NEP 48 — Spending NumPy Project Funds

Author

Ralf Gommers <ralf.gommers@gmail.com>

Author

Inessa Pawson <inessa@albuscode.org>

Author

Stefan van der Walt <stefanv@berkeley.edu>

Status

Draft

Type

Informational

Created

2021-02-07

Resolution

Abstract

The NumPy project has historically never received significant unrestricted funding. However, that is starting to change. This NEP aims to provide guidance about spending NumPy project unrestricted funds by formulating a set of principles about what to pay for and who to pay. It will also touch on how decisions regarding spending funds get made, how funds get administered, and transparency around these topics.

Motivation and Scope

NumPy is a fiscally sponsored project of NumFOCUS, a 501(c)(3) nonprofit organization headquartered in Austin, TX. Therefore, for all legal and accounting matters the NumPy project has to follow the rules and regulations for US nonprofits. All nonprofit donations are classified into two categories: unrestricted funds which may be used for any legal purpose appropriate to the organization and restricted funds, monies set aside for a particular purpose (e.g., project, educational program, etc.).

For the detailed timeline of NumPy funding refer to NumPy funding – history and current status.

Since its inception and until 2020, the NumPy project has only spent on the order of $10,000 USD of funds that were not restricted to a particular program. Project income of this type has been relying on donations from individuals and, from mid 2019, recurring monthly contributions from Tidelift. By the end of 2020, the Tidelift contributions increased to $3,000/month, and there’s also a potential for an increase of donations and grants going directly to the project. Having a clear set of principles around how to use these funds will facilitate spending them fairly and effectively. Additionally, it will make it easier to solicit donations and other contributions.

A key assumption this NEP makes is that NumPy remains a largely volunteer-driven project, and that the project funds are not enough to employ maintainers full-time. If funding increases to the point where that assumption is no longer true, this NEP should be updated.

In scope for this NEP are:

  • Principles of spending project funds: what to pay for, and who to pay.

  • Describing how NumPy’s funds get administered.

  • Describing how decisions to spend funds get proposed and made.

Out of scope for this NEP are:

  • Making any decisions about spending project funds on a specific project or activity.

  • Principles for spending funds that are intended for NumPy development, but don’t fall in the category of NumPy unrestricted funds. This includes most of the grant funding, which is usually earmarked for certain activities/deliverables and goes to an Institutional Partner rather than directly to the NumPy project, and companies or institutions funding specific features. Rationale: As a project, we have no direct control over how this work gets executed (at least formally, until issues or PRs show up). In some cases, we may not even know the contributions were funded or done by an employee on work time. (Whether that’s the case or not should not change how we approach a contribution). For grants though, we do expect the research/project leader and funded team to align their work with the needs of NumPy and be receptive to feedback from other NumPy maintainers and contributors.

Principles of spending project funds

NumPy will likely always be a project with many times more volunteer contributors than funded people. Therefore having those funded people operate in ways that attract more volunteers and enhance their participation experience is critical. That key principle motivates many of the more detailed principles given below for what to pay for and whom to pay.

The approach for spending funds will be:

  • first figure out what we want to fund,

  • then look for a great candidate,

  • after that’s settled, determine a fair compensation level.

The next sections go into detail on each of these three points.

What to pay for

  1. Pay for things that are important and otherwise won’t get done. Rationale: there is way more to be done than there are funds to do all those things. So count on interested volunteers or external sponsored work to do many of those things.

  2. Plan for sustainability. Don’t rely on money always being there.

  3. Consider potential positive benefits for NumPy maintainers and contributors, maintainers of other projects, end users, and other stakeholders like packagers and educators.

  4. Think broadly. There’s more to a project than code: websites, documentation, community building, governance - it’s all important.

  5. For proposed funded work, include paid time for others to review your work if such review is expected to be significant effort - do not just increase the load on volunteer maintainers. Rationale: we want the effect of spending funds to be positive for everyone, not just for the people getting paid. This is also a matter of fairness.

When considering development work, principle (1) implies that priority should be giving to (a) the most boring/painful tasks that no one likes doing, and to necessary structural changes to the code base that are too large to be done by a volunteer in a reasonable amount of time.

There are also many tasks, activities, and projects outside of development work that are important and could enhance the project or community - think of, for example, user surveys, translations, outreach, dedicated mentoring of newcomers, community organizating, website improvements, and administrative tasks.

Time of people to perform tasks is also not the only thing that funds can be used for: expenses for in-person developer meetings or sprints, hosted hardware for benchmarking or development work, and CI or other software services could all be good candidates to spend funds on.

Whom to pay

  1. All else being equal, give preference to existing maintainers/contributors.

  2. When looking outside of the current team, consider this an opportunity to make the project more diverse.

  3. Pay attention to the following when considering paying someone:

    • the necessary technical or domain-specific skills to execute the tasks,

    • communication and self-management skills,

    • experience contributing to and working with open source projects.

It will likely depend on the project/tasks whether there’s already a clear best candidate within the NumPy team, or whether we look for new people to get involved. Before making any decisions, the decision makers (according to the NumPy governance document - currently that’s the Steering Council) should think about whether an opportunity should be advertised to give a wider group of people a chance to apply for it.

Compensating fairly

Note

This section on compensating fairly will be considered Draft even if this NEP as a whole is accepted. Once we have applied the approach outlined here at least 2-3 times and we are happy with it, will we remove this note and consider this section Accepted.

Paying people fairly is a difficult topic, especially when it comes to distributed teams. Therefore, we will only offer some guidance here. Final decisions will always have to be considered and approved by the group of people that bears this responsibility (according to the current NumPy governance structure, this would be the NumPy Steering Council).

Discussions on remote employee compensation tend to be dominated by two narratives: “pay local market rates” and “same work – same pay”.

We consider them both extreme:

  • “Same work – same pay” is unfair to people living in locations with a higher cost of living. For example, the average rent for a single family apartment can differ by a large factor (from a few hundred dollars to thousands of dollars per month).

  • “Pay local market rates” bakes in existing inequalities between countries and makes fixed-cost items like a development machine or a holiday trip abroad relatively harder to afford in locations where market rates are lower.

We seek to find a middle ground between these two extremes.

Useful points of reference include companies like GitLab and Buffer who are transparent about their remuneration policies (3, 4), Google Summer of Code stipends (5), other open source projects that manage their budget in a transparent manner (e.g., Babel and Webpack on Open Collective (6, 7)), and standard salary comparison sites.

Since NumPy is a not-for-profit project, we also looked to the nonprofit sector for guidelines on remuneration policies and compensation levels. Our findings show that most smaller non-profits tend to pay a median salary/wage. We recognize merit in this approach: applying candidates are likely to have a genuine interest in open source, rather than to be motivated purely by financial incentives.

Considering all of the above, we will use the following guidelines for determining compensation:

  1. Aim to compensate people appropriately, up to a level that’s expected for senior engineers or other professionals as applicable.

  2. Establish a compensation cap of $125,000 USD that cannot be exceeded even for the residents from the most expensive/competitive locations (8).

  3. For equivalent work and seniority, a pay differential between locations should never be more than 2x. For example, if we pay $110,000 USD to a senior-level developer from New York, for equivalent work a senior-level developer from South-East Asia should be paid at least $55,000 USD. To compare locations, we will use Numbeo Cost of Living calculator (or its equivalent).

Some other considerations:

  • Often, compensated work is offered for a limited amount of hours or fixed term. In those cases, consider compensation equivalent to a remuneration package that comes with permanent employment (e.g., one month of work should be compensated by at most 1/12th of a full-year salary + benefits).

  • When comparing rates, an individual contractor should typically make 20% more than someone who is employed since they have to take care of their benefits and accounting on their own.

  • Some people may be happy with one-off payments towards a particular deliverable (e.g., “triage all open issues for label X for $x,xxx”). This should be compensated at a lower rate compared to an individual contractor. Or they may motivate lower amounts for another reason (e.g., “I want to receive $x,xxx to hire a cleaner or pay for childcare, to free up time for work on open source).

  • When funding someone’s time through their employer, that employer may want to set the compensation level based on its internal rules (e.g., overhead rates). Small deviations from the guidelines in this NEP may be needed in such cases, however they should be within reason.

  • It’s entirely possible that another strategy rather than paying people for their time on certain tasks may turn out to be more effective. Anything that helps the project and community grow and improve is worth considering.

  • Transparency helps. If everyone involved is comfortable sharing their compensation levels with the rest of the team (or better make it public), it’s least likely to be way off the mark for fairness.

We highly recommend that the individuals involved in decision-making about hiring and compensation peruse the content of the References section of this NEP. It offers a lot of helpful advice on this topic.

Defining fundable activities and projects

We’d like to have a broader set of fundable ideas that we will prioritize with input from NumPy team members and the wider community. All ideas will be documented on a single wiki page. Anyone may propose an idea. Only members of a NumPy team may edit the wiki page.

Each listed idea must meet the following requirements:

  1. It must be clearly scoped: its description must explain the importance to the project, referencing the NumPy Roadmap if possible, the items to pay for or activities and deliverables, and why it should be a funded activity (see What to pay for).

  2. It must contain the following metadata: title, cost, time duration or effort estimate, and (if known) names of the team member(s) to execute or coordinate.

  3. It must have an assigned priority (low, medium, or high). This discussion can originate at a NumPy community meeting or on the mailing list. However, it must be finalized on the mailing list allowing everyone to weigh in.

If a proposed idea has been assigned a high priority level, a decision on allocating funding for it will be made on the private NumPy Steering Council mailing list. Rationale: these will often involve decisions about individuals, which is typically hard to do in public. This is the current practice that seems to be working well.

Sometimes, it may be practical to make a single funding decision ad-hoc (e.g., “Here’s a great opportunity plus the right person to execute it right now”). However, this approach to decision-making should be used rarely.

Strategy for spending/saving funds

There is an expectation from NumPy individual, corporate, and institutional donors that the funds will be used for the benefit of the project and the community. Therefore, we should spend available funds, thoughtfully, strategically, and fairly, as they come in. For emergencies, we should keep a $10,000 - $15,000 USD reserve which could cover, for example, a year of CI and hosting services, 1-2 months of full-time maintenance work, or contracting a consultant for a specific need.

How project funds get administered

We will first summarize how administering of funds works today, and then discuss how to make this process more efficient and transparent.

Currently, the project funds are held by NumFOCUS in a dedicated account. NumFOCUS has a small accounting team, which produces an account overview as a set of spreadsheets on a monthly basis. These land in a shared drive, typically with about a one month delay (e.g., the balance and transactions for February are available at the end of March), where a few NumPy team members can access them. Expense claims and invoices are submitted through the NumFOCUS website. Those then show up in another spreadsheet, where a NumPy team member must review and approve each of them before payments are made. Following NumPy bylaws, the NumFOCUS finance subcommittee, consisting of five people, meets every six months to review all the project related transactions. (In practice, there have been so few transactions that we skipped some of these meetings.)

The existing process is time-consuming and error-prone. More transparency and automation are desirable.

Transparency about project funds and in decision making

To discuss: do we want full transparency by publishing our accounts, transparency to everyone on a NumPy team, or some other level?

Ralf: I’d personally like it to be fully transparent, like through Open Collective, so the whole community can see current balance, income and expenses paid out at any moment in time. Moving to Open Collective is nontrivial, however we can publish the data elsewhere for now if we’d want to. Note: Google Season of Docs this year requires having an Open Collective account, so this is likely to happen soon enough.

Stefan/Inessa: at least a summary overview should be fully public, and all transactions should be visible to the Steering Council. Full transparency of all transactions is probably fine, but not necessary.

The options here may be determined by the accounting system and amount of effort required.

NumPy funding – history and current status

The NumPy project received its first major funding in 2017. For an overview of the early history of NumPy (and SciPy), including some institutions sponsoring time for their employees or contractors to work on NumPy, see 1 and 2. To date, NumPy has received four grants:

  • Two grants, from the Alfred P. Sloan Foundation and the Gordon and Betty Moore Foundation respectively, of about $1.3M combined to the Berkeley Institute of Data Science. Work performed during the period 2017-2020; PI Stéfan van der Walt.

  • Two grants from the Chan Zuckerberg Foundation to NumFOCUS, for a combined amount of $335k. Work performed during the period 2020-2021; PI’s Ralf Gommers (first grant) and Melissa Mendonça (second grant).

From 2012 onwards NumPy has been a fiscally sponsored project of NumFOCUS. Note that fiscal sponsorship doesn’t mean NumPy gets funding, rather that it can receive funds under the umbrella of a nonprofit. See NumFOCUS Project Support for more details.

Only since 2017 has the NumPy website displayed a “Donate” button, and since 2019 the NumPy repositories have had the GitHub Sponsors button. Before that, it was possible to donate to NumPy on the NumFOCUS website. The sum total of donations from individuals to NumPy for 2017-2020 was about $6,100.

From May 2019 onwards, Tidelift has supported NumPy financially as part of its “managed open source” business model. From May 2019 till July 2020 this was $1,000/month, and it started steadily growing after that to about $3,000/month (as of Feb 2021).

Finally, there has been other incidental project income, for example, some book royalties from Packt Publishing, GSoC mentoring fees from Google, and merchandise sales revenue through the NumFOCUS web shop. All of these were small (two or three figure) amounts.

This brings the total amount of project income which did not already have a spending target to about $35,000. Most of that is recent, from Tidelift. Over the past 1.5 years we spent about $10,000 for work on the new NumPy website and Sphinx theme. Those spending decisions were made by the NumPy Steering Council and announced on the mailing list.

That leaves about $25,000 in available funds at the time of writing, and that amount is currently growing at a rate of about $3,000/month.

Alternatives

Alternative spending strategy: not having cash reserves. The rationale being that NumPy is important enough that in a real emergency some person or entity will likely jump in to help out. This is not a responsible approach to financial stewardship of the project though. Hence, we decided against it.

Discussion

References and Footnotes

1

Pauli Virtanen et al., “SciPy 1.0: fundamental algorithms for scientific computing in Python”, https://www.nature.com/articles/s41592-019-0686-2, 2020

2

Charles Harris et al., “Array programming with NumPy”, https://www.nature.com/articles/s41586-020-2649-2, 2020

3

https://remote.com/blog/remote-compensation

4

https://about.gitlab.com/company/culture/all-remote/compensation/#how-do-you-decide-how-much-to-pay-people

5

https://developers.google.com/open-source/gsoc/help/student-stipends

6

Jurgen Appelo, “Compensation: what is fair?”, https://blog.agilityscales.com/compensation-what-is-fair-38a65a822c29, 2016

7

Project Include, “Compensating fairly”, https://projectinclude.org/compensating_fairly

8

This cap is derived from comparing with compensation levels at other open source projects (e.g., Babel, Webpack, Drupal - all in the $100,000 – $125,000 range) and Partner Institutions.

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