Infrastructure Productivity with a Long Persistent Effect

  • Tsukai Makoto
  • Kobayashi KiyoshiEmail author
Part of the Advances in Spatial Science book series (ADVSPATIAL)


Appropriate investment and management of infrastructure is an important issue in national or regional planning. Aschauer (1989) reported two important findings: that infrastructure productivity was significantly higher than expected, and that fewer investments in the USA after 1970 would result in less growth of national productivity. Since then, measurement of infrastructure productivity has become a focal issue in national or regional management policy, and a large number of empirical studies have accumulated; see Sturm (1998). An aggregate production function approach for time-series applied in Aschauer’s study, however, was criticized by economists, or econometricians. Among the assumptions inherent in the production function approach, “constant return to scale” and “competitive input factor market” are often viewed with suspicion from the viewpoint of the endogenous growth theory by Romer (1986) and Lucus (1988) or from an uncompetitive structure of infrastructure market, respectively. Basu and Fernald (1997) empirically tested these assumptions with the production function approach, based on data from 34 sectors of US industries. They concluded that in some segmented sectors of industry, the constant return to scale assumption was violated but that it held true in most sectors. Holtz-Eakin and Lovely (1996) analyzed the effect of infrastructure on economic activities by using a general equilibrium system explicitly considering scale economies. They showed that infrastructure decreases the input factor cost, increases the variety of industries, and increases the number of newly founded companies. Haughwout (2002) applied the spatial equilibrium theory considering regional monopolistic power over input factor market, and measured infrastructure productivity. This study showed that infrastructure provided significant marginal benefit on regional economic activities. Most studies have reported that there is a positive production/cost reduction effect caused by infrastructure, but unfortunately, some studies have paid little attention to the data generation process that significantly influences findings through model specifications. Recent American, European, and Japanese studies about the production function approach have been widely reviewed by Ejiri et al. (2001).


Private Capital Persistent Effect Residual Series Gross Regional Product Production Function Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Agiakloglou C, Newbold P (1994) Lagrange multiplier tests for fractional difference. J Time Series Anal 15:253–262CrossRefGoogle Scholar
  2. Almon S (1965) The distributed lag between capital appropriation and expenditures. Econometrica 33:178–196CrossRefGoogle Scholar
  3. Aschauer D (1989) Is public expenditure productive? J Monet Econ 23:177–200CrossRefGoogle Scholar
  4. Baltagi B, Song S, Jung B (2001) The unbalanced nested error component regression model. J Econom 101:357–381CrossRefGoogle Scholar
  5. Barkoulas T, Baum F, Chakraborty A (2001) Waves and persistence in merger and acquisition activity. Econom Lett 70:237–243CrossRefGoogle Scholar
  6. Basu S, Fernald J (1997) Returns to scale in U.S. production: estimates and implications. J Polit Econ 105:249–283CrossRefGoogle Scholar
  7. Beran J (1992) Statistical methods for data with long-range dependence. Stat Sci 7:404–427CrossRefGoogle Scholar
  8. Bhardwai G, Swanson N (2006) An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series. J Econom 121:539–578CrossRefGoogle Scholar
  9. Box P, Jenkins M, Reinsel C (1994) Time series analysis: forecasting and control, 3rd edn. Prentice hall, New YorkGoogle Scholar
  10. Box-Steffensmeier M, Tomlinson R (2000) Fractional integration methods in political science. Elect Stud 19:63–76CrossRefGoogle Scholar
  11. Cheung Y, Lai K (2001) Long memory and nonlinear mean reversion in Japanese yen-based real exchange rate. J Int Money Finance 20:115–132CrossRefGoogle Scholar
  12. Chung F, Baillie R (1993) Small sample biases in conditional sum-of-squares estimators of fractionally integrated ARMA process. Empir Econ 18:791–806CrossRefGoogle Scholar
  13. Davidson J, Sibbertsen P (2005) Generating schemes for long memory processes: regimes, aggregation and nonlinearlity. J Econom 128:253–282CrossRefGoogle Scholar
  14. Dfrenot G, Guegan D, Peguin-Feissolle A (2005) Long-memory dynamics in a SETAR mode – applications to stock markets. Journal of Financ Mark Inst Money 15:391–406CrossRefGoogle Scholar
  15. Duggal V, Saltzman C, Klein L (1999) Infrastructure and productivity: a nonlinear approach. J Econom 92:47–74CrossRefGoogle Scholar
  16. Ejiri R, Okumura M, Kobayashi K (2001), Productivity of social capital and economic growth: state of the art. J Infrastruct Plann Manage 688/IV-53:75–87 (in Japanese)Google Scholar
  17. Everaert G, Heylen F (2001) Public capital and productivity growth: evidence for Belgium, 1953–1996. Econ Model 18:97–116CrossRefGoogle Scholar
  18. Godfrey L (1979) Testing the adequacy of a time series model. Biometrica 66:62–72CrossRefGoogle Scholar
  19. Gonzalo J, Lee T (1998) Pitfalls in testing for long run relationships. J Econom 86:129–154CrossRefGoogle Scholar
  20. Gourierouex C, Monfort A (1997) Time series and dynamic models. Cambridge University Press, CambridgeGoogle Scholar
  21. Granger CWJ, Joyeux R (1980) An introduction to long-memory time series models and fractional differencing. J Time Series Anal 1:15–29CrossRefGoogle Scholar
  22. Haughwout A (2002) Public infrastructure investments, productivity and welfare in fixed geographic areas. J Public Econ 83:405–428CrossRefGoogle Scholar
  23. Henry T, Olekalns N (2002) Does the Australian dollar real exchange rate display mean reversion? J Int Money Finance 21:651–666CrossRefGoogle Scholar
  24. Holtz-Eakin D, Lovely M (1996) Scale economies, returns to variety, and productivity of public infrastructure. Reg Sci Urban Econ 26:105–123CrossRefGoogle Scholar
  25. Hosking J (1981) Fractional differencing. Biometrika 68:165–176CrossRefGoogle Scholar
  26. Hosking J (1996) Asymptotic distributions of the sample mean, auto-covariances, and autocorrelations of long-memory time series. J Econom 73:261–284CrossRefGoogle Scholar
  27. Igresias P, Jorquera H, Palma W (2006) Data analysis using regression models with missing observations and long-memory: an application study. Comput Stat Data Anal 50:2028–2043Google Scholar
  28. Jhun M, Song S, Jung B (2003) BLUP in the nested regression model with serially correlated errors. Comput Stat Data Anal 44:77–88CrossRefGoogle Scholar
  29. Lucus R (1988) On the mechanics of economic development. J Monet Econ 22:3–42CrossRefGoogle Scholar
  30. Michelacci C (2004) Cross-sectional heterogeneity and persistence of aggregate fluctuations. J Monet Econ 51:3121–1352CrossRefGoogle Scholar
  31. Minotani C (1995) Differencing and Integral for economic analysis. Taga-Shuppan, TokyoGoogle Scholar
  32. Moran P (1948) The interpretation of statistical maps. J R Stat Soc B 10:243–251Google Scholar
  33. Munnel H (1992) Policy watch: infrastructure investment and economic growth. J Econ Perspect 6:189–198CrossRefGoogle Scholar
  34. Patel A, Shoesmith G (2004) Term structure linkages surrounding the Plaza and Louvre accords:evidence from Euro-rates and long-memory components. J Bank Finance 28:2051–2075CrossRefGoogle Scholar
  35. Robinson P, Hidalgo F (2003) Time-series regression with long-range dependence. In: Robinson PM (ed) Time series with long memory. Oxford University Press, Oxford, pp 305–333Google Scholar
  36. Romer M (1986) Increasing returns and long-run growth. J Polit Econ 94:1002–1037CrossRefGoogle Scholar
  37. Shiller J (1973) A distributed lag estimator derived from smoothness priors. Econometrica 41:775–778CrossRefGoogle Scholar
  38. Smith J, Taylor N, Yadav S (1997) Comparing the bias and misspecification in ARFIMA models. J Time Series Anal 18:507–527CrossRefGoogle Scholar
  39. Strum E, de Haan J (1995) Technological change and aggregate production function. Rev Econ Stat 39:312–320Google Scholar
  40. Sturm E (1998) Public capital expenditure in OECD countries. The causes and impact of the decline in public capital spending. Edward Elger, CheltenhamGoogle Scholar
  41. Tanaka K (1999) The non-stationary fractional unit root. Econom Theory 15:549–582CrossRefGoogle Scholar
  42. Tsukai M, Ejiri R, Okumur M, Kobayashi K (2002) Productivity of infrastructure and spillover effects. J Infrastruct Plann Manage 716/IV-57:53–67 (in Japanese)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Graduate School of EngineeringHiroshima UniversityKusatsuJapan
  2. 2.Graduate School of ManagementKyoto UniversityKyotoJapan

Personalised recommendations