IRES Chapter VII — Data Collection and Compilation

Chapter VII (PDF pp. 100–113) discusses the role of legal frameworks and institutional arrangements in energy data collection, followed by data collection strategies, data sources, and data compilation methods, so that countries can learn from one another’s practices and improve the completeness and international comparability of energy data [IRES, Ch. VII, para. 7.1, PDF p. 100, 2018].

This digest covers the full chapter across two ingest passes: Sections A (legal framework), B (institutional arrangements) and C (data collection strategies) — paras 7.2–7.35, PDF pp. 100–106 — followed by Sections D (data sources) and E (data compilation methods) — paras 7.36–7.69, PDF pp. 106–113.

A. Legal framework (paras 7.2–7.5)

Full detail on Legal Framework and Institutional Arrangements. A strong legal framework — statistical laws plus applicable energy, customs and other national legislation — is one of the most important prerequisites for a sound national statistical system in general and a national system of energy statistics in particular [IRES, Ch. VII, para. 7.2, PDF p. 100, 2018]. Establishing a legal framework that makes reporting of energy data mandatory, while adequately addressing confidentiality, is of great importance to compiling high-quality energy statistics [IRES, Ch. VII, para. 7.3, PDF p. 100, 2018]. The framework should also describe how responsibilities for collection, compilation and maintenance of the different data components are divided among government bodies [IRES, Ch. VII, para. 7.4, PDF p. 100, 2018]. It is recommended that national agencies responsible for energy statistics actively participate in the discussions on national statistical legislation, with a view to mandatory reporting and adequate confidentiality protection [IRES, Ch. VII, para. 7.5, PDF p. 101, 2018] — recommendations tracker row VII/7.5.

B. Institutional arrangements (paras 7.6–7.14)

Full detail on Legal Framework and Institutional Arrangements. A legal framework is a necessary but not sufficient foundation; appropriate institutional arrangements among relevant governmental agencies are of paramount importance for effective statistics [IRES, Ch. VII, para. 7.6, PDF p. 101, 2018]. A national system of energy statistics comprises the various agencies engaged in collecting, compiling and disseminating energy data — chiefly national statistical offices and energy ministries, but increasingly also chambers of commerce, industry associations and regional offices [IRES, Ch. VII, para. 7.7, PDF p. 101, 2018]. Sound institutional arrangements enable collection, standardization, integration and dissemination of scattered information, promote harmonization with international standards, and reduce both collection cost and response burden [IRES, Ch. VII, para. 7.8, PDF p. 101, 2018]. Governance requires a clear lead agency responsible for coordination [IRES, Ch. VII, para. 7.9, PDF p. 101, 2018]. It is recommended that countries develop an appropriate interagency coordination mechanism that monitors system performance, motivates member participation, and has authority to implement improvement recommendations, including on statistical capacity [IRES, Ch. VII, para. 7.10, PDF p. 101, 2018] — recommendations tracker row VII/7.10. National systems range from centralized (one institution handles the whole statistical process) to decentralized (several institutions handle different parts) [IRES, Ch. VII, para. 7.11, PDF p. 102, 2018]. Effective institutional arrangements are characterized by three traits: (a) a single agency (or clearly identified agencies plus consistency mechanisms) responsible for dissemination; (b) clear definition of each agency’s rights and responsibilities; and (c) formalized (and complementary informal) working arrangements between agencies [IRES, Ch. VII, para. 7.12(a)–(c), PDF p. 102, 2018]. It is recommended that countries treat the establishment of such institutional arrangements as a high priority and periodically review their effectiveness [IRES, Ch. VII, para. 7.13, PDF p. 102, 2018] — recommendations tracker row VII/7.13. The agency with overall responsibility should periodically review definitions, methods and outputs against international recommendations and best practice [IRES, Ch. VII, para. 7.14, PDF p. 102, 2018].

C. Data collection strategies (paras 7.15–7.35)

The collection of energy data can be complex and costly, so countries should undertake it on the basis of well-thought-out strategic decisions on scope and coverage, organization of the collection process, selection of data sources, and use of reliable collection methods [IRES, Ch. VII, para. 7.15, PDF p. 102, 2018].

C.1 Scope and coverage of data collection (paras 7.16–7.30)

Full detail on target population split across Energy Data Reporter Groups and the remaining five dimensions on Scope and Frequency of Data Collection. Scope and coverage are defined along six dimensions: conceptual design, target population, geographical coverage, reference period, frequency, and point in time of collection [IRES, Ch. VII, para. 7.16(a)–(f), PDF pp. 102–103, 2018]. It is recommended that countries distinguish at least three reporter groups — energy industries, other energy producers and energy consumers — as applicable [IRES, Ch. VII, para. 7.18, PDF p. 103, 2018] — recommendations tracker row VII/7.18. The chapter also covers collection from the informal sector using the Fifteenth ICLS definition [IRES, Ch. VII, paras 7.25–7.26, PDF p. 104, 2018], geographical coverage [IRES, Ch. VII, para. 7.27, PDF p. 104, 2018], reference period [IRES, Ch. VII, para. 7.28, PDF pp. 104–105, 2018], the three frequency tiers — annual, infra-annual and infrequent [IRES, Ch. VII, para. 7.29, PDF p. 105, 2018] — recommendations tracker row VII/7.29, and point in time of collection [IRES, Ch. VII, para. 7.30, PDF p. 105, 2018].

C.2 Organization of data collection (paras 7.31–7.35)

Full detail on Scope and Frequency of Data Collection. Proper organization starts with identifying production, supply, transformation and consumption flows for each fuel and mapping their potential data sources [IRES, Ch. VII, para. 7.31, PDF p. 105, 2018]. Collection methods — statistical business registers, administrative data, censuses or sample surveys — should be selected according to the nature of the energy activity, data availability and budget [IRES, Ch. VII, para. 7.32, PDF p. 105, 2018]. It is recommended that energy data collection be integrated into the wider national statistical system, with close collaboration between energy statisticians and compilers of industrial, household, labour-force and financial statistics [IRES, Ch. VII, para. 7.33, PDF pp. 105–106, 2018] — recommendations tracker row VII/7.33. Establishing or improving the regular data collection programme should be part of a long-term strategic plan [IRES, Ch. VII, para. 7.34, PDF p. 106, 2018]. An integrated approach is especially important for energy consumption data, given the number of consumers relative to suppliers and the value of exploiting existing business surveys [IRES, Ch. VII, para. 7.35, PDF p. 106, 2018].

D. Data sources (paras 7.36–7.60)

The generation of energy statistics is based on data collected from two main sources: statistical data sources (censuses and/or sample surveys, collected exclusively for statistical purposes) and administrative data sources (data created originally for purposes other than the production of statistics) [IRES, Ch. VII, para. 7.36, PDF p. 106, 2018].

D.1 Statistical data sources (paras 7.37–7.54)

Full detail on Statistical Data Sources and, for the sampling frame it recommends, Statistical Business Register. Statistical data sources are surveys of the units in a population, conducted either by census or sample survey [IRES, Ch. VII, para. 7.37, PDF p. 106, 2018]. It is recommended that countries make efforts to establish a programme of sample surveys integrated into an overall national sample survey programme of enterprises and households [IRES, Ch. VII, para. 7.39, PDF p. 106, 2018] — recommendations tracker row VII/7.39. Survey design proceeds through a sequence of steps — identifying information needs, establishing periodicity [IRES, Ch. VII, para. 7.41, PDF p. 107, 2018] — recommendations tracker row VII/7.41 — selecting data items from the Chapter VI reference list, selecting the target population/sample, designing the questionnaire, and piloting/training [IRES, Ch. VII, paras 7.40–7.45, PDF pp. 107–108, 2018]. Enterprise surveys may be list-based or area-based [IRES, Ch. VII, para. 7.46, PDF p. 108, 2018], and it is recommended that list-based enterprise survey frames be derived from a single general-purpose statistical business register [IRES, Ch. VII, para. 7.47, PDF p. 108, 2018] — recommendations tracker row VII/7.47 — falling back to an amended economic-census list where no current register exists [IRES, Ch. VII, para. 7.48, PDF p. 108, 2018] — recommendations tracker row VII/7.48. The chapter also covers ad hoc energy statistics surveys [IRES, Ch. VII, paras 7.49–7.50, PDF pp. 108–109, 2018] and household/mixed household-enterprise surveys [IRES, Ch. VII, paras 7.51–7.54, PDF p. 109, 2018].

D.2 Administrative data sources (paras 7.55–7.60)

Full detail on Administrative Data Sources. Publicly controlled administrative data sources arise from governmental agencies acting under legislation and/or regulation [IRES, Ch. VII, para. 7.55, PDF p. 109, 2018], with advantages [IRES, Ch. VII, para. 7.57, PDF pp. 109–110, 2018] and limitations [IRES, Ch. VII, para. 7.58, PDF p. 110, 2018] discussed alongside named examples — customs records, VAT, excise duty and carbon tax, regulated meter-operator systems [IRES, Ch. VII, para. 7.59, PDF p. 110, 2018]. Privately controlled administrative data sources (e.g., trade-association data) should be accessed cooperatively, with the statistical agency verifying data quality and objectivity independently [IRES, Ch. VII, para. 7.60, PDF p. 110, 2018].

E. Data compilation methods (paras 7.61–7.69)

Full detail on Data Compilation Methods. Data compilation covers (a) data validation and editing, (b) imputation of missing data, and (c) estimation of population characteristics [IRES, Ch. VII, para. 7.61, PDF p. 110, 2018]. Validation and editing rules include checking that the sum of available supplies equals the sum of recorded uses [IRES, Ch. VII, paras 7.62–7.64, PDF p. 110, 2018]. Imputation should have three desirable properties [IRES, Ch. VII, para. 7.66(a)–(c), PDF p. 111, 2018], and it is recommended that imputation methods comply with the requirements of other international economic-statistics recommendations such as IRIS [IRES, Ch. VII, para. 7.67, PDF pp. 111–112, 2018] — recommendations tracker row VII/7.67. Grossing-up procedures estimate total-population characteristics from sample values, and it is recommended that specialist expertise always be sought for this task [IRES, Ch. VII, para. 7.68, PDF p. 112, 2018] — recommendations tracker row VII/7.68. Outlier treatment closes out the chapter [IRES, Ch. VII, para. 7.69, PDF p. 112, 2018].

Page map

Section	Page
A — legal framework	Legal Framework and Institutional Arrangements
B — institutional arrangements	Legal Framework and Institutional Arrangements
C.1 — target population (reporter groups)	Energy Data Reporter Groups
C.1 (remainder) + C.2 — scope dimensions, frequency, organization of collection	Scope and Frequency of Data Collection
D.1 — statistical data sources	Statistical Data Sources, Statistical Business Register
D.2 — administrative data sources	Administrative Data Sources
E — data compilation methods	Data Compilation Methods

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