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Updating the Sri Lankan Register of Industry and the Role of IT. By Alex Korns UNIDO consultant ICES Session 65 June 21 2007. Outline. 1. The system for register updating 2. Testing in Sri Lanka 3. The IT application in Sri Lanka 4. Earlier experience in Indonesia 5. Conclusions.
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Updating the Sri Lankan Register of Industry and the Role of IT By Alex Korns UNIDO consultant ICES Session 65 June 21 2007
Outline • 1. The system for register updating • 2. Testing in Sri Lanka • 3. The IT application in Sri Lanka • 4. Earlier experience in Indonesia • 5. Conclusions
Introduction • Needed: A system for inter-censal updating of the regis-ter of large and medium manufacturing establishments. • The main instrument for register updating in many devel-oping countries is a decennial economic (or industrial) census, involving door-to-door listing. • A register quickly becomes obsolete if not updated, especially given the rapid turnover of establishments in many developing countries and the tendency for their numbers to grow rapidly. • In the absence of updating, there will be a tendency to underestimate the size of the manufacturing sector and its annual rate of growth in non-census years.
The challenge of register updating in a developing country • Needed: teamwork by a small group of skilled staff who understand the updating system and can make purpose-ful decisions. • Use of administrative sources for updating involves a purposive approach that is not part of the basic statistical training, nor taught in statistical academies. • In the absence of an independent measure of the popu-lation of establishments, managers lack a clear target. • Due to labor supply constraints in developing countries, there is a preference for statistical operations involving routine data-collection by numerous field staff under the guidance of a few managers. It is difficult to adapt the updating system to such a paradigm, but IT can help.
Administrative sources for updating • Most medium and large establishments are registered with one or more government agencies. Registration leads to the creation of administrative data sources. • An effective source needs to provide an up-to-date address, an activity code, and a size indicator. Absent these 3 prerequisites, a data source is not very useful. • An up-to-date activity status code (active, closed, etc?), telephone number and reliable establishment name are also important. • Sources may be single or multiple. A common source in many countries is the register of companies. It often lacks the 3 prerequisites and is therefore ineffective.
Topic of this paper • This paper is about a system for register updating for medium-sized industrial registers in countries that lack a single administrative source for updating. • IT plays an important role in the system, both for guiding officials in what needs to be done each year, step by step, and for facilitating the work. • Operation of the system requires 2 kinds of data: • A core register that is to be updated. This is commonly managed by the national statistical office. • External lists of establishments or enterprises from administrative sources, based on self-registration.
Six Procedures Six procedures are then applied to the external lists • Importing, parsing and editing the external lists. • Matching external lists against the core register. • A list of unduplicated candidates for addition to the register is prepared from the external lists. • The candidates are prioritized based on various indi-cators (source, size, year starting production, etc) • High-priority candidates are field checked, on the spot or by phone. • Qualifying candidates are added to the register.
Updating for closures • In many developing countries, the Annual Survey of Industry (ASI) covers all L & M manufacturing establish-ments. • Often unclear whether establishments that do not res-pond to the ASI have closed or remain active. This ham-pers register updating and imputation for nonresponse. • A null response form can solve the problem. For estab-lishments that do not provided a completed ASI ques-tionnaire, the enumerator uses the form to report whe-ther the establishment is still active and in scope. • Incomplete coverage of closures when the ASI sample is selected by sampling instead of with a simple cutoff, or when enumerators lack time or funding to visit all sample establishments.
Nonresponse • ASI nonresponse is a major problem in developing coun-tries, often one that gets more attention from manage-ment than register updating. • High nonresponse rates can discourage local statistical offices from reporting new establishments, for fear dis-coveries will worsen the response rate. • An application for register updating can also support a efforts to reduce nonresponse. E.g., track response rates for individual enumerators and tag difficult cases. • An ASI call center can support efforts to raise response rates and economize on field visits. The register applica-tion can be adapted to the call center requirements.
Census listing • Countries that do update their register between census-es face special technical issues in integrating the two sources. • The preferred (but less-frequently used) technique is to carry the register list sorted by small areato the field dur-ing enumeration. This should ensure a good linkage between the census listing and the old register. • The more common technique is to go to the field with a “blank slate,” that is, without the old register. The census produces a new list that should, in principle, be more complete than the old register, but that in practice often misses many establishments in the old register. • The old list is frequently discarded in the aftermath of a census. Much valuable information is thus lost.
Introduction for Sri Lanka • Manufacturing accounted for 18 % of GDP in 2006, probably an understatement. • Census of Industry in 1983 and in 2003-04. • Incomplete register updating between censuses. • UNIDO projects at the Department of Census and Statistics (DCS) to develop a system for register updating: • Phase 1 – 2002-03, for Western Province only. Pro-totype system for register updating in Dbase and Visual Basic. • Phase 2 – 2005-07, for the whole country. System for register updating in SQL Server and dot Net.
Core register in SRL • A core register had to be created in the aftermath of the 2003-04 census. This was done by laboriously matching data from the pre-census register with census data and combining the unmatched records. • The core register provides about 100 variables: • Identification numbers, including numbers for the census and the old register. • Activity status, month starting commercial produc-tion, month of closure, ISIC 3 codes & main product. • 20 variables for the name, address, telephone & fax numbers of the establishment and a similar number for its head office. • 26 variables for historical data for employment & ASI response status.
External sources in SRL DCS has used four external data sources, as follows: • The Board of Investment was most effective. Addresses. phone numbers and activity status are up to date. • The Ministry of Industrial Promotion (MIP) provides data on establishments registered with it; but many records are outdated, especially for activity status and phone. • The Ceylon Electricity Board provides data on industrial customers. Up-to-date for activity status but mostly lack phone numbers. Establishment names are unreliable, as connections are often registered in the owner’s name. • The Employees’ Provident Fund (EPF) provides data on establishments covered by the government-mandated social security system. No phone number, an often out-dated address, and an unreliable set of activity codes. With a very low success rate in phase 1, EPF was not used again in phase 2.
Matching in SRL • The sources all provided data in electronic format. After importing into the DCS system, parsing and editing, the lists from external sources were matched against the DCS core register. • Matching was computer-assisted. The phase 2 applica-tionused a matching likelihood index (MLI). For names and addresses, similarity was calculated with a bigram algorithm. Words appearing frequently (e.g., garments) were given a lower weight. • Operators reviewed cases with a high MLI and classified them as either a match, nonmatch or pending. Pending cases were printed out for further analysis, often based on information to be elicited in phone calls.
The list of candidates • In phase 2, DCS entered 7308 records from 3 external sources. Matching reduced the number of unduplicated records from external sources to 4913 candidates for addition to the register. • Not all candidates could be checked, so they were priori-tized based on the quality of the source (BOI was top-rated), size of employment or power usage, and regis-tration year (preference went to the most recent years). • High priority candidates (883) were designated for field visits. Medium priority candidates (705) were designated for phone checks. • Low-priority candidates (3328) could not be checked, for lack of resources. However, a random check of these candidates would have been useful for estimating the number of establishments missed by the register.
Results of field and phone checks • 699 establishments were discovered, equal to 44 per-cent of candidates checked. Total employment was 134,000 -- an average of 192 workers per establishment. • The most surprising finding came from the age distribu-tion of discoveries. Half of them started commercial pro-duction before 2001, while only 29 percent started in 2003 or after. This indicated that many discoveries were in-scope at the time of the 2003 census. • In a mature system for discovering new establishments, the backlog of undiscovered establishments should be small. The finding suggests that the backlog of undiscov-ered establishments may still be large for Sri Lanka.
Closures in SRL • ASI response rates chronically low. Only 42 percent of active establishments responded to the 2005 ASI. • DCS used the null response form to document reasons for nonresponse for 2,367 nonrespondents and 271 cases that had either closed or gone out of scope. • The form is supposed to be filled out during a field visit; however, there are grounds for concern that many null response forms were filled out without a field visit. In such cases there is no positive, concrete evidence that the establishment was still in operation. • Accordingly, it is possible that many closures or “out of scope” cases were simply not observed.
Call center for the ASI • In an effort to improve the response rate, the UNIDO project helped DCS set up a call center for the ASI. About 3000 calls were placed, with most establishments receiving only one call. • Most establishments said they hadn’t received the mail-ed questionnaire; questionnaires were resent. • 13 % refused to have a dialog or to respond to the survey, 20 % responded with combative questions, 30 % agreed to respond but sounded negative, while 37 % agreed to respond. • Given the negative attitudes, it was concluded that DCS would need to place an average of 5-7 calls per estab-lishment in order to have a substantial impact on res-ponse rates.
Register Updating in Indonesia • A similar system was implemented in Indonesia during 1990-94 & reported at ICES I. The system succeeded in discovering thousands of missed large and medium manufacturing establishments, bringing the total number from 12,000 up to 20,000. • “A Workable System for Updating Indonesia’s Manufacturing Directory”, ICES, 1993. • Establishment numbersfor Indonesia stopped growing after 1995. The updating system initiated during 1990-94 continued to be implemented, but became less effective, most likely due to inadequate training and supervision. • Response rates have been much higher in Indonesia than in Sri Lanka. Reached more than 90% under the Suharto government, now down to 70%.
Register Updating cont. • In the 1996 Economic Census, few discoveries of indus-trial establishments not already in the register, indicating that the updating system had succeeded. • In the 2006 Economic Census, however, many industrial establishments were discovered that were not already in the register, confirming that updating was no longer so successful. • More specifically, the 2005 register showed only 21,000 industrial establishments, while the census found about 31,000 in 2006. The discrepancy is under investigation.
Conclusions • Administrative listsprovide excellent sources for updat-ing statistical registers in developing countries. • A field check is needed before the candidate can be added to the register. • A null response form is needed to document closures. • Problem of how to combine administrative sources & economic census. • But is the system sustainable? Experience in Sri Lanka and Indonesia has shown that the system is workable but requires much training & supervision. Should man-agement attention falter, the system may be forgotten or lose effectiveness in discovering new establishments. • IT can provide solutions that limit the burden on training, supervision and management. Further work is needed to perfect a robust IT solution for developing countries.
