When it comes to Equity, people need to put their money where their mouths are. A key way that businesses can make sure they aren’t unfairly paying some people more than others based on their age, sex, or race is called a ‘pay equity analysis’.
谈到股权，人们需要把钱放在嘴边。 企业可以根据自己的年龄，性别或种族，确保他们向某些人支付的酬劳不公平的一种关键方法称为“薪资公平分析”。
Undergoing a pay equity analysis can help you correct inequitable (and often illegal) imbalances in employee pay. Its main focus is to separate the good reasons you might be paying an employee more (skill, education, experience, performance, seniority, etc.) from the bad ones: age discrimination, sex discrimination, race discrimination and different-ability discrimination. It doesn’t matter if the discrimination is overt and intentional, or, more commonly, entrenched and unconscious. When a pay equity analysis is well done, it will identify any pay gaps that shouldn’t be there.
进行薪酬净额分析可以帮助您纠正员工薪酬中不平等(通常是非法的)的不平衡。 它的主要重点是将您可能会付给员工更多薪水的良好原因(技能，学历，经验，绩效，资历等)与不良原因区分开：年龄歧视，性别歧视，种族歧视和不同能力的歧视。 歧视是公开的和故意的，还是更普遍的是根深蒂固和无意识的，都没有关系。 做好薪酬净额分析后，它将确定所有不应存在的薪酬差距。
In our 4 part series, We All Count is going to walk you through just how your company might go about it. The process we share is applicable to a gender pay equity analysis, a race pay equity analysis, and an intersectional pay equity analysis.
在我们的第4部分系列文章中，我们将全力以赴，逐步向您介绍贵公司的发展方向。 我们共享的过程适用于性别薪酬公平性分析，种族薪酬公平性分析和交叉薪酬公平性分析。
Part One: Gather Your Materials
第一部分：收集您的资料
To do a good pay equity analysis you need three datasets.
为了进行良好的薪酬净值分析，您需要三个数据集。
Dataset #1: Individual wage/salary
数据集1：个人工资/薪水
Administrative and HR data that records each team member’s hourly wage or salary, and bonuses. This data will give us a clear picture of what each employee makes. You almost certainly have this on a spreadsheet somewhere already.
记录每个团队成员的小时工资或薪水以及奖金的行政和人力资源数据。 这些数据将使我们清楚地了解每个员工的收入。 您几乎可以肯定已经将其保存在电子表格中了。
Dataset #2: Job titles/positions
数据集2：职位名称/职位
Administrative and HR data that records each team member’s job title, department, and, if possible, recent promotions. If your organization has multiple locations, a record of where they work is good too since different sites can legally have different local pay scales. This is another dataset you can assemble yourself, although it can get tricky if you have bad ‘job title hygiene’ where you have lots of different roles and responsibilities that overlap which also vary in pay. Do your best to tidy up this dataset, but don’t worry, this is an area where your pay equity analysts can help. This data will be used to create the job categories that will get compared. We need to identify ‘equal work’ in order to check for ‘equal pay’.
管理和人力资源数据，记录每个团队成员的职务，部门以及最近的晋升(如果可能)。 如果您的组织有多个地点，则在其工作地点的记录也很不错，因为不同的地点在法律上可以具有不同的本地薪级。 这是您可以自己组装的另一个数据集，但是如果您的“职务职称卫生状况”不好，在其中您有许多不同的角色和职责重叠且薪资也各不相同的话，它可能会很棘手。 尽力整理这个数据集，但是请不要担心，这是您的薪酬平等分析师可以提供帮助的领域。 该数据将用于创建要比较的职位类别。 我们需要确定“同工同酬”，以便检查“同工同酬”。
Dataset #3: Relevant Social Identities
数据集3：相关的社会身份
This is where you should have a company from outside yours collect this data. There are privacy and confidentiality issues and we’ve found that employees are more comfortable and trusting sharing sensitive identity information with people they don’t also work with or for.
在这里，您应该由外部公司来收集此数据。 存在隐私和机密性问题，我们发现员工更加自在，并信任与自己或不与之共事的人共享敏感的身份信息。
If you are only trying to address gender pay inequality, you could only collect that information, but it’s almost always a good idea to look at all of the relevant legally protected social identities (race, age, ability status, religion, gender orientation) as well as any other equity you care about (immigration status, sexual orientation, indigenous status, etc.). By collecting more than one identity factor, you can do an ‘intersectional’ analysis looking at whole people instead of one piece of them at a time (more on that later). It’s not only more fair, it’s much more efficient to do one good pay equity analysis than many incomplete ones over time.
如果您只想解决性别薪酬不平等问题，则只能收集该信息，但是将所有受法律保护的相关社会身份(种族，年龄，能力状况，宗教，性别取向)视为一个几乎总是一个好主意。以及您关心的任何其他权益(移民身份，性取向，土著身份等)。 通过收集多个身份因素，您可以进行一次“交叉”分析，而不是一次查看整个人，而是查看整个人。 这样做不仅公平，而且随着时间的推移，进行许多良好的股权净值分析比进行许多不完全的效率更高。
Once you’re armed with this information, you can move on to the next phase of a pay equity analysis!
掌握了这些信息之后，您就可以进入下一阶段的薪酬净额分析！
If you are looking for some help getting a gender or race pay equity analysis done, you can contact us here.
如果您需要帮助以完成性别或种族薪资净值分析，可以在这里与我们联系。
翻译自: https://medium.com/the-innovation/part-1-how-to-do-pay-equity-analysis-for-gender-and-race-e2bb90181535
相关资源：模型models，包括：图像分类、性别判断、年龄估算