E. AGGREGATED COSTS STRUCTURE IN RESIDENTIAL HOUSING 3.42 In this section we review the role of energy in overall housing and utility costs. This analysis is important for further simulations of the link between expected growth in domestic prices for gas and electricity and a future increase in housing costs. For the purposes of such an analysis, it is important to account for full gas and electricity consumption in the sector, that is, both their direct and their indirect (as input for production of other utility services) consumption.
3.43 The first column in Table 3.11 presents the conventional cost structure in Russia’s housing sector. It is worth noting that the share of housing maintenance in total costs is quite low, less than one-fourth. In developed economies this share often amounts to 50 percent of the total. Non-maintenance costs are much higher in Russia because of several factors: (i) climate, (ii) major energy inefficiencies in the sector, and (iii) the under-estimation of actual maintenance costs in the existing maintenance fees/tariffs, which cover operating costs (though not in the full amount) but exclude provisions for rehabilitation and major repair costs, land fees, and insurance payments. The implementation of tariff reforms would require a major increase in housing maintenance fees.
Table 3.11: Cost Structure in the Housing and Utility Sector, per 1 Square Meter of Residential Housing, as of end 2002 (%) Cost structure that accounts for Conventional cost structure indirect consumption of electricity and gas Electricity 9.4 15.Water and sewerage 13.6 9.Heating and hot water 48.6 34.Gas 5.8 19.Housing maintenance 22.6 20.Total 100 Source: IUE.
3.44 The second column in Table 3.11 presents the estimates for the full electricity and gas intensity of the Russian housing sector. While the costs of directly consumed electricity and gas make up only 15 percent of total, the costs of full consumption of these energy inputs amount to 35 percent. This is because all main utility services are quite energy intensive.
Electricity costs account for nearly 30 percent of the total costs in water and sewerage. The share of heat generated in gas-operating boilers reaches 60 percent. Gas accounts for percent of the total heat costs in such boilers. Respectively, 50 percent of electricity is generated by gas-operating power plants and gas accounts for 35 percent of the generation costs of these plants.
3.45 The estimates in Table 3.11 were used directly as weights in our simulations of future housing costs, which were based on the utilization of three primary independent variables:
growth in gas and in electricity prices, and growth in tariffs in the rest of the sector. The last variable reflects a need for tariff adjustment to ensure proper maintenance and rehabilitation of the housing stock and utility networks, but it is unrelated to the costs of energy inputs.
Budget spending on utility services provided to budget organizations 3.46 The Russian budget statistics do not generate consolidated estimates for federal budget expenditures on utility services consumed within the public sector. This is because utility payments are budgeted as parts of the budgets of individual ministries and agencies, and the Ministry of Finance does not provide for across-the-board consolidation of such spending.
However, the budget reporting at the subnational level provides sufficiently adequate data on government utility spending.
3.47 The consolidated estimates of the budget costs of utility payments were developed by the IUE based on the detailed analysis of the budget execution for 2001. Respective expenditures made by both regional and municipal governments were extracted from individual regional consolidated reports on the annual budget execution. Respectively, estimates for the 2001 federal budget spending on utility services were developed using several assumptions on the unit costs of such services in the sectors, for which direct reports are unavailable. The latter includes expenditures under the Ministries of Defense and Interior.
Annex 3.2 presents a description of the various assumptions made.
3.48 Consolidated budget expenditures on utility services for 2002 and 2003 were generated based on the simplified procedure, using the base estimates for 2001 and general trends in budget spending at all levels of the government in 2002-03, as well as the dynamics of utility prices.
3.49 Total budget expenditures on utility services have been slightly larger than 1 percent of GDP in recent years (Table 3.12). Half of these expenditures are made by municipal budgets. The analysis also suggests that at the subnational level (both regional and municipal) the largest component of the total utility spending relates to the costs of operating budget institutions in the education and health sectors, which account for about 70 percent of the total utility expenditures.
Table 3.12: Budget Expenditures on Utility Services Provided to Budget Organizations, by the Level of Government (billion RUR and %) In bl Rbl In % of GDP 2001 2002 2003 2001 2002 89.9 130.8 152.3 1.0 1.2 1.Total expenditures - Federal budget 29.1 43 46.9 0.3 0.4 0.- Consolidated regional budget 60.8 87.8 105.4 0.7 0.8 0. including:
3.50 At the federal level, utility spending on education and health is considerably lower and accounts for less than 40 percent of the respective total. This is because of the completely different structure of government functions at the federal level. The specific federal items that have significant utility costs relate to defense and law enforcement, including the operation of the penal and penitentiary system. This group of government functions accounts for nearly one-third of all federal budget spending on utility services.
3.51 It is worth noting that, owing to a considerable improvement in budget discipline in Russia since the late 1990s, actual government payments for utility services have improved considerably. The level of current non-payments has become negligible. The stock of government arrears for utility payments declined to below 0.6 percent of GDP by the end of 2002 (Table 3.7, above). Most of the remaining quasi-fiscal financing takes places through cross-subsidization (Table 3.6 above): in many regions budget organizations have benefited from the same low utility tariffs as households.
3.52 As is shown below, we expect that under the reform scenarios the unit costs of HUS would increase by about 90 percent by 2006 relative to the 2002 level.52 Even adjusting for the expected modest efficiency gains in the sector, our estimates suggest that full adjustments in energy and utility prices would result in the total HUS costs to the government reaching 1.9-2.0 percent of GDP, with half to be incurred by municipalities. This amounts to 0.8-0.percent of GDP in additional expenditures for Russia’s consolidated budget. We estimate that about half of these incremental costs could be compensated through additional taxes paid by energy firms and utility providers that are the primary beneficiaries of the proposed tariff reform. This leaves a residual fiscal gap of about 0.4-0.5 percent of GDP. In the longer term, we expect that most of this gap would disappear as a result of the expected rationalization and consolidation of the budget sector, first of all in health and education. But public sector rationalization could become a relatively lengthy process. Meanwhile, the government has to find ways to finance the gap.
This cost increase reflects some compensation for under-investments in the previous period. See below for more details on cost assumptions.
F. MODEL FOR SIMULATING BUDGET IMPLICATIONS FROM INCREASES IN HOUSING COSTS Lessons from the earlier simulations of housing reforms in Russia 3.53 Given the long history of the government’s attempts to accelerate housing reforms, which would include reforms in financing through higher cost recovery by tenants and reduced subsidies, it is not surprising that there has been some experience of quantitative analysis of the potential impact of the proposed tariff increases.53 Most of this work has been undertaken initially by the staff of the Urban Institute, as part of the housing reform program sponsored by USAID.
3.54 The primary focus of the earlier work was related to the introduction of the housing allowances programs in particular regions and municipalities, as well as to the analysis of the actual efficiency of such programs. This required developing the procedures for: (i) estimating the variation in future reform impacts across particular types of housing and specific household groups, (ii) forecasting changes in the demand for housing allowances, and (iii) making projections for trends in the total requirements for budget financing, including costs of both conventional budget subsidies and social assistance programs, such as housing allowances. These studies have been based on various types of the survey data on household incomes, expenditures, and housing conditions, which have helped provide an important understanding regarding the comparative advantages of specific statistical sources.
3.55 The main lessons from the earlier simulations of housing reforms, which were fully incorporated in this paper, could be summarized as follows:
• Estimates of the current level of housing costs to households have to be imputed, and they should not be directly based on expenditure values self-reported in the household surveys. The conventional household surveys in Russia, including both the RLMS and the regular Household Budgetary Survey (HBS), bring an unacceptable level of distortions when they deal with the issues related to housing and utility spending.
Apparently the basic question, “How much did you spend on housing and utilities last month” was interpreted quite differently by respondents. This is because of the multiplicity of available discounts and benefits (lgoty) to households, the high incidence of arrears and late payments, and the still relatively low share of housingrelated payments in total spending for many households.
• Accounting for cross-regional variations in costs and incomes is important. There is a striking cross-regional variation in all key parameters that determine a potential reform impact on both the population and the fiscal system. In particular, regional differences are high with respect to average housing costs, average income levels, and income inequality. As a result, as was shown, for example, in World Bank (1998b), while on the average the reform impact could be modest, some regions (particularly Siberia and the Far East), could be badly affected by the shift to full cost recovery in housing.
• Regional income distribution is a key determinant of a demand for housing allowances. Relatively simple models that are based on i) forecasting of average regional housing costs, and ii) the aggregated income distribution by main income groups, proved to be sufficiently accurate in generating projections for both number See specifically Housing Allowance Program (1996), Kolodeznikova and Struyk (1997), World Bank (1998b), IUE (2003b).
of future applicants and average size of their allowance. Switching to more detailed (household-level) information on incomes and housing conditions greatly complicates the analysis and does not bring significant improvements in the accuracy of projections.
Model: Estimating future HUS costs3.56 The base model for estimating future average costs of operating the residential housing stock had the following structure:
HUS (t) = (H(t)* x1 + E(t)* x2 + G(t)* x3) * Sav(t), (1) where HUS (t) – a housing cost index that reflects an increase in full average costs of operating 1 square meter (sq. m) of the housing stock in year t relative to the base year (2002), H(t) – an increase in unit non-energy costs of operating housing in year t relative to the base year, E(t) and G(t) -- increases in unit costs of electricity and gas, respectively, in year t relative to the base year, x1, x2, and x3 – shares of main components of housing costs (non-energy, electricity and gas, respectively - see section 3 for additional information) in the total costs, Sav(t) – parameter of cost savings in year t, which reflects the expected efficiency gains (primarily energy savings) as a result of reforms, as a percent of the base 2002 costs.
3.57 All costs are estimated in constant 2002 prices. Regional average costs HUS(r, t) are determined based on actual cross-regional variations in unit costs in the base year 2002:
HUS(r, t) = HUS(t)*cost(r) Where cost (r) - the coefficient of regional costs that reflects the 2002 ratio between unit costs in region r and average costs for Russia.
3.58 Future growth in electricity and gas tariffs, E(t) and G(t), is determined by two factors:
- e1/g1 – expected average real growth in domestic energy prices, - e2/g2 – expected effect of elimination of cross-subsidization that would provide for a higher growth in residential tariffs relative to their average growth.
3.59 Future growth in non-energy unit costs H(t) is determined by:
- h1 – expected real growth in tariffs for non-energy services in the sector, mostly related to a partial compensation for earlier under-financing in the sector; h1 was selected at the level of 1.32 (i.e., for the period 2004-06 non-energy costs in the sector would be growing at a rate that is 32 percentage points higher than general inflation), which would compensate for about a quarter of earlier deferred inflation.
- h2 -- expected effect of the elimination of cross-subsidization in non-energy services (primarily water) that would provide for a higher growth in residential tariffs relative to their average growth.
A detailed description of the model is provided in IUE (2003a).
E(t) = e1(t) * e2(t) G(t) = g1(t) * g2(t) H(t) = h1(t) * h2(t) 3.60 Projections for costs of housing and utility services for budget organizations are also based on the above HUS(t) index (i.e., it is expected that future costs of HUS for budget institutions would grow at a similar rate as those for households).
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