Satisfacción con el desempeño de los egresados

Frame

by Frederic Vogel

The Global Strategy begins with a long-term vision to integrate agriculture into the national statistical system on the basis of an MSF. The Global Strategy introduces a bold concept: the economic and social dimensions of agriculture are linked to land cover and to the environmental impact of agriculture. To support this concept, the Global Strategy calls for the use of geo-referenced information on holdings, households and enterprises, including satellite and airborne imagery.

This Handbook relies on the existing large body of statistical theory on sampling and survey methods; no new theory is presented. Instead, the goal was to translate this theory into a common language related to agricultural statistics. For this reason, this Handbook is considered to be a living document, which can be updated as new theories and methods are developed and new problems emerge.

The goals set by the Global Strategy may appear daunting to developing countries; for this reason, it is suggested that the MSF be developed in stages. It should also be recognized that the development will require multiple years. This Handbook first provides basic principles on the definitions of sampling frames, sampling units, reporting units, variables of interest, and their application in the use of area and list sampling frames. Multiple frame sampling is also defined.

Statistical organizations often develop sampling frames for specific purposes, without considering the overall data needs of policymakers and other data users. Chapter 2 provides a detailed review of the background information necessary to develop and use the MSF. This is an essential exercise, which must be carried out with full input from policymakers and the data user community. It should be an iterative process, in which the first set of requirements is reviewed together with the statistical requirements for providing the data. This first review, applied against the frame development methods provided in this Handbook, may require more than one round, because the demand

While this Handbook focuses on the development of the MSF, it also reviews the properties of the estimators for the various sampling designs available, because these may affect the choice of sample frame. Chapter 3 outlines these sampling methods, which range from simple random and systematic to the use of sampling using PPS. All of these can be applied in a single stage design or in one or more stages, in which the first stage may be a large land cluster or an administrative unit (such as a county). A common mistake is that, in the effort to achieve cost efficiencies, multiple stage sampling is implemented without considering the impact on the resulting sampling variability. Chapter 2 and Annex A provide an overview of the variance components inherent in two-stage sample designs. Multi-stage sampling provides a valuable tool for developing countries, but its strengths and weaknesses must be understood. A related issue is how sampling and non-sampling errors contribute to total survey variability.

In recent years, there have been rapid advancements in the availability and quality of technical tools that support the development of MSFs, ranging from developments in computer processing capabilities and software to the availability and quality of satellite imagery. Chapter 4 provides guidelines on the use of GIS and their data layers as they apply to both area and list frames. Another valuable instrument is GPS, which supports frame development and data collection. The Chapter concludes with a review of the use of remote sensing for developing sampling frames. Chapter 5 provides a thorough review of the different methods of using list frames to build and use MSFs. Methods based on the use of population censuses and agricultural censuses are described in detail. Two-stage sampling is also illustrated, together with methods to determine the size and scope of PSUs. The chapter distinguishes between list frames of rural households (population censuses), agricultural holdings (agricultural censuses) and farms that are commercial entities (business registers). While countries are encouraged to implement an agricultural census in such a manner that it can also be used to build a sampling frame, they are not always aware of the coverage errors, misclassification errors and duplication that may be inherent in list frames. A common problem affecting list frames is their rapid obsolescence as individuals or households change location or composition, with additions or deletions of units leading to problems in estimation due to multiplicity. The Chapter examines the impact of different types of multiplicity in detail. Issues of under- and over-coverage are presented, with a review of non-sampling errors. The Chapter concludes with a discussion of the need to maintain and update list frames.

One of the earliest forms of probability sampling for agriculture was the area frame that was developed in the US and known as the MSF for agriculture. Chapter 6 provides guidance on developing and using an area sampling frame and builds on the technology described in Chapter 4. Several different types of area frames are defined; these differ in terms of the choice of the basic sampling unit and respective reporting units, the use of single or multi-stage sampling and the use of stratification. The Chapter also outlines the strengths and weaknesses of the data collection methods associated with area sampling frames, mainly the use of direct observation as opposed to farmer interviews. The Chapter concludes with methods to link area frames with census or administrative information using EAs. Chapters 5 and 6 review in detail the available methodology for the development and use of list and area frames. Both have strengths and weaknesses. Lists of farms with associated auxiliary data on size measures can be more statistically efficient for sample survey purposes than other forms of sampling. However, they rapidly become out-of-date and are prone to under-coverage. Area frames may be complete, but are better suited to measurement of small farms and commodities that are widely distributed in the population. Sample sizes must be sufficiently large to control sampling variability, if there are large farms in the population. Multiple frame sampling was introduced as a method that exploits the strengths of individual frames, while allowing sampling flexibility for each frame. Chapter 7 provides the principles of multiple frame sampling and an in-depth review of the estimators. One conclusion drawn is that the choice of estimator should be based on simplicity. The Chapter gives an overview of common problems in the application of multiple frame sampling due to the requirement of identifying the overlap between frames. While the term “multiple frame sampling” implies that more than two frames can be used, the complexities in determining the overlap between frames appears to favour limiting the choice to two frames. The

general conclusion is that the list frame containing mostly large commercial farms and farms producing important but rare items should be kept as small as possible. Annex C summarizes a number of country experiences; several countries have stated that they are gradually adopting multiple frame sampling.

In deciding which master sampling frame to use in a given country situation, it is essential to perform a careful analysis, in terms of availability of resources, material available, institutional support, the scope of the statistical system and objectives of the surveys to ensure that the options selected are suitable and sustainable. The guidelines are provided for the use of the Handbook and the implementation of the MSF.

1. Conduct a thorough review of the statistical methods, including censuses and surveys, used for agriculture in your country. Where relevant, separate reviews should undertaken by/for the relevant National Statistical Office and by/for the statistical unit within the Ministry of Agriculture. Both the methodology used and the data provided must be reviewed.

2. Review other censuses and surveys in your country with a focus on sample frames. Examples are the population census, national household surveys, and price collections for the Consumer Price Index.

3. Review administrative data and other possible sources for building and/or updating a list of farms or agricultural holders.

4. Obtain information on census or survey systems in countries of similar size, form of agriculture, and capabilities. 5. Compare findings from steps 1, 2, 3 and 4 above with the methods described in this Handbook to find out where

similar methods are used and build off their experiences.

6. Follow the guidelines on obtaining background information on your country’s requirements for data on agriculture, as described in Chapter 2. This information should then be contrasted with data currently available. 7. Identify overlaps in the statistical systems where resources can be combined to build an MSF.

8. Determine the requirements for geo-referencing agricultural and/or population census EAs. Identify how this can assist other parties in the national statistical system.

Following steps 1-8 above, there should be enough information to make a first recommendation on the choice of frame (list or area) or on a form of multiple frame sampling. Seek a peer review; revise as necessary. Begin implementation in a portion of the country.

The final choice of MSF should take into consideration not only the costs of frame development and data collection, but also the costs required for maintenance and updating. The proposals should be realistic and reflect national capabilities, and include an indicative budget and timeframe for implementation. An effective MSF will facilitate the integration of agriculture into the national statistical system and will benefit the entire statistical system.

References