Data quality and limitations

Matlab area was chosen by the ICDDR,B as a field site for population policy

research because it is among the few localities in the Third World where

demographic data are known to be accurately collected and complete (D’Souza

1981). Despite the advantages of Matlab as a research site, certain limitations are

contiguous. Diffusion of project-related services occurs across project boundaries

through intervillage migration, mainly through marriage. In this connection Phillips

et al. (1988) argued that contaminating factors tend to understate the treatment

effect, rather than lead to spurious project results.

Data gathered through the DSS system are cleaned routinely both for logical and

between-records errors. DSS data cleaning is so thorough (see section 3.2.1) that

those who have used these data have found few errors. For the present study few

unmatched cases were identified (less than 2 per cent).

3.5.1 Reporting of births

Previous experience from developing countries has shown that birth history data

contain two kinds of error: errors of omission and errors in reporting dates of

occurrence. According to Chidambaram, Cleland and Verma (1980), displacement of

date of birth was more prevalent than omission of births in WFS data. Older women

tended to displace the dates of their early births towards the survey date. The present

data set is free from this type of error because it has been collected through a vital

registration system. As recording of events relies on fortnightly household visits, the

method of data collection does not allow errors of displacement to occur. Moreover,

other studies also make field visits, and errors detected by them are considered

discreditable to DSS workers. Evaluation reports are prepared by the DSS manager

at the end of each year after considering omissions and misreporting of events

detected by DSS supervisory staff and by other studies; omission and misreporting

3.5.2 Reporting of contraceptive use

Underreporting of contraceptive use is not uncommon in Bangladesh (Green 1969;

Stoeckel & Choudhury 1973). Using data from a panel study in Dhaka, Green (1969)

estimated underreporting of contraceptive use at 13 to 22 per cent for males, and 26

to 35 per cent for females. On the basis of tape-recorded transcripts of Bangladesh

Fertility Survey data, Thompson, Ali and Casterline (1982) indicated that the

contraceptive knowledge and use section of the interview was distinguished by a lack

of discussion. There was evidence that items in this section caused considerable

discomfort for both the interviewer and the respondent, which might have caused

underreporting of contraceptive use. There has been no assessment made of the

extent of underreporting of contraceptive use in the present study. However,

contraceptive prevalence rates calculated for the treatment area in 1984 using the

service records and the IDS survey data were found to be close (44 compared to 39

per cent); this difference may be due to sampling error. It is not known to what

extent underreporting exists in the comparison area, but it is believed that as ICDDR

workers visit these households often, respondents would be less embarrassed than

Bangladeshis in other parts of the country when reporting contraceptive use.

3.5.3 Reporting of reproductive preferences

As the ICDDR has been operating the FPHSP in the treatment area, critics might

have thought about contamination of reproductive preference data in this area.

Because of this presence, when ICDDR field workers inquired about reproductive

preferences, respondents may have been tempted to give responses they thought

would satisfy the interviewers. However, this is not the case. Reproductive

surveys were found to be similar in the two areas, indicating that respondents in the

treatment area did not tailor their preferences to satisfy interviewers.

The present study used three concepts to measure reproductive preferences, desired

family size, ideal family size and desire for more children. According to Lightboume

(1984), the quality of data on reproductive preferences can be ascertained to some

extent through non-response levels, test-retest reliability studies and inter-item

consistency analysis. However, the Matlab data do not allow test-retest reliability

studies.

Over the period between 1975 and 1990, the non-response rate for desired family

size declined dramatically, probably mainly because people became more aware that

family size could be controlled. In the 1975 CDP data the non-response rate for

desired family size was 17.1 per cent in the treatment area and 27.2 per cent in the

comparison area, while comparable non-response rates for the 1990 KAP were 1.7

and 3.4 per cent. In the 1984 IDS data the non-response rate for desire for more

children was 3.2 per cent in the treatment area and 4.0 per cent in the comparison

area, while comparable non-response rates for the 1990 KAP were 1.2 and 2.3 per

cent.

An inter-item consistency check was made on the 1990 KAP data; responses to the

question on desire for more children were compared with a variable indicating

whether desired family size exceeded, equalled or was less than the actual number of

living children. Of those who desired more children, 82 per cent in both areas

reported a desired family size in excess of actual family size; 11-12 per cent reported

a desired family size equal to actual family size and 5 per cent reported a desired

family size less than actual family size and were therefore inconsistent (Table 3.2).

Of those who wanted no more children, 55 per cent in the treatment area and 61 per

size, while 41 per cent in the treatment area and 37 per cent in the comparison area reported equal desired and actual family sizes; only 3 per cent in the treatment area and 2 per cent in the comparison area were inconsistent, reporting desired sizes that exceeded actual sizes.