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Mr. Chu, economist in the Commodities Division of the Research Department, is a graduate of Kyung Hee University (Seoul) and Columbia University. Before joining the Fund, he was an instructor at Columbia University.
Mr. Feltenstein, economist in the Special Studies Division of the Research Department, received degrees from Harvard and Yale Universities. Before joining the Fund, he taught at the University of Massachusetts at Amherst.
The authors would like to thank Phillip Cagan, Michael Parkin, and John Whalley for their helpful comments and suggestions on an earlier draft of this paper. They are also grateful to a number of colleagues in the Fund and to the members of the monetary workshop at the University of Western Ontario, where an earlier version of this paper was presented, for valuable comments.
The most extreme selective price controls came during the Social Contract of 1973 to mid-1976.
It is, of course, theoretically possible for the loss-making public enterprise to be financed through the private capital market, but in the Argentine case, such financing was not generally forthcoming.
As we mentioned before, the assumed exogenous nature of the changes in foreign exchange reserves is not correct for a part of the sample period.
The aggregate loss, a key variable in the model, should not be interpreted as the aggregated value of the actual “accounting” losses of the firms. It is an aggregate of the ex ante losses that would be incurred by the firms under a Leontief system with exogenous real values added and distorted actual prices. Actual losses of firms may have been smaller than these ex ante losses, not only because of subsidies but because of input prices maintained under the zero-profit levels by price control. Actual losses may have been larger than ex ante losses for those industries in which the practice of delayed payment for purchase of inputs was widespread and in which input prices were inflated in anticipation of inflation. The aggregate loss is referred to hereafter as “computed aggregate loss.”
Decomposing a change in high-powered money into only these three factors is an obvious simplification. There apparently have been other sources of increase in high-powered money. The estimated equation, however, suggests that these three factors adequately explain the variations in the rate of increase in money during the sample period.
The government deficit incurred by subsidization of public enterprises is represented in this study by the government’s current transfer payments to public firms. In reality, the subsidization also took other forms, such as exemption of some public enterprises from import tariffs and toleration of long delays in payment of taxes during inflationary periods. These other forms of subsidies should also have increased the government deficit to some extent. They are not included in the estimate of the subsidies in the model, however, because it is difficult to measure them.
To obtain a reasonable estimate of the length of the lag, equations with various lengths of lag were estimated in various forms by both the ordinary least-squares and the instrumental variables estimation techniques. The data strongly supported the one-year lag.
The money supply equation (8) is not an accounting identity. First of all, the equation linking the money supply and high-powered money in equation (1) would not be an identity unless the money multiplier k were constant. Furthermore, the equations introduced in (7), especially the second one, are essentially stochastic, although to simplify notation they are specified without error terms.
See Section III for a description of the impacts of changes in various policy variables and the Appendix for an analytical, as well as an empirical, examination of the characteristics of the model.
This result is demonstrated in the Appendix.
Value added vaj refers to total value added rather than value added per unit of output.
These 23 sectors are as follows: *(1) agriculture, hunting, and forestry, *(2) mining, *(3) food, drinks, and tobacco, *(4) textiles, *(5) clothing and shoes, *(6) wood products and furniture, *(7) paper and printing materials, *(8) hides and skins, *(9) rubber, *(10) chemical products, *(11) petroleum derivatives, *(12) non-metallic minerals, *(13) metal, *(14) machinery, (15) machinery and electrical apparatus, (16) transportation equipment, (17) others, *(18) electricity, gas, and water, *(19) construction, (20) commerce, restaurants, and hotels, (21) transportation and communication, *(22) housing, (23) personal and financial services. The sectors preceded by asterisks are those for which official price indices are available. (See Banco Central de la República Argentina (1976).)
More correctly, this difference represents the required total credit creation brought about by the particular price distortion per unit of output.
See the Appendix for a derivation of output unit that is consistent with the price indices.
The total loss Dppt is an estimate of the losses incurred by public and private enterprises. It is not unreasonable to assume that the losses of the public enterprises would be promptly subsidized by the government. Therefore, the amount of actual current transfer payments of the government to public enterprises is subtracted from the computed aggregate loss. The remainder Dt is an estimate of the losses of private industries.
At one point during 1975, the actual price of output in the agricultural sector had fallen to barely 30 per cent of its zero-profit level.
The IV estimators for the coefficients would be consistent under standard conditions if the instruments were not correlated with the errors. (See Amemiya (1974) for a description of the conditions for IV estimators of the parameters of general nonlinear models.) In this study, distributed lags of the basic exogenous variables (gt, gt-1, gt-2, dt-4, dt-5, dt-6, bt, bt-1 and bt-2) and a dummy variable (explained later) are used as instruments for both equations, because by assumption they are uncorrelated with the errors and because the time paths of the endogenous variables for which the instruments are used are characterized largely by the past time paths of these exogenous variables.
It often happened that suppliers of inputs, knowing that they would be paid only after a long delay, deliberately raised their prices to compensate for the anticipated increase in the general price level. Hence, the actual repayment of a loss was usually larger than the value of that loss at the time it was incurred.
Although the D-W statistic cannot be used to test serial correlation of the error for the real balance equation because the equation has a lagged dependent variable and because the estimation method is an IV technique, a useful asymptotic relationship can be established: It can be shown that the D-W statistic computed for a first-order MA process converges in probability to 3 if the MA parameter is 1, to 2 if the parameter is 0, and to 1 if the parameter is -1. The MA parameter suggested by the D-W statistic in equation (32) is greater than 1, but a perfect compatibility cannot be anticipated in this case because the estimates are not constrained.
In all simulations in this section, gt, dt, bmt, and dmt, (dummy variable) are assumed to be exogenous, and all simulations are dynamic from the beginning of the simulation period (the first quarter of 1967) to the end (the fourth quarter of 1976). The assumption of the exogenous nature of bmt is introduced because the paper mainly concerns itself with the impacts of price distortions on inflation.
The error ωt = ηt – λ ηt–1 for the real balance equation and the i.i.d. error ∊t of the money supply equation are suppressed for all quarters in the simulations. In a more accurate simulation, the residual
The significance of the distortion variable (dmt-4) in explaining the money supply is indicated by the estimated money supply equation. The Appendix introduces the results of simulations conducted for a similar purpose.
See the Appendix for a further discussion of the results.
This tax is always feasible, that is, it does not take more than the total income of consumers, since, in the extreme case, when pa = 0, we have cx - (pe – pa) y = cx – pey = 0.
The stable equilibrium point could be in other quadrants. It is conceivable that both μt and πt, could be negative. According to the results obtained, however, μt was always positive, and πt was negative for only two quarters during the period 1964-76 in Argentina.