A vertical social accounting matrix of the U.S. economy

Abstract

This article presents an adaptation of the square social accounting matrix used in economic planning and programming to the rectangular or vertical transaction matrix used in post Keynesian monetary economics. The objective is to obtain a simple and intuitive framework to organize macroeconomic data in terms of the main institutional sectors of the economy, showing how production, distribution, demand, and financing are inter-related. The article presents the social accounting matrix of the U.S. economy as an example and use it to analyze the trends in net lending and household net income since 1960.

Keywords:

• Social accounting matrix
• U.S. economy
• post Keynesian

Notes

1 According to the BEA’s website, the IMA “are part of an interagency effort to further harmonize the BEA National Income and Product Accounts (NIPAs) and the Federal Reserve Board Financial Accounts of the United States (FAUS).” Bond et al. (2007) and Yamashita (2013) presented the methodology of the U.S. IMA. As the BEA itself warns, there are discrepancies between some numbers from IMA and NIPA sources. We will work only with NIPA numbers in this paper and indicate the source table accordingly. The data comes from the BEA NIPA tables, as of December 2016, and are available from the author upon request.

2 The households sector can also be divided by income cohort, say, poor, middle, and rich, using household surveys (Taylor et al. 2014) or tax data (Piketty, Saez, and Zucman 2016) as a guide.

3 In view of recent macroeconomic developments, the most useful division is between financial and nonfinancial firms, which has been recently expanded in the BEA’s IMA (Kornfeld et al. 2016).

4 For example, private and government consumption have different price indexes, and so on. The NIPA data contains price deflators for almost all expenditures, but this would just complicate the analysis without changing its meaning.

5 All data used in this paper come from 10 BEA tables: Tables 1.5.5, 1.10, 1.12, 1.13, 2.1, 3.1, 4.1, 5.1, 7.10, and 7.11.

6 For some economies, we can start allocating part of the net operating surplus to households and the government. This is a point of attention in applying the methodology of this paper to other countries than the United States.

7 The BEA’s statistical discrepancy equals GDP minus gross domestic income (GDI). A negative statistical discrepancy means, therefore, that GDP was smaller than GDI in the period under analysis.

8 The current structure of the BEA’s NIPA does not present bilateral dividends and interest flows. Because of this, Table 2 presents only the total receipts and payments by institutional sector.

9 In 2015, 29% of the total interest payments ($1,010 billion) were imputed flows due to financial services and “owner-occupied-housing.” In the later, households receive an imputed rent for the homes they own. For a detailed analysis of imputed interest in economic analyses of capital income, see Shaikh (2016, Appendix 6.7).

10 With four institutional sectors, we should have 12 bilateral flows of other transfers in Table 3. We present only nine bilateral flows because the BEA data contains only the net values of some transfers between our four institutional sectors.

11 Capital transactions consists of transfers of capital and other operations involving nonproduced nonfinancial assets.

12 For another way to match the NIPA with the FoF data, see Taylor (2004, chapter 1).

13 According to the Fed’s FoF, in 2015 most of this “instrument” discrepancy came from foreign deposits, fed-funds and repo operations, taxes payable and “miscellaneous assets.”

14 For example, allocating all residential investment to households. The post-1960 data show that household and residential investment are close, but do not necessarily coincide.

15 This confirms the analysis of Barbosa-Filho et al. (2014).

16 This is approximately 0.7% of the rest of the world’s GDP assuming that the United States currently represents 25% of nominal world income, as shown by the recent International Monetary Fund data.

17 By wage repression, I mean structural changes that reduces workers’ bargaining power in the labor market. In the United States, this has been driven mostly by four factors since the 1980s: technological change or the “information revolution,” trade opening or “globalization,” the relative increase in the demand for services, and the procapital changes in labor and tax legislation. For a model of employment and the labor share of income applied to the United States, see Barbosa-Filho (2015).

18 The third component of household final income consists of net taxes, contributions, benefits, and other current transfers received, described in Table 3.

19 Other BEA series indicate that half of this fall was due to the reduction in farm income in terms of GDP.