ASTI Industry Indonesia Presentation

8/9/2019 ASTI Industry Indonesia Presentation

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QUARTERLY MANUFACTURING INDUSTRIAL SURVEY:METHODS, PROBLEMS, AND SOLUTION

THE CASE OF INDONESIA

SUB DIRECTORATE SMALL-SCALE AND COTTAGE INDUSTRY STATISTIC S BPS - STATISTICS INDONESIA


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QUARTERLY MANUFACTURING

INDUSTRIAL SURVEY

1


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In the beginning PJP I, the Indonesian Economic was still relied on theagricultural sector, which contributed over 50% to the GDP. Whilecontribution manufacturing industrial sector was only less than 9%.

BPS: 2

The industrial sector is a leading sector as a main supporting Indonesian

Economy => The existence of Quarterly Production Indices as a promptindicator which useful for monitoring the economy.

In 2000 BPS introduced a new quarterly system with a monthly sub-system

that gives an aggregate monthly index .

BACKGROUND

Entering the PJP II (1994) => contribution industrial sector to GDPcontinued to increase (23.5%), while agricultural sector tended to decline(17.4%).

Industrialization process => Economic structure has shifted fromagricultural sector to industrial sector. Contribution industrial sector(20.96%) is bigger than agricultural sector (19.66%).


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SOURCES OF MANUFACTURINGINDUSTRY DATA

Data on Large & Medium manufacturing industries is collected every year,while Small & Cottage industries could not be collected every year.

Large & Medium Manufacturing Industrial Survey relied on the cencusebasis, while Small & Cottage Industrial Survey relied on sample basis.

Data sources of manufacturing industrial survey => relied on the establishment record. Administrative records from association of several industries => needed

just for standard of comparison => check for plausibility.

Manufacturing industry is categorized into four groups according to thenumber of employees. Large & Medium establishment cover those u 20

persons engaged, Small and cottage establishment cover those e 19 personsengaged.

3

- Directory of Large & Medium establishments => Updated every year.- Large & Medium Manufacturing Industrial Survey => Relies on cencus

(covers all establishments).- Response rate of the annual survey around 84% - 91% from the target.

- Timeliness around 18 months after the reference year.


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- Large & Medium establishments => is a smallest part from the total number of establishments ( less than 1%).

- Cottage industries => is the biggest part (90 .

26 %)- Small scale industries => is the second biggest part (8.77%).

- Large & Medium establishments => is a biggest absorber employements (44%)- Cottage industries absorbs around 4 0 .2 8%, and - Small-scale industry absorbs around 1 6 % employments.

- Number of manufacturing industries in 1998 amounted to 2 .22 million units .- Absorbing around 9.43 million persons.

4PROFILE OF MANUFACTURING INDUSTRY

- Total value of gross output of the manufacturing industries in 1998 wasaround Rp. 474,4 2 4 billion.

- Large & Medium establishments => The biggest share total of output of themanufacturing industry (91%).

- Small-scale and Cottage industries constibuted only 9% to the total output.


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5

- Number of establishments, the Large & Medium Manufacturing Industry=> was dominated by sub-sector 31, 32 and 33 => Each sub-sector has

share around 25%; 22% and 14% from the total number of establish-ments.

PROFILE OF MANUFACTURING INDUSTRY

- Sub-sector 35,38 and 36 has share was around 11.15%, 10.99% and 9%.

- Sub-sector 34: less than 5%, sub-sector 39: less than 3%.

- More over the sub-sector 37 which was only less than 1%.


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Sampling frame based on the 199 6 Annual Large & Medium Manufacturing Industry Survey.

Methodology of Sample Selection:- A rough target sample of 99 2 establishments was choosen whisch should

allow timely processing based on pas experience. Based on this sample, thelevel of representation of the sample was determine to be at the 3-digit ISIC,not at the 5-digit ISIC currently used.

- Establishment were then sorted by declining output, and a cut-off point ( u Rp. 141.55 million in 199 6 ) was determined above which all establish-ments are choosen with certainty. The number of such establishments was2 36 => given a code C1

6METHOD OF MANUFACTURINGINDUSTRY SURVEY

Quarterly Survey


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Quarterly Survey

Methodology of Sample Selection:

- The top 1 % establishments group which have extraordinarily high output per worker, then was choosen and was added to the sample with certainty.The number of establishments added was 131 => given a code C 2 (those establishments group have a special behavior => special treatment)

- Remaining establishments were then sorted by 3-digit ISIC, and within each ISIC by descending output per worker. The remaining number of establish-ments to be selected (99 2 - 2 36 - 131) were then selected with PPS, and theywere given a code of S.

- Establishments selected with certainty were then given a sampling weight of 1, and those selected by PPS were given a weight equal to the invers of their

probability of selection.


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Quarterly Survey

Number of establishments has been selected by sub-sector ------------------------------------------------------------------------------------------

Sub-sector Number of establishments Distribution (%)

------------------------------------------------------------------------------------------31 210 21.1732 195 19.6633 97 9.7834 45 4.5435 195 19.66

36 43 4.3337 39 3.9338 160 16.1339 8 0.80

-------------------------------------------------------------------------------------------Total 992 100.00

=====================================================


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9METHOD OF MANUFACTURING

INDUSTRY SURVEY

Quarterly Survey

Methodology of Sample Selection:

- In line with the sampling design, the unweighted output of establishments in thesample represents 6 1.7% of total output in the population.

- Target publication schedule => 3-3.5 months after the end of the referencequarter for the preliminary version and 6 months for the final figures.

- The methodology of computing Quarterly Production Indices was based oninter-month commodity growth. Previously was based on estimation of intraquarterly commodity growht and inter quarterly growth.

- Quarterly Production Indices are calculated by averaging of three monthly indi-ces of the concerned quarter using the Discrete Divisia procedure.


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Quarterly Survey

Steps are required to compute the Quarterly Production Index based onthe Monthly Index:

- The first step includes calculating commodity growth between the two relevant months.

- The second step includes calculating an establishment index by aggregating commodity growth using their relative value weights.

- The third step includes aggregating to the 5-digit ISIC level to measure growth for that particular ISIC.

- The fourth step includes computing a total index for all establishments.

- Finally, by averaging of three monthly indices on the concerned quarter, then

would be Quarterly Production Index result.


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Monthly survey as a sub-sample of the Quarterly survey

- Using the same selection methodology which used in the Quarterly survey. Theonly difference being that while the Annual Survey was the frame for the quar-terly, the Quarterly itself was the frame for the Monthly Survey .

- Target sample of 195 establishments, is representative for 1-digit ISIC level monthly production index result, and could be handle for a monthly survey.

- Establishment were then sorted by declining output, and cut-off point (Rp.9 0 7.9billion) was determined above which all establishments are choosen withcertainty. The number of such establishments was 11 => given a code C1.

- Remaining establishments were then sorted by descending output per worker.The remaining number of establishments to be selected (195-11) were then se-lected with PPS => given a code S.

- 797 establishments are compiled for a quarterly survey.

- 195 establishments are compiled for a monthly survey .


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12QUESTIONNAIRES AND MAJOR VARIABLES

Questionnaire format:- Periodicity => Quarterly- Quarterly index computation => Inter month commodity growth

=> The questionnaire requires data for every month in the quarter,=> In addition to the last month of the previous quarter (overlapping months)

=> Cover three main variables:- Quantity of production => Commodity growth factors- Value of production => Current weight value- Number of employees => For editing purpose

The main reasons for adopting those format:

- With information for four consecutive months (including one overlappingmonth) in one questionnaire, a quarterly growth rate for an establishmentcan be calculated from a single questionnaire rather than having to com-pare two questionnaires. This has two important advantages:

a. The role of inconsistent nomenclature and classification from quarter

to quarter is eliminated.


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1 3QUESTIONAIRE AND MAJOR VARIABLES

b. More data are used in calculating the index, since calculations neednot be limited to establishments that respond in both quarters.

- Comparing data for overlapping months in various quarters provides anindication or response reliability.

- The questionnaire has additional fields for calculating the implicit unit priceof various commodities reported => an easy way to detect implausibility orinconsistency in reported data.

- Many establishments may find it easier to report monthly rather than quarter-ly because their books are arranged that way.


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1 4QUESTIONAIRE AND MAJOR VARIABLES

f ig u r e 1

S a m p l e Q u e s t io n n a i r e f o r Q u a r t e r I , 2 0 0 0P R O D U C T I O N A N D T O T A L E M P L O Y M E N T P E R M O N T H

1 9 9 9 2 0 0 0 N o T y p e o f P r o d u c t

D e s c r ip t i on U n i t

D e c e m b e r J a n u a r y F e b r u a r y M a r c h

( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) ( 6 ) ( 7 ) ( 8 )

1 . Q u a n t i t y

V a l u e m i ll io nR p

P r i c e / U n i t

2 . Q u a n t i t yV a l u e m i ll io n

R pP r i c e / U n i t

. . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . .

6 . Q u a n t i t y

V a l u e m i ll io n

R pP r i c e / U n i t

7 . O t h e r * ) V a l u e m i ll io nR p

8 . T o t a lq u a n t i t y

9 . T o t a l v a l u e V a l u e m i l l io nR p

1 0 . E m p l o y e e s P e r s o n

* ) I f t h e v a l u e o f p r o d u c t i o n o f s e v e r a l c o m m o d i t ie s i s le s s t h a n 2 % o f t h e t o t a l v a l u e o f p r o d u c t io n , p l e a s e

c l a s s i f y t h e s e u n d e r O t h e r ( N o . 7 ) .


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1 5DATA COLLECTION METHOD

Periodicity:Data on Quarterly Survey are collected in two ways :- First, 797 establishments are compiled every quarter.- Second, the remaining establishments (195) are compiled every months.Notes: After three months then the data which collected every month could

be consolidated with data which are collected every quarter for com-

putting the Quarterly Production Index.

System data collection:- Quarterly Survey => Direct enumeration.- Monthly Survey => Combination of the mailling, facsimile, and email

system.

System of directory updating:Directory of the Quarterly and Monthly Sample are maintained and keptupdated, for cases in which some sample establishments has died or is non-active.


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1 6

Purposes of sample directory:- Useful for monitoring of document reponses for each establishment.- Measuring of response rate of documents survey from the target sample in

each Province.

Improving the response rate :Based on the response rate, policy actions could be taken for improving theresponse rate:

- To inform the BPS Province to handle or troubleshoot the problem, espiciallyfor lack of response and non-response cases.ponse.

- To visit some establishments in order to improve their cooperation and dataintegrity.

- To contact establishments (via contact person), to ask about progress andproblems in filling up the questionnaire.

- To ensure establishments, that their report is important for to computingproduction index, industrial policy decisions, for itself, and so on.

DATA COLLECTION METHOD


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1 7ADJUSTMENT FOR NON-RESPONSE

Adjustment methodology:The response rate of the Quarterly Survey is always under from the targetsample. Usually after 3 to 4 months of the reference quarter, the response ratewas still around 40% to 50%. Therefore, in order to maintain reliability andrepresentativeness, then the adjustment for non-response was needed.Adjustment for non-response distinguishes between establishments selected withcertainty and those selected with PPS.

- Establishment with code of C1, sampling weights of respondents in a parti-cular quarter are adjusted by multiplying them by a ratio of total output of all establishments has code of C1 in a particular ISIC to total output of

actual respondents in that quarter in that ISIC.

- Establishments with code of S, sampling weights of respondents are multi-plied by the invers of the response rate in that quarter in a particular ISIC.


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1 8ADJUSTMENT FOR NON-RESPONSE

Therefore, its more better for always be maintain of the res-ponse rate than must be done adjustment for non-response.

There are the informal approach which need to be done:

=> To develop and improve better relationships with contactpersons.- Indirect communication with establishments (by phone).- Direct communication with establishments (visited).

=> Give and take principal must be taken.- BPS giving the relevant publications which useful for

establishments, while the establishments giving the datareport to BPS.


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1 9

Data preparation:Data consistency and plausibility are detected following these two steps:- First, manually system => Before the entry.- Second, computerized system => After the entry (data processing).

Before the entry, data consistency and plausibility must be evaluated mannu-

aly to ensure that the data have been toleraby consistence:- Calculate average price per unit of every commodities in every month

within one quarter, then comparing one to the others.

- Compare the productivity per worker between months within one qua-ter, to ensure that production per month is plausible.

- Examine the onsistency possibility of quantity and value of productionfor every commodity by comparing them with the previous quarter, andmake the necessary correction if they are inconsistent.

DATA PROCESSING AND DISSEMINATION


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If data are suspected as being inconsistent:

- BPS comes back and asks the contact person to ensure that the correctioncan be made appropriately.

- Indicators of the previous quarter usually are used for correction.

- Moreover, to ensure that the data is plausible, data are crosschecked againtsadministrative records of a limited number of industry associations.

Data entry:

- Data of every establishment are entered twice by independent data entryoperators, and result are then compared one to another.

- Any discrepancies are then checked and corrected by the computerizedsystem which has been developed in the data entry module.

20DATA PROCESSING AND DISSEMINATION


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Data entry module has three main feature:

- It validates quantity and value totals per commodity. That allowsdetection of inconsistencies between individual commodity entries

and commodity totals in the questionnaire.

- As quantity and value data per commodity are entered, the pro-gram flashes the implicit unit prices. Inconsistencies can then bedetected and corrected.

- It is interactive, i.e. entry erros can be corrected on the spot.


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Data Processing:- Data processing uses the same manner and software as the ones previously

used.- The previous growth computation uses inter quarter growth and intra

quarter growth => Then to be change base on commodity growth factors

between two consecutive months in the reference qua-ter.

Step of computation:- Commodity growth factors are computed for every establishments.

- An establishments index is then calculated, aggregating commoditygrowth factors using the Discrete Divisia procedure .- Establishments indices are then aggegated using the same procedure to pro-

duce a 3-digit index, these are in turn aggregated using the same procedureto produce a 2 -digit then a 1-digit index.


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2 3DATA PROCESSING AND DISSEMINATION

Program data processing has an three modules:- First, response module it produces two tables:

- Provides response rates by province, to measure the relevant perfor-mance of various provincial office.

- Produces table of share of respondents in industry output are brokendown by selection criteria (ISIC and category C1 or other).

Therefore, the response module useful for BPS to monitor the progress of thesurvey (progress of the response rate).

- Second, data entry module:

- Allows detection and correction of inconsistent commodity entriesand unit prices.

- Thirt, growth computation module:- Produces inter month measures of monthly growth quantities, unit

value indices and employment indices.


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Limits on various variables

> Caused of outlying observations:- Unsustainable movements in production in a paticular quarter- Strong seasonality in production (cases of sugar)

- Data error- If production was zero in certain month in within one quarter

> Reducing the influence of outlying observations:- For reducing of these cases affected, then in the system data processing

places limits on three types of variables:- Growth rates of commodities => -2.50 to 2.50 (is still the same)- Establishment growth rates => 0.25 to 4.00 (is still the same)- Establishment weight => 0.05 to 4.00 ( previously 0.1 - 4.00)


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- Appropriate with the sample design, the Quarterly Production Index waspublished at the most, in 3-digit ISIC. While the Monthly Production Indexwas published was the most, in 1-digit ISIC.

- Those index would be release every quarter/month .

- After 3-4 months of the reference quarter, these indices are still consideredpreliminary figures and become the final figures after 6-7 months.

- After the quarterly production indices turn to final figures, then the month-ly production indices of the following quarter are revised, and become thefinal figures (Monthly Survey as sub-sample Quarterly Survey).

- Data are release simultaneously to all users in the publication of MonthlyEconomic Indicator, Monthly Statistical Bulletin, and can be accessed thro-ugh Internet on BPS website: (http://www.bps.go.id).


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2 6PROBLEMS AND SOLUTION

The main problems :- Lower of awareness entrepreneur side- Non-response- Late or lack of response.- Inconsistency and plausibilitys data reported.

Solutions for handling those cases:

- Take special efforts for visit establishments, to improve the relation-ships and cooperativeness.- Improve the monitoring activities of the document receiving, and

keep contacts with establishments by phone or email to remind theirduties to report.

- Improve the monitoring of the data quality to detect and evaluate

data plausibility and consistency.- In the futures, BPS need to take special efforts to intensively improve

statistics awareness of respondent (including establishments).- Ideally, a special telephone number and a toll-free telephone number

facilities should be avaliable in BPS central office, to accelarate com-munication with establishments and to be used by establishment that

have chosen to use facsimile to send the questionnaire.


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Thankyou for your attentions

SUB DIRECTORATE SMALL-SCALE AND COTTAGE INDUSTRY STATISTICS BPS - STATISTICS INDONESIA

ASTI Industry Indonesia Presentation

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