Knowledge that Transforms

W OULD a movement from fixed to flexible exchange rates increase or decrease the stability of economic activity? This paper presents calculations of how increased responsiveness of exchange rates to balance-of-payments pressures would have affected macro-economic stability for most of the world's developed nations in the recent past. It also tries to assess how openness affects the optimum responsiveness of the exchange rate to these pressures from the standpoint of macro-economic stability, an important issue in the debate over what constitutes an optimum currency area.' Our point of departure is the model which J. L. Stein (1963) built over a decade ago in order to relate macro-economic stability to the exchange-rate system. He argued that a balanceof-payments deficit under fixed exchange rates would cause currency depreciation if the exchange rate were flexible and depreciation stimulates output. Also, noting that the reverse is true for a surplus under fixed rates, he observed that in order to foster the stability of real absorption in a Keynesian economy,2 one would want the domestic currency to depreciate (appreciate) during periods when the level of output is below (above) normal. Thus, he concluded, Keynesian economies should opt for flexible or fixed exchange rates according to whether or not fluctuations in output under fixed rates (Y) and fluctuations in the excess supply of foreign exchange under fixed rates (i.e., the balance of payments surplus) (s) tend to have the same sign. In other words, Stein's rule prescribes a flexible rate for any Keynesian economy in which cov (Y, s) > 0. However, as this paper shows, if maximization of the stability of output is the goal, a better rule prescribes a flexible rate for only those economies in which the variance of what output would be under flexible rates is less than what the variance of output would have been under fixed rates. This latter rule is superior to Stein's version because there may be only a weak correlation between what output would have been under fixed rates and the state of the foreignexchange market; when the correlation is weak, the effect of induced changes in the exchange rate will be just like that of any other random disturbance: namely, to increase the variance of output. Thus, even if the covariance is positive, if the correlation is less than one, adoption of a flexible exchange rate may still increase the variance of output.3' 4

POLICIES to promote price and income stability in agriculture have often been justified by the belief that stability would help farmers make better consumption and investment decisions. However, review of literature makes it abundantly clear that the consequences of instability are matters of debate among economists. For instance, Caine (1966, p. 16) believes that a main evil resulting from fluctuations in income is lowering of the level of capital expenditure. Others argue that farmers adapt to the exigencies of fluctuating income and that instability, per se, has little influence on consumption and investment (e.g., Campbell, 1964, p. 59).1 In this paper, consumption and investment functions are estimated for two groups of southern Minnesota farmers with contrasting degrees of income stability. Since various hypotheses exist about investment and consumption behavior, alternative models are outlined in the first section. Consequently, this paper provides empirical evidence for evaluating alternative models as well as assessing the effects of instability. The data and the estimation procedures are briefly described in the second section, and the empirical results are presented in the third.

IN 1961, Arrow, Chenery, Minhas, and Solow (ACMS) (1961) introduced their now familiar production function V y[8 K-P + (1-8)L-P]-1/P (1.) where V is value added per man-year, K is capital, L is man-years of labor, and y, 8, and p are the efficiency, distribution, and substitution parameters, respectively. It is well known that the elasticity of substitution, 1/(1 + p), can be estimated by estimating b in the profit maximizing conditi'on log (V/L) _log a + b log w + u (2) where w is the annual wage rate of production workers. fact, ACMS obtained very good results by using international data from 19 countries for various census years between 1949 and 1955. These data represented up to 24 ISIC industries at the three-digit level. 1963, C. E. Ferguson (1963) used U.S. Census of Manfactures data to fit the regression equation (2). Whereas ACMS obtained good results, Ferguson was disappointed in his: In the entire list of 129 items, R2 is significant at P < .05 in only 50%o of the cases. The bcoefficient is significant 70% of the time . . . But in more than half of these, b was not found to be significantly different from one (1963, p. 306). Ferguson recognized a possible reason for such results. The requires different relative factor prices for different observations. With only a little variation in the wage rate, the regression coefficients will have large standard errors. Unfortunately, when Ferguson's paper appeared there was no way to correct or improve the sample. Now, it is possible to pool time-series and cross-sectional data and to recognize the possibility of cross-sectional heteroscedasticity and time-wise autoregression of the disturbance terms. Jan Kmenta has termed this a cross-sectionally heteroscedastic and time-wise autoregressive model (1971, p. 509). We shall use this to estimate the elasticity of substitution, b, in a modification of regression equation (2). We expect that the increased variability of the independent variable will improve the results. This system of production functions for various indtustries provides a classic example of a case where the method of seemingly unrelated regressions may be applied. Thus, we also obtain two-stage Aitken estimates of the elasticity of substitution' using the pooled data. Estimation of the elasticity of substitution by pooling time-series and cross-section data requires a modification of regression equation (2). As the regression stands, there is an implicit assumption of no technological progress over time. This assumption is removed by specifying the as log (V/L)_ log a + b logw + c2T2 + C3T3 + C4T4 + u (3) where T2, T3, and T4 are dummy variables representing the years 1958, 1963, and 1967, respectively.' The introduction of the dummy variables into equation (3) allows for the possibility of technological progress in each of the cross-section years of 1958, 1963, and 1967. Although this results in a loss of three degrees of freedom, it does allow our to capture the influence of technological progress. The Received for publication August 8, 1973. Revision received for publication October 12, 1973. * We have benefited greatly from the comments offered by our colleagues,. David Denslow and Frank Sloan. The encouragement and helpful suggestions of the late C. E. Ferguson, Jan Kmenta, John Moroney, and an anonymous referee are gratefully acknowledged. Jerry R. Jackson provided invaluable assistance in the computations. Of course, we must exonerate everyone but ourselves of all blame for what follows. An earlier version of this paper was presented at the annual meetings of the Econometric Society in December 197 1. 1 We are indebted to an anonymous referee for suggesting this means of accounting for technological progress.

INFORMATION on patterns of structural change during modern economic growth is summarized in Kuznets (1967 and 1971). The use of secular data to identify such patterns, however, must be limited, for relatively few countries have compiled suitable long-term records. Thus, attention has focused upon an alternative data source, namely, intercountry data for evidence on past and future trends in industrial structure (Temin, 1967; Kuznets, 1967, pp. 431-436; Chenery-Taylor, 1968, p. 391; Houthakker, 1965, p. 277). Despite the frequent use of cross sections to infer intertemporal patterns, relatively little empirical and theoretical work has been devoted to the interpretation of cross-section patterns vis-a-vis intertemporal patterns. Exceptions to this rule are Kuh (1959), Houthakker (1965), Temin (1967), Maizels (1963), Kuznets (1971, chapter. 4), and Chenery-Taylor (1968). The strikingly different results obtained by Kuznets (1971) and Chenery-Taylor (1968) concerning the compatibility of intercountry versus intertemporal patterns raise the basic issue which this study addresses.'

IN recent years there has been a rapidly growing interest in the relative efficacy of monetary policy vis-a-vis fiscal policy as a vehicle for achieving economic stability and regulating economic activity. Several widely discussed empirical studies of this issue have used GNP as the dependent variable in a single-equation least-squares regression with various measures of monetary and fiscal policy as independent variables.' One common objection to this approach is that it is not clear what the structural model is from which this so-called reduced form equation is derived. Another obvious criticism is that the various contemporaneous (as opposed to lagged) measures of fiscal and particularly monetary policy may be significantly influenced by movements in the dependent variable, GNP, thus violating one of the basic assumptions underlying the singleequation least-squares procedure. The major purpose of this paper is to develop and estimate a reduced form which takes some steps toward meeting the above objections, and which hopefully sheds some new light on the relative importance of monetary and fiscal influences on economic activity. By disaggregation and the use of a reduced form framework generically related to that of an earlier study of man-hour behavior (Waud 1968), we seek to reduce the possibility of reverse causation and thereby the problem of single-equation least-squares bias in examining this issue. On the basis of the empirical findings reported here, fiscal influences and monetary influences on economic activity are both significant and appear equally important. Hence our findings are in sharp contrast to those of Andersen and Jordan (1968, 1969), for example, and certainly do not support the contention that monetary influences are much stronger than fiscal influences -a notion which seems to have gained much currency in recent years. In section I the rationale and framework for the analysis is presented. Section II discusses the various measures of monetary and fiscal influences, as well as the problem of bias which may plague studies which use a single-equation least-squares framework to examine the relative importance of monetary and fiscal influences. The estimation of the model is discussed and the results presented in section III. Concluding remarks are made in section IV.

RECENT studies by Teigen (1964), Smith (1967) and Modigliani, Rasche and Cooper (1970) suggest that money supply like money demand is sensitive to the interest rate. This has two implications: (a) the supply of money may not be an exogenous variable; and (b) the ordinary least squares estimates of the money demand function with the interest rate as an explanatory variable may suffer from simultaneous-equation bias. In this case-study of India we shall investigate some of these familiar issues in monetary economics as well as some monetary issues peculiar to developing economies. In developed countries, in general, discrepancies between physical and monetary flows of production, consumption and income are only marginal.' In developing countries sizable proportions of income and consumption originate through non-monetary transactions like self consumption of goods and services and barter trade. These proportions generally decline with economic development.2 The transaction demand for money therefore increases partly because of growth of national income and partly because of a rise in the degree of monetization.3 As such, the relevant concept of income in monetary analysis of developing countries is a monetized component of national income and not total national income. This factor has been generally ignored in empirical studiesprobably because of the lack of data on monetized income. In this study we have estimated the money demand function with monetized income data and have shown how it differs from that estimated with national income data.4 Another important characteristic of developing economies is the dual nature of organized and unorganized money market interest rates. In the organized market the speculative demand for money varies with interest rates on financial assets. Interest rates in the unorganized market are primarily related to risks and returns on real assets which having inelastic supply like land.5 The supply of money does not effect these interest rates significantly. Therefore, the Keynesian liquidity preference hypothesis may hold good in developing economies, if at all, in the organized market rather than the unorganized market. In section II we formulate the money supply and the money demand functions for the Indian economy within the framework of a HicksHansen-Modigliani-type model. The ordinary least squares (OLS) and the two-stage least squares (2-SLS) estimates of these functions with alternative definitions of money are analysed in section III. The impact of nonmonetized income and unorganized market interest rate on money demand are analysed in section IV. Section V is devoted to the analysis of forecasts and multipliers of 'money proper (currency plus demand deposits). The main conclusions are presented in section VI.