Руководство мвф по статистике государственных финансов

International Monetary Fund

17.2.5 Summary of institutional facts

Appendix 4.A List of variables and data sources

7.6 Government Policy Parameters

We obtain the ratio of the government debt Btr as a fraction of GDP from the International Monetary Fund (IMF)’s World Economic Outlook Database (WEO, 2013). We use the net debt variable that represents the gross debt net of financial assets. The average over the period 1990–2010 was 48%, 37%, and 51% in High-, Middle-, and Low-Income regions. For China, only gross debt data is available, which is 14% of GDP. Since we do not have the data for the government’s financial assets, we assume a net debt of 10% of GDP in the baseline calibration.

The total government expenditures as a fraction of GDP are obtained from the WEO for the period 1980–2010, and from the IMF Government Finance Statistics (GFS, 2014) and the China Statistical Yearbook of the National Bureau of Statistics of China (n.d.) for earlier years (1970–1980). The average over 1970–2010 was 36%, 23%, 30%, and 22% of GDP in each of the four regions, respectively. Since these figures represent general public expenditures, which include spendings for social security and interest payment, we compute the ratio of the government expenditures Gtr to GDP so that the total expenditures match the ratios reported above. The ratios of Gtr to GDP are 29.2%, 22.5%, 28.8% and 21.4% for each of the four regions.^m

Based on the study of OECD, the replacement rate of pensions to the average earnings is set at κtH=58% in High-Income region.ⁿ Unfortunately, similar systematic studies on the replacement rates for other regions are not available. The average replacement rate is likely to be much lower than in High-Income region due to two factors. First, the disproportionate role of self-employment and informal production means that a vast part of the working population is not covered by a public pension system. Second, the involvement of governments in the pension sphere is limited: in Asia, only Korea and Taiwan operate a defined benefits PAYG scheme with universal coverage; Latin America is the region with the largest number of pension systems already reformed toward substantial privatization (see Mohan, 2004, for the Asian experience; see Corbo, 2004, for the Latin American experience). We set the replacement rate of the three regions at κtr=10% in the first steady state, the value used in Attanasio et al. (2006).^o

For tax rates of each region, we use various data sources for the period of 2000–2010 and estimate effective tax rates following the method of Mendoza et al. (1994). We use the OECD Revenue Statistics (2013a) database for tax revenues, in particular for High-Income and Latin American countries, integrated with consistent data from IMF GFS for Low and other Middle-Income countries. Detailed national accounts data on households, enterprizes, and government are taken from the OECD National Accounts Statistics (2013b) and the UN National Accounts Statistics (2013b) databases. Equivalent data for China are not available and we use the estimates of Cui et al. (2011) for the effective tax rates of the country. Capital income tax rates τar are 35.7% for High-Income region, 15.5% for Middle-Income region, 13.5% for Low-Income region and 25.7% for China. Consumption tax rates τcr are set at 9.7%, 16.0%, 6.3%, and 7.7%, respectively. The labor income tax τw,tr in each region is determined in the equilibrium path of the model to satisfy the government budget constraint, as presented and discussed in the next section.

§9.4.4 Statistical Reporting

The basic standards for macroeconomic statistical reporting are set out in the System of National Accounts (SNA) (European Commission et al. 2009) under the joint responsibility of the United Nations, the IMF and a number of other multilateral bodies. For non-EU governments, the key source of guidance for the format of statistical reporting, based on the SNA, is the IMF’s Government Finance Statistics (GFS) Manual (IMF 2014). This is aimed at ‘helping national authorities to strengthen their capacity to formulate fiscal policy and monitor fiscal developments’.

There is no worldwide agreement yet among statisticians on the treatment of PPPs and thus the SNA, and the GFS Manual based on this, are currently non-prescriptive. The IMF’s GFS Manual generally takes a risk/reward approach to determining economic ownership but recognises that each case needs to be considered on its merits as the relative importance of each different risk/reward factor will have a bearing on the overall determination of economic ownership and therefore balance-sheet treatment. The factors involved are closely related to the conditions prescribed by IPSAS 32 for financial accounting. So, GFS rules and IPSAS 32 should usually lead to the same result.

If limits (e.g. imposed by the IMF) exist on levels of public deficit and public debt and a country is close to these limits, how a PPP project is classified for statistical-reporting purposes is particularly important. There may be an incentive to ‘financially engineer’ a structure to get the project classified as off balance sheet so that it does not contribute to the reported level of public debt. If risk transfer is the deciding factor, this may lead to paying the project company for a risk that it is not well-equipped to assume, resulting in poor VfM. It also puts pressure on the process of measuring risk and the integrity of the VfM assessment by creating an incentive to ensure the assessment ‘proves’ VfM, when this may not be the case.

However, the bias to use PPPs by the prospect of off-balance-sheet treatment can sometimes be overstated. A study of the impact of the requirement that all French local-government PPPs should be classified as on-balance sheet as of 2010 (Buso et al. 2016) showed that this did not lead to materially fewer PPPs. In other active PPP markets, such as Canada or Germany, PPP programmes have developed even though almost all projects have always been and continue to be classified as on the public-sector balance sheet.

International Data Sources

International sources of data on federalism are more widely scattered than are data on the United States, so it is difficult to evaluate all of them here. As in the United States, in general, data on fiscal federalism is easier to come by than are data on electoral behavior or public opinion.

4.7.2.2 Database development

Among the factors of production, there are three types of labor: those with less than completed secondary education (unskilled), with completed secondary education but not completed tertiary (semi-skilled) and with completed tertiary (skilled). Each of these labor types is therefore linked directly to an educational cycle. The growth in the labor force and changes in its composition will in part depend on the functioning of the education system in the model.⁸¹ The remaining factors include public capital stocks by government activity, a private capital stock and a natural resource used in oil and gas extraction.

The institutions include the government, a household (the private domestic institution, which represents both households and domestic enterprises) and the rest of the world. Each institution has its own capital account. Taxes have been disaggregated into direct, import and commodity taxes/subsidies. There is one private investment account and eight public investment accounts (one for each government sector). Lastly, the SAM includes accounts for domestic and foreign interest payments.

The determinants in the MDG functions include the provision of relevant services (in education, health and water and sanitation) and other indicators as per capita consumption and the size of the capital stock in public infrastructure, also allowing for the presence of synergies between MDGs, i.e. the fact that achievements in terms of one MDG can have an impact on other MDGs. For example, improvements in water and sanitation (i.e. MDG 7) will reduce under-5 mortality (MDG 4). In the cases of health and water and sanitation (i.e. MDG 4, 7w and 7s), service provision is expressed relative to the size of the population. For MDG 2, the treatment is slightly more complex. The arguments in Table 4.20 determine the shares of children that enter basic school (out of the cohort of 6-year olds) and successfully complete their current grade (among those enrolled in the first basic cycle). The shares that repeat their current grade or drop out from it are determined residually. The service level is measured per enrolled student – an indicator of educational quality. MDG 4 is included as a proxy for the health status of those enrolled. Wage incentives – an indicator of payoffs from continued education – are expressed as the ratio between the wages for labor at the next higher and the current levels of education.

For the secondary and tertiary cycles, the same set of arguments enter functions that determine the shares of enrolled students that pass as well as the shares of graduates from the previous cycle that enter the first grade of these two cycles. The only differences are that the arguments for services (per enrolled student) and wage incentives are redefined to be relevant to these higher cycles and that no continuation rate is defined for the tertiary cycle (as it is viewed as a terminal cycle).

MAMS typically focuses on the net (on-time) primary completion rate as its main MDG 2 indicator; the net enrolment rate, which is the official indicator, is a less informative measure of the extent to which the relevant age group is able to complete the six-year primary cycle.⁸⁴ More specifically, in any year, the net completion rate is defined as the share of the students that would complete primary school on time if this year’s net intake and grade promotion rates were to prevail during the coming six years.⁸⁵ In addition, MAMS reports other indicators related to the primary cycle, the gross enrollment rate and the gross completion rate; the latter is used by the World Bank as an alternative MDG 2 indicator.

Generally speaking, the functions for educational outcomes and the other (i.e. non-education) MDGs have all been calibrated to ensure that, under base-year conditions, base-year indicators are replicated and that, under a set of other conditions identified in the Yemen Needs Assessment Report (see MOPIC, 2005), the target is fully achieved. Specifically, the Yemen Needs Assessment Report provides estimates of government sectoral spending needs (current and capital) for the period 2006–2015, which are used to calibrate the logistic functions in the MDG module of MAMS.

Finally, the MAMS Poverty Module was calibrated under the assumption that the household income distribution follows a log-normal distribution (see Section 4.4). Specifically, a poverty rate of 42.4% for 2010 and a Gini coefficient of 0.411 were used (see Government of Yemen et al., 2007; MOPIC, 2011); these two pieces of information were used to estimate the shape of the log-normal distribution and the value of the poverty line. As explained, this approach assumes that the income distribution within the representative household does not change over time.