Monitoring year-to-year variation in structural business statistics

Contribution to Q2008 – Rome, 9 July 2008Session: Editing and Imputation IGuy.Vekeman@ec.europa.eu, ESTAT – G1

Structural Business Statistics (SBS)

Yearly statistics covering the ‘Business Economy’ (NACE sections C-K).

Geographical coverage: EEA + candidate countries Many characteristics: financial, employment… Multiple breakdowns: activity, size class, region

Produced by National Statistical Institutes, using uniform definitions (but data collection methodologies may vary)

Role of ESTAT: collecting, validating data flows, confidentiality treatment, publishing data series.

Actors

Data validation: a ‘macro editing’ tool

Main variation causes of aggregates in data flows:

– performance of the individual enterprises

– change in the composition of the set of enterprises

– raw data error (misreporting)

– data processing error (editing flaw).

Essential characteristics of macro-editing tool

not to overload correspondents with false alerts suitable threshold to single out influential anomalies

Previous practice

Symmetric [-20%, +20%] confidence interval Applied to all (but a few) characteristics Possibly generating hundreds (if not thousands) of

“anomalous” variations Skilful application required by ESTAT database manager Small aggregates vary more -> Unreasonable burden for

NSI of small countries

Factors influencing evolution of SBS data

Macro-economic– Economic growth– Inflation PPI/CPI (SBS data are in current prices)– Currency fluctuations

Micro economic– Prospering of enterprises– Business demography in the sector

Administrative: business register related– Registering enterprises / deregistering merged, closed

down or suspended units– Activity classification of enterprises

<- can be compensated for

Heuristics: Basic assumptions

Assumptions:– year-to-year variations (YTYV) of individual enterprises = set of random

observations– Enterprises very unevenly distributed in size and the YTYV of large

corporation influential on the sector average. – Economies of scale come to our rescue: since large YTYV more typical

for small companies.– variance of average: YTYV ~ 1/n

– Standard deviation on the average

Knowing economic growth G and inflation I, change of the aggregates could be estimated.

– So can we expect Vt є [Vt-1* (1+Gt)*(1+It)*(1 ± 2.σ/√nt-1)] with 95%

probability ?

No, because of several sources of bias

n1/ ~ YTYV

Heuristics: sources of bias

Non-financial business economy: NACE C-K \ J: not a full coverage

Stratification by NACE: non-random sample -> heavily biased sector evolution, moreover: – We use one unique ‘inflation’ number (CPI) instead of array of

sectoral PPI

GDP is a sum of values added. Other characteristics: possibly different evolution

Result of bias: expectation value => expectation interval

Heuristics: variability of characteristics

A few characteristics can be negative of close to zero:– Change in stocks or work in progress (frequently)– Gross operating surplus (rarely)– Value added (almost never)

Consequences:– Volatile characteristics -> large % YTYV– Variance increase of the characteristics

Measures taken:– Dropping volatile characteristics– Widening confidence limits of expectation interval (lack

of predictability ≈ extra bias source)

Heuristics: Bringing it together

(Standard) Confidence interval limited by a Standard lower boundary (SLB) and standard upper boundary (SUB)

Adapted boundaries: number of enterprises in year t-1

SLB / ( ) ; SUB * ( )

2. σ imply 95% confidence limits, leaving 5% anomalies (too many) … but we have no idea about σ.

=> 2. σ is considered a parameter: We fit the value 4 to obtain an 80% reduction of the number of ‘anomalies’ as compared to previous practice.

1

.21

tn

1

.21

tn

Heuristics: Method applied

Standard Confidence Interval:

– width depending on characteristics

– tuned using CPI and/or growth data (compare in national currency)

– Symmetrical on log-scale

Tuned interval for ‘Business demography’ characteristics.

– SLB / ( ) < (nt/nt-1) < SUB*( )

Tuned interval for Financial characteristics– [SLB / (1+…) * (1+real growth) * (1+inflation rate) ; SUB*(1+ …) * (1+real

growth)*(1+inflation rate)]

Tuned interval for Employment characteristics– [SLB / (1+…) * (1+real growth); SUB*(1+ …) * (1+real growth)]

)1(

41

tn )1(

41

tn

Characteristics Inflation? Growth? SLB SUB

Number of enterprises N N 0.82 1.22

Turnover Y Y 0.82 1.22

Purchases Y Y 0.82 1.22

Value added Y Y 0.77 1.30

Personnel costs Y Y 0.82 1.22

Number of employees N Y 0.82 1.22

Turnover / person empl. Y N 0.85 1.18

Purchases/ product.value N N 0.85 1.15

Confidence interval standard lower and upper boundaries

Implementation and discussion

Deterministic method => programmed in Access for distribution Test more tolerant on small aggregates => Reduced burden for small MS

(confirmation in ‘2003-04 field test’) Raising awareness on influential changes 'macro-editing tool‘: signalling suspicious aggregates:

– Business demographic change?– Micro-data to be reviewed? Selective editing of ‘suspect’ subset.– Same ‘macro editing tool’ front end (NSI) and back end (ESTAT) ->

shorter validation cycle Field test: Number of anomalies varies between 0.37% and 4.6% (!) Correlation low (0.15) between ‘country size’ (number of inhabitants) and

anomaly frequency: small and large MS are treated on equal footing.