Development of Survey Instrument.
The first draft of the household survey was developed in English in July, 1993. Training of enumerators, based on this draft, began on August 2, 1993. The month of August was devoted to training the enumerators and pre-testing the questionnaire. The first pre-test of the questionnaire took place in mid-August. The household questionnaire was almost completely precoded to eliminate coding errors and time delays. A category labeled "other: specify" was added to several questions. For those questions for which answers were not mutually exclusive, we precoded them with letters, rather than numbers, to allow for unambiguously coding of multiple answers. To minimize nonsampling errors, the questionnaire was in a form that reduced to a minimum the number of decisions required of interviewers while in the field. In anticipation of pages becoming detached from the questionnaire, every page contained a space for the household number and the last digit of the cluster code. Despite the fact that questions were written exactly as they were supposed to be asked by the interviewer, interviewers were granted some flexibility to give the interview greater semblance to a conversation, rather than an inquisition.
Pre-Test of Questionnaire.
The "pre-pre-test" of the questionnaire (August 16, 1993) was done only to discern whether the questions were understood, how long the administration of the survey required, whether all responses had been anticipated, which sections needed to be stressed during the training, etc. In this pre-pre-test, each questionnaire required an average of 4 hours to complete, far longer than the planned 1.5 hour maximum. The survey was consequently shortened and streamlined.
The true pre-test was conducted in two different types of clusters: Ubungo ward in DSM (urban) and Kibaha in the Coast Region (rural) over a period of two days. We chose these clusters because they are representative of two distinct groups, so a broader spectrum of answers and problems with the instrument could be anticipated. In the pre-test each questionnaire required an average of 2.5 hours. After a couple weeks of interviewing, the enumerators became more familiar with the instrument, resulting in their spending an average of 1.5 to 2 hours per questionnaire.
During the pre-test, each supervisor was asked to comment on each interview. The supervisor was asked to pay special attention to questions that seemed to make the respondent uncomfortable, that the respondent had difficulty understanding, or that the respondent seemed to dislike. The supervisor also evaluated which sections seemed to go slowly, had the most difficult questions, or provided insufficient opportunity for a complete response.
Revision of questionnaire.
Given the results of the two pre-tests, several areas for improvement in the questionnaire were identified. Perhaps most importantly, the willingness-to-pay amounts were adjusted. The sample distributions of the maximum willingness-to-pay questions were analyzed, and, based on that analysis, we decided to change some of the values. For example, in the child spacing question, the "pay Tsh 1,000" responses unexpectedly accounted for a large share of the bids. Thus, we provided the option of paying more by introducing "pay Tsh 50,000" and "pay Tsh 25,000" as answer choices. For the other contigent valuation sections--health and education--the first pre-test determined that there was also a large lumping of responses at the high end of the scale. We adjusted the ranges accordingly, although there remains some lumping at the high end in the final data.
We also changed the order of the sections. Based on the pre-test and judgment of the field workers, we decided to first ask the questions in the individual section, then the contigent valuation questions, then the household questions. Because the respondents enjoyed the contigent valuation questions so much, this decision helped increase interest in the questionnaire and re-energized the respondent before proceeding with the household questions--the last part of the questionnaire. The final survey instrument, incorporating all of the changes dictated by the pre-tests and other expert advice, was completed on September 12, 1993.
Translation of the survey instrument was a joint effort of the enumerators and supervisors. Given the specific characteristics of the Kswahili language, this was a much better approach than asking one translator to translate from English to Kswahili, and another one to translate from Kswahili to English. The "group" translation, involving those who would ask the questions, was intended to avoid different interpretations of the same question and achieve uniformity. In this way the enumerators were able to better convey the message/objective of each question.
The majority of the interviews were conducted in swahili. In very few cases, because no one in the selected household could speak swahili, the need arose to use interpreters.
Our initial plan called for the field work to start no later than August 29. However, unforeseen circumstances, including both financial and logistical problems, delayed the first field trip. Both the money and the materials were available by September 6, and five of the six teams left for Tanga region on that day. Initially we had planned to have the sixth team based full-time in Dar es Salaam; however, tighter time constraints imposed by the above and subsequent delays eventually made it necessary to send the sixth team into the field as well, as detailed below.
Description of questionnaires
The main objective of the survey was to obtain data on the use of, and spending on, the social sectors. The primary emphasis was on education and health--the areas in which the major gaps in availability of data were identified. The survey was divided into five major components, each of which was further subdivided, as described below:
I. Individual Questionnaire
A. Household Roster;
B. Information on parents of children between 7 and 15 years of age;
C. Information on the utilization of, and spending on, education services;
D. Information on the utilization of health services for those reported ill in the month previous to the interview;
E. Information on the utilization of, and spending on, prenatal care, delivery, and family planning.
II. Contingent Valuation Questions on:
A. Primary Health Facility (includes modules allowing respondents to assess desired characteristics of facilities, to reveal their willingness to pay for health services, and to provide information on the available health care facilities);
B. Primary Education Facility (includes modules allowing respondents to assess desired characteristics of schools and curriculum, to reveal their willingness to pay for education services, and to provide information on the available schools and curriculum);
C. Demand for Child Spacing;
D. Envisaged, Required Income Level.
III. Household Questionnaire
A. Land and livestock ownership;
B. Household income and economic activities;
C. Annual expenditures;
D. Monthly expenditures;
E. Weekly expenditures;
F. Housing characteristics and expenditures;
G. Mortality: deaths in the last 12 months.
IV. Community Price Questionnaire
V. Cognitive Test:
The design of the questionnaire took advantage of the huge volume of work done on household questionnaires over the past decade. The next paragraphs highlight areas in which this survey is different from the Social Dimensions of Adjustment (see Delaine et al. 1992) or Living Standard Measurement-type of surveys (e.g. Ainsworth et al. 1992; Grosh 1991). Where appropriate, a summary of the reasons for the difference in approach is also presented.
The Yellow Card. A yellow-colored Household Roster card was included in the questionnaire. The interviewer had to copy some of the information--age, gender, and name--from the household roster onto this yellow card. The removable Household roster card was then used throughout the rest of the questionnaire for reference as to which members of the household were eligible for particular sections and what their ID numbers were.
Income Questions. It was decided that our survey, unlike the majority of the surveys we reviewed, would not include data to be used to estimate income levels. Gathering complete and accurate income data is a very time-consuming activity in countries where few receive wages and the majority are self-employed and engaged in non-market activities. In analysis, income questions are difficult to use; monetary incomes are often calculated to be negative; and, when much of the sample works outside the formal market economy, these problems are compounded. Given our time and budget constraints it was not logical for us to try to measure income.
We did include a few questions about sources of income. In this section, our objective was not to gather the information necessary to estimate income levels, but rather to ascertain the main economic activities in which the household engaged, and how the household ranked them in order of importance. For those growing crops, we wanted to establish the relative importance of each crop and the proportion of it that was marketed.
Expenditures Section. The three principal issues which had to be resolved regarding the expenditure section were as follows:
(i) How to organize the expenditures in terms of levels of observation: individuals or households, and the recall period;
(ii) Whether to split consumption expenditure and consumption of home production, or to ask them in the same question; and
(iii) How to take into account seasonality of food consumption.
Accurate and complete measurement of expenditures is essential. To maximize the accuracy of the information gathered, different types of expenditures were organized into different levels of observation, depending on the consumption item for which we were measuring expenditures. For example, better estimates of consumption are likely to be obtained by adapting the period of recall to the frequency of purchasing the good. Accordingly food expenditures had a week-long recall period, while education expenditures had a one-year recall period. For some items, we gathered information at the individual level- -e.g. education and health--and for some, at the household level--e.g. housing and utilities. For food items, we split consumption expenditure and consumption of home production. For items such as education, for which expenditures are more likely to be cash expenditures, we did not split them. However, we explicitly included the following instructions with the expenditure questions: "Please include contributions of labor and other non-cash items, which we will convert to shillings."
To address the problem posed by seasonality of consumption, we phrased the question as "During a typical week this past year, did anyone in this household acquire or spend money on..." Also it must be kept in mind that not all consumption expenditures are recorded in Part 3, Sections C, D, and E of the Consumption Section. Expenditures on education can be obtained from Section 1, Part C: Schooling, while housing expenditures were included in Section 3, Part F: Housing.
The survey asked only a few questions on ownership of durables and none regarding their acquisition cost or present value. There is evidence from Grosh, Zhao, and Jeancard (1995) that information on durables does not change the results of welfare ranking. This is another area in which we chose to shorten the questionnaire and the length of interview. 10. The Contigent Valuation Questions. One of the distinguishing features of the HRDS survey is the use of contigent valuation questions to better understand households' perception and valuation of some services available to them, which characteristics they value the most, and how far actual levels of provision are from desired levels. A three-step process was followed. In the first step, the respondent was given 20 chips (or shillings), representing a budget constraint. The enumerator then showed him or her a card with 5 pictures, representing 5 characteristics of a health facility (or of a primary school). The respondent was asked to allocate the 20 shillings among the 5 characteristics. In the second step, the enumerator asked how much the respondent was willing to pay for a visit to a health facility--or, in the case of education, for one year of tuition in a primary school--that matched the most important characteristics for the respondent. In the third and final step, the respondent was asked to characterize the closest health facility (and closest primary school) in terms of the five characteristics that he/she was previously asked to rank. This information should provide a picture of what households consider important in primary health and education services, and of how well the available facilities meet the household's desires. We tried to select characteristics that designers of health and education services tend to emphasize as important. For health, we mixed characteristics of public and private goods.
Respondent Rules. Different respondents were chosen for different sections to increase accuracy. For some topics, information from proxy or household respondents will be less accurate or less applicable (e.g. the contigent valuation questions on child spacing from a 10-year-old boy). In the header on each page of the questionnaire, there is a space to identify the respondent. This information can be useful for some types of analysis in which it is important to know characteristics of the specific respondent. In the introduction to each section, the preferred respondent is explicitly stated. Accordingly, for Section 1, Part A, the head of the household is the preferred respondent; but in Part D, the preferred respondent is "Each eligible individual, with assistance of the head of household if necessary."
The Community Questionnaire. We asked community questions to all selected households in a cluster because of the statistical principle that multiple answers to the same question provide better information, on average, than asking only a single "principal informant" these questions. Also, for some questions--e.g. distance to the closest village health center--it is important to obtain answers from each household. In some rural clusters in Tanzania, houses can be 15 miles apart. Therefore important intercluster inequalities may be neglected if not all households are questioned. The price questions, because they do not vary much across households in a cluster, were answered either by a principal respondent or through inspection in the local markets or shops.