comparing nia and gaap data for industries

Framework for Comparing NIA and GAAP

Framework for Comparing Data from National Income Accounts (NIA)

and Company Financial Statements (GAAP)

Summary: What are the similarities and differences in data values obtained from NIA

versus GAAP based sources?

The World Industry Service (WIS) features data and forecasts for industries, with

history values drawn from each country’s National Income Accounts (NIA). The data

from NIA are the supply side of GDP, and represent all the entities that operate in

every aspect of legal commerce.

The industry data in WIS is often compared to similar indicators obtained from the

financial statements of individual companies, or groups of companies, presented in

the format of Generally Accepted Accounting Principles (GAAP). Indeed both the NIA

and GAAP accounting systems feature similar structures (double entry), and similar

sounding indicators (revenues, costs, profits).

We have identified 5 factors that determine the main relationships among data

obtained from the NIA versus GAAP based sources. These 5 factors comprise a

framework for understanding and describing the data involved in both of the

accounting systems.

1. What is the Universe of Coverage? The NIA based data in WIS includes all

economic activity in each country, regardless of forms of ownership

However GAAP based statements are sometimes limited to publically traded

companies, can exclude privates and/or government entities. Also public traded

peer groups often disproportionately exclude small and medium sized companies.

2. How is Geography Treated? GAAP based data refer to location of the

country’s incorporation, but NIA has geography defined according to the

location of activity, regardless of where the ownership of company is located.

Note the use of a global total eliminates this factor. When all the companies, in all

the industries, in all countries, are added together into one big peer group total,

then any differences in treatment of geography become irrelevant.

Created on 02 Jan 2014 for Mark Killion, CFA


3. Industry designations can be a factor for understanding what is being included

in each data set. Sometimes labels and phrases can sound similar, but the

activity recorded can be treated quite differently:

Some companies are conglomerates, operate in many markets and industries, thus

are difficult to assign to a single industry. The NIA, by contrast, allocates each

business activity from each company, measured at the ‘establishment’ level, to their

appropriate industry classification.

Companies are free to organize and name their internal structures in the way they

deem best, whereas the NIA will strictly allocate each activity in the company,

measured at the level of ‘establishments’, to the appropriate industry or industries

within the ISIC classification scheme

4. Forecasts of industries from NIA are more reliably constructed and simulated

than are similar forecasts for industries, derived from adding “bottom up”

projections for numerous companies into industry peer group totals

Top down consistency versus bottom up herding

5. The Indicators used in NIA and GAAP have generally similar interpretations,

although some indicators are better aligned than others

The NIA is best aligned with GAAP’s Income Statement, a bit less so with the Cash

Flow statement. The NIA does not line up at all with the Balance Sheet; instead NIA

tries to exclude the direct impacts from the balance sheet.

The main differences relate to NIA’s focus on current operating activity, whereas in

GAAP several indicators from the income statement include impacts from balance

sheets, such as inventories, asset valuation and goodwill.

The indicators of revenues, costs and output are treated in a similar manner in both

the GAAP and NIA systems. However, the indicators of CapEx and Profits are not so

readily or easily compared. For example, the CapEx in NIA includes purchases of

machinery, equipment, construction or similarly productive and long-lived assets, as

does GAAP. However the CapEx from GAAP includes other items, not counted in NIA,

such as rental expense, and purchase of financial assets.

Thus the data for CapEx in GAAP has a bias to be larger in size, when rolled up to

industry or country peer group totals, than does the CapEx indicator in GDP and NIA.



An excellent handbook is available from the United Nations that covers the source of

differences and treatments between the GAAP and NIA systems. This handbook can

serve as reference for understanding how methods impact data values:

http://unstats.un.org/unsd/publication/SeriesF/SeriesF_76e.pdf

To illustrate this framework, we assembled a series of examples, or case studies, in

which we illustrate the treatment and impact of the 5 factors. We show how the NIA

based data in WIS line up with GAAP based data from companies, and ‘peer group’

totals of companies. Each case study illustrates the impact of the 5 factors in our

framework, and discusses how they impact the comparisons of data.

A. First study illustrates how well the top-line revenue from company financial

statements, when collected and summed from all public companies in the

world, and organized into the GICS industry classification scheme, compare to

the similar indicator from NIA based data sources

1. Universe is all publically traded companies. That excludes privates, but otherwise

the coverage is pretty similar between NIA and the peer group data sets.

2. Global totals eliminate distortion from geography

3. Companies are organized into peer group industries, using the GICS classification

scheme, so pretty good alignment with NIA in WIS

4. Companies are history only, but the WIS also has forecasts for NIA based industries

5. The Indicator is revenues, which is similarly measured in both systems

B. Second study shows how the history data from Brazil’s energy giant, Petrobras, compares to the NIA based data for Brazil’s energy sector. This illustrates how industry data from NIA compare to similar metrics from a single company, measured under GAAP. All the more illuminating because Petrobras dominates Brazil’s domestic energy market.

1. Petrobras dominates the oil and gas sector in Brazil, so universe of coverage is

similar in both data sets

2. Most of Petrobras’ operations are domestic, so treatment of geography is similar

3. There are significant differences in the manner in which Petrobras organizes and

names it business segments, versus the conventions used in Brazil’s NIA

4. Both data sets are commonly forecast

5. One indicator is revenues, which is similar in comparison. However the other

indicator is CapEx, which shows material differences in their data values


http://unstats.un.org/unsd/publication/SeriesF/SeriesF_76e.pdf


C. The United States Bureau of Economic Analysis (BEA) shows how the profits

indicator is best compared between a “peer group” of 500 companies, those

which comprise the S&P500, versus profits data for the whole U.S. economy

1. The S&P 500 include the largest publicly traded companies in the United States, but

the exclude mid-size and small companies, also exclude foreign and public ownership

2. BEA data is limited to U.S., whereas data on S&P500 reflect global operations

3. Industries are not considered

4. Both data sets are commonly forecast

5. The indicator of Profits shows material differences in methods used and items

included in the calculations. However, despite differences in their methods, and

dollar values, there is strong correlation in the pattern of growth seen in both

D. Equity analysts’ forecasts of company sales and profits are collected, by several

data providers, and averaged into a “consensus forecast”. This collection of

consensus forecasts, for individual companies, is then often rolled up to

industries, or for the whole stock market. How do these “bottom up” forecasts

differ from the “top down” forecasts made with data from NIA sources?

1. The universe of coverage for bottom up consensus forecasts is restricted to those

public companies which have a following of equity analysts, which are the persons

that are making the ‘consensus’ forecasts

2. Geography is treated differently in NIA versus GAAP (same as previous case study)

3. The industry classification used here is GICS, which does work well for classifying

both companies and industries into a common set of industry listings.

4. Forecast of top down are more reliable and consistent than are an average of

forecasts for individual companies, that are added up to an industry total

5. The indicator of profits in particular is less reliably forecast from GAAP based

accounts, as opposed to NIA data, as compared to the indictor of revenues.



First Case Study shows how revenues from a ”Peer Group” of similar companies,

representing an industry, compare to the similar indicator from National Income

Accounts (NIA, which is used in the World Industry Service (WIS)

Companies that operate in similar markets are often combined (added) together into

a Peer Group composite total, for example to use as sector benchmark in the risk

analysis of individual companies. Other examples are seen in surveys and rankings of

companies, such as those among the top 100, or top 1000. The companies named

are often rolled up into peer group totals, and cited for their size and growth.

The choice of which companies to include in the Peer Group has a “more than is

otherwise obvious” impact on the results. The wider is the universe of coverage, the

better is the match with NIA. In this case study we used global totals, all countries, to

allow for a cleaner comparison.

The data source for the company revenues is Thomson Reuters’ Worldscope.

Company revenues generally lines up well with the gross output indicator from the

NIA system. However the universe of coverage in the Worldscope database is limited

to public companies, it therefore excludes privately owned or government entities.

We used the GICS (Global Industry Classification Scheme) to organize companies into

sector based peer groups. Each company is assigned to an industry designation

within the GICS industry classification scheme.

Number of Publically Traded Companies Reporting Financial Results in the GICS Sectors of Energy, Utilities and Consumer Staples

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Energy Utilities Consumer Staples



This case study shows the results of constructing the peer groups for 3 of the main

sectors in the GICS: Energy, Utilities and Consumer Staples. The graph above lists

the number of companies that are included in each of the 3 sector peer group totals.

After assembling the company peer groups in the GICS, we did the same for the

national income accounts (NIA), converting from the ISIC used in WIS into the

equivalent industry in the GICS classification. This mapping from ISIC to GICS was

developed by WIS, and is proprietary to IHS.

For example, the GICS Energy sector includes both the upstream extraction, which is

in the ISIC C11 for Mining of Oil and Gas, as well as downstream refining, which is in

the ISIC D23 for Manufacture of refined petroleum products.

Similar mappings are made for the ISIC based data in WIS into Consumer Staples and

Utilities, and indeed into all of the 10 Sectors in GICS, so that all of the ISIC categories

are assigned to their analogous slots in the GICS. Each of the 3 sectors shown below

have a display of their nominal revenues, measured in US$, and another measured as

growth rates, each over the time period 2000-2012.

The graphs show that the two data sets match up very well, at the global total, in

both the US$ level of revenues and the growth rates. The NIA version of data is

slightly larger in size, as expected, when compared to the company peer groups in

GICS. Nevertheless the correlation in the patterns of growth among the data sets is

very strong.

This confirms that the indicator of revenues matches up well, for global totals, and at

the level of sector/industry, when GAAP based data for company peer groups are

compared to NIA data for industries.



Global Sales by Sector: National Income Accounts v. Company Peer Groups (Company data from Thomson Reuters, NIA data from World Industry Service)

Global Sales - Billions of US$

Thomson Reuters - Blue WIS - Red

Global Sales – Annual % Change

Thomson Reuters – Blue WIS - Red

Energy Sector

Consumer Staples Sector

Utilities Sector

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Second Case Study: How to compare Industry data from NIA to similar metrics from

a single company, measured under GAAP? In particular when it is a large company

that dominates a country’s domestic market?

This example looks at Brazil’s giant energy company Petrobras, which accounts for

the biggest majority of Brazil’s oil and gas sector. While the 5 factors for comparison

of GAAP and NIA apply to companies of all sizes, the large presence of Petrobras in

Brazil’s energy markets magnifies the impacts.

For example, Petrobras’ Annual Report has this self-description: “The Brazilian federal

government holds a monopoly over the exploration, production, refining and transportation of

crude oil and oil products in Brazil … operates substantially all of the refining capacity in Brazil …

also involved in the production of petrochemicals. We distribute oil products through our own “BR”

network of retailers and to wholesalers ... We supplied almost all of the refined product needs of

third-party wholesalers, exporters and petrochemical companies, in addition to the needs of our

Distribution segment … (we) operate a large and complex infrastructure of pipelines and terminals

and a shipping fleet to transport oil products and crude oil to domestic and export markets.”

The data covering Petrobras are sourced from their company financial accounts,

published in annual reports, in the format of GAAP. The NIA data in WIS is sourced

from Brazil’s national statistics agency IBGE. Note that both of these data sets

originate from the Brazil government. Petrobras is largely state-owned, and Brazil’s

congress must approve its budget every year, even though a portion of Petrobras

shares do trade publically on the local stock exchange.

In addition, the industry data on Brazil comes from the national statistics agency,

IBGE, whose budget is also approved annually by congress. Since both data sets

come through the same owner and reporting structure, the comparison of data boils

down to a matter of differences in methods, definitions and classifications.

classifying NIA industries for the country versus the naming of Petrobras’

business segments

accounting methods used in GAAP versus those in NIA

Some different meanings for words and labels that are nonetheless used in

both of the data sets (e.g. CapEx).

Our conclusion is that data for Petrobras is best compared with a composite of all oil

and gas related activity In Brazil, as reported by IBGE. This “Oil and Gas Composite”



from IBGE includes mining (extraction), refined petroleum products, wholesale and

retail of petroleum products, and pipeline transport.

In addition, the best comparison between the two data sets is in terms of growth

rates. While the size of data values can be somewhat different, due to factors

discussed below, their growth rates and other stylized metrics are nonetheless highly

correlated over time, as demonstrated by the following graph,

The main reason the comparison is best kept at the level of “entire company” and

“entire oil and gas sector” is due to the difficulties that arise when attempting to drill

down into a more granular comparison among ISIC industries versus individual

business units of Petrobras.

The next chart illustrates this factor, showing the R$ amounts for composite totals for

Petrobras versus IBGE’s data for Oil & Gas composite. The graph shows they are very

similar in size, in composite total, but there are significant differences between the

sub-industries in IBGE versus Petrobras’ various business units, despite their similar

sounding names.

For example, the name of the E&P (exploration and production) division in Petrobras

sounds as though it should match up closely with the NIA’s industry for Mining of Oil

and Gas (ISIC C11). However in practice the data from IBGE for C11 O&G Extraction is

much more narrowly defined than is Petrobras’ E&P division.

0 5 10 15 20 25 30

Petrobras

IBGE, O&G Composite

Petrobras

IBGE, O&G Composite

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Brazil's Energy Sector Compound Annual Percent Change, 2000-12



The differences are in the methods for industry classification used by IBGE to

construct the NIA for Brazil. The ISIC-based industries in NIA are very well defined,

follow internationally consistent norms, in particular with a narrow focus. However a

company such as Petrobras can structure and name its operation according to what it

believes is best for shareholders.

There are no international guidelines for companies in naming or organizing their

own business segments. However the NIA assembled by IBGE follows international

conventions on industry classification, outlined in the UNSNA treaty among members

of the United Nations.

The Petrobras business segment called E&P includes a lot of activities that are not

classified with the ISIC C11 for Brazil’s Oil and Gas Mining. For example, these

activities are included in the Petrobras E&P business unit, but are not classified with

ISIC C11 mining of O&G:

- Test drilling and boring: NIA includes this in ISIC 451, within the Construction sector

- Geophysical surveying and mapping: NIA includes this in ISIC 7421, Engineering Services

- Oil and gas well exploration: NIA includes this in ISIC 7421, Engineering Services

Similarly, the Petrobras segment for refining and marketing owns and operates a lot

of capacity in ports, shipping and transportation. But in the IBGE these activities are

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Petrobras IBGE, O&G Composite

Petrobras

Revenue Capital Spending

Petrobras v. IBGE Oil &Gas Composite 2012 Revenue and Investment, Millions of R$

Other

Refining, Transportation and Marketing

Exploration and Production

Pipeline Transport

Wholesale and Retail of Petroleum Products

Refined Petroleum Products

Oil and Gas Extraction

IBGE, Oil and Gas Composite

Petrobras Financial Statements



located in the transportation sectors ISIC I61 and I63. Also Petrobras generates

electricity, albeit mainly for own use. Nonetheless in IBGE that activity is allocated to

the Electricity Generation in ISIC E40.

These factors amount to a significant bias in favor in the R$ size of data from

Petrobras’ GAAP statements, over that from the IBGE’s NIA based Oil and Gas sector.

This is confirmed by comparing their respective history data.

On the other side of the scale, the industry data from IBGE for Pipelines includes

some that are not used for Energy. There are pipelines in Brazil used for metals

slurry and other products. This is a small bias in size in favor of the size of IBGE data

over Petrobras.

Perhaps the biggest factor for consideration is the treatment of CapEx in company

financial statements versus the NIA methods. For example, Revenues is an indicator

which is measured and interpreted very similarly in both of the accounting systems.

Therefore their data can be fairly and readily compared, which is confirmed by our

chart comparing Petrobras revenues to that from Brazils NIA for Oil &Gas.

However, the indicator of CapEx is not so readily comparable to the “Fixed

Investment” from GDP and NIA. The main reason is the NIA focuses on purchase of

tangible physical productive assets, such as purchases of machinery, equipment,

software or construction. By contrast, GAAP includes items not in the CapEx from

NIA, such as rental expense (NIA has this as OpEx from the leasing industry), also

purchases of land, and financial assets, such as shares in companies.

In practice, Petrobras’ financial statements show a heavy reliance on leasing of

equipment, especially in their E&P division, as opposed to outright purchases. For

example, well over half of the offshore platforms in use by Petrobras are not directly

owned by them. Instead that equipment is leased from others, yet are included in

the company level CapEx reported by Petrobras.

Thus the data for CapEx in GAAP have a material bias to be larger in size, when rolled

up into industry or country peer group totals, than does the CapEx indicator in NIA.



Third Case Study was published by the Bureau of Economic Analysis in their April

2001 Survey of Current Business. This link is for the original article:

http://www.bea.gov/scb/pdf/NATIONAL/NIPAREL/2001/0401cpm.pdf

In this article the BEA makes a conceptual analysis and then a comparison of data for

the indicator of profits. The comparison is between profits from a “peer group” of

500 companies, those which comprise the S&P500, versus the same indicator from

the NIA for the whole U.S. economy.

Overall there is a striking correlation in the two data sets, yet the NIA version of

profits is shown to be a generally cleaner and more consistent measure of profits

than is the same indicator from GAAP based financial statements.

In this example, there is no designation of industry sub segment; instead the 500

companies are those named in the S&P500 index, which is commonly used as a

benchmark for the stock market. These are compared to the NIA-based profits data

for whole U.S. economy.

The designation of geography does have a small role in the interpretation of results.

The GAAP based data for profits from the S&P500 companies refers to the sum of

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NIPA Profits from IRS via BEA (Bill.$, RHS)

Two Measures of U.S. Corporate Profits


http://www.bea.gov/scb/pdf/NATIONAL/NIPAREL/2001/0401cpm.pdf


their global activities. However the data for NIA generally refers to the location of

activity, no adjustment is made for overseas flows of profits.

The measurement of revenues is very similar in GAAP versus NIA data. However, the

treatment of, and definitions for, the indicator of profits can be quite different in the

two accounting systems.

The NIA system focuses on current operating profits, which means there are no ties

or linkages from balance sheet measures to profits in the income statement. By

contrast, GAAP based financial statements have several relationships between

balance sheets versus the income and cash flow statements. Also, NIA profits are not

distorted by management's "discretion" in the use of accounting techniques,

whereas GAAP requires this judgment from each company’s management.

This list summarizes the material differences in the treatment of profits within NIA

system versus GAAP financial accounting.

1 – NIA does not allow adjustments for capital gains and losses, or inclusion of bad

debt expense. This description is from the U.N. Handbook on NIA:” Capital gains and

losses are not included in the NIA profits measures, because they result from the revaluation and sale of

existing assets rather than from current production. Similarly, bad debt expenses are not deducted in

calculating the NIA profits measures, because these charges represent a rearrangement of assets and

liabilities in the Nation’s balance sheet rather than costs of current production.”

2 – Inventory valuation and depreciation have different treatments in the 2 systems

3 – Extraordinary items, goodwill and charges for restructuring are all excluded from

NIA profits calculations, yet are a featured part of GAAP.

4 – The cost of goods sold from NIA will not include the cost of transporting goods

from suppliers to purchasers, whereas GAAP does include these for companies. In

the NIA, ‘freight-in cost’ is treated as an intermediate cost of traders, not as COGS.

5 – GAAP profits allow deductions from taxes on income that are not recognized by

profits measured by the NIA:

Tax deduction for capital loss of preceding years (i.e. tax loss carried forward) is not recognized by NIA

Tax deduction for increasing employment (i.e. employment tax credits): in the NIA, this deduction is

treated as other subsidies on production

Tax deduction for charitable contribution, and Irregular taxes on wealth or assets, is treated in NIA as

capital transfers, thus not impacting NIA profits



6 – GAAP based profits include the impact from pension obligation, so the

appreciation of securities in corporate-sponsored, defined-benefit pension plans can

result in increased earnings under GAAP accounting, but not under NIA.

These factors together support our conclusion that NIA and GAAP measured profits

have several very material differences in their respective accounting practices, more

differences than is observed in the indicator of sales or revenues.

In addition, the NIA’s focus on current ongoing activity and costs is the main attribute

that allows the profits indicator form NIA to be a much a cleaner and more reliable

measure of operating profitability than is the GAAP based version of profits.

Perhaps the closest match in the GAAP based financial statements to the operating

profits in the NIA is the equivalent of “operating net income plus depreciation”. This

measure in GAAP avoids impacts from extraordinary adjustments and balance sheet

influence, and also avoids the distortion from different treatment of ‘depreciation’

that is also seen in GAAP versus NIA.



The Fourth Case Study looks at a comparison between the “bottom up” techniques

for calculation of profits forecasts, measured in GAAP terms, versus the “top down”

methods used by WIS to forecast industry profits, using the NIA based data.

Portfolio managers and analysts need an expected growth rate of sales and earnings

to determine the value of securities. A common metric for this is the "Analysts'

Earnings Expectations", which represent "bottom up" projections of forecasts, based

on consensus estimates for individual companies. These projections of company

profits are added together into peer groups, sectors, and up to a market total.

Consensus "Bottom Up" projections are thus comprised by the collection of all

analysts' estimates for individual companies. These “Bottom Up” forecasts by

analysts, on the profits of companies, are not constrained by any control totals or use

a common view of economic, sector-specific and inter-industry factors. The analysts

can forecast company profits in a freely independent manner

This causes the projections drawn from “Bottom Up” forecasts of companies to be

seriously flawed in methodology and application, with well known "Blue Sky"

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WIS Consensus

One-Year Growth Forecast for U.S. Profits

Consensus Forecasts for 3047 companies, organized into GICS Sectors,

Versus Forecasts of Sector Profits in WIS



optimistic biases, and a lack of common, or harmonized, assumptions, and other

distortions such as pervasive "herding" among analysts1.

The data being forecast by equity analysts is based upon company financial reports,

which themselves can sometimes contain biases and inaccuracies that distort the

true underlying rate of operating profits growth.

Consensus estimates also exclude failed companies, due to the fact that they no

longer exist. Survivorship bias causes the results to skew higher because only the

companies which were successful enough to survive, and large enough to have a

following among equity analysts, are included in the consensus forecasts.

Fortunately, WIS employs a better tool and more accurate process, and uses a more

consistent measure of profits, so avoids many of these pitfalls. The National Income

Account (NIA) profits measure provides the better benchmark of underlying profit

trends, and a better framework for calculating sector and market growth potentials.

A “Top Down” approach to forecasting profits, such as the global framework in WIS

that uses NIA based data, avoids the systemic problems of “Bottom Up” consensus

forecasts of GAAP based profits.

1 Darrough, M., and T. Russel. “A Positive Model of Earnings Forecasts: Top Down versus Bottom Up.” Journal of

Business, vol.75, no. 1 (2002); Beckers, S., M. Steliaros, and A. Thomson. “Bias in European Analysts’ Earnings

Forecasts.” Financial Analysts Journal, vol. 60, no. 2 (March/April 2004)


comparing nia and gaap data for industries

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