Finding data for all counties in IFs to initialize the financial flows of the social accounting matrices with the pre-processor (Hughes and Hossain 2003) is not just challenging, but impossible. IFs draws on data from a variety of sources, including the World Bank’s World Development Indicators, the OECD, and the Global Trade and Analysis Project. The IMF’s Government Finance Statistics (GFS are at http://www.imf.org/external/data.htm) provides a number of important series including the ability to begin differentiating central and local government revenues and expenditures, important in IFs as we increasingly represent states, provinces or other jurisdictions within countries. At this writing, most analysis is done at the general government level (central plus local) and local variables are rather preliminary and the distinction is primarily made using data from the IMF’s GFS.

The pre-processor reads total government revenue from the SeriesGovtCalcRevTot%GDP table of the IFsHistSeries.mdb data file and reads central government revenue from SeriesGovtCalcRevCen%GDP. It computes the local portion as the residual. Whenever values are missing in tables for these variables or others in the pre-processor, holes are filled for countries with values generally estimated from a cross-sectional relationship with GDP per capita at PPP.

Various revenue streams for general government are initialized in the preprocessor with data from the World Development Indicators or the IMF’s GFS. These include taxes from firms (SeriesTaxCorp%Tot) from the IMF, social security and welfare taxes (TaxSocSec%CurRev) from WDI, and indirect taxes (TaxGoodServ%CurRev) from the WDI and originally the IMF GFS. Because many countries do not have direct taxes on households and data are poor, they are set as residual for revenue generation

Similarly, the pre-processor reads total government expenditures from SeriesGovtCalcExpendTot%GDP and central expenditures from SeriesGovtCalcExpendCen%GDP. When both expenditure values exist in the data, local is again the residual. If total expenditures do not exist, then local is computed based on some data about the growth of local revenues and expenditures in North, Walli and Weingast (2009: 10):

In this case, total expenditures are initialized as the sum of central and local.

Expenditures break down into direct government consumption (GOVCON) in categories of military, health, education, infrastructure, R&D, and other and into government to household transfers (GOVHHTRN). Data for the various categories come primarily from WDI sources and GOVCON is set as the larger of the sum from the categories and a direct specification of it in the WDI data (SeriesGovCon%GDP). The health and education expenditures are assigned to GOVCONLOCAL, but GOVCON itself remains general government (central plus local). The pre-processor assures that GOVEXP is at least 1 percent more of GDP than is GOVCON, to make some headroom for government transfers.

Data on government transfers are again not very strong. We identify social security/welfare payments (GOVSSWEL) and pension payments (GOVHHPenT) as the two categories, based on WDI data in SeriesGovSSWelBen%Exp and SeriesGovtPension%GDP, respectively.

A note on forecasting central and local government finance

The process described above initializes four local government finance variables for a country as a whole, namely

GOVREVLOCAL (local government revenues)

GOVEXPLOCAL (local government expenditures)

GOVCONLOCAL (local government direct consumption)

GDSLOCAL for categories health and education (local government consumption by type)

In addition three variables have been added as placeholders that would add complexity and possible utility to representation of government finance at the sub-national level:

GOVREVCENFRLOCAL (government revenue at the central level from the local—transfers)

GOVREVLOCALFRCEN (government revenue at the local level from the central—transfers)

GOVREVLOCALTAX (government revenue raised through local taxes)

Possible futures steps in forecasting central/local government finance

At this point no dynamics have been added to the model for the four initialized variables, so they are displayed as constant across time. It would be easy to add dynamics that made them constant shares of GOVREV, GOVEXP, GOVCON, and GDS, respectively. It would take very considerably more work to make truly dynamic the central and local shares. There are two very different circumstances for such representations:

1. In integrated local representation without regionalization of countries. Steps of complexity: (a) local shares could be raised or lowered with functional forms or exogenous multipliers, with no forward linkages in model version with no specific representation of sub-units, having no real model impact; (b) passing through local changes to total revenues or expenditures as straight increases or decreases; for instance, if total expenditures were pushed up by an exogenous increase in local expenditures, it could have broad impacts on revenues as well as on the targets of those expenditures. But it is not clear that (b) would add much model value because exogenous changes to total revenues and expenditures are already possible.

2. In regionalized (states, provinces, districts) local representation. Using South Africa as an example, this is heavily complicated by the fact that the model cannot now represent both central government (e.g. national level government in Pretoria) and local government finance (e.g. in Western Cape), much less the interaction of the two and the possible implications for other provinces. The total government numbers being brought into the model for all SA provinces represent values related to provincial-level initialization.

A first step might be (a) to provide initialization data for as many as possible of the 7 variables for SA provinces and make sure the preprocessor uses these when available instead of estimates; (b) to initialize total government for all SA provinces as mirrors of SA national government, making all provincial total variables essentially identical shares of the national total percentage rates. The two steps are complicated, but reasonably limited.

A second and considerably larger step might be (c) to allow manipulation of provincial local expenditures with pass through to total (actually the provincial shares of the total) as indicated in point 1 above.

A much bigger step would be (d) to think about trade-offs and interactions across expenditures and revenues within and across local and total levels. It would then be possible, for example, to push up a specific consumption expenditure (say that on education) and boost educational performance and downstream impacts of that within one region, representing the trade-offs with (i) other expenditures in that region (this can already be done, but not always with meaningful provincial financial numbers) (ii) offsetting revenue increases at the local level (assuming there is any local sourcing of revenue); (iii) transfers from the regional total representation to or from the local; (iv) some combination since there would be many possible ones. In general, representing trade-offs outside of the specific region would be especially messy.

Given the lack of meaningful forecasting of local government finance variables at this time, the discussion on the forecasting of government finance will discuss only total government variables.