CSRHub Blog Research on ESG metrics and comments on sustainability best practice

The following is part 3 of a 3-part series on “Big Data.”

The following is part 2 of a 3-part series on “Big Data.”

We have previously defined “Big Data” and shown how we feel a Big Data system built by CSRHub could help address some problems that exist in collecting corporate social responsibility (CSR) and sustainability data on companies.  We have also further described the problems with the currently dominant method of gathering this data—an analyst-based method.

CSRHub uses input from investor-driven sources (known as “ESG” for Environment, Social, and Governance or “SRI” for Socially Responsible Investment), non-governmental organizations, government organizations, and “crowd sources” to construct a 360 degree view of a company’s sustainability performance.

The illustration below shows the steps in our process.

The steps are:

1. Convert measurement from each data source into a 0 (low) to 100 (high) scales. This requires understanding how each source evaluates company performance.
2. We next connect each rating element with one or more of our twelve subcategory ratings. (Some elements may also map partially or exclusively to special issues such as animal testing, fracking, or nuclear power.)
3. We compare each source’s ratings with those for all other sources. Each company we study gives us more opportunities to compare one source’s ratings with another.  The total number of comparisons possible is very large and growing, exponentially.  We use the results of our comparison to adjust the distribution of scores for each rating source so that they fall into a “beta” distribution that has a central peak in the 50s.
4. Some sources match up well with all of our other data. Some sources don’t line up.  We add weight to those who match well but continue to “count” those who don’t.
   
   

We then repeat steps A to D as many times until we have found a “best fit” for the available data.  Each time we add a new source, we go through an initial mapping, normalization, and weighting process.

An Example

It may help explain our data analysis process by using a specific example.  (Note that we are using an example from 2014 for a company that has since been split into two parts.  This avoids any risk of an implied criticism of an existing company.)  Hewlett Packard was a heavily tracked company prior to its split.  We had 154 sources of data for this company that together provided 17,571 individual data elements.  Only 62 of these data sources provided data for our July 1, 2014 rating—the rest of the data sources provided data for previous periods (our data set goes back to 2008).  The 62 current data sources provided 575 different types of rating elements and a total of 610 different ratings values that did not affect/apply to special issues.

After their conversion to our 0 to 100 scale, we mapped the rating elements into our twelve subcategories.  We had 1,403 ratings factors.  We selected our subcategories to allow an even spread of data across them. You can see that we had a reasonably even spread for Hewlett Packard:

Before we could present a rating, we needed to check first that we had enough sources and enough “weight” from the sources we had, to generate a good score.  In general, we require at least two sources that have good strength or three or four weaker ones, before we offer a rating.  As you can see, we had plenty of sources to rate a big company such as HP.

Even after normalization, the curve of ratings for any one subcategory may have a lot of irregularities.  However, we had enough data to provide a good estimate of the midpoint of the available data, for those ratings we collected and adjusted.  Below you can see that some sources had a high opinion of HP’s board while others had a less favorable view.  The result was a blended score that averaged to less than the more uniform Leadership Ethics rating.

The overall effect of our process is to smooth out the ratings input and make them more consistent.  As you can in the illustration below, the final ratings distribution was organized well around a central peak.  The average overall rating of 64 was below the peak, which was around 80.  The original average rating was 61.

By making a few assumptions about how the errors in data are distributed, one can assess the accuracy of ratings.  In a previous post, we showed that CSRHub’s overall rating accurately represents the values that underlie it to within 1.8 points at a 95% confidence interval.  Our error bars have continued to decline and now are less than 1 point at this confidence interval.

In our next post, we will discuss the benefits and drawback of using this complex and data intensive approach to measuring company CSR performance.

Did you miss part 1? See part 1 here.

Bahar Gidwani is CTO and Co-founder of CSRHub.  He has built and run large technology-based businesses for many years. Bahar holds a CFA, worked on Wall Street with Kidder, Peabody, and with McKinsey & Co. Bahar has consulted to a number of major companies and currently serves on the board of several software and Web companies. He has an MBA from Harvard Business School and an undergraduate degree in physics and astronomy. He plays bridge, races sailboats, and is based in New York City.

CSRHub provides access to the world’s largest corporate social responsibility and sustainability ratings and information.  It covers over 17,400 companies from 135 industries in 134 countries. By aggregating and normalizing the information from 530 data sources, CSRHub has created a broad, consistent rating system and a searchable database that links millions of rating elements back to their source. Managers, researchers and activists use CSRHub to benchmark company performance, learn how stakeholders evaluate company CSR practices, and seek ways to improve corporate sustainability performance.

The following is part 1 of a 3-part series on “Big Data.”

“Big Data” is a useful tool for rating corporate social responsibility (CSR) and sustainability performance.  We believe that the Big Data system that CSRHub has developed is one answer to dealing with the rise in new ratings systems (it seems there is a new one announced each month) and with the disparities in scores that occur among these different systems.

In 2001, Doug Laney (currently an analyst for Gartner), foresaw that users of data were facing problems handling the Volume of data they were gathering, the Variety of data in their systems, and the Velocity with which data elements changed.  These “three Vs” are now part of most definitions of the “Big Data” area.

Ratings in the CSR space appear to be a candidate for a big data solution to its three “V” problems.

• Volume: There are many sources of ratings.  CSRHub currently tracks more than 530 sources of CSR information and plans to add at least another 30 sources over the next six months.  Our system already contains more than 120,000,000 pieces of data from these sources that touch more than 150,000 companies.  We hope eventually to expand our coverage to include several million companies.
• Variety: Each of these 530+ sources uses different criteria to measure corporate sustainability and social performance.  A number of comprehensive sustainability measurement approaches have been created.  Unfortunately, each new entrant into the area seems compelled to create yet another system.
• Velocity: With hundreds of thousands of companies to measure and so many measurement systems, the perceived sustainability performance of companies constantly changes.  Many of the available ratings systems track these changes only on a quarterly or annual basis.
  
  

Most systems for measuring the CSR and sustainability performance of corporations rely on human-based analysis.  A researcher selects a set of companies to study, determines the criteria he or she wishes to use to evaluate their performance, and then collects the data needed to support the study.  When the researcher can’t find a required data item in a company’s sustainability report or press releases, he or she may try to contact the company to get the data. 

Some research firms try to streamline this process by sending out a questionnaire that covers all the things they want to know.  Then, they follow up to encourage companies to answer their questions and follow up again after they receive the answers, to check the facts and be sure their questions were answered consistently.  An NAEM survey showed that its members were seeing an average of more than ten of these results in 2011. This number has continued to grow and some large companies say they receive as many as 300 survey requests per year.

NAEM Green Metrics That Matter Report—2012 for 35 members.

Both the direct and survey-driven approaches to data gathering are reasonable and can lead to sound ratings and valuable insights.  However, both are limited in several important ways:

• The studied companies are the primary source of the data used to evaluate them.  While analysts can question and probe, they have no way to determine how accurately a company has responded.
• Different areas of a company may respond differently to analyst questions.  It’s hard to determine objectively from the outside, which area of a company has the right perspective and which answer is correct.
• When companies get too many surveys and requests for data, they stop responding to them.  This “survey fatigue” leads to gaps in the data collected.  Note that only a few thousand large companies have full-time staff available to answer researcher questions.
• Often analysts cannot financially justify studying smaller companies.  There is little interest in smaller companies from the investor clients who pay for most CSR data collection.  As a result, most analyst-driven research covers a subset of the world’s 5,000 largest companies.  There are only a few data sets bigger than this, and they cover only limited subject areas.  There is very little coverage for private companies, public organizations, or companies based in emerging markets.

Large Companies Get Heavy ESG Attention

• A human-driven process will always involve a certain amount of interpretation of the data.  This in turn can lead to biases that are hard to detect and remove.
• Each human-driven result is based on its own schema and therefore they are hard to compare.  Companies do not understand why their rating varies from one system to the next and this reduces their confidence in all ratings systems.
  
  

It may be useful to take a look at some details of one company’s approach to a “Big Data” based analysis of CSR ratings. Our next post explains how CSRHub applies its methodology to address “Big Data” problems while also noting that every system has some limitations.

Bahar Gidwani is CEO and Co-founder of CSRHub.  He has built and run large technology-based businesses for many years. Bahar holds a CFA, worked on Wall Street with Kidder, Peabody, and with McKinsey & Co. Bahar has consulted to a number of major companies and currently serves on the board of several software and Web companies. He has an MBA from Harvard Business School and an undergraduate degree in physics and astronomy. He plays bridge, races sailboats, and is based in New York City.

CSRHub provides access to the world’s largest corporate social responsibility and sustainability ratings and information.  It covers over 17,400 companies from 135 industries in 134 countries. By aggregating and normalizing the information from 530 data sources, CSRHub has created a broad, consistent rating system and a searchable database that links millions of rating elements back to their source. Managers, researchers and activists use CSRHub to benchmark company performance, learn how stakeholders evaluate company CSR practices, and seek ways to improve corporate sustainability performance.

CSRHub CEO and Co-Founder, Bahar Gidwani, will be speaking at a One-Day Training Program presented by Governance & Accountability Institute and Global Change Associates, titled Into to ESG, Sustainable & Impact Investment Training. This training is hosted by Zicklin School of Business at Baruch College/CUNY on June 15, 2017.

Bahar will be speaking at the session focused on Bridging the Gap: Sustainability vs. Profitability.

June 15^th:

1:55pm- Bridging the Gap: Sustainability vs. Profitability

Bahar Gidwani, Founder & CEO, CSRHub

Register here, with CSRHUB20 for 20% off!

See the full interview with Bahar Gidwani and Hank Boerner on this upcoming event here. 

Bahar Gidwani is CEO and Co-founder of CSRHub.  He has built and run large technology-based businesses for many years. Bahar holds a CFA, worked on Wall Street with Kidder, Peabody, and with McKinsey & Co. Bahar has consulted to a number of major companies and currently serves on the board of several software and Web companies. He has an MBA from Harvard Business School and an undergraduate degree in physics and astronomy. He plays bridge, races sailboats, and is based in New York City.

CSRHub provides access to the world’s largest corporate social responsibility and sustainability ratings and information.  It covers over 17,400 companies from 135 industries in 134 countries. By aggregating and normalizing the information from 530 data sources, CSRHub has created a broad, consistent rating system and a searchable database that links millions of rating elements back to their source. Managers, researchers and activists use CSRHub to benchmark company performance, learn how stakeholders evaluate company CSR practices, and seek ways to improve corporate sustainability performance.

Much has been written about America’s President, Donald Trump, and the effects his tenure will have on the world.  We at CSRHub recently had an interesting opportunity to examine the relationship between President Trump’s businesses and corporate social responsibility (CSR).

A US group called “Grab Your Wallet” has launched a web site that looks for companies that are controlled by the Trump family or benefit from being associated with it.  Their premise is that shoppers, investors, communities, employees and other stakeholders could signal their displeasure with Mr. Trump and his policies by boycotting companies associated with him.  Of course, those who support Mr. Trump could use the same list as a guide to companies they should support.  This dual perspective caused us to bring Grab Your Wallet in as a special issue that indicates whether or not a company is “Trump Involved.”

A different group of companies  distinguished themselves recently by filing an amicus curiae motion in support of blocking Mr. Trump’s proposed ban on travel from certain predominantly Muslim countries.  While participation in and support of legal efforts to block the Trump ban does not indicate that a company directly opposes all aspects of Mr. Trump’s policies, it does suggest a desire for distance from them.

Using CSRHub’s data, it is easy to compare and contrast the perceived sustainability performance of these two groups of companies.  At a top overall ratings level (using our average user ratings weight profile), the CSRHub-rated companies within the amicus curiae group had an average rank among the 17,000+ companies we track of 56%.  In contrast, the CSRHub-rated companies tagged by Grab Your Wallet as involved with Mr. Trump had an average rank 16% lower.

(Note that both lists contained many smaller companies that are not yet rated by CSRHub.)

The two groups seemed to be fairly similar in their treatment of environmental issues and reasonably close on governance matters.  But, the Trump Involved companies were perceived to be dramatically lower on social issues—6% worse on community matters and 8% worse on employee-related topics.

CSRHub includes six subfactors within the social area.  As you can see, the Trump Involved companies have especially weak scores relating to Community Development & Philanthropy, Compensation & Benefits, and Training Health & Safety.

Most of the Trump Involved businesses have a broad portfolio of products and services.  Their financial exposure from a Grab Your Wallet boycott could be minimal.  However, these business may want to examine more closely how they interact with their communities and their employees.  They may not be hearing signals of dissatisfaction from these stakeholders that may be material to their overall business activities.

Bahar Gidwani is CEO and Co-founder of CSRHub.  He has built and run large technology-based businesses for many years. Bahar holds a CFA, worked on Wall Street with Kidder, Peabody, and with McKinsey & Co. Bahar has consulted to a number of major companies and currently serves on the board of several software and Web companies. He has an MBA from Harvard Business School and an undergraduate degree in physics and astronomy. He plays bridge, races sailboats, and is based in New York City.

CSRHub provides access to the world’s largest corporate social responsibility and sustainability ratings and information.  It covers over 17,400 companies from 135 industries in 134 countries. By aggregating and normalizing the information from 530 data sources, CSRHub has created a broad, consistent rating system and a searchable database that links millions of rating elements back to their source. Managers, researchers and activists use CSRHub to benchmark company performance, learn how stakeholders evaluate company CSR practices, and seek ways to improve corporate sustainability performance.