide Albany into the six census tracts. In the graphs that follow, the census tracts will be ordered from left to right by their average household income, as in the table below:

Figure 2: The six Albany census tracts and descriptions, ordered from low- to high-income.

Albany population, households and K-12 enrollment

The first three graphs below display some general population statistics about Albany’s six census tracts. Keep in mind this is data from 2014-18 survey. The 2015-19 survey will be released on Dec. 10. The 2020 census data will become available in the Spring of 2021. The subtitles show the citywide totals:

Graph 1: Population of Albany by six census tracts. City total is 19,758.

In Graph 1 above, note that the most populous census tract is NW-Condos. It contains 24.6 percent of Albany’s population. In addition to the three Pierce St. condo complexes, this tract also includes apartments and a mix of single family homes, from small bungalows to larger houses on Albany Hill.

Graph 2: Number of Albany households by census tract. City total is 7,391.

The largest number of households in Albany is also in the NW-Condos census tract. Graphs 1 and 2 have similar shapes, which indicate the number of persons per household is similar across census tracts. The average number of persons per household in Albany is 2.67. This figure varies from a low of 2.49 in NW-Condos to a high of 2.87 in SE-St. Mary’s.

Graph 3: K-12 enrollment. City total is 3,905.

In Graph 3 above, K-12 enrollment can include students in private schools, although the majority of students are enrolled in Albany public schools. (The 2019-20 enrollment of AUSD is 3,586, according Ed-Data.) Note that the percentage of students from SW-UC Village is only 9.3 percent of the total Albany enrollment. The families of these students are sometimes criticized because UC is exempt from local property taxes. This is not quite true. First of all, UC Village residents pay sales taxes just like any other Albany residents. In addition, the per-student state funding for Village kids is the same as other AUSD students. Finally, the new mixed-use development there does pay local taxes because the land is being used for commercial purposes.

The ethnic composition of Albany’s census tracts

I have ordered the following four graphs, graphs 4-7, in declining order by population of the racial and ethnic groups. Note that In census data the term Hispanic is an ethnic concept, not a racial one. Hispanics can be of any race. Of course, race itself is no longer considered a scientific concept.

Graph 4: White non-Hispanic population. City total is 9,098.

The SE-St. Mary’s census tract is home to the largest White Non-Hispanic population in Albany. In Albany as a whole, 46.0 percent of the population is White Non-Hispanic. This is much higher than the figure for Alameda County (31.8 percent) or California (37.5 percent). But Albany’s percentage is much lower than the United States as a whole (61.1 percent).

Graph 5: Asian/Pacific islander population. City total is 5,994.

In Albany, the Asian/Pacific Islander population is concentrated in the NW-Condos census tract and the two other lower-income tracts (72.8 percent). In Albany as a whole, the percentage of Asian/Pacific Islanders is 30.3 percent, about the same as Alameda County (30.4), but much higher than the state as a whole (14.7 percent) or the nation (5.6 percent).

Graph 6: Hispanic population. City total is 2,501.

Albany has a relatively low Hispanic population (12.7 percent), lower than Alameda County (22.5 percent), California (38.9 percent) or the nation (17.8). There is a fairly large Hispanic population in the SE-St. Mary’s census tract. Otherwise Hispanics are concentrated in the two census tracts west of San Pablo Ave (57.0 percent).

There is a mixture of housing types in all our census tracts. The SW-UC Village tract contains a disproportionate share of younger student families and their children, but it also contains our assisted living center for seniors. All our census tracts border on either San Pablo Ave, Solano Ave, or both. Many apartment buildings are located on our commercial corridors. There are a few large apartments along Solano in the SE-St. Mary’s census tract, but fewer in the NE-AHS tract. This might explain the difference in the Hispanic population of our two highest-income districts. But this is speculation–it is difficult to tell from the data we have.

Graph 7: African-American population. City total is 490.

Albany has a low percentage of African-American residents, at 2.5 percent of the population. This percentage is lower than Alameda County (10.8 percent), California (5.8 percent) or the nation (12.7 percent). Of Albany’s 490 African-American residents, 41.7 percent live in one census tract, N-Plaza to Solano. Albany’s African-American population peaked in WWII when housing was built for shipyard workers at the current location of University Village. This population declined steadily after WWII and continues to fall in the sustained aftermath of the 2008 financial panic, and with the rise of the tech industry and high housing prices in the inner Bay Area.

The racial and ethnic percentage composition of Albany’s census tracts compared to the citywide averages

In the following four graphs, graphs 8-11, I use the same data used in the four graphs above, graphs 4-7, but instead of counting people, I calculate the percentage breakdowns in each census tract and compare them to the citywide average (orange line). This controls for the different sizes of the census tracts, allowing trends to become more apparent. In the graphs below, note how many trends are monotonic or nearly so. That is, they tend to trend either up or down from one side of the city to the other.

Graph 8: The percentage of White non-Hispanic residents in each census tract (blue bars) compared to the citywide average (orange line).

As we move from the census tract that contains University Village to the one that contains St. Mary’s high school, Albany gets progressively more White non-Hispanic. The percentage rises from about one-third of the population west of San Pablo Ave to two-thirds of the population east of the BART tracks.

Graph 9: The percentage of Asian/Pacific Islander residents in each census tract (blue bars) compared to the citywide average (orange line).

As we travel across our census tracts, the percentage of Asian/Pacific Islanders tends to drop, from about 43 percent in NW-Condos to about 16 percent in SE-St. Mary’s.

Graph 10: The percentage of Hispanic residents in each census tract (blue bars) compared to the citywide average (orange line).

As we travel across our census tracts, the percentage of Hispanics declines, but does increase in the highest-income census tract, SE-St. Mary’s. However, it still remains below the citywide average there.

Graph 11: The percentage of Black or African-American residents in each census tract (blue bars) compared to the citywide average (orange line).

The two census tracts between San Pablo Ave and the BART tracts contain above- average percentages of African-American residents, while the other four to the east and west have below-average percentages.

Other related census tract trends

The final four graphs, graphs 12-15, show related information about Albany’s census tracts that is also mostly monotonic as we travel across the city.

Graph 12: Percent foreign-born residents by census tract.

The number of foreign-born residents declines as we move from west to east in Albany, from almost 47 percent west of San Pablo Ave to a little more than 13 percent in the SE-St. Mary’s census tract.

Graph 13: Percent of households in single-family homes by census tract.

The percentage of households living in single-family homes rises as we move from west to east in Albany. The citywide average is 53.8 percent, but it is almost 90 percent east of the BART tracks.

Graph 14: Median household income by census tract.

Graph 14 above shows a monotonic increase in median income. In Albany, as in nearby cities in the East Bay, incomes tend to rise as you travel from west to east, and as altitude increases.

Graph 15: Percent of households with annual incomes greater than $200,000 by census tract.

Graph 15 takes another look at income, this time as the percentage of households with income great than $200,000 annually. The bars tend to rise more steeply than in Graph 14. This is mostly likely because with respect to medians, averages are more affected by outliers–in this case very high incomes. Note that in the SE-St. Mary’s census tract, 35 percent of the households have incomes above $200,000 annually.

Albany is geographically segregated

Many Albany residents like to think of our city as being diverse, and it is–if you only look at the city as a whole. If you zoom down the census tract level, Albany is a geographically segregated city. As we move from west to east, Albany becomes whiter and higher-income, with a higher proportion of native-born residents living in single-family homes. There is nothing unusual about this. You’ll find a similar pattern in Berkeley and Oakland, and in Contra Costa County cities of El Cerrito and Richmond.

To see why I am concerned, see the map below. Currently, All five members of the city council, and three of the five school board members, reside in the two highest-income census tracts in Albany:

Map 2: The approximate location of Albany’s city council and school board members.

Note that five new city council and school board members have been elected and will be seated in December, 2020. I don’t know the exact addresses of the new members, but the net result should be a shift of one or two dots from east of the BART tracks to the census tracts between BART and San Pablo Ave.

Slightly less than 30 percent of Albany residents live in its two highest-income census tracts east of the BART line, yet the majority of city council and school board members live there. This is a problem, especially since the income and ethnic/racial characteristics of the east side and west side of Albany are very different. Albany’s elected officials should be more representative of the city as a whole. I think Albany’s citizens should be concerned about this.

Will our switch to ranked choice voting somehow solve this problem? I doubt it. The reality is that a significant portion of the voters do not follow local government issues closely and the easiest way to get their attention and win their votes is through campaign spending. Campaign expenditures for city council races (and school district parcel taxes) have been growing in recent years, and this puts lower-income candidates and their neighborhood donors at a disadvantage.

The final campaign expenditure reports for the last election are due on February 1, 2021, and can be viewed on the city’s website soon after that date. Although I’ll no longer be on the council then, I’ll review the expenditure reports and post what I discover here. We will have to wait and see how these issues play out over the coming months.


Ranked choice voting in Albany?

Dear Readers, Below you will find a long discussion of ranked choice voting in Albany. Be warned, this piece is about 4,400 words. But I think it’s important to look at some examples of ranked choice voting, and how it would work in our city. For a shorter take, please see my ballot statements here and here. If you get bored or run out of time, you can scan the subheadings or skip down to the closing thoughts at the end. All words in purple are hyperlinks.

This November in Albany, in the form of Measure BB, we are being asked to consider changing to a different set of voting rules known as ranked choice voting (RCV). The proposed RCV system we are being asked to consider will certainly be more expensive than our current system, adding an extra $26,000 cost for each election, but there is no guarantee is will be any better. All voting systems are imperfect, and a system that is suitable for one city may not be the best choice for another city.

Social Choice Theory

The imperfection of all voting systems was established by Nobel Laureate economist Kenneth Arrow with his famous Impossibility Theorem. In his 1951 book, Social Choice and Individual Values, Arrow jump-started the field of social choice theory. This body of thought attempts to provide a systematic framework to explore how well individual votes and other preferences can be aggregated to the societal level. Arrow’s Impossibility Theorem showed that all voting systems have fundamental flaws, although these might not always be serious. Arrow famously stated, “Most systems are not going to work badly all of the time. All I proved is that all can work badly at times.”

Later another Nobel Laureate, the Indian economist Amartya Sen, extended social choice theory to examine problems in developing countries like the failures in institutions that lead to poverty and famine. You can read more about Arrow (here and here) and Sen (here and here).

There are dozens of alternative voting models. RCV is just one. Before we jump down the alternative voting rabbit hole, it’s a good idea to read about Arrow’s Impossibility Theorem to better understand the complexities of social choice theory. Don’t believe me? Read this. But I’ll leave it up to you to browse the literature. You can also start here or here, or search for “ranked choice voting” in Google Scholar.

Ranked choice voting in Albany

For now, let’s focus this conversation on Albany. The Albany City Council and the Charter Review Committee have looked at the issue of RCV several times over the years. Here is a recent example from the March 19, 2018 city council meeting, item 8.1. The staff report is a useful overview.

Albany is a charter city — like many older cities, Albany has its own set of bylaws, or charter. Most newer cities are general law cities that rely on the State of California’s rules for cities. Currently, general law cities are not allowed to use RCV, and most recent governors, including Jerry Brown and Gavin Newsom, have vetoed RCV bills for general law cities because of their concerns that RCV is overly complicated and will lead to voter confusion.

Because Albany is a charter city, we have the option of using RCV. However, this requires study by our charter review commission, consultation with our city attorney, and a modification of the city charter — which requires a vote of the citizens of Albany. Both the city council and the charter review committee have looked at the issue of RCV repeatedly, and in general have not been interested in adopting it.

This year a group of advocates calling themselves Voter Choice Albany began collecting signatures for a citizen’s initiative to put RCV on the Nov. 2020 ballot. A citizen’s initiative requires signatures from ten percent of Albany’s registered voters, plus some extras as a buffer. In Albany, that’s about 1,200 signatures. The group claims it had collected about 600 signatures before it had to stop because of the Covid pandemic. 

Soon after their signature-gathering moratorium, the members of Voter Choice Albany approached the city council and asked it to place their initiative on the ballot as a council-initiated proposal. When the council did not express much interested in doing so, Voter Choice Albany threatened to sue the city if it did not place the measure on the ballot.

Given that lawsuits are expensive regardless of the outcome and given that Albany’s citizens will sign just about any petition, the council concluded that Voter Choice Albany would have probably eventually found their 1,200 signatures. Therefore, the city decided this was not a fight worth having and agreed to put the issue to the voters. That is how we find ourselves where we are today.

I have many issues with RCV that I’d like to discuss. As I mentioned above, for brief explanations, please see my ballot statements here and here. I’ll go into more detail below:

The human problem that RCV doesn’t solve

Albany’s adults are usually focused on their families and careers. Serving in a volunteer capacity on a city commission and gaining the experience necessary to serve on the city council can be a thankless task. But it is an important one. Getting residents involved in the workings of city governance is part of that long process.

If we don’t do this work, we are often left in the few months before elections seeking people who are willing to run. By the time election day rolls around, this problem is either solved or it’s not. At that point, no fancy voting algorithm is going to solve the problem for us. It’s too late. RCV is not a solution to our fundamental problem of getting citizens involved.

Single-seat ranked choice voting (instant runoff voting)

RCV as proposed for Albany comes in two flavors — single-seat RCV, also known as instant runoff voting, and a more complicated version called at-large RCV that is rarely used in the United States. You can read more about how they work at the RCV advocacy website, fairvote.org. It is a well-organized site that provides lots of information. Here is what I discovered there:

Nationally, 23 jurisdictions presently or imminently use RCV to elect officials. Of those, 17 exclusively use single-seat RCV (instant runoff voting).

To give you some perspective, the nine-county Bay Area alone has about 100 jurisdictions, including cities and counties. There are thousands of public jurisdictions in the United States.

Instant runoff voting makes a lot of sense for cities that have been conducting primary elections followed by runoff elections. Holding two elections can strain the resources of both cities and candidates, and the instant runoff process saves them both time and money.

However, Albany doesn’t use primary and runoff elections for council members, so one of the main justifications for switching to RCV is missing in our city. If Albany were to switch to instant runoff voting from its current at-large system, it is not clear what we would gain. In practice, the vote-transfer process of RCV doesn’t appear to make much difference.

According to fairvote.org, “There have been 15 RCV races in the U.S. which were won by a candidate other than the first-round leader. That’s 4.2 percent of the 353 single-winner RCV races since 2004.”

In the Bay Area, four cities that vote by districts use instant runoff voting — Oakland, Berkeley, San Leandro and San Francisco. San Francisco has been using RCV since 2004. Oakland, San Leandro and Berkeley have been using it since 2010. According to online voter registrar records, there have been 60 RCV elections in San Francisco and 83 in the East Bay, a total of 143.

In 94.4 percent of these elections, the candidate who won the first round of counting either prevailed in the first round (just like in a conventional election) or won after additional rounds of counting rank-choice ballots. In only 5.6 percent of the elections (8 of 143) did a candidate who did not take the lead in the first round come from behind to win. Both nationally and in the Bay Area, the extended ballot counting of RCV only affects the outcome of about one election in twenty.

At-large ranked choice voting

The RCV initiative on the ballot this November in Albany also allows our city to implement the even more complicated at-large version of RCV. Referring again to the fairvote.org website:

Two jurisdictions exclusively use multi-winner RCV (single transferable vote) – Cambridge, MA and Eastpointe, MI.

Two use a combination of single- and multi-winner RCV – Minneapolis, MN and Palm Desert, CA.

Two use a form of multi-winner RCV called preferential block voting – Payson, UT and Vineyard, UT.

Albany’s RCV advocates are asking the voters to make Albany only the fifth city in the country to adopt at-large RCV. It would be an experiment, and one that, in my opinion, will not do much for Albany. Here is an example of how at-large RCV work from the fairvote.org website.

The example above involves six candidates for three seats in a partisan election. This is not how elections work in Albany. First of all, our elections are non-partisan. Secondly, it would be rare to have six candidates for an Albany election. Finally, we are left to assume that one Republican winning thanks to RCV is a better outcome than electing three Democrats. I’m not sure why, in general, that should be the case.

It can be difficult to picture how adding second- or third-choice candidates, or even lower-ranked candidates, from the ballots of eliminated candidates can shape the eventual outcome of the election. There are three conditions that typically apply in the unusual case that counting the subsequent ranked choices beyond the first round makes a difference:

1) If, after the first round, two candidates are nearly tied.

2) If there are enough candidates to create a depth of ranked ballots that are capable of making a difference.

3) If the voting patterns in the subsequent rounds are sufficiently different from the patterns in the first round.

A useful analogy is to the counting of vote-by-mail (VBM) ballots after the polls have closed. In the past, VMBs were mostly absentee ballots, but over time more and more voters have switched to becoming permanent VBM voters. Imaging the following scenario:

In an election, 100,000 voters go to the polls. After the polls close, it is announced that Candidate A leads Candidate B by 51,000 votes to 49,000. The candidates are nearly tied, separated only by 2,000 votes (as in #1 above). The voter registrar’s office announces that there are 3,000 VBMs that need to be counted. There are enough remaining votes to make a difference (as in #2 above). If Candidate B was the choice of all the VBM voters, she would win by 52,000 to 51,000 votes. However, in order squeak out a victory, she would need only 2,501 of the VBM votes to win.

 If Candidate B does get 2,501 VBM votes and Candidate A gets 499, then Candidate B wins by 51,501 to 51,499. In this example, Candidate B would have to take more than five-sixths of the VBM ballots (83.33 percent). During the in-person ballot box voting, she only earned 49 percent of the vote (as in #3 above). Therefore, it seems unlikely that Candidate B will be the eventual winner.

Simulated examples from Albany elections

Keeping these concepts in mind, let’s apply them to a hypothetical Albany example. In the 2018 city council election, there were two open seats and three candidates. Here are the three candidates and their vote totals:

Peggy McQuaid          4,716

Rochelle Nason           4,245

Preston Jordan            4,009

Total votes                  12,970

Note that this election was not held under at-large RCV rules, but let’s use this as an example of how an at-large RCV election would play out. In an at-large RCV election with three candidates, in addition to making a first-rank vote, voters also would make second- and third-ranked choices. In an at-large RCV election with two open seats, any candidate that gets more than one-third of the vote is automatically elected. In this case, one-third rounded up is 4,324 votes. Peggy McQuaid is elected with 392 over-votes, or votes over the minimum she would have needed. Neither Nason nor Jordan are over the threshold of 4,324 votes. Nason is 79 votes short, and Jordan is 315 votes short.

In the next step, McQuaid’s 392 over-votes must be redistributed to the remaining candidates in proportion to the second-rank choices of the voters who ranked her first. We have no information on those choices, but we can make some reasonable guesses. Nason would need 79 of McQuaid’s 392 over-votes, or 20.15 percent, to have the 4,324 ranked choice votes necessary for election. Jordan would need 315, or 80.36 percent.

Given that the two female candidates were both incumbents, it is reasonable to assume Nason would get more than 20.15 percent and would be declared the second winner. In this example, 1) the leaders were not close to being tied, 2) there were enough ranked ballots to make a difference, and 3) the voting patterns were unlikely to be sufficiently different from the first-round votes. Therefore, the conventional election and an at-large RCV election would have yielded the same results.

Here is another example based on Albany’s 2018 school board elections:

Hinkley                       4,922

Duron                          4,575

Doss                            3,092

Blanchard                   2,940

Stapleton-Gray            2,438

Total votes                  17,967

Again, this election was not held under at-large RCV rules, but let’s use this as an example of how an at-large RCV election would play out. In at-large RCV election with five candidates, in addition to making a first-rank vote, voters also would make second- through fifth-ranked choices. In an at-large RCV election with three open seats, any candidate that gets more than one-quarter of the votes is automatically elected. In this case, one-quarter rounded up is 4,492 votes. Hinkley and Duron are elected with 430 and 83 over-votes respectively. Doss is short 1,400 votes, Blanchard is short 1,552 votes, and Stapleton-Gray is short 2,054 votes.

In this election, there was one slate—the Albany Teacher’s Association (ATA) slate of Hinkley, Duron and Doss, and the two white male incumbents. For the sake of simplicity, let’s assume that the over-votes in the ATA slate all stayed within the slate. In other words, all of the ATA slate voters placed Hinkley, Duron and Doss in first- through third-ranked positions and the incumbents in fourth and fifth positions.

If so, when the over-votes are distributed at the end of the first round of voting, Doss gets all of Hinkley’s 430 and all of Duron’s 83 over-votes. That brings Doss’s vote tally to 3,605 votes, still 887 votes short of the threshold level of 4,492.

The candidate with the least number of votes, Stapleton-Gray, is now eliminated, and his votes are redistributed to the second-ranked candidates of the 2,438 voters who voted for him. Again, we have no information on those, but we can make some educated guesses. If Doss gets 887 of the 2,438 transferred votes, (36.38 percent), then he is over the threshold, and he is the third candidate elected.

However, if the voters tended to vote for the incumbents as a slate, and therefore Blanchard gets at least 1,552 of Stapleton-Gray’s transfer votes, (63.66 percent), then that puts Blanchard across the threshold and Blanchard is elected rather than Doss. This example points out two things contrary to misperceptions about RCV. First, slates form and they matter, and RCV doesn’t necessarily favor candidates of color.

Note in this example that 1) two of the candidates were nearly tied, 2) because there were two more candidates than open seats, there were enough ranked ballots after the first round to make a difference, and 3) if the voting in subsequent rounds were sufficiently different than the first round, RCV can matter. If Doss was elected in this scenario, then the conventional and at-large RCV results would have been the same.  

An example of when at-large rank choice voting works

Imagine a small agricultural town in the Central Valley. Although the Latino population is in the majority, not all the adults are documented, so they have only 40 percent of the 10,000 voters in town, while the Anglos have 60 percent, or 6,000. Voting is at-large, just like Albany’s current system. There are five seats on the city council to be filled, so each voter can vote for up to five candidates. Both the Anglos and the Latinos run five-candidate slates.

Let’s further assume that Anglos only vote for Anglo candidates, while all Latinos only vote for Latino candidates. All voters use all five of their votes. Within the two groups, all candidates are equally popular. Under these assumptions, it is easy to predict the outcome of the election. All five Anglos are elected with 6,000 votes each, while none of the Latinos are elected because they only got 4,000 votes each. This is known as block voting.

In towns like this, Anglo-only city councils can be sustained for decades. The State of California stepped in to remedy this type of voter suppression with the California Voter Rights Act of 2001 (CVRA), which typically requires such cities to move to district elections. In this case, our little Central Valley city could be split into five voting districts with roughly equal populations. Two might be Latino majority, two Anglo majority, and one mixed. With district-based elections, Latinos can easily win two or more seats on the city council.

But what if Anglo and Latino families all live in mixed neighborhoods? The town could consist of single-family homes occupied by Anglos, with one apartment building on each block occupied by Latino farm workers. In that case, moving to voting districts will not remedy the voter suppression. Since the town is homogeneous, all potential districts would have the same ethnic balance.

In this situation, at-large RCV make sense. Under this model, all voters in town are given ballots with 10 lines to rank-order their choice for the five Latino and five Anglo candidates. The 6,000 Anglo voters rank order the Anglo candidates first through fifth, and the Latino candidates sixth though tenth. The 4,000 Latino voters do the opposite. At the end of the first round, each Anglo candidate has 6,000/5 or 1,200 votes. Each Latino candidate has 4,000/5 or 800 votes. The election threshold under at-large RCV rules is 10,000/6, or 1,667 votes. No candidates win in the first round.

As the ranked votes from last-place candidates are transferred, Latinos win two seats, which require 3,334 of their 4,000 votes. Anglos win three seats with 5,001 transferred votes out of their total of 6,000 votes. Note that each group is represented proportionately. Latinos have 40 percent of the voters and 40 percent of the five seats, while Anglos have 60 percent of the voters and 60 percent of the five seats.

This proportional outcome is the story RCV supporters like to tell. However, in real life, there are not just two interest groups. They can be a mix of Anglo and Latino, male and female, low-income and high-income, gay and straight, meat-eating and vegetarian, and so forth. When voter identities are more complicated, it is not clear how well RCV does to proportionately represent all the possible groupings, especially if, as in Albany, there are typically only two or three seats open in each election.

The at-large ranked choice voting record of Cambridge, MA

Cambridge is the city across the Charles River from Boston. It is the home of Harvard University and the Massachusetts Institute of Technology. Cambridge is one of four cities in the United States that uses at-large RCV voting. The city has used at-large RCV continuously since 1941. The more recent election results are online. I found the City of Cambridge’s RCV voting records for their School Committee and their City Council for the five elections that occurred in the odd years from 2011-2019.

Let me discuss the School Committee first. There were six members elected in every odd year in the five elections from 2011-2019. The number of candidates ranged from a minimum of 9 and a maximum of 12. I listed the top six vote-getters in Round 1 of the voting and compared that list to the list of the six elected winners at the end of the at-large ranked-choice voting process. Here is what I found:

In four of the elections, those occurring in 2011, 2013, 2015 and 2019, all six of the Round 1 winners were eventually elected. The at-large RCV process didn’t change the outcome. In 2015, the sixth- and seventh-placed candidates at the end of Round 1 were separated by only 12 votes, and in the at-large RCV process, the seventh-placed candidate acquired a few more votes than the sixth-place candidate and was elected instead.

Now for the Cambridge City Council elections: Using at-large RCV, Cambridge elects nine members to its city council in odd years. I found records for the same time period as the school committee elections, 2011-2019. The total number of candidates in each election varied from 18 to 26. In three of the elections, 2011, 2013 and 2017, all nine of the top vote earners in Round 1 were elected to the council. The at-large RCV process made no difference.

In 2015, the 11th-placed Round 1 candidate replaced the ninth-placed candidate during the at large-RCV process. In the 2019 election, the 10th-placed Round 1 candidate prevailed over the seventh-placed Round 1 candidate to earn a seat on the city council. The at-large RCV process made a minor difference in two elections.   

Note that even though there was a considerable depth of candidates to rank order, it seldom mattered. Also note that with nine open city council seats, any candidate who accumulates more than 10 percent of the votes is elected. Unlike with single-seat instant runoff voting, where one candidate eventually achieves a majority, with nine open seats, no candidate is required to get anywhere near a majority to be elected.

Also notice how different the reality of at-large RCV in Cambridge is when compared to our hypothetical model of a Central Valley farm town. In our farm town example, the ranked-vote transfer process radically altered the eventual winners. In real life in Cambridge, the differences were minor to non-existent.

A brief note on Eastpointe, Michigan

Eastpointe, Michigan, is a city of 32,500 in the Detroit area. According the latest census data, it is now 49 percent African-American. According to the fairvote.org website, Eastpointe is the only jurisdiction other than Cambridge that exclusively uses at-large RCV. This is not quite accurate. As part of a settlement with the federal Dept. of Justice, Eastpointe agreed to to switch to at-large RCV for electing its four city council members. Unlike Albany, Eastpointe has a directly elected mayor, one who is not elected using RCV.

The first election under the new rules took place in November 2019. There were four candidates for two seats on the city council, elected by at-large RCV. The two winners were a white female city council incumbent and a white male. The two candidates who did not win were both African-American, one male, and one female. The conventionally elected mayor was also a city council member who became the first African-American female mayor of the city (more here and here).

We have to keep in mind that this is the first at-large RCV election in Eastpointe. However, it is worth noting that this result is far from what the advocates have advertised about what to expect from an at-large RCV election. It was the conventional election that elected an African-American mayor, and the at-large RCV election that chose two white candidates over two African-Americans.

Closing thoughts about RCV

The advocates of RCV make the assumption that ranked choices provide more information in an election, and that more information is good. But there is another possibility. Perhaps beyond their second- or third-ranked choice, the voters do not research their choices and are confused about the attributes of their lower-ranked candidates. If so, it’s likely they just guess, or fill in their lower-ranked choices randomly.

If so, the RCV process incorporates some information along with a lot of noise. That would explain why RCV elections, either the instant runoff or at-large versions, seldom overturn the first-round winners. For all its technical sophistication, RCV cannot overcome the simple human problem of voters who lack either the resources or the enthusiasm to carefully study all the candidates.

In addition, the notion that every vote counts in RCV is generally not true. In single-seat RCV (instant runoff voting), if one candidate gets more than 50 percent of the vote, the election is over, just like in a conventional election. In at-large RCV with two open seats, if two candidates get more than one-third of the votes each, the election is over, just like in a conventional election. In an at-large RCV election with three empty seats, if three candidates get more than one-quarter of the votes each, the election is over, just like a conventional election. If I didn’t vote for one of the winners in the examples above, none of my votes counted.

At-large RCV creates incentives for candidates to spend more money. In the 2016 council election I joked that my plan was to spend as little money as possible and still come in third. That is exactly what I achieved. If that election was held under at-large RCV rules, I would have had much more incentive to spend more to try to clear the safe-harbor hurdle of getting one-quarter of the votes, which would have protected me from any vote-redistribution surprises. The amount of money candidates need to raise to compete for a volunteer elected position in Albany is a major reason more residents don’t run. RCV won’t help this problem, and may make it worse.

As in the past, this November we are having barely competitive elections for both the city council and school board. For the city council, there are four candidates running for three seats. For the school board, there are three candidates running for two seats. In elections with only one more candidate than open seats, at-large RCV elections are unlikely to yield a different result than conventional elections. That’s because only over-votes of the leading candidates can make a difference, and this is unlikely to happen because there are usually relatively few over-votes, and they tend to be distributed like the first-round votes.   

What do we make of at-large RCV? At least for Albany, I think it is much ado about very little. It does little or nothing to encourage citizen participation in local government. And it does little or nothing to change the outcome of elections. It is a Rube Goldberg machine. Perhaps that explains with so few public jurisdictions in the United States use it.

The advocates for RCV in Albany are asking the citizens to become human guinea pigs in an experiment that isn’t that useful to start with. In addition, Albany residents will have to pay $26,000 per election for the privilege of participating in the experiment. I just don’t see any good reasons to do this, so my advice to Albany voters is to vote no.


California’s COVID-19 deaths keep rising as counties reopen

I’ve put together some a simple chart and two tables to follow up on my last post. With the possibility of reopening some California counties, it’s a good idea to see how they are doing. To start, the chart below shows the top 16 counties by the number of COVID-19 deaths in California.

Chart 1: COVID-19 deaths for 16 California counties with largest numbers of deaths. Blue denotes total deaths until April 20, 2020. Gold indicates deaths in the following week ending April 27.

By both its size and number of deaths, LA County is in a category all its own. The county added 325 deaths in one week, a jump from 619 to 944 COVID-19 deaths. Table 1 below lists the data for those 16 counties in Chart 1, which include more than 95 percent of all COVID-19 deaths in California.

Table 1: Data for the 16 counties included in Chart 1 above, including land areas in square miles and and deaths per million people.

It is helpful to put California counties into four groups. LA County alone is a region, with about one-quarter of the population of the state and more than half the cases. The other regions are described below (note the typo in the title–the data is actually from 4/27/2020).

Table 2: All 58 California counties sorted into four regions. LA County is its own region. Note typo in title, deaths are from the 4/27/2020 LA Times count.

LA county is similar in population to both Sweden (10.23 million) and Greece (10.72 million). In LA County there have been 944 deaths. Sweden has been hailed as a model by pundits who don’t seem to have examined the data closely. Sweden has had 2,568 COVID-19 deaths, almost three times the number in LA County. A better but lesser known model is Greece, with only 140 deaths.

Closer to home, a quick check of Table 1 above reveals that LA County has a death rate of 92 deaths per million, while its neighbor to the south, Orange County, has only 12 deaths per million. This is true even though Orange County has a higher population density. However, after a crowded weekend on Orange County beaches Governor Newsom ordered them to close temporarily. We’ll see if the crowds brought an increase of COVID-19 cases and deaths to what has been a relative safe haven in crowded coastal Southern California.

The LA region and South Bay/Sacramento region have very similar death rates at about one-third the rate of LA County. The remaining 42 counties together have had 76 deaths, only 4.3 percent of the total. The death rate is low, 9.3 deaths per million. Part of the reason for the low death rate is that people are spread very thinly across those 42 counties with a population density of 73.9 people per square mile. That’s about 7.9 million people, one-fifth of the state’s population, spread across more than 106,700 square miles. If that was a rectangle 100 miles wide, it would have to be 1,067 miles tall. Old-fashioned, labor-intensive contact tracing, what’s been called “shoe-leather epidemiology,” will require lots of trained workers willing to travel many, many miles. Newer technologies may not help that much (LA Times, requires registration). Even so, Modoc County, in the far northeastern corner of the state, has reopened.

Meanwhile, construction will begin again in the Bay Area, a move that the East Bay Times questioned in an editorial. The real issues concern testing, whether we have enough test kits, how fast we can process them, and how accurate they are. The rule of thumb for a disease of low prevalence (less than 10 percent of population infected) is that if the true (unknown) prevalence of the disease is roughly the same as the false positive rate of the test, then a positive test result is wrong almost 50 percent of the time.

Here’s an example: Assume one percent of Californians in a random sample have COVID-19. A test accurately reveals the one percent that are infected (i.e. no false negatives). However, 99 percent of the people in the sample don’t have COVID-19. If the test has a one percent false positive rate, then 0.99 percent of the sample will have a false positive result. The study’s results show that 1.99 percent of the sample tested positive, yet we know the true positive rate is only one percent, and almost half the people with positive results really don’t have the disease.

This implies that people will have to be tested more than once, and tested repeatedly, just like professional athletes are tested for performance-enhancing drugs. And even if we start testing people for antibodies to the disease, either from having COVID-19 or getting (someday) a vaccination, we’ll still have to test them to verify that their immunity remains, a least until we have enough experience with the new vaccines and have vaccinated a sufficient proportion of the population.

So far, humankind has only eliminated one virulent disease by vaccination and outbreak tracing–smallpox. We are close to eliminating a second disease, polio, but mostly due to warfare and poverty in some developing countries, finishing the job is proving to be tough. Due partly to anti-vaccination hysteria, we still suffer from occasional outbreaks of whooping cough and measles. COVID-19, even with plentiful and reliable testing and effective vaccines, may be with us for years.


California Counties and COVID-19

There have been several newspaper articles (examples here and here and here) that speculate that population density encourages the spread of COVID-19 by making social distancing more difficult. New York City is the prime example in the U.S. I think it’s fair to say that the YIMBYs and other pro-growth urbanists have taken the position that the problem is not density but “crowding,” which is a more amorphous concept. By crowding I think they mean not enough housing and too many people per housing unit. If that’s the case, then here in California we have the county-level data to examine this question. That’s what I’ve done in the charts below.

The LA Times reports COVID-19 deaths by county and updates the information at least daily. On the evening of April 20, 2020, the online edition reported 619 COVID-19 deaths in California. From the California Dept. of Finance demographics group I found data on county population and average number of people per household. And from Wikipedia I found data on the land area of all 58 California counties. The LA Times excludes five small counties that don’t report data. For those 53 remaining counties I have created a spreadsheet that lists COVID-19 deaths, population, area, and household size, and I calculate population density and deaths per million people.

In Chart 1, I winnowed the data down to the 18 counties that have reported five or more deaths. With respect to the 53 reporting counties, the subset of 18 includes 96 percent of the deaths, 84 percent of the population, but only 44 percent of the land area. In other words, I am excluding the large, lightly populated rural and wilderness counties. A quick look at Mono County explains why. This county has the highest death rate in California, at 73.4 per million people. However, Mono County has had only one death in a population of 13,616 people. Los Angeles is the county with the second highest death rate, at 60.4 per million people. But LA Country has over 10 million people and 619 deaths. I have excluded these rural counties to make the charts less cluttered. 

Chart 1: COVID-19 deaths vs. population for 17 California counties, Los Angeles excluded.

It’s always a good idea to look at the raw data, which I have done in Chart 1 (above). Here’s a handy description: California has about 40 million people and 1,200 COVID-19 deaths. That’s a death rate of 30 per million. Of these amounts, LA County has about one-quarter of the state’s population and almost half of the COVID-19 deaths. I’ve included in the chart a blue line which indicates the average death rate of 30.69 deaths per million in the 53-county data set. I’ve had to exclude LA County because due to its size it is way off the chart. Of the remaining 17 counties that dot the chart, note that two Silicon Valley counties, Santa Clara and San Mateo, lie above the line. Riverside County, just east of LA County, is also above the line. That means their death rates are higher than average.

Orange County is the obvious outlier below the line. This is curious because the county just to the north, LA County, is a huge outlier in the opposite direction. One possible explanation for this is that COVID-19 deaths are counted where they occur in hospitals, and not where the victims lived. If Orange County residents are traveling to LA County hospitals and dying there, that would explain, to some extent, why both counties are outliers. Due to privacy concerns and lack of time during the crisis, coroners may not be reporting deaths based on where people lived. A final thing to note is that San Francisco lies below the average line.

Chart 2: COVID-19 deaths per million vs. population per square mile. San Francisco excluded.

Chart 2 compares the death rate with population density. In this chart, San Francisco County is the excluded outlier, which a population density almost 19,000 people per square mile, which puts it way off the chart to the right. That’s because San Francisco is only 47 square miles and is the only county in California that is also a city. As we’ve discussed, San Francisco’s death rate is below average (22.6 deaths per million). Again, the obvious outlier is Orange County, which is second only to San Francisco in population density, while LA is third. If you hold your hand over the Orange County dot, it’s more obvious that there is a correlation between population density and the death rate. And as before, note that LA is an outlier the the upper part of the graph.

Chart 3: COVID-19 deaths per million people vs. persons per household.

Chart 3 compares the death rate to average persons per household. It contains all 18 counties. Most of the large counties lie near the vertical straight line at 3 persons per household (the state average 2.986). Almost all of the 18 counties in the sample have average household sizes between 2.8 and 3.2. Note that, as before, LA and Orange counties are outliers, LA with a high death rate and Orange County with a low one. The counties to the right tend to be more rural, located in Central Valley and Inland Empire counties, with lower incomes and perhaps more children.

Note that Tulare County is also an outlier. The county is the home of Sequoia National Park and the agricultural city of Visalia. Its relatively high death rate may be due to the high number of cases in nursing homes in the county. Although rural, Republican and anxious to reopen, the county’s Highway 198 is a major gateway to Sequoia and Kings Canyon National Parks and sees throngs of tourists during the summer months.

The three counties to the left in the graph are an interesting group. Placer County lies along the I-80 corridor from Roseville to Lake Tahoe and includes the NW shore of the lake and the ski resorts south of Truckee. Marin and San Francisco counties are located in the Bay Area and are two of the wealthiest counties in California. Although their population densities are very different (18,806 for San Francisco, 506 for Marin), their persons per household numbers are very similar (2.350 for San Francisco, 2.441 for Marin). It’s important to note that in Marin County, only the north-south Highway 101 corridor is densely populated. Most of the rest of the county consists of dairy farms, protected agricultural land and a vast network of regional, state and national parks.

Meanwhile, in San Francisco, the proportion of children has been shrinking for years (see this). The local wisdom is that twenty-somethings meet in San Francisco, get married and move to the East Bay to raise their families. This party explains the low number of persons per household. However, cities in general have lower numbers of persons per household. Berkeley has only 2.28 persons per household, while Albany has 2.57 (and less than 10 COVID-19 cases). The main message of Chart 3 is that if important aspects of crowding are captured by household size, then crowding (unlike population density) doesn’t have much effect on the COVID-19 death rate.

Now for the caveats: County data is far from ideal. First, California counties vary wildly in size, from San Francisco at 47 square miles, to San Bernardino at 20,062 square miles, the largest county in the United States. In addition, population density varies within counties. The populated one square mile of my little town of Albany has about 20,000 people, with a density greater than that of San Francisco. Yet the county in which we are located, Alameda, has an average population of 2,262 people per square mile. Any serious analysis of the spread of COVID-19 will someday require more disaggregated geographical data, perhaps at the city, census tract, or zipcode level.

The COVID-19 pandemic is far from over. Various counties have taken different approaches along different times to sheltering in place, and that will matter, too. The snapshot in time that I’ve been describing may look very different a month from now. As the virus moves inland from more densely populated coastal regions to the more remote inland counties like Tulare, we may see a dramatic late surge in cases and deaths. While it might seem better to track cases and not deaths, testing remains much too unavailable and inconsistent. Death is a lagging indicator of the progress of the pandemic, but it is a certain one. You are either dead or you’re not, and by now we know how to tell a COVID-19 death from those caused by other medical problems.

Finally, the distinction between density and crowding is mushy. A commuter may live in a quiet suburb, but commute to San Francisco on a crowded BART train. And for some urbanists, crowding is the point–crowded bars, crowded concerts and crowded sporting events are not considered negatives. But at the aggregated county level, the COVID-19 death rate appears to correlate more closely with population density than household size.

I’m happy to send the spreadsheet that I used to create these charts to anyone who would like a copy. I’ll update this information every week or so.


Here’s why I’m not voting for AUSD Measure B

This will be short note, I’m afraid. I’m up to my eyeballs in writing projects, so I have to make this quick. The Albany Unified School District (AUSD) has put a parcel tax on the March 2020 ballot as Measure B. After having given it some thought, and after doing some background research, I’ve decided to vote against Measure B. Here’s why:

Measure B replaces the existing Measure LL which will sunset (terminate) in July of 2021. If Measure B simply asked to continue at the same inflation-adjusted level as Measure LL, I would have no trouble endorsing it. However, according my current property tax bill, we now pay $318 annually for Measure LL, while Measure B will start at $448 annually, and increase of 41 percent.

If Measure B guaranteed that the new money would be held in a restricted fund to deal with pension costs, I would vote yes. But it doesn’t. There needs to be some justification for an increase of this amount, and I’m not seeing it. For comparison, note that in the 2018 elections, the City of Albany asked to voters to extend our half-percent sales tax (Measure L) and our parks and open space funding (Measure M), but at the existing rates.

I’ll be the first to admit that when I was on the school board from 2002-06 we used the same sort of heart-warming photos of cute Albany kids in our campaign literature. But times have changed. Local governments are facing a serious pension-funding crisis, one that will play for a decade or longer (here and here and here). AUSD’s literature would be more honest if it featured age 60+ folks like me holding up signs that say “SAVE OUR PENSION SYSTEM.”

In Albany we have some folk wisdom that needs a critical second look. Do we have great public schools? Depends on your benchmarks. Compared to our surrounding schools districts–Berkeley and West Contra Costa, we do have high-performing schools. But compared to schools nationally, Albany schools are solid mid-pack peformers, as this interesting graphic from the NY Times shows. Albany is sometimes compared to another small local city, Piedmont. Oddly enough, the wealthy Piedmont school district tends to underperform when measured against its peers.

And are Albany teachers underpaid? Again, it depends on the comparison. The Albany teacher’s step-and-column table is here. Similar pay schedules for nearby districts are here for Berkeley (scroll down to Appendix 12), Piedmont and West Contra Costa schools. The steps (rows) indicate years of service, while the columns indicate the amount of education.

Albany teachers are paid at least as well as teachers in nearby districts. However, compared to other public organizations, Albany teachers are paid fairly well. For example, I retired as a UC Berkeley science writer and editor after 20 years of working at UC. My pay was roughly the same as a Column 3 Albany teacher with 14 years of experience.

For more comparisons, the City of Albany’s salary schedule is here. The Sacramento Bee maintains a database of public sector employee compensation (here) but it is annoying to use. This database is easier to use, and it is looks accurate (including for my data), but it is put together by a conservative political organization in Nevada.

There are a few caveats that I should mention. First, as a UC employee, I continued to pay into the social security system, so when I retired, I got my UC pension and I am eligible for social security benefits. There is an employer match in social security, so my pay + benefits are higher because of that. When a teacher joins the STRS pension system, they stop paying into the Social Security system. This creates some unusual incentives.

For teachers who enrolled in STRS in 2012 or earlier, their pension payment is based on their highest one year of pay. This leads to the what called spiking, or pay scales with a big bump in the final year. Albany’s step-and-column is a good example. After remaining relatively flat for several years, in the final year, the annual pay jumps by almost $5,000. For teachers hired after 2012, the pension benefit is based on the highest three years of salary, which is the typical practice for public sector organizations like UC and city governments.

More generally, the STRS pension system creates incentives for a steep system of steps, meaning starting teachers get paid less, while senior teachers get paid more. If teachers also paid into the social security system and got full benefits from both (as I do), the incentives for spiking and steeper steps would be reduced. This would benefit starting teachers, who get paid considerably less than senior teachers.

Now, a note on our property tax bills. I just crunched the numbers for 2019-20 (we paid the 1st installment already, and have the 2nd installment due in April). On the left hand side of our bill, the ad valorum portion, the county gets one percent of our assessed value (less the $7,000 homeowner exemption). For me that’s 56.31 percent of my total tax bill. The county gets a bit of parcel tax revenue, but it’s pretty small. From both ad valorum and parcel taxes, the school district gets 20.98 percent of my property taxes. That amount would rise to 22.17 percent if Measure B passes. The city gets 16.56 percent.

Finally, it’s is important to note that California school districts get significant funding from the state in the form of average daily attendance (ADA) money. For AUSD, that amounts to a little more than $9,000 annually per student. If AUSD is turning to Albany residents for extra funding at the top of the business cycle when state budget has a healthy surplus, what is going to happen when the next recession inevitably occurs as our public pension obligations are increasing?

When I was on the school board, the district was run Dr. William Wong, whose management goals were shaped by running poor rural school districts in Southern California. Wong ran a tight ship financially. From my conversations with various Bay Area educators, AUSD since then has developed a reputation for (how to put this politely) getting looser with its financial management. But then if you have a group of soft-hearted, naïve citizens willing to bail you out with parcel taxes, why bother to run a tight ship?

If Albany residents are going to be the funders of last resort for our local government agencies, we need to start thinking seriously about how we will address our long-term funding needs. I don’t think Measure B does that. Albany residents will have plenty of opportunities to tax themselves in the coming decade. For now we might want to hold off.


The naive economics of SB 50


San Francisco State Senator Scott Wiener, along with our own State Senator Nancy Skinner (in the news recently), have resubmitted their zoning bill SB 50, which was converted to a two-year bill at end of the last legislative session. The final version of the bill is not yet available, but the flaws in previous incarnations of this bill no doubt will remain.

The rhetoric from the bill’s supporters has been sloppy enough that I think it’s time to frame the issues the bill raises in the rigorous analytic framework of neoclassical economics. SB 50’s emphasis on housing supply recalls the supply-side economics of the Reagan administration. But neither supply-side economics nor SB 50 are based on mainstream economics. In what follows, I’ll lay out the groundwork my analysis, which will be familiar to any undergraduate economics major. I know because in the early 1990s, I taught economics at UC Berkeley as an graduate student instructor and as an acting instructor.

Here’s my first question, which is one that could have been drawn from a quiz early in an intro econ course: In a market with a standard downward sloping demand curve and upward sloping supply curve, in order to lower equilibrium price and raise equilibrium quantity, is it sufficient to shift the supply curve outward? If not, what other conditions must be assumed?

Figure 1: SB-50’s implicit vision of the housing market.

Figure 1 describes, in a standard intro econ graph, the question posed. In a housing market, assume a fixed demand curve (D), and an outward shift in supply from S1 to S2. The price of housing falls from P1 to P2, while the quantity of housing supplied rises from Q1 to Q2.

This is a result that proponents of SB 50 like to assume. But the result rests upon a very strong, and very unrealistic assumption–that the demand curve for housing is fixed. In the Bay Area, the demand for housing has shifted outward at a dramatic rate, driven by the growth of large monopolistic tech firms like Apple, Google and Facebook, and by the billions of dollars of venture capital being funneled to tech startups here. This growth requires more tech workers, more office buildings and ultimately more housing.

The choice of the expression “housing crisis” was a deliberate, misleading attempt on the part of SB 50 supporters and other pro-growth advocates to shape the debate. The state’s Office of Housing and Community Development (HCD) instead uses the term “housing shortage.” Statewide, the shortage is the result of both a physical shortage of housing and an income mismatch that HCD estimates requires the construction of 1.5 million units of affordable housing for the poorest of California’s residents.

If simple poverty is the major problem for the whole state of California, in the Bay Area the problem is mostly due to relative poverty–the influx of a highly paid cohort of tech workers crowding out lower-income residents. This is not a housing crisis–there was no hurricane or outbreak of mutant termites that destroyed thousands of apartments. What we have is a venture-capital driven influx of tech workers. It would be more accurate to call this a “housing demand shock.”

Figure 2: In the real world, demand for housing is shifting outward.

Here’s how we could graph a housing demand shock. For simplicity, in Figure 2 above, the supply curve of housing is held constant, while the housing demand shock shifts the demand curve from D1 to D2. While the assumption that the supply curve is constant is too simple, it is very realistic to assume that the demand for housing in California, and especially in the Bay Area, has been shifting outward. In this example, like the example in Figure 1, the equilibrium quantity of housing supplied rises from Q1 to Q2. However, unlike in Figure 1, the equilibrium price rises from P1 to P2.

The outward shifts in supply and demand both cause quantities to rise, while these outward shifts have contradictory affects on prices. To explore this more fully, let’s combine shifts in demand and supply together in one graph.

Figure 3: Demand and supply both shift outward, but demand shifts out more.

In this example, demand and supply curves both shift outward. Equilibrium quantities rise as before, and prices rise somewhat. The price response is moderated by the relatively small outward shift of the supply curve.

When both supply and demand curves are shifting out, it is the relative size of the shifts that matter. By now the reader can probably see that if the S2 supply curve continued to shift out far enough, with its intersection point moving down the D2 demand curve, the new price would be lower, not higher. The reader is encouraged to draw diagrams of their own, not only shifting the positions of the curves, but also drawing them steeper (more price-inelastic) or flatter (more price-elastic).

Three general points should be made here: 1) It is unreasonable to assume that housing supply can shift rapidly enough to accommodate a housing demand shock driven by volatile capital flows. This is especially true because builders of new affordable housing shared in very little of this largess. 2) It is generally faster to build office buildings than new communities. With respect to communities, office buildings require far less services like police and fire departments, schools, parks and libraries and utilities. 3) When they ignore the demand issues, SB 50 proponents violate one of the fundamental concepts of economics–that in a market, prices and quantities are set by the interaction of supply and demand, and not by supply conditions alone.

At least in San Francisco, SB 50 isn’t the only game in town. An initiative that explicitly links housing demand and supply will be on the ballot in March 2020. Sponsored by the community development organization Todco, Measure E will cap office construction unless the city meets its affordable housing goals (Links here and here).

Figure 4: Converting office space to housing, especially affordable housing.

While capping office construction to allow new housing to catch up is an idea worth supporting, restoring the jobs/housing balance could still take years–and without something like the Todco proposal it may never happen at all, since SB 50 makes no attempt to control housing demand.

An intriguing possibility to speed up that process is suggested by Figure 4. What if we could increase housing supply and simultaneously decrease housing demand by converting offices to apartments? In such a scenario, small office buildings could be completely retrofitted and converted to apartments, while whole floors of taller office buildings could be converted.

The advantages to this plan are many. Since building any new housing is very expensive, money would be saved by utilizing existing buildings. Downtown office workers could walk to work (or perhaps just take an elevator), and their presence in the neighborhood in the evenings would create a lively after-work social scene with new bars, restaurants and shops. If some of the new apartment units were affordable, inclusionary and affordable housing programs could be tapped for revenue to subsidize the retrofitting.

Of course, this would mean that some tech businesses would leave the city, a trend that is already beginning. But is this so bad? By moving to cities with lower housing costs, tech workers could afford houses instead of apartments, and move into neighborhoods with good schools and other amenities. And they might not have to move far. Oakland, Concord and Walnut Creek, Sacramento, Las Vegas, Phoenix and Austin all are possibilities. It makes sense to move jobs to where housing is more available.

In the story told in Figure 4, as offices start to close and workers move to other cities, the demand curve shift inward from D1 to D2. Housing quantities and prices both fall temporarily. But as the former office spaces are converted to housing, and the supply curve shifts from S1 to S2, housing prices continue to fall while musicians, artists, people of color, students, new immigrants and commuters are drawn back to the city. This is a story of the degentrification of San Francisco.


If SB 50’s advocates fail in part because they do not grasp the interaction of supply and demand, there remains a deeper failure. Upzoning will not effectively increase housing supply, at least not for many decades, if at all. Zoning is a constraint, although a complicated one. But in the current housing market in the Bay Area, zoning is mostly a non-binding constraint. That is why changing zoning laws will not be effective in the short run. By definition a “crisis” is something happening here and now and requires effective solutions in the short run. Zoning changes are not that solution.

To discuss this, I want to introduce the topic of constrained optimization in a form that is familiar–the model of consumer behavior in standard neoclassical economics.

Figure 5: A simple model of consumer choice in the present of a budget constraint.

Figure 5 presents a typical graph that could be found in many intro econ textbooks. A consumer is faced with choosing how many mangoes and avocados to purchase. The goal is to maximize utility subject to a budget constraint. To keep things simple, let’s just assume the consumer budgets six dollars per week on fruit, and they only like to eat mangoes and avocados. Mangos cost $1 each and avocados cost $2 each. If they spend their entire fruit budget of $6 per week, the consumer could buy six mangoes or three avocados, or some combination of the two. Their purchase decision will lie along the blue budget constraint line.

The graph also features a series of indifference curves which display the consumer’s preferences between mangoes and avocados. Along each curve, the consumer is equally satisfied with the options available. As we move to indifference curves further up and to the right, the consumer’s satisfaction, or utility, goes up. That’s another way of stating that more is better, a basic the assumption in these models. The consumer maximizes their utility by reaching the highest indifference curve possible without violating their budget constraint. In this example, our consumer will choose to buy two avocados and two mangoes.

Now let’s assume that due to the popularity of avocado toast, the local grocery where our consumer buys fruit limits customers to one avocado per day, or seven per week. We represent this new constraint on the graph as a dashed vertical blue line at the number seven on the horizontal avocado axis. Now the figure contains two constraints, one binding, one non-binding.

In this example, the budget constraint is the binding constraint, and the store’s limit is non-binding. But if our consumer was shopping for a family, their budget for fruit might be far larger and their budget constraint could lie much farther to the right. If, for example, our family shopper had a fruit budget of $60 per week and wanted to purchase 20 mangoes and 20 avocados, their choices would be constrained by the grocery store’s limits. In that case, the budget constraint would be non-binding, and the store’s limit would be binding.

Here is another example in a different context: Assume you are a very fast center fielder playing for a major league baseball team. The center field wall is short and you a facing a team with lots of power hitters. For you the outfield wall will be a constraint if the opposing team’s hitters are whacking balls into the grandstands. On the other hand, if you are playing a team that focuses on line drives and high batting averages, the outfield wall may not be a constraint because balls aren’t being hit that far.

The important point to remember is that removing non-binding constraints does not change the equilibrium outcome. Whether or not a constraint is binding or non-binding depends on the situation. In large, complex, multi-dimensional models, it is often not obvious which constraints are binding, and computer algorithms are used to determine the optimum outcome. In the real world, building housing is subject to many constraints. In the current Bay Area context, zoning rules are typically not the binding constraints for two reasons. First, there are many other constraints that are binding. Second, zoning rules don’t work quite in way that many SB 50 supporters seem think they do.

In an excellent letter date June 14, 2019 (here), the City Council of Rohnert Park sent to various legislators a list of the many constraints on building new housing. Excerpts from the letter appear in italics below:

There is a flood of proposed legislation in California intended to address housing that are a result of a misdiagnosis of the root causes of the housing shortage. The bills seem to assume that a lack of approvals is unduly constraining housing construction. In reality, it is a complex problem with many contributing factors to the housing shortage including:

• An economic expansion including significant regional construction demand in Silicon Valley and San Francisco for office buildings and campuses

A lack of specialty trade construction subcontractors

• A lack of construction workers

• Immigration uncertainty and hostility from federal government

• Cost, long delays, and uncertainty associated with the California Environmental Quality Act lawsuits

• Tariffs and trade uncertainty driving up materials costs

• A building boom to replace homes lost due to wildfires

• Lack of available sites due to land use protections such as urban growth boundaries, community separators, etc.

• High costs associated with mitigating water, sewer, transportation, and environmental impacts including endangered species (e.g. California tiger salamander, various vernal pool wild flowers)

• State regulatory requirements such as low-impact-development storm water requirements

• Affordable housing inclusionary requirements added to market rate housing projects

• Loss of redevelopment which was the greatest affordable housing producer in the history of California

• Federal tax reform which lowered the value of affordable housing tax credits leading to a widened funding gap for affordable housing projects

Increased local government capital project spending from new gas taxes, regional tolls and other revenue improvements

• Whole-house-vacation-rentals taking housing stock off the market

Lender reticence to extend credit to construction projects post 2008 melt-down

Lack of affordable housing gap funding.

Rather than address those issues within its control, some state legislators are seeking to impose “by-right” development projects on local governments, elimination of fees, removing parking, overriding local plans, and limiting public input.

As the letter describes, there are many binding constraints that prevent housing from being built, constraints which SB 50 does little or nothing to address. But even if all these constraints could be removed, there is still another problem. Upzoning–allowing multifamily and other large housing developments in neighborhoods in which they were previously restricted–requires homeowners to do nothing.

Let me give an example from my own neighborhood. I live in an R-1 neighborhood where only single-family homes can be built. My 1,100 sq. ft. house was built in the 1920s. If my neighborhood was upzoned to R-2 zoning (which allows for multifamily housing), what would I be required to do? Nothing. Upzoning removes a constraint–if my neighborhood became R-2, I could sell my house to someone who plans to build a duplex. But I’m not really interested in doing that. I think I’d rather sell to a family who wants to live in the perfectly adequate house that is already here, and until then I might add some plumbing to my backyard studio to convert it a legal accessory dwelling unit (ADU), which I would probably continue to use as an occasional guest house.

For me, R-1 zoning is a non-binding constraint. As in the examples above, if you remove a non-binding constraint, it doesn’t change the outcome. Some people seem to think that zoning is like eminent domain, where the state can condemn your house, force you to sell, and then demolish it to make room for a freeway (or an apartment building). That’s not how zoning works. Upzoning allows someone to build something bigger on my property, but it can’t require me to let them do it, or to sell to them. I still maintain my property rights.

Even if I wanted a duplex where my little house exists now, there is another problem. I might not be able to find a developer who would want to build it. The project very likely wouldn’t be profitable. Let’s just say because I live in a town with good schools within walking distance, with a charming walkable commercial district nearby, I could sell my house to a young family for $1 million. As an alternative, I could sell it to a developer who wanted to build a duplex.

First the developer would have to pay $1 million for the property. Then they would have to demolish the old house and build two new units. That’s expensive. Then they would have to find a buyer for the project. The problem is that privacy, space and aesthetics are all what economists call normal goods–as your income rises, you demand more of them. If you tear down a charming 1920s bungalow and replace it with a boxy duplex, you are destroying the very characteristics that made the property valuable in the first place.

Given how valuable single-family homes are in the Bay Area, and how expensive it is to build for all the reasons listed above, upzoning R-1 neighborhoods like mine might lead to very little building in the short run. In the long run it might lead to more, but as John Maynard Keynes famously said, “In the long run we are all dead.” If we really want to solve our “housing crisis,” solutions that take several decades are inadequate to the task.

However, the combination of upzoning and gentrifying low-income neighborhoods, typically occupied by families of color, could be profitable under SB 50. That’s why low-income community organizations tend to be among the most vociferous opponents of SB 50. Various versions of the bill in the past have attempted to mollify these critics, but the neighborhood groups are right to be extremely skeptical. They have been hesitant to abandon their positions on the bill (and possibly their positions in their old neighborhoods).

For a good example how and where such problems could emerge, consider Minneapolis. Advocates for upzoning consider the city a model. Minneapolis recently banned single-family zoning in favor of allowing residential triplexes “by right,” which means the city has very limited ability to block the projects. In an article that is both fascinating and disturbing, a Minneapolis planning commissioner, architect and resident of low-income North Minneapolis, dissects this policy (here).

To summarize, the arguments for SB 50 fail for two reasons. First, expanding supply will not bring down prices unless demand is constrained. Second, although zoning is a type of constraint, in the current situation, it is not a binding constraint. However, several other constraints are binding. Upzoning R-1 residential neighborhoods does not require a homeowner to move or prevent them from selling their house to a new owner who might live in it for decades.

If SB 50 is ineffective in bringing about its stated goals, what then is the real purpose of the proposed legislation? The real purpose of SB 50 is to destroy local control and small-homeowner property rights. Real democracy exists at the local level. But for corporate real estate developers and their sycophants (see here and here), local democracy is a nuisance. If democracy, at least on paper, must exist, they would prefer its decision-makers to be housed a compact space, like a state capital building, where they become easier targets for lobbyists and campaign-funding checks.

On the other hand, under local democracy, there are too many decision makers, and too many homeowners, to be bought off easily. Influencing local government officials is like herding cats, and homeowners are a group of independent and opinionated Jeffersonian free holders (at least after the mortgage is paid off). Local governance is messy. Some corporate real estate interests would prefer to do away with local governance and small homeowners altogether, and, judging from the legislation they support, require the little people to live in large, drab apartment blocks like those in the old East Berlin, or to house tech workers in Shenzhen-style worker barracks–quick to build, no design review required. SB 50, along with related bills like Skinner’s SB 330, take a giant step in that direction.


Since legislation like SB 50 and SB 330 are not the solution, it’s a good idea to step back and ask how we got into this mess. If we don’t understand how we got here, if we don’t understand the nature of the malady, we will keep on prescribing for ourselves the wrong remedies.

First, a note about rural California. In many respects, the problem there is not that the rich are getting richer, but that the poor are getting poorer (here and here). Although it is true that lack of affordable housing can exacerbate rural poverty, the opposite is also true–lack of effective demand due to poverty can reduce the amount of new housing. Poverty is both a cause and effect of the rural housing shortage.

Economic relationships in which cause and effect flow in both direction are difficult to disentangle. But in the real world, they are common. In his classic essay, “Politics and the English Language,” George Orwell stated this succinctly:

“But an effect can become a cause, reinforcing the original cause and producing the same effect in an intensified form, and so on indefinitely. A man may take to drink because he feels himself to be a failure, and then fail all the more completely because he drinks.”

In California, the combination of rural poverty and lack of housing are nothing new. Does anyone believe that in 1962, when Dolores Huerta and Cezar Chavez began organizing the United Farm Workers, those farm workers were better housed than they are today?

Here in urban coastal California, things have changed. As I mentioned earlier, the rise of demand for housing has been driven by the growth of large monopolistic tech firms like Apple, Google and Facebook, and by the billions of dollars of venture capital being funneled to tech startups here. The clustering of firms based on emerging technologies has been happening at least since the industrial revolution, and analyzing this new round of tech clustering is keeping economic geographers busy.

One of the new aspects of tech clustering in the United States is that it’s happening during an era of weak antitrust enforcement. Especially given the privacy issues engulfing Google and Facebook, is there any economic efficiency argument for Google Maps and Gmail to be run by the same company? How about Facebook and WhatsApp? The gigantism of tech firms is now drawing the attention of Congress.

New York University finance professor Thomas Philippon is the best known current thinker exploring the failures of U.S. antitrust policy. As he notes in an Atlantic magazine article, “In 1999, the United States had free and competitive markets in many industries that, in Europe, were dominated by oligopolies.Today the opposite is true.” A New York Times article about Philippon’s work on corporate concentration states, “Philippon’s biggest contribution is to explain that it isn’t some natural result of globalization and technological innovation. If it were, the trends would be similar around the world. But they’re not. What explains the difference? Politics.”

Facebook, Google, Apple and other tech firm are not immutable forces of nature. Our rules, or rather the lack of enforcement of them, have led to their growth. Bay Area citizens have every right to use the rule of law to restrain the behavior of, and the problems created by, these behemoths of the Bay.

Many of the problem big tech creates are what economists call negative externalities.The negative externality most often in the news today results from the burning of fossil fuels. The price of burning coal, oil and gas does not include the social and environmental damage caused by increasing levels of carbon dioxide in the Earth’s atmosphere, or their contribution to climate change. One partial solution would be to charge a carbon tax to increase the cost of burning fossil fuels and internalize those costs in the higher price.

Writing in the Jan. 18, 2018, edition of the New York Times, columnist E. Tammy Kim tied the logic of taxing negative externalities to the tech housing demand shock:

“A half-century ago, it seemed inconceivable that factories, smelters or power plants should have to account for the toxins they released into the air. But we have since accepted the idea that businesses should pay the public for the negative externalities they cause. Today, corporations must answer for increased rents and evictions, and for worsening traffic jams. Like air and water pollution, these costs are shared by all of us.”

Approximately 11 months later, in Dec. 2019, a report from the Brookings Institution mirrored the concerns of Kim:

“At the economic end of the equation, the costs of excessive tech concentration are creating serious negative externalities. These range from spiraling home prices and traffic gridlock in the superstar hubs to a problematic “sorting” of workers, with college-educated workers clustering in the star cities, leaving other metro areas to make do with thinner talent reservoirs.”

The Bookings report stresses subsidies to develop new regional growth centers. Their goal is to:

Assemble a major package of federal innovation inputs and supports for innovation-sector scale-up in metropolitan areas distant from existing tech hubs. Central to this package will be a direct R&D funding surge worth up to $700 million a year in each metro area for 10 years. Beyond that will be significant inputs such as workforce development funding, tax and regulatory benefits, business financing, economic inclusion, urban placemaking, and federal land and infrastructure supports.

The New York Times’s Kim endorses not the “corporate takeover of housing policy” (as the advocates of SB 50 suggest), but taxation of negative externalities in existing tech centers:

What is needed in Seattle — as well as San Francisco; Austin, Tex.; New York City; Boulder, Colo.; and other urban areas where the rapid influx of high-paid tech workers has made housing unaffordable for nearly everyone else — isn’t a corporate takeover of housing policy but, rather, a per-employee “head tax” that would fund real investments in affordable housing, which should be a public good.

These two policies are complimentary. In addition to taxing tech’s negative externalities to subsidize affordable housing, the tax revenue could fund the development of new regional growth centers–although if state taxing and funding mechanisms were used, the new regional grow centers would have to be in California.


In this blog post I have attempted to explain how the advocates for SB 50 do not understand the basics of supply and demand. These advocates misunderstand or ignore the many constraints to building housing, and they do not have a clear understanding of zoning or the unintended consequences of upzoning. In their arguments they fail to recognize the broader economic context that includes antitrust and negative externalities.

SB 50 cannot fulfill its stated mission of reducing housing costs in the short run. However, if enacted, the bill could be effective in its intended, long run mission–removing local control of land use and encouraging the corporate takeover of housing policy.

NOTE: Jan. 14, 2019: I made changes to two paragraphs, at the suggestion of a reader. Typos continue to be corrected as I find them. Jan 15: I added link to Nancy Skinner news story in first paragraph.


Please help the city fight off a special district annexation

Imagine that Amazon notified you that starting right now, every week they would send you merchandise and bill your credit card for it–even though you had not ordered any of the items and you didn’t want them. In reality, Amazon can’t get away with doing that because it’s illegal. But the same rules don’t apply in the wacky world of special districts.

Special districts–water districts and other small government agencies–can “annex” your city and charge your property tax bill for the services, even though your city didn’t request them. As a resident, you don’t get to vote. Your city council members don’t get to vote. Whether or not a city can be billed for such unwanted services is up to a special organization called a LAFCo, a local agency formation commission. Alameda’s LAFCo is here.

Alameda County has a special district for mosquito control, the Alameda Country Mosquito Abatement District (MAD). However, the county also provides mosquito control through its broader vector control program. For many years Albany has relied on the country vector control program. It has been one-stop-shopping for us, and the city has been happy with the services provided. We have no interest in being part of an additional mosquito control program, especially one that we’ll pay for with new parcel taxes (although the taxes are only $4.24 annually–at least for now).

In order to prevent annexation, the city has to fight back by requesting something called a protest hearing. I know this must sound bizarre, but special district rules are arcane. And in order to fight against this unwanted annexation of our city, I’m asking for your help. Here’s the official word from the city about how you can file your protest. You should be receiving a postcard with the same information, but just in case, here it is again:




The Alameda County Mosquito Abatement District has applied to annex the City of Albany to become part of the Mosquito Abatement District (MAD). If this application is approved, Albany property owners will have to pay a new fee for services from MAD. The Albany City Council has submitted a letter opposed to this annexation. 

Albany has for many years received mosquito abatement services, from Alameda County Vector Control, and these services are included in our overall package of services from Alameda County. 

The MAD seeks to take control of Albany’s mosquito abatement by ‘annexing’ Albany and obligating Albany property owners with an additional fee.    

The Local Agency Formation Commission (LAFCo) is the agency responsible for administering the protest hearing. LAFCo will hold a public hearing regarding the proposed annexation.  The protest hearing will be held Wednesday, January 8, 2020 from 6:00 p.m. to 8:00 p.m at the Alameda County Administration Building Board of Supervisors Chambers, 5th floor, 1221 Oak Street, Oakland.

We encourage you to please consider filing a protest.

How to File a Protest: To be considered valid, a protest must be written and filed by either a landowner or a registered voter, within the area included within the reorganization. Protests may either be mailed to the Alameda LAFCo at 1221 Oak Street, Room 555, Oakland, CA 94612 or delivered to the LAFCo Executive Officer at the protest hearing. Each protest must be signed and dated, must state whether it is made by a landowner or a registered voter, and must include the name and address of the protester and a street or parcel number identifying the location of the land. A registered voter’s protest must show the name and address appearing on the affidavit of registration. Disclosures related to expenditures made for political purposes related to the subject change of organization must comply with the Political Reform Act (California Government Code Section 81000 et. seq.). Only written protests that are received prior to the end of the hearing on January 8, 2020 will be accepted as timely.

For additional information, please contact Alameda LAFCO Executive Officer Rachel Jones at (510) 271-5142 or rachel.jones@acgov.org.


Yes, you understand correctly. In order to protest as a citizen, you have to write a letter with specific information and either mail it or bring it to the meeting. It makes you suspect these rules haven’t been updated since before the internet was invented. At least you are not required to travel to the meeting in a horse and buggy. For more on special district lunacy, see this episode of John Oliver’s HBO comedy show.

Please consider writing a letter and sending it early enough that it gets to the Alameda County Administration Building before January 8. It doesn’t have to be perfect, it just has to be done. In your letter, you might want to stress that the issue is not the mosquito district’s competency–it’s their redundancy. Our little city doesn’t have the resources to pay twice for the same services. Although mailing a letter or bringing one with you to the meeting is a pain, it might be one of the most cost-effective letters you ever write.

Addendum: January 23, 2020. At the Alameda LAFCO meeting on Jan. 8, 2020 the board voted to force Albany to join the Mosquito Abatement District. It took me a few phone calls and emails to discover what the process was. It turns out, assuming we have about 10,000 registered voters or property owners, one-quarter of them would have either had to send a letter or attend the meeting in person. That’s 2,500 people. Less than 2,500 people and the annexation happens. Between 2,500 and 5,000 people, a vote is held. If more than 5,000 people show or send letters, the annexation would have been cancelled. LAFCO received a total of ninety one written protests–8 registered voters, 83 property owners. Many thanks to those of you who protested. This whole process is bizarre. It reminds me of something out of a movie I saw recently, the black comedy “The Death of Stalin.” The only bright note is that any increases in fees will be subject to a 2/3 majority vote of the whole county.


Housing Crisis? Not so fast…

Here in California we are bombarded with news about our “housing crisis.” State politicians have used the housing crisis as justification for removing local control of zoning and handing carrots to developers. We are told that the Bay Area is the “epicenter” of the housing crisis.

Politicians and pundits who use this overblown language should review some of the reports available from state agencies and business sources. Those reports paint a far more nuanced picture.

The reports show:

1) San Francisco is not the epicenter of the affordable housing shortage. The opposite is true.

2) The state does not have a housing crisis. It does have a severe shortage of affordable housing for our lowest-income residents. This is due a combination of a physical housing shortage and simple poverty. There is not a shortage of market-rate housing.

3) 2018 population estimates show that population growth has slowed dramatically statewide, but the decline varies from county to county. Factors including fires, expensive housing, and the search at the urban boundaries for cheaper housing. Housing projections need to take these new figures into account.

4) Hundreds of thousands housing units have been proposed in California—more than enough to meet growth in housing demand statewide since 2010. While some projects are working their way through local government approval processes, most of them have been approved. For most of these projects, the construction phase is the bottleneck, not local government.

5) Growth is not an act of God. The jobs/housing ratio in the Bay Area is out of balance. The trend toward the “Manhattanization” of San Francisco has been fought sporadically for decades, and is now back on the agenda. There needs to be a serious, competent, open and democratic planning process for growth, both at the regional and state level.

This analysis is based on the following four widely available reports. However, the data has been combined to tease out some conclusions that are not well understood:

1) HCD report: California’s Housing Future: Challenges and Opportunities, Final Statewide Housing Assessment 2025. California Department of Housing and Community Development.

2) LAO report: The 2019-20 Budget: Considerations for the Governor’s Housing Plan. Legislative Analyst’s Office.

3) DOF report: E-5 Population and Housing Estimates for Cities, Counties, and the State, 2011-2019 with 2010 Census Benchmark. State of California Department of Finance, Demographics Research Unit.

4) CIRB report: New Development in California 2018. California Homebuilding Foundation, Construction Industry Research Board.

San Francisco is not the epicenter of the housing crisis

Under the standard definition, any household that spends more than 30 percent of its gross income on rent is considered rent-burdened. The chart below displays the percentage of low-income households that are rent-burdened in California. The chart is taken from the LAO report, but the graph has been truncated to save space. The original chart on page 7 of the report lists several more highly cost-burdened California counties.

Source: LAO report, p. 7.

Note that the least burdened county is San Francisco (which is both a city and a county). Low-income San Francisco residents on average have a rent burden that is lower than any other California county, and lower than the rest of the United States. This is probably due to a combination of low-income residents hunkered down in rent-controlled apartments, while highly paid techies are paying market rates.

According to the report, “The figure demonstrates that low-income households experience similar levels of rent burden across communities—suggesting that insufficient income creates housing affordability challenges even in low-cost communities that have a sufficient supply of housing. Because of this, construction of affordable housing has a key role in helping low-income households afford housing.”

The HCD report takes a slightly different approach, but the results are much the same. In the chart below, counties are ranked by the average percentage of income spent on housing and transportation. As in the LAO chart above, this version of the chart has been truncated to save space. Several more highly rent-burdened counties can be found in the original chart on page 34 of the HCD report.

Source: HCD report, p. 34.

The report shows that not only does San Francisco have a low rent burden, it also has a low transportation cost burden due to access to mass transit and jobs that are close to housing. Together these two charts show that despite the myths, San Francisco is not the epicenter of the state’s housing crisis.

California has a severe shortage of affordable housing

Statewide there is a shortage of 1.5 million housing units for very low- and extremely low-income residents. These income categories are defined based on household income with respect the local area median income (AMI). The county is usually the area used in the definition. Extremely low income is defined as less than 30 percent of AMI, while very low income is 30 to 50 percent of AMI.

An example will help make this clear. Let’s assume an AMI for a particular county is $100 thousand annually for a family of four. Then an extremely low income household makes $30 thousand annually or less, a very low income household makes $30 to $50 thousand annually, a low income household makes $50 to $80 thousand annually, a moderate income household makes $80 to $120 thousand annually, and an above moderate household makes more than $120 thousand annually.

A household at the upper boundary of the moderate income category must spend on rent at least 30 percent of annual income, or $3,000 per month, before it is considered rent-stressed. On the other hand, a household at the upper boundary of the extremely low income category must spend on rent more than $750 per month to be considered rent-stressed. In reality, the AMI for Alameda and Contra Costa counties is slightly higher than these figures. This is due in part to high tech salaries in the Bay Area. In at least nine tech companies, the median salary is more than $200 thousand annually.

The chart below compares incomes and housing availability statewide:

Source: HCD report, p. 30.

There is a lot going on in this graph, so it is useful to work through it carefully. On the left is the bar that shows renter households by comparison to AMI. On the right the bar displays rental units that are affordable for renters in the various income categories.

As the legend on the right shows, in every group except the above moderate group, some households must pay more for housing than they can afford. For example, note the upper boundaries of the light blue bars. For low-income groups and below, there is a shortage of 960 thousand units statewide—the difference between the top of the light blue bars on the left and right. For moderate income households (green bars) and below, there is still a smaller shortage of 61 thousand rental units statewide.

The gap of 1.5 million housing units is shown by the blue arrow. Note that for low, moderate and above-moderate income households, the bars on the right (light blue, green and yellow) are thicker on the right than on the left—within those groups, there is enough housing. If there was enough affordable housing for very low and extremely low income households, all income categories would have sufficient housing (these numbers are for the state as a whole and figures can vary by region).

Taking a broader view, this chart details the mismatch between renter incomes and rental housing supply. One solution is to expand the bottom of the right hand bar—increase the amount of affordable housing. An alternative would be to shrink the bottom of the left hand bar—increase incomes to push renters into higher income categories. As the LAO report stated, the real culprit here is poverty (see here, here and here). But in either case, California doesn’t need to subsidize more market-rate housing. It needs to concentrate on housing for its lowest-income residents.

California’s population growth has slowed dramatically

Housing estimates have to account for population growth. In recent years, this issue has grown more complicated. Population growth has slowed tremendously in the last few years and is falling well behind projections, but in very uneven ways.

Source: DOF E-5 Population and Housing Estimates.

The table above is derived from the population estimates of the California Department of Finance demographic unit. It shows the figures for the state, selected large counties and all nine Bay-Area counties. Population for the state and various counties is shown for the years 2010, 2018 and 2019 (in blue on the left). On the right (in green) we see the total percent change from 2010-19, the annual average for the nine-year period, and the annual percent change for 2018-19.

Let’s start with the far right hand column. Last year’s statewide population growth figure of 0.47 percent is the lowest figure ever recorded. The state population growth rate is about half what had been forecast in long-range estimates. Only two counties, Riverside and Sacramento, had population growth rates of greater than one percent. Also note that three counties with negative growth last year all suffered from devastating fires.

In addition to the three counties with fires, four counties had slower growth than the statewide average—Marin County and the three Silicon Valley counties of Santa Clara, San Francisco and San Mateo. In Silicon Valley, the lack of population growth is partly due to people leaving because they can no longer afford to live there.

Next, it is useful to compare the last two columns. In the state as a whole and in every county except one, last year’s growth rate is below the 9-year average. Population growth has slowed down. The only exception is Sacramento County. The region has become a popular destination for people exiting the Bay Area to look for cheaper housing. Estimates of future housing needs must be adjusted for these slower population growth trends.

Proposed housing is sufficient to meet statewide growth since 2010

A comparison of population growth data, along with proposed housing projects, tells another interesting and complex story. The table below is derived from the Dept. of Finance population figures combined with estimates from the Construction Industry Research Board (CIRM) report of housing projects in the pipeline.

When reviewing these numbers, it’s important to keep in mind two things. First 2010 is the base year for the DOF population reports. California’s housing shortage existed even then. Renter’s income began to lag behind rising rents at least a decade before 2010 (HCD report, p. 29, fig. 1.23). Second, the number of proposed housing projects exceeds population growth in some Silicon Valley counties. If built, this new housing would help restore the jobs/housing balance in those counties, reducing the need for commuting.

Sources: DOF E-5 Population and Housing Estimates and CIRB report.

In the left hand column are the number of persons in 2019 living in households. Note that these numbers are slightly lower than the previous population statistics because they exclude people living in dormitories, assisted living centers, prisons and other group settings. The figures in the green section show the number of housing units in 2019, and how many housing units we would need to keep up with population growth from 2010-19. In the state as a whole, and in every county in the table, there is a shortage—since 2010, housing growth has not kept up with population growth.

The first column in the blue area shows the size of the shortfall, and compared it to the number of housing units that are in the approval and construction pipeline. In some cases, there is more than enough housing in the pipeline to meet population growth, and in some cases it is the opposite. In Los Angeles and Sacramento counties far more housing has been planned than is needed to meet population growth since 2010. The East Bay counties of Contra Costa and Alameda are a little behind. Interestingly, the Silicon Valley counties of Santa Clara, San Mateo and San Francisco all have far more housing in the pipeline that they need to accommodate population growth.

Statewide, there are 801,300 housing units in the pipeline. Of that number, according to the CIRM report, 451,000 are either under construction or are awaiting developers to pull the permits and begin work. In other words, cities have done their jobs and have signed off on 451,000 new housing units statewide—more than enough to meet population growth since 2010. The bottleneck for those units is the construction industry, not local governments. The HCD report states California needs to build 180,000 housing units annually. The number of units the in construction backlog (downstream of local governments) is 2.5 years of needed housing production. In the business press, the problems with the construction industry are well known. For more on the construction industry, see these two articles in the San Francisco Business Times (here and here).

The need for democratic growth planning

Building more housing and increasing housing density in the Bay Area are often justified because they will reduce rents. But if adding density will reduce rents, why does Manhattan, the most densely populated place in the United States, still have high rents? That is the question that the Bay Area’s pro-growth advocates have yet to answer. The relationship between increasing urban population density and lower rents is not straightforward.

The housing crisis has been talked about almost exclusively in terms of supply. Sooner or later, anyone who has taken an economics course will began to wonder about the demand side of the housing market, or put another way, the job/housing balance. Growth is not an act of God. Do we accept growth at any cost? Do we want to turn the San Francisco into Manhattan and the rest of the Bay Area into Brooklyn, Bronx and Queens? Who gets to decide?

To help clarify these issues, the chart above demonstrates a stylized version of San Francisco’s office and housing markets. On the vertical axis are office units. On the horizontal axis are housing units. We define these such that one more office unit requires one more unit of housing for balance. Each office unit provides space for one more person to work, and each housing unit provides space for one more person to live. The thick grey line is the balanced-growth expansion path. Along this line rents will stay reasonable. The points on the upper right indicate places where there are more office units than housing units, and rents are high. To the lower left, there is a surplus of housing units, and rents are low.

Let’s start on the lower left, at point (0,0). Assume some time in the halcyon past San Francisco had job/housing balance. But with a sudden burst of commercial development, 4,000 office units were developed, but only 1,500 housing units. We find ourselves at point A. Rents have risen, low-income residents are being displaced, artists are moving away, etc.

From here there are two possible courses of action. The first would be to get back on the balanced growth path quickly. Commercial growth could be discouraged and held to 1,000 new office units. Meanwhile to preserve housing possibilities for current residents, 3,500 affordable units would be produced. Balanced growth and reasonable rents would be reestablished at point B with a new total of 5,000 office units and 5,000 housing units.

Another alternative would to add 3,500 more market rate units while adding 6,000 more office units. The end result would be 10,000 office units and 5,000 housing units. This is point C. Note that at point C there are double the number of office units with respect to point B. This is the road to Manhattan. Point C has moves us even further away from the balanced growth line than point A. Yet getting to point C requires building just as many housing units as getting to point B.

For developers, point C is advantageous. First, because of the influx of well-paid tech workers, new housing can be market rate. Second, with the job/housing ratio even more out of balance, rents will continue to rise, allowing developers and their allies to continue to decry NIMBYism, impact fees, high construction costs, etc. The reality is that as long as the tech boom continues, developers can maintain the housing “crisis” simply by expanding office construction faster than housing construction can keep up.

But balanced grow per se is not the complete answer. Even if we can stay on the balanced grow path, there remains a question—how much balanced growth do we want? In the short term even balanced growth puts stresses on the kind of infrastructure local governments provide. Think of it this way: When you buy a house, you don’t stop there. You have to turn your house into a home. You furnish it inside with carpets, furniture, beds, kitchenware and eating utensils.

When a developer builds a new neighborhood, all those houses have to be furnished on the outside to turn that collection of houses into a community—infrastructure like water and electricity, sewer systems, police and fire services, park, libraries and schools. Building communities is expensive and takes more time than building offices.

In the long run, too much growth, even balanced growth, leads to crowding. Californians don’t just live to work. They want to get outdoors and enjoy weekends and vacations. Many people already feel that the Bay Area is too crowded (here and here). In addition, even balanced growth can start bumping into environmental constraints—sea level rise, droughts, fire, earthquakes. None of these are made easier to mitigate by more growth and more population.

To focus exclusively on California’s “housing crisis” obscures as much as it illuminates. The issues are far broader, and require a more informed and democratic discussion of how much California should grow, and how it needs to change to adapt to its future. Those are the issues our legislators and all Californians should be discussing.


A review of Albany campaign spending in the 2018 election

It’s time once again for me to post the amounts spent by candidates for Albany city council and school board positions. I typically do this after every election. This information is public record and can be found on the Albany city clerk’s website here.  The candidates are required to complete their post-election reporting by the end of January, and the city posts the information in February. The spending numbers below were taken from each candidate’s Form 460, page 3, line 11.

There were three candidates who ran for two city council positions, Preston Jordan, Peggy McQuaid and Rochelle Nason. McQuaid and Nason were both incumbents, and both won re-election. McQuaid and Nason both agreed to the voluntary campaign spending limit of $6,000. This limit does not include the $980 required by the state for the 250-word ballot statement. Jordan did not agree to adhere to the limit. Their vote tallies (high to low) and spending are listed below.

McQuaid $3,183  votes: 4,716

Nason $6,915.27  votes: 4,245

Jordan $35,379.60  votes: 4,009

For comparison, here are the spending amounts and vote totals for the three candidates who won election in 2016:

Nick Pilch $16,076.48 votes: 5,386

Pete Maass $7,185.47 votes: 5,328

Michael Barnes $1,990.69 votes: 3,589

My spending in 2016 was low because I created my own simple campaign literature in Photoshop and InDesign and I used my surplus yard signs from the 2012 election. In addition, my strategy was to spend as little as possible and still come in third, which is what I accomplished. That seems like the rational approach to me.

I will point out that the two big spenders in 2016 and 2018, Nick Pilch and Preston Jordan, are the co-founders of the advocacy organization Albany Strollers and Rollers. I think we set the voluntary campaign limit in the right place. Pilch and Jordan, who spent much more than $6,000, didn’t seem to get much bang for the buck from their additional spending.

Here are the amounts spent for the Albany Unified School District elections:

Sara Hinkley $4,635.38  votes: 4,922

Clementina Duron $(NA)  votes: 4,575

Brian Doss $(NA) votes: 3,092

Charlie Blanchard $(NA) votes: 2,940

Ross Stapleton-Gray $(NA) votes 2,438

All of the school board candidates except Hinkley spent less than $2,000, so they were not required to file a Form 460. The Albany Teacher’s Association filed a Form 460 to report its campaign expenditures on behalf of the following candidates:

Sara Hinkley $965.20

Clementina Duron $1,370.20

Brian Doss $1,370.20