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

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

By Bahar Gidwani

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:

•  Convert measurement from each data source into a 0 (low) to 100 (high) scales.  This  requires understanding how each source evaluates company performance.
•  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.)
•  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 around 50.
•  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.  Hewlett Packard is a heavily tracked company.  We have 154 sources of data for this company that together provide 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 do not affect/apply to special issues.

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

Before we can present a rating, we need to check first that we have enough sources and enough “weight” from the sources we have, 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 have 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 have enough data to provide a good estimate of the midpoint of the available data, for those ratings we report.  Below you can see that some sources have a high opinion of HP’s board while others have a less favorable view.  The result is a blended score that averages 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 see in the illustration below, the final ratings distribution is organized well around a central peak.  The average overall rating of 64 is below the peak, which is 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.

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

See part 1, Using “Big Data” to Rate Corporate Social Responsibility: One Company’s Approach.

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. Bahar is a member of the SASB Advisory Board.  He plays bridge, races sailboats, and is based in New York City.

CSRHub provides access to corporate social responsibility and sustainability ratings and information on 9,200+ companies from 135 industries in 106 countries. By aggregating and normalizing the information from 348 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 change the world.

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

By Bahar Gidwani

We have previously defined “Big Data” and shown how we feel it 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.  To better understand this process, let’s consider an example.

Hewlett Packard is a heavily tracked company.  We have 56 sources of data for this company that together contribute 494 different rating elements.  We map each of these elements into one of twelve different CSR subcategories.  For instance, here are mappings for 20 of the elements that contribute to the Hewlett Packard rating:

Some of these data elements could map to more than one subcategory.  For instance, a company that is on the list of “Best Workplaces for Commuters” would get credit both for its energy saving effort (in “Energy & Climate Change”) for the benefit its programs bring to its employees (in “Compensation & Benefits).

The list above includes examples of each of the three main contributors to the system: Investment-related sources (Asset4/Thomson Reuters, Carbon Disclosure Project, GovernanceMetrics International/Corporate Library, IW Financial, MSCI, Trucost, Vigeo); Activists and NGOs (Accountability, BSR, CorporateRegister, CR 100, EIO, FCPA, Top 50 Socially Responsible); and Government & Consumer (Better World, Black Engineer, EICC, EPEAT, UN Global Compact, UNODC, Working Mother).  The completed mapping process connects the 494 data elements from the 56 sources for HP into our twelve subcategories in 971 different ways.

While investment-related sources contribute more data elements than the other types, there are at least some of each type present in each subcategory.  Another way to look at this is to see that many sources contribute to each subcategory:

Each value from each data element is converted into a zero to 100 rating (zero = lowest, 100 = highest).  These scores are then adjusted by comparing them to each other.  In the example above, there are 11 sources for HP’s board performance.  Suppose three of them gave it a great rating, six a medium rating, and two a poor one.  Computer analytics would guess that the six scores that agree are correct and that HP’s board rating is in the medium range.  The assumption is that three sources tended to be biased towards high scores and two towards low scores.  This chart shows the actual distribution of scores at the subcategory level, along with a calculation of the “normal” error curve that results.

When the analysis is repeated across thousands of companies, a picture emerges as to which sources tend to be overly positive or negative and which tend to predict the “mean” of the other sources.  All sources can be adjusted, based on this feedback—moving them up or down so they more accurately match the opinion of all other sources.  After a large number of iterations in this process, there is a consensus score for each subcategory for each company analyzed.

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.

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

Bahar Gidwani is a Cofounder and CEO of CSRHub. Formerly, he was the CEO of New York-based Index Stock Imagery, Inc, from 1991 through its sale in 2006. He has built and run large technology-based businesses and has experience building a multi-million visitor Web site. Bahar holds a CFA, was a partner at Kidder, Peabody & Co., and worked at McKinsey & Co. Bahar has consulted to both large companies such as Citibank, GE, and Acxiom and a number of smaller software and Web-based companies. He has an MBA (Baker Scholar) from Harvard Business School and a BS in Astronomy and Physics (magna cum laude) from Amherst College. Bahar races sailboats, plays competitive bridge, and is based in New York City.

CSRHub provides access to corporate social responsibility and sustainability ratings and information on nearly 5,000 companies from 135 industries in 65 countries. By aggregating and normalizing the information from over 170 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 change the world.

This post is the first in a series of three about expanding the sustainability data industry. It was also published through Triple Pundit. 

By Bahar Gidwani

We know a lot about the sustainability performance of about 5,000 large companies. We know something about the sustainability performance of another 16,000 smaller companies and organizations. But, our world economy includes millions of economic entities. How can we get data on their social and sustainability performance?

I believe there is a strong and growing interest among consumers, social groups, and companies about sustainability issues. For instance, Google search interest for the term “sustainability” is pretty high.

Another place to gauge interest is via social media groups. For example, there are already more than 2,000 groups related to sustainability on LinkedIn.

A number of sources provide data to satisfy this interest in corporate social responsibility (CSR) and sustainability. There are about ten major Socially Responsible Investment (SRI) research firms, a lot of news and blog sites and a number of activist and not for profit (NFP) groups. Our CSRHUB web site currently ingests about 100 of these sources—and we expect to add another 20+ sources soon.

Unfortunately, even with almost two million pieces of CSR and sustainability data from our sources, it is not possible for CSRHUB to rate most companies. We currently rate about 5,000 companies on CSRHUB and have data on another 16,000. However, there are at least 30,000 publicly-traded companies in the world and a lot that are big enough to be important players in the world’s economy. I’ve used one million to represent these companies in my diagram below. But it could be five million or fifty million. Just think of how many companies and organizations there are in the world that make products or provide services.

If we had complete information on the social performance of most large organizations, we would be able to do some pretty useful things.

• We could describe the entire supply chain that underlies the products we consume. We could know how much carbon is used to make a car, how much water goes into producing and delivering an orange and whether or not child labor was involved in any part of making our laptop computer.
• Consumer purchases could be tied to consumer social preferences. A buyer could avoid purchasing products that are associated with animal testing, tilt towards companies that contribute positively toward their communities or buy only from employers who provide day care for their employees.
• Other types of economic activity could be tied to a social viewpoint.  Individuals could accept ads from companies that meet their social standards and avoid ads from those who don’t.  Employers could recruit candidates who already work at companies that share their cultural norms. Investment bankers could test whether two companies that wanted to merge shared similar views on sustainability.

Something this ambitious must be taken a step at a time. In the next two posts in this series, we will more fully describe the data we currently have and then discuss some things that could expand what is available.