Rice Price Variability in the Data

An important prerequisite for the successful identification of behavioral responses is that there be sufficient price variation across the cross-section data.^ Time-series data are usually relied upon for this purpose because relative prices often vary over time. In cross- sectional data one can hope for spatial price variation and possibly some temporal variance due to seasonality effects, captured by the length of

the period over which the data is collected.

In recent studies using various SUSENAS survey years, a large group of researchers have implicitly assumed that price differentials are large enough to estimate price elasticities (see Alderman and Timmer, 1979, 1980; Boediono, 1978; Byron, 1984; Chernikovsky and Meesook, 1984; Case, 1987; Cheung, 1987; Deaton, 1988b; and Dixon, 1982). They have likewise implicitly assumed that such price variability can be safely attributed to spatial and seasonal effects and not to the presence of quality differences and heterogeneous goods in the data. The plausibility of this last assumption, and the consequences for demand estimation of its not being correct, are examined in the next section. Each of these studies asserts that prices vary sufficiently but this is nowhere explicitly investigated. Certainly the fact that the 1981 SUSENAS is conducted over a series of subrounds spanning a period of 12 months ensures that a degree of seasonal variation will be present in the data. However, on its own, this is unlikely to be sufficient. It is thus

necessary to look elsewhere to answer the question of what evidence there is for spatial price variation.

Two possible sources of price variability in a static setting include transport and distribution costs, and government market intervention. It is often asserted that in low income countries,

regional price differentials can be expected to be considerable, in large part because poor marketing and distribution infrastructure lead to

market segmentation and high transport costs. In addition, governments in these economies (as indeed, in the majority of developed economies) endemically interfere in food production and consumption processes.

The Indonesian archipelago is geographically dispersed, comprising many islands and including both surplus and deficit rice growing areas

(in Java as well as in the Outer Islands). One would expect such geographical segmentation to add to transport costs. Transport costs will clearly be higher between some of the Outer Islands, but even on Java, Hears (1981) judges that the main roads "provide only a reasonably adequate system to meet all rice marketing needs" (p.196). He claims that maintenance is poor and that weather conditions render travel slow and at times impossible. He concludes that "Transport services account for an important portion of marketing margins" (p.225).

With the exception of Hears' (1981) book which is wide-ranging and general, there have been few recent empirical studies of issues such as the level of market integration, price variability and transport costs on Java. Studies done in the early 1970's such as that of Goldman (1974), Timmer (1974), and Arndt and Sundrum (1975), argued that regional price differentials and seasonality effects were large even in Java. In Arndt and Sundrum's words they were "particularly high for food items,

especially rice" (p.40). However, these conclusions cannot be assumed to hold fully for 1981, given the changes that had taken place in rice

markets by that date: the emergence of near self sufficiency, the

widespread adoption of high yielding varieties which allowed an increased number of harvests per year, and the increasing success of government interventionist goals and concurrent higher use of input subsidies.

More recently. Hears (1981) argues that transport costs continued to remain high in the late 1970's, despite great improvements in public transport achieved in the 1970's. He also states that because the government through BULOG (the National Stock Authority), maintained

different ceiling prices in deficit and surplus urban areas, price variation between 1975 and 1979 (the last year for which figures were available to him) in these urban centers, far surpassed the amount

required to cover transport costs. Although government intervention has aimed to stabilize prices between a fixed ceiling and floor price level

(to benefit consumers and producers respectively), the policy and its price consequences have tended to vary considerably over regions (Hughes, 1985). In fact, historically, official stabilization measures appear to have had more influence on seasonal price movements than on regional price differentials, which partly explains why the responsibility

for storage has increasingly been transferred to the public authorities (Hears, 1980, 1981). Independent price information sources point in the same direction. Table 3.2 presents 1981 retail prices for medium quality rice for rural areas in the provinces of Java, collected by the Bureau of Statistics. (Note the temporal as well as the spatial variance.) Mears finds the same large variation in local government rice price statistics at the kcJDupaten level on Java. (He dismisses some of these as resulting from personnel incompetence, although no evidence is presented.) The SUSENAS also points to high regional price differentials (Table A3.1.1 in Appendix 3.1), for both the rice aggregate (local,imported, high quality, and sticky rice) and local rice on its own.

The evidence on how well integrated markets were in 1981 is not conclusive and there are disagreements among researchers. However, transport costs appeared to remain high and government intervention to be inconsistent across regions and between urban and rural areas. Whatever the causes, sufficient regional price differentials do appear to exist for rice in Java to enable successful estimation of demand parameters

TABLB 3,2; 1981 Rural Retail Prices for Medium Quality Rice (Rps/kg)

Rural: West Central East

Java Java Java

Jan 248.7 225.8 236.3 Feb 248.3 223.4 243.9 Mar 241.1 202.8 237.6 Apr 230.9 197.9 228.2 May 228.1 201.2 225.7 June 227.8 » 201.6 229.8 July 231.3 199.6 230.7 Aug 228.7 200.3 233.2 Sept 233.8 202.8 236.9 Oct 247.3 225.9 246.0 Nov 256.3 232.2 252.4 Dec 262.3 237.7 258.6

using SUSENAS cross-sectional data.