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What does a relative price of investment wedge reveal about the role of investment-specific technology?

  • Joel Wagner EMAIL logo

Abstract

In order to identify investment-specific technology (IST), most DSGE models assume a perfect inverse relationship between IST and the relative price of investment (RPI). This paper explores this relationship and provides evidence that the RPI also responds to changes in market power, which I find constitutes a third of volatility in the RPI. To corroborate this conclusion, two competing models are produced; the first is a two-sector model with a wedge separating the identification of IST with the inverse of the RPI. The RPI wedge is then estimated using Bayesian estimation techniques. A second, richer two-sector model is produced, where firms can vary markups depending on the number of competitors. This paper finds that changes in relative markups are highly correlated with the RPI wedge and help explain the sudden increase in the RPI following the Great Recession in the United States. In addition, with endogenous price markups, non-IST shocks can explain over a third of the volatility observed in the RPI, with marginal efficiency of investment contributing approximately 30 percent of the volatility in the RPI.

JEL Classification: E32; L11; L16

A Appendix

A.1 Bayesian estimation data

  1. Nominal Gross Domestic Product, BEA, NIPA Table 1.1.5, Line 1, 1948:Q2-2016:Q3, Seasonally Adjusted at Annual Rates

  2. Nominal Personal Consumption Expenditure on durables, BEA, NIPA Table 1.1.5, Line 4, 1948:Q2-2016:Q3, Seasonally Adjusted at Annual Rates

  3. Nominal Personal Consumption Expenditure on non-durables, BEA, NIPA Table 1.1.5, Line 5, 1948:Q2-2016:Q3, Seasonally Adjusted at Annual Rates

  4. Nominal Personal Consumption Expenditure on services, BEA, NIPA Table 1.1.5, Line 6, 1948:Q2-2016:Q3, Seasonally Adjusted at Annual Rates

  5. Nominal Gross Private Domestic Investment, BEA, NIPA Table 1.1.5, Line 7, 1948:Q2-2016:Q3, Seasonally Adjusted at Annual Rates

  6. Real Personal Consumption Expenditure on durables, BEA, NIPA 1.1.6, Line 4, 1948:Q2-2016:Q3, Chained 2009 Dollars Seasonally Adjusted at Annual Rates

  7. Real Personal Consumption Expenditure on non-durables, BEA, NIPA 1.1.6, Line 5, 1948:Q2-2016:Q3, Chained 2009 Dollars Seasonally Adjusted at Annual Rates

  8. Real Personal Consumption Expenditure on services, BEA, NIPA Table 1.1.6, Line 6, 1948:Q2-2016:Q3, Chained 2009 Dollars Seasonally Adjusted at Annual Rates

  9. Real Gross Private Domestic Investment, BEA, NIPA 1.1.6, Line 7, 1948:Q2-2016:Q3, Chained 2009 Dollars Seasonally Adjusted at Annual Rates

  10. Private Non-Farm Hours Worked Major Sector Multisector Productivity Index Base Year 2009 1948:Q2-2013:Q4 BLS PRS85006033 Seasonally Adjusted at Annual Rates

  11. Price deflator for personal consumption expenditure on non-durables and services, (3+4)/(7+8)

  12. Price deflator for gross private domestic investment and consumer durables, (2+5)/(6+9)

  13. Current population survey civilian non-institutional population, 16 years old and over, 1948 through to 2015.

  14. Real per capita output Yt is (1)/ (13)/(11)

  15. Real per capita consumption Ct is (3+4)/(13)/(11)

  16. Real per capita investment RPItIt is (5+2)/(13)/(11)

  17. The relative price of investment (12)/(11)

A.2 The steps involved in estimating Markups μtC and μtI

The methodology used here to estimate the elasticities τI and τC follows Floetotto, Jaimovich, and Pruitt (2009). To calculate the number of firms operating within each sector, I first calculate the number of firms operating within each of the non-agriculture Standard Industrial Classification (SIC) supersectors. Then I subdivide each sector by its contributions to either consumption or investment production, using data from the input-output use tables available from the Bureau of Economic Analysis (BEA). Lastly, I weigh each sector by their relative contribution to either consumption or investment, then sum across the thirteen SIC supersectors to derive the total number of consumption and investment firms each quarter.

  1. The number of firms within each SIC supersector is derived by adding expansions (firms that hired employees), contractions (firms that laid off employees), openings (new start-ups) minus closures (firms that closed).

  2. Input-Output Use Table BEA Before Redefinitions 1992–2015. The input-output use tables break down each of the 13 supersectors’s product going toward Personal Consumption Expenditure or Private Fixed Investment. Then each industry is weighed by their relative contribution to the consumption sector (Personal Consumption Expenditure)/(Personal Consumption Expenditure + Fixed Private Investment), and investment sector (Fixed Private Investment)/(Personal Consumption Expenditure+ Fixed Private Investment). Lastly, we sum across supersectors to derive NtC and NtI.

  3. Each variable is log differenced, denoted by I^t, C^t, N^tC and N^tI.

  4. Then N^tC is regressed on C^t and N^tI on I^t.

  5. Using the conditions

    (55)C^t=(τC(μC1))(1τC)N^tC
    (56)I^t=(τI(μI1))(1τI)N^tI.

    With μI=μC=1.3, values for τC and τI are calculated.

Then markups μ^tC and μ^tI are calculated by using equations (57) and (58)

(57)μ^tC=(1τCμC)(τCμC)C^t
(58)μ^tI=(1τIμI)(τIμI)I^t.

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Article Note

The views expressed in this paper are those of the author. No responsibility for them should be attributed to the Bank of Canada.


Published Online: 2019-04-17

©2019 Walter de Gruyter GmbH, Berlin/Boston

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