What was the framework in which the first historical statistical project was conducted? In the preface for Long-term Economic Statistics, the editors describe their general purpose as follows: "This series...takes modern economics as its foundation for framing national income accounts structures, and for statistically tracing the shape of development of the Japanese economy since Meiji..." They did not, they added, "simply gather national income statistics and their structural aspects, but comprehensively compiled and organized them..." (see "A Preface from the Editors" on p. iii of each volume).
Within this general framework, then, what plan was undertaken for compiling agricultural statistics? The objective, according to the introductory remarks by the authors of LTES-9, Agriculture and Forestry, was "to prepare a continuous time series going back to the early years of Meiji covering data related to agricultural and forestry production and inputs as well as factor costs, and do so in conformity with national income accounts" (LTES-9, p. vii). However, there are two qualifications regarding this goal.
The first qualification is that because the system of national income accounts constitutes a basic framework, anything more general such as, for example, institutional or structural statistics would be excluded. This qualification provides an important starting point for the Asian-LTES project. Of course, if general historical statistics could be included in institutional or structural form, their value would be immeasurable. Therefore, our basic approach should be to use the statistical time series estimated for LTES as a basis for Asian-LTES, and to compile other institutional or structural statistics on a supplementary basis.
The second qualification regards the point that "emphasis should be placed on continuous long-term statistical time series from the early years of Meiji." Since the 1950s, Japan's statistical data has been greatly improved in terms of both quality and quantity, so emphasis was placed not on comprehensive inclusion of the abundant statistics of recent years but on the continuity of statistical time series from early Meiji. In short, improving existing postwar time series was not the goal.
This second qualification is also applicable to Asian-LTES. As in Japan, statistical compilation in other Asian countries has been substantially improved at all levels in recent years, not just in breadth of coverage but in accuracy as well. Given this situation, attempting to improve statistical time series for recent years should not be the task of our project. On the contrary, our aim should be to compile time series using the reliable statistics from the earliest point in time for which they exist. However, we also want to compile compatible time series while maintaining reliability.
The second qualification is illustrated in Figure 1, which depicts a conceptual outline. The vertical axis, moving downward, represents the passage of time, while the horizontal axis represents the volume of existing raw data. As time progresses downward, the volume of the raw data grows and its precision improves apace. The lower part of the triangle includes this increasingly dense data. The equilateral rectangle represents the time series estimated by Asian-LTES. Considered from a more recent perspective, the scope appears to be limited, but if we are going to emphasize long-term continuity, we will need to conduct extensive estimations for earlier periods. Thus there is no point in uselessly extending the scope of the project.
In Part 3 (Statistical Tables) of LTES-9, there are 39 statistical tables related to agriculture, and they include a total of 476 data time series. If we group the tables into major categories, we have (1) time series for agricultural output and prices (Tables 1-13); (2) times series for current inputs in agriculture (Tables 14-27); (3) time series related to agricultural capital stock (Tables 28-31); (4) basic statistical time series for arable land and other items not covered in the first three categories (Tables 32-34); and (5) other time series (Tables 35-39).
This category indicates the value of agricultural production, agricultural price indices, volume of agricultural production, and the amount of value added for agriculture. Only data for "production of agricultural products in volume" (96 items in ten categories) are compiled here, and remaining time series are estimated from this production data and from separately estimated price data and other information. Several issues or technical difficulties were raised in this process. For instance, the distinction between agricultural and forestry products is often unclear in the case of items like shiitake mushrooms. Another problem is determining whether products processed by farm households should be classified as processed agricultural goods (agricultural products) or as manufactures. LTES-9 classifies processed agricultural goods as manufactures, while straw goods are included with agricultural products. Since there are many regions throughout Asia where farm households extensively engage in processing agricultural products, the income generated from these activities is important in regional economies. The issue raised in LTES-9 is important and thus should be kept in mind for the Asian-LTES project.
Current inputs are categorized as inputs supplied within the agricultural sector and as inputs provided by the non-agricultural sector. The former include seed, silkworms, green manure, domestic fodder crops (grains, pulse, and tubercrops) and others. The latter include pesticides, fertilizer, imported fodder crops such as bran, and oil cake. However, in LTES-9, internally supplied intermediate products like compost were excluded from the list. There is a large quantity of statistical data for the most important agricultural factor of production, fertilizer, and it is analyzed in detail.
The objective of Asian-LTES estimates for "agricultural products," as learned from LTES, should be: to compile estimates which conform to the national income account structure, while extending continuous long-term time series data for agricultural output, input, and price factors as far back in time as possible.
Given this objective, we can present two qualifications.
Agricultural statistics are based in widely varied geographic and cultural differences of many countries reflecting a wide diversity of historical origins. Hence different classification systems tend to take on their own particularities. At the same time, those systems often convey important information regarding those countries' agricultural systems. Thus, we should not distort the distinctive agricultural information systems of different countries, but seek to develop a means of compiling statistics which will render this information mutually comparable.
Fortunately, the Food and Agriculture Organization (FAO) of the United Nations has compiled a detailed classification system for agricultural products. Each agricultural product is classified by a four-digit code (FAOSTAT CODE) in one of twenty groups. Further, there is a corresponding code for each trade classification code (Standard International Trade Classification, Revision 2, Revision 3, HS) for each FAOSTAT CODE. With these tools, we can estimate each country's agricultural products time series according to its customary classifications, and then, by annotating the appropriate FAOSTAT CODE to the estimated time series, we can seek to maintain compatibility between the trade statistics system and the time series data for different countries.
In brief, when more detailed or different classifications of a country's statistics are available, we need not process or aggregate the time series for that country; the best approach is to present the times series as they are with notes which identify the products' standardized classifications according to the FAOSTAT CODE.
With regard to units of measurement, statistical time series have adopted units of measurement for area, weight, and volume used in the original time series while also using metric conversions to create parallel time series. Moreover, when data is provided primarily in terms of volume, it is necessary to employ a weight-volume conversion factor. Other conversion factors should be reported as necessary. For example, rice production statistics are often shown in terms of unhusked rice, so a conversion ratio to milled rice is necessary. Conversion ratios will vary by region, era, production season, and varieties. If a conversion rate is available, trade statistics, which are often shown in terms of milled rice, can be consolidated with production statistics and it becomes possible to conduct such tasks as making estimates of domestic consumption volumes and other matters.
Because time series for capital stock are needed to adduce "net value added," even LTES estimated them as provisional and partial estimates due to difficulties in estimating reliable data on depreciation. For Asian-LTES, it may be possible in some countries to estimate these series on the basis of certain assumptions.
Seikei University, Department of Economics