oppgavegiveren: meeting user needs cost-effectively

Meeting User needs cost-effectively

Toward increased coherence in the international Trade by Enterprise Characteristics (TEC) framework

Nordic Statistical Association 2013 14th – 16th august 2013

Increased user-demands for trade data disseminated at the micro-level

The question is not solely “what do countries trade?” but “what kind of enterprises is behind that trade?” (size, activity, ownership etc.)

Respond: the Trade by Enterprise Characteristics (TEC) database since 2005

Links micro-data from ITGS with the Statistical business register (SBS), however:

Despite high degree of coherence, several methodological challenges remain unsolved

Services are not included but STEC-database in pipeline

Short introduction and outline

2

The paper include two different issues regarding

Trade by Enterprise Characteristics

This presentation will present these two issues

independently:

1. The issue of complex enterprises and estimated trade

2. The issue of adapting the TEC framework to the ITSS

Short introduction and outline

3

SBR’s handling of complex enterprises is insufficient

for this purpose

“Complex enterprises” identified by use of the

superior number (UHO-system) which is unique to

the Danish ITGS

The UHO system provides an automated method for

identifying complex enterprises by providing a

linkage of legal units that are financially interrelated

(“enterprise group”) through the allocation of a

superior number (UHO-number).

Treatment of complex enterprises

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508 complex enterprises (out of ~8000 enterprises) with 1986 legal numbers

26 pct. of total exports, 22 pct. of total imports

Most have only two legal numbers (291) but 58 have more than 5 legal numbers

These complex enterprises often report trade on a legal number with no or very few employees; and often legal units within the same complex enterprise have many employees but no trade

→ A lot of trade is matched with a “wrong” legal number in the SBR


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Suggested solution

The high-number of complex enterprises combined

with a relatively low number of “very complex

enterprises” suggests that a two-string approach

combining an automated process with a manual

“check-and-adjust” follow-up on the most

complicated enterprises is appropriate


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Step 1: Establish the link between traders and enterprises on a monthly basis.

Step 2: Use the one-to-one and many-to-one linkages to calculate the median trade M (import and export taken together) per person employed per activity group.

Step 3: For all traders in one-to-one or many-to-one linkages between traders and enterprises allocate monthly trade to the one and only identified enterprise.

Step 4: For all enterprise in one-to-many linkages between traders and enterprises calculate an estimated trade E by multiplying M with the number of persons employed by the enterprise.

Step 5: For all traders in one-to-many linkages between traders and enterprises allocate trade proportionally to E over all identified enterprises.


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~10 pct. of the total sum of estimated trade is

allocated to specific UHO-numbers, i.e. estimated

trade is currently impossible to allocate to the

enterprise level

The estimation-process as such is based on

individual UHO-numbers…

…but the subsequent allocation of estimated trade to

specific countries and goods is not, which impedes

the allocation of such trade to individual enterprises

Allocation of estimated trade to appropiate legal numbers

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Allocation of estimated trade to the enterprise-level

should be secured through an improvement of the

current system for allocation of estimated trade to

specific countries and goods.

When all estimated trade is allocated to specific

UHO-numbers, it can then be allocated further to

legal units by the automated process.

Allocation of estimated trade to appropiate legal numbers

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Conclusions

The issues of complex enterprises and allocation of

estimated trade when compiling TEC data need to be

addressed to secure a more robust TEC statistics

Not least seen in the light of new data requirements (export

intensity and more detailed classification by activity sector)

Complex enterprises and estimated trade

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Different sources in the ITGS and the ITSS

…leads to new challenges

Distributing estimated trade to enterprises not included in

the sample

Representivity of population

Adapting TEC framework on the ITSS

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Short presentation of the sources for the ITSS


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ITS survey

Travel

statistics

ITGS

Other sources

ITSS

Total population

~40.000 enterprises

Survey

~1.500

enterprises

Trade reported by ~1500 enterprises in the ITS

survey can be linked with SBR

Covers roughly 80 pct. of total service trade

Represent around 40000 enterprises

Other sources cannot be linked with SBR

Some cannot be linked due to the current method

- E.g. ITGS, government services and some insurance services

Other due to nature of the transaction

- Mainly the travel account


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A closer look at the ITS survey

Directly reported trade covers ~85 pct. of the total trade in

the ITS survey (~68 pct. of the total ITSS)

~15 pct. has to be distributed to the rest of the population,

i.e. the ~38500 enterprises (40000 – 1500)

No correlated administrative data source available to help

distribute the estimated trade

Using a non-correlated variable, such as employees, has

certain drawbacks

- No “zero reporters” since all enterprises have employees

- Variance from the non-correlated variable is inherited by the ITS


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What you cannot do when distributing with a non-

correlated variable

Count the number of enterprises engaged in ITS

Classify ITS by the non-correlated variable (e.g. by size if

number of employees are the non-correlated variable used

for the distribution of trade)

Classify ITS by other variables that are highly correlated

with the variable used for the distribution of trade

What you can do

Classify trade on a more aggregated level that are not highly

correlated with the variable used for the distribution of trade


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ITS survey – Trade by activity for 2010

NACE rev. 2 Import Export

Division Mill. DKK

A Agriculture, forestry and fishing 720 076 1 149 996 B Mining and quarrying 2 127 235 1 075 557 C Manufacturing 28 099 256 29 167 990 D Electricity, gas, steam and air conditioning supply 1 830 779 2 025 810 E Water supply; sewerage, waste management and remediation activities 102 853 250 789 F Construction 679 574 373 834 G Wholesale and retail trade; repair of motor vehicles and motorcycles 12 326 885 21 171 280 H Transportation and storage 135 667 463 193 802 038 I Accommodation and food service activities 523 956 C J Information and communication 15 613 411 15 137 626 K Financial and insurance activities 9 668 767 6 386 485 L Real estate activities 1 073 651 518 929 M Professional, scientific and technical activities 8 843 855 13 190 761 N Administrative and support service activities 9 083 828 5 571 938 O Public administration and defence compulsory social security 2 739 659 1 345 873 P Education 475 920 221 081 Q Human health and social work activities 25 798 274 022 R Arts, entertainment and recreation 368 794 303 333 S Other service activities 253 755 303 107 U Activities of extraterritorial organisations and bodies C C

Unknown acitvity 3 900 061 4 496 905

ITS survey updated roughly every 5 years

Population is ”frozen” at a given year (current population is

from 2007)

Mergers, exit, and entry change the composition of the

population over time


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Matching ITSS population with SBR

Enterprises not found in SBR 2010 3.710 Total number Enterprises in 2007 snapshot 40.653

Enterprises in SBR 2010 not represented by ITSS population 4.501

Enterprises matching SBR 2010 and ITSS population 35.063 Share of total enterprises in 2007 snapshot 86,2 %

Conclusions

~68 pct. of the Danish ITSS can be directly linked with SBR

and produce unbiased TEC statistics

~12 pct. can be indirectly linked using a non-correlated

variable and can be used to produce some TEC statistics on

a more aggregated level depending on the variable used

~20 pct. cannot be linked with the SBR due to

methodological issues or by the nature of the trade

ITS survey loses representivity over time but the effect is

quite small (1.3 pct. of total trade in 2010)


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oppgavegiveren: meeting user needs cost-effectively

Business

trade data

countries trade

enterprises survey

monthly trade

trade covers

identified enterprises

issues of complex enterprises

international trade