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Information and communication technologies over the business cycle

  • Benedetto Molinari , Jesús Rodríguez-López EMAIL logo and José L. Torres
Published/Copyright: July 2, 2013
The B.E. Journal of Macroeconomics
From the journal The B.E. Journal of Macroeconomics Volume 13 Issue 1

Abstract

This paper quantifies the relative importance of different sources of technological progress as determinants of short-run fluctuations in the US economy. In particular, it focuses on the role of the technical innovations associated with information and communication technologies (ICT). The paper points to three main findings. First, neutral technical change is the main determinant of the US aggregate fluctuations, and its contribution remained constant throughout the postwar sample. Second, the importance of ICT increased significantly during the last decades of the considered sample, which nowadays is responsible for approximately 1/5 of GDP fluctuations. Third, the variance reduction of exogenous shocks typically associated with the last decades of the postwar sample, mainly comes from ICT and neutral shocks, whereas the volatility of innovations in traditional capital remained relatively stable. Overall, we conclude that attention should be focused on identifying those incentives behind the adoption of knowledge and technology, an issue related to the neutral progress, rather than the quality or technology embedded in capital goods such as ICT assets.

Keywords: business cycle; information and communication technologies; investment-specific technical change; neutral progress; JEL classification: E32; O47
Corresponding author: Jesús Rodríguez-López, Department of Economics, University Pablo Olavide, Ctra Utrera, Km. 1, 41013 Sevilla, Spain, e-mail:
  1. 1

    Investment aggregates include both private and government expenditures in capital assets. Non-ICT investment also accounts for inventories and consumers’ expenditures in durable goods.

  2. 2

    This is a simplifying assumption that has a negligible impact on quantitative results. Gort, Greenwood, and Rupert (1999) estimated that NIPA price for nonresidential structures should be quality adjusted by 1% yearly, with small annual variations.

  3. 3

    Following McConnell and Pérez-Quirós (2000) and Stock and Watson (2002), the first quarter of 1984 is considered the switching point.

  4. 4

    In Cummins and Violante (2002) the depreciation rates are time-varying, while we assume them constant.

  5. 5
  6. 6

    NIPA data use an economic rate of depreciation for structures of dstr=0.056. Gort, Greenwood, and Rupert (1999) propose a physical measure of this rate that is much lower than the NIPA rate and increases with the age of the structure. This rate steadily increases from 0.8% for a brand new building to 4.3%.

  7. 7

    Using the estimated coefficients in the joint null hypotheses γ12=γ13=0, γ21=γ23=0, and γ31=γ32=0 cannot be rejected at any significance level, while they are all rejected using coefficients. In particular, we find that A48–74∼AR(1), as usually assumed in the RBC literature.

  8. 8

    The identification assumptions in the SVAR are tested using a LR test of the null hypothesis that all the empty cells in the orthogonalization matrix H are significantly equal to zero.

Comments and suggestions from Víctor Ríos-Rull, Fabio Canova, Claudio Michelacci, Ana Sánchez, participants at the Workshop on Dynamic Macroeconomics (Punta Umbría, 2009) and those from an anonymous referee are all gratefully acknowledged. We wish to thank Raúl Santaeulalia-Llopis for the help in managing the dataset. Financial support from Research Grant P07-SEJ-02479 and from Ministerio de Educación SEJ2006-04803/ECON are acknowledged.

Appendix

A Data transformation

Data on gross GDP, consumption expenditures, investment expenditures in a variety of assets and the consumer price index for nondurable and services come from the National Income and Product Accounts (NIPA) of the Bureau of Economic Analysis (BEA). The total hours worked is proxied using the aggregate hours index (PRS85006033) from the Bureau of Economic Statistics (BLS).

Assets are classified as belonging to one of three categories: (i) information and communication technologies (ICT) assets, (ii) non-ICT assets, and (iii) structures. The ICT category includes three assets: (i.1) hardware, office equipment and peripherals; (i.2) communication equipment and (i.3) software licenses. The non-ICT category includes (ii.1) transport equipment, (ii.2) machinery and (ii.3) other equipment. These, together with structures, sum to seven assets. As in expression (1) on Section 2, the ISTC in asset j, Qj,t, is defined as the ratio between constant quality consumption, PCt, and the relative price of quality adjusted investment qj,t, Qj,t=PCt/qj,t. We assume that all assets except structures have embedded ISTC.

Let denote the annual quality adjusted price of asset j provided by Gordon (1990) and Cummins and Violante (2002), 1947–2000. These series do not include quality adjustment for structures and are separated into 26 assets that can be assigned to six equipment categories of the BEA database. The following is a detailed schedule indicating the aggregation we have undertaken:

  1. Hardware equipment includes three sub categories: (a) computers and peripheral equipment, (b) instruments and photocopies and (c) office and accounting equipment.

  2. Communication equipment.

  3. Software licenses include three assets: (a) pre-packaged software, (b) custom software and (c) own software. These series are available for the period from 1960 to 2000.

  4. Transport equipment includes (a) trucks, buses, and truck trailers, (b) autos, (c) aircraft, (d) ships and boats and (d) railroad equipment.

  5. Machinery and equipment include (a) fabricated metal products, (b) engines and turbines, (c) metalworking machinery, (d) special industrial machinery, n.e.c., (e) general industrial equipment, including materials handling equipment and (f) electricity transmission and distribution.

  6. Other equipment includes (a) furniture and fixtures, (b) tractors, (c) agricultural machinery, except tractors, (d) construction machinery, except tractors, (e) mining and oilfield machinery, (f) service industry machinery, (g) electrical equipment, n.e.c. and (h) other equipment.

In what follows, we will refer to items i=1…6 as categories is. The first step consists of aggregating the 26 assets into the six categories. We use a Törnqvist price aggregate that weights the growth rates of the price index of investment in assets j, qi,j,t, belonging to category i based on their nominal shares

where si,j,t is the nominal investment share of asset in year (Table 5.3.5. Private Fixed Investment by Type and Detailed Investment in Private Nonresidential Fixed Assets from BEA). Note that ∑jsi,j,t=1. The quality-adjusted price index for total investment is recovered recursively,

with i=1, 2, …,6.

For each category i we select 1995 as the base year, qi,1995=1.

A price index for consumption, PCt, is constructed using a Törnqvist price index aggregate that weights the growth rates of price indexes for nondurable consumption (food, clothing and shoes, and other goods) and services (household operations, transportation, medical care, recreation, and other services) based on their nominal shares. Let PCi,t be the price index for nondurable consumption/service good i in year t. Let be the corresponding nominal share of good i in period t. Thus, the growth rate of the price index for consumption is

The level of quality-adjusted price index for total investment is recovered recursively,

where PC1995=1.

From (43) and (45) we can measure the ISTC for asset j=1…6, according to expression (1). Using the nominal investment series for categories 1 through 6, we have aggregated the quality adjusted price series into ICT and non-ICT assets

with j=1 (hardware), 2 (communication) and 3 (software), where sict,j,t is the nominal investment share of asset j within the ICT chapter in year t. Note that For the Non-ICT assets we have

with j=4 (transport eq.), 5 (machinery eq.) and 6 (others). In the same fashion, snict,j,t is the nominal investment share of asset j within the non-ICT chapter in year t, and In both (46) and (47) the shares of nominal investment are now borrowed from the BEA database.

Therefore, we end up with seven series of assets: three ICT assets, three non-ICT assets and structures. We use these quality adjusted prices to deflate the series of nominal investment. Nominal investments in structures are deflated using the consumption price index for non durables and services.

Finally, for those years uncovered by the Cummins-Violante database, i.e., from 2002 to 2008, we extend the series of prices according to the long-run specification proposed by Cummins and Violante (2000):

j=Transport, Machinery, Others,

where is the Cummins-Violante quality adjusted price of asset j, is the NIPA price of asset j, and Δln(yt–1) is the lagged growth rate of the US GDP (Table 1.1.3. BEA Real Gross Domestic Product Quantity Indices). We estimate the long-term relationship in (48) with OLS. These estimates are shown in Table A.1. All coefficients are statistically significant except those associated with the growth rate of output, bj,3. The lagged values of the NIPA prices are not very significant. Note that this extension only includes the non-ICT assets as for the ICT equipment the NIPA prices are quality-adjusted and updated. Using these estimates and the NIPA prices of the six assets, we extend the database from 2001 to 2008.

Table A.1

OLS estimates of quality adjusted prices.

Non-ICT assets
VariableTransportMachineryOthers
Constant1.68(19.30)1.48(14.59)0.88(16.48)
0.94(4.65)0.99(6.02)1.22(10.81)
0.37(1.81)0.19(1.14)–0.11(1.02)
t×100–3.63(19.29)–3.19(14.58)–1.92(16.39)
Δ ln (yt–1)–0.13(0.45)0.25(0.95)–0.15(0.90)
0.960.980.99

Table A.2 reports the price based measure of the ISTC for the six categories of assets according to (13). These results do not differ from those reported by Cummins and Violante (2002, see their table 2). Hardware and Communication equipment, and Transport equipment and Machinery have a non negligible ISTC, mainly after the 1980s. This is an important issue, as noted by Cummins and Violante (2002), since the use of NIPA price for growth accounting decomposition may be misleading when concluding that the upsurge in the US productivity growth after the mid 1990s was almost solely due to the ICT assets. On the contrary, the use of quality adjusted prices evince that the ISTC has been embedded in every type of assets.

Table A.2

Investment specific technical change by asset.

1948–200848–6061–7071–8081–9091–0001–08
Hardware18.3–15.922.815.622.114.3
Communication9.3–5.47.99.013.813.2
Software4.1–3.84.44.94.12.6
Transport eq.3.73.74.62.13.34.64.1
Machinery2.51.52.81.52.23.64.4
Others1.81.71.80.42.02.53.0

These annual series of prices have been transformed into quarterly series using Denton’s (1971) method.

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Published Online: 2013-07-02
Published in Print: 2013-01-01

©2013 by Walter de Gruyter Berlin Boston

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https://doi.org/10.1515/bejm-2013-0064
Keywords for this article
business cycle; information and communication technologies; investment-specific technical change; neutral progress; JEL classification: E32; O47

Articles in the same Issue

  1. Masthead
  2. Masthead
  3. Advances
  4. How have global shocks impacted the real effective exchange rates of individual euro area countries since the euro’s creation?
  5. Employment by age, education, and economic growth: effects of fiscal policy composition in general equilibrium
  6. Overeducation and skill-biased technical change
  7. Strategic wage bargaining, labor market volatility, and persistence
  8. Households’ uncertainty about Medicare policy
  9. Contributions
  10. Deconstructing shocks and persistence in OECD real exchange rates1)
  11. A contribution to the empirics of welfare growth
  12. Development accounting with wedges: the experience of six European countries
  13. Implementation cycles, growth and the labor market
  14. International technology adoption, R&D, and productivity growth
  15. Bequest taxes, donations, and house prices
  16. Business cycle accounting of the BRIC economies
  17. Privately optimal severance pay
  18. Small business loan guarantees as insurance against aggregate risks
  19. Output growth and unexpected government expenditures
  20. International business cycles and remittance flows
  21. Effects of productivity shocks on hours worked: UK evidence
  22. A prior predictive analysis of the effects of Loss Aversion/Narrow Framing in a macroeconomic model for asset pricing
  23. Exchange rate pass-through and fiscal multipliers
  24. Credit demand, credit supply, and economic activity
  25. Distortions, structural transformation and the Europe-US income gap
  26. Monetary policy shocks and real commodity prices
  27. Topics
  28. News-driven international business cycles
  29. Business cycle dynamics across the US states
  30. Required reserves as a credit policy tool
  31. The macroeconomic effects of the 35-h workweek regulation in France
  32. Productivity and resource misallocation in Latin America1)
  33. Information and communication technologies over the business cycle
  34. In search of lost time: the neoclassical synthesis
  35. Divorce laws and divorce rate in the US
  36. Is the “Great Recession” really so different from the past?
  37. Monetary business cycle accounting for Sweden
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