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Mismeasuring long-run growth: the bias from splicing national accounts—the case of Spain

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Abstract

Comparisons of economic performance over space and time largely depend on how statistical evidence from national accounts and historical estimates are spliced. To allow for changes in relative prices, GDP benchmark years in national accounts are periodically replaced with new and more recent ones. Thus, a homogeneous long-run GDP series requires linking different temporal segments of national accounts. The choice of the splicing procedure may result in substantial differences in GDP levels and growth, particularly as an economy undergoes deep structural transformation. An inadequate splicing may seriously bias the measurement of GDP levels and growth rates. Alternative splicing solutions are discussed in this paper for the particular case of Spain, a fast-growing country in the second half of the twentieth century. It is concluded that the usual linking procedure, retropolation, is seriously flawed as it tends to bias GDP levels upwards and, consequently, to underestimate growth rates, especially for developing countries experiencing structural change. An alternative interpolation procedure is proposed.

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Notes

  1. Actually, Maddison made earlier attempts to produce comparable estimates for real product per head expressed in US relative prices derived from Paasche PPPs (Maddison 1982, 1991a). Currently, the Maddison Project aims at carrying forward his project (see Bolt and van Zanden 2014).

  2. See, nonetheless, Jerven (2013) detailed discussion of the problems affecting developing countries’ national accounts.

  3. Harrison’s (1998) adjusted splicing of two overlapping GNP series for the Soviet Union provides another exception.

  4. Revisions of Italian historical national accounts have neglected the splicing problem. Nonetheless, a linear interpolation between benchmarks is carried out in Baffigi (2013: 178–179).

  5. Improving the comprehensiveness, reliability, and comparability of national accounts estimates through the use of new statistical sources, the inclusion of new concepts, and the adoption of new computation procedures, often due to the adoption of new or updated international standards, are the technical reasons provided by national statistical offices for their periodical revisions of national accounts’ benchmarks and the resulting breaks in GDP time series.

  6. Were this approach accepted in the case of Nigeria presented above, the historical series would be systematically re-scaled by 59.5 %, the ratio between the new (2010) and the old (1990) benchmark series at 2010 (the overlapping year) (Kale 2014).

  7. For the case of Spain, cf. Uriel (1986), Corrales and Taguas (1991), INE (1992), and Uriel et al. (2000). In the Netherlands, a pioneer country in national accounts, it was only after the 1993 SNA classification that the retropolation method was challenged (den Bakker and van Rooijen 1999).

  8. This linkage procedure helps to understand the one-sided upward revisions Boskin (2000) finds in US national accounts.

  9. A as far as I know, Maddison (1991b) presented the first methodological discussion along these lines and spliced GDP series through interpolation for the case of Italy.

  10. If, alternatively, the interpolation approach were accepted for the case of Nigeria, the historical series would keep the original level for 1990, while the 59.5 % gap at 2010 would be distributed over 1991–2009.

  11. Being aware of this problem, De la Fuente Moreno (2014: 116) suggests an alternative approach in which rather than ρ a parameter H (half-life) “that measures the persistence over time of the measurement error” at the linking point is employed. In my view, this solution is still discretional.

  12. Series constructed with different benchmarks’ prices and quantities are named after the year, e.g. CNE70, that is, Contabilidad Nacional de España (National Accounts of Spain) with 1970 as the base year.

  13. For all these benchmark years, input–output tables are available, except for 1964 and 1986, for which the closest ones are those for 1962 and 1966, and 1985, respectively.

  14. Such is the approach implicitly supported by Uriel (1986) and Uriel et al. (2000). This procedure has the advantage of being less time-consuming and not altering the yearly rates of variation resulting from the ‘old’ benchmark series.

  15. See INE (1992). The National Statistical Institute (INE) never produced a new spliced series of the latest base year CNE00 back to 1964, 1970, or 1980. The quarterly national accounts provided spliced series from 1980 onwards, but without a detailed explanation of the splicing procedure.

  16. No mention of any methodological adjustment was made in the splicing through interpolation of CNE80 and CNE86.

  17. It should be noted that since there were minor methodological and statistical changes between CNE00 and CNE08, the major revision embodied in CNE10 led to a new interpolation between CNE00-CNE08 and CNE10 that was extended over the years 1995–2009.

  18. The same procedure was applied to the gap between CNE00 and CNE95 in 2000, CNE08 and CNE00 in 2008, and CNE10 and CNE00-08 in 2010, with the statistical gap distributed over the intermediate years 1996–1999, 2001–2007, and 2001–2009, respectively. The Spanish Statistical Institute notes, “The [remaining] differences between both estimates [CNE00 and CNE95 in the year 2000] are due to the statistical changes, and given that information is not available regarding how and at what time they have been generated, it is assumed that this has occurred progressively over time, from the beginning of the previous base” (INE 2007: 5).

  19. This percentage increase for 1970 results from successively multiplying the ratios of adjacent benchmarks at overlapping years, that is, CNE10/CNE08 in 2010, CNE08/CNE00 in 2008, CNE00/CNE95 in 2000, CNE95/CNE86 in 1995, CNE85/CNE80 in 1985, CNE80/CNE70 in 1980, and CNE70/CNE64 in 1970, [1.0338 × 0.9997 × 1.0323 × 1.0439 × 1.0118 × 1.0016 × 1.1378 = 1.2841]. If alternatively, CNE10/CNE00 in 2010 is used, the results alters slightly [1.0254 × 1.0323 × 1.0439 × 1.0118 × 1.0016 × 1.1378 = 1.2741] (see Table 2).

  20. For the years 1980–1986, CNE86 provides spliced series derived from interpolating CNE86 and CNE80.

  21. Unfortunately, national accounts explanatory notes do not address this issue.

  22. The reason is that the methodological adjustment of the ‘old’ benchmark series to the ‘new’ benchmark implies that an “error” exists in the initial year of the old estimates.

  23. Cf. Cassing (1996) for a discussion of alternative deflation procedures. See, alternatively, David (1962) and Fenoaltea (1976) for a defence of single deflation as a way of avoiding negative values of real value added.

  24. In the dual approach to computing total factor productivity (TFP), over time changes in TFP are measured as the differential between the rate of variation of the output price and that of weighted input prices. In other words, a faster decline (less marked increase) of output prices than of inputs prices, due to input savings, reflects TFP growth.

  25. The 1950s, especially since 1953, were years of rapid growth and structural change in which double deflation would make a difference over single deflation. Unfortunately, lack of data prevents this option. Cf. Prados de la Escosura (2003).

  26. By double deflation it is meant that real gross value added is obtained as the difference between output at constant prices and intermediate consumption at constant prices, that is, each of them independently deflated with their own price indices. Cf. also Gandoy and Gómez Villegas (1988). Occasionally, when strong discrepancies between output and inputs prices were observed, and data availability allowed it, CNE70 used double deflation but, in any case, never over the years 1978–1981. In the case of agriculture, real value added was properly assessed in CNE70, as the purchases of industrial and service inputs represented a small share of final output. As for services, the difficulties to produce double-deflated value added series, comparable to those for agriculture and manufacturing, persisted over time.

  27. Cf. Krantz (1994).

  28. Although, fortunately, from 1980 onwards, CNE80 provided industrial value added computed through the standard double deflation procedure, double-deflated value added figures for construction and services were still problematic. Cf. INE (1986) for a discussion of CNE80.

  29. Also van Ark (1995) chose Gandoy (1988) series over the original national accounts. Among van Ark’s reasons are the downward bias in the growth rates of industrial production indices and its failure to adjust to the emergence of new products and quality changes.

  30. For the reasons to keeping original CNE70 gross value added for agriculture and services see footnote 27. For a discussion of the problems in measuring services’ gross value added through double deflation, see Mohr (1992).

  31. There is no discrepancy between CNE58 and CNE64 estimates at their overlapping year, 1964.

  32. Such a correction would not happen with the conventional retropolation approach.

  33. It is worth mentioning that the resulting discrepancies between obtaining GDP through aggregation of its spliced components and splicing GDP directly are negligible. Thus, additive congruence has not been imposed. By additive congruence it is meant that the addition of the different components of a given magnitude (output or expenditure) must be equal to its aggregate value (GDP). This is obtained by distributing, proportionally to their relative weight, the deviations of the addition of the linked components’ values from the aggregate magnitude (Cf. Corrales and Taguas 1991). This is implicitly done, however, for each of the sub-components of GDP components.

  34. In the case of ‘hybrid’ nonlinearly interpolated series, I have spliced CNE10 and CNE00* (that is, CNE00-08 adjusted for methodological differences with CNE10) over 2000–2010; and CNE00* and CNE95* (CNE95 adjusted for methodological discrepancies with CNE00*) over 1995–2000.

  35. Sündbarg (1908) estimates are reproduced in Maluquer de Motes (2006: 145). I have used the average birth and death rates in 1858–1860 for the years 1850–1857, except in the case of 1855–1856 for which the death rate (45 per 1000) estimated for 1855 as a consequence of cholera epidemics by Pérez Moreda (1980: 398) has been used. I have also used the average of birth and death rates in 1870 and 1878–1880 for the years 1871–1877 in which data on total births and deaths are missing.

  36. For 1850–1881, Figures of Spanish immigration in Argentina, Uruguay, Brazil, and the USA provided by the recipient countries’ official statistics were completed with emigration to Cuba in 1860–1861 from Anuario(s) Estadístico(s) that was assumed to remain constant over the period. Emigration to Algeria was derived from Spanish arrivals in Alger and Oran for the years 1872–1881, while the figures for 1850–1871 were estimated under the arbitrary assumption that the share of emigrants who remained in Algeria after one year of residence was similar to the one over the period 1872–1881 (25 %). Estimates for returned migration was computed by assuming that the average returns from America for 1869–73 were acceptable for 1850–1868, while 92 % of emigrants to Algeria returned home within the first year. A consistency check of the yearly migration data was performed using the migration balances from population censuses along the lines described in Sánchez-Alonso (1995). Data for returned migration from America, 1869–1881, were taken from (Yáñez Gallardo 1994, p. 120). Data on migration to Algeria over 1850–1881 comes from Vilar (1989).

  37. Ortega and Silvestre (2006) consider the 162,000 net migration figure during 1940–1944 grossly underestimated. Pérez Moreda (1988: 418) suggested a maximum permanent exile of no more than 190,000 people, a figure below the 200,000 provided by Tusell (1999) and much lower than a post-Civil War exile estimate (300,000) (Tamames 1973).

  38. GDP levels in 2011 have been converted into ‘international’ dollars using EKS purchasing power parity exchange rates taken from the latest ICP round (World Bank 2013) http://siteresources.worldbank.org/ICPEXT/Resources/ICP_2011.html and projected backwards with GDP volume series from the spliced national accounts back to 1958 and, then, from Prados de la Escosura (2003) back to 1850. I have divided the resulting GDP series by population to derive per capita GDP estimates at 2011 EKS dollars. The French series of real GDP per head come from the Maddison Project, http://www.ggdc.net/maddison/maddison-project/home.htm, completed with data from Conference Board http://www.conference-board.org/data/economydatabase/. The levels of GDP per head derive from ICP2011. A caveat is needed about this kind of exercise. Using per capita income levels obtained through backward projection of PPP-adjusted GDP levels (that is, at “international” prices) for a benchmark year (say, 2011 or 1990) with volume indices derived at national prices implies a huge index number problem that grows as the time span considered widens. The reason is that this procedure implicitly assumes that the basket of goods and services and the structure of relative prices for the benchmark year remain unaltered over time (something definitively wrong as long-run growth is about change in relative prices) (Prados de la Escosura 2000). However, the widespread acceptance of fixed PPP-adjusted benchmark estimates projected backwards with volume indices from historical accounts has led me to use this approach exceptionally to illustrate my point.

  39. Alternatively, I have carried out the exercise with the 1990 ICP benchmark estimate favoured by Maddison (Maddison Project) and the previous ICP round for 2005 used in the Penn Tables 8.0 (Feenstra et al. 2013) with similar results.

  40. In practical terms, the adjusted was carried out with the ratio between GDP at market prices and factor cost.

  41. Actually, CONSadd equals the differential between the revised GDP estimates (\({\text{GDP}}_{\text{mp}}^{r}\)) and CNE70 GDP (\({\text{GDP}}_{\text{mp}}^{{{\text{cen}}70}}\)) plus the estimated additional investment (GCFadd).

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Acknowledgments

Earlier versions of the paper were presented at the ‘Maddison Memorial Conference’, International Institute of Social History, Amsterdam, November 2010, and the International Economic History Association Congress, Helsinki, August 2006. I acknowledge participants and, especially, Bart van Ark and Albert Carreras for their remarks and suggestions. I have also benefited from exchanges with Ángel de la Fuente, Antonio Díaz Ballesteros, and David Taguas+.

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Correspondence to Leandro Prados de la Escosura.

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This paper has been written to honour the memory of Angus Maddison.

Appendices

Appendix 1: Revised CEN70 series (CEN70R) from the expenditure side

CEN70 GDP estimates on the expenditure side were also adjusted. While Gandoy (1988) provides alternative value added series at factor cost for industry (\({\text{VA}}_{fci}^{G}\)) and construction (\({\text{VA}}_{fcc}^{G}\)), Gómez Villegas (1988) presents new series for fixed domestic capital formation in industry (\({\text{GCF}}_{i}^{G}\)) and construction (\({\text{GCF}}_{c}^{G}\)). Thus, in order to adjust the aggregate figure for investment in CNE70 (GCFcen70), I firstly computed the share of value added at market prices (VAmp) allocated to investment in industry and construction, according to Gandoy (1988) and Gómez Villegas (1988), (\({\text{GCF}}_{i}^{G} /{\text{VA}}_{{{\text{mp}}i}}^{G}\) and \({\text{GCF}}_{c}^{G} /{\text{VA}}_{{{\text{mp}}c}}^{G}\)), which implied adjusting value added to include taxes on production and imports net of subsidies.Footnote 40 Then, I applied this share to the difference between the value added estimates at factor cost in Gandoy’s (\({\text{VA}}_{{{\text{fc}}i}}^{G}\) and \({\text{VA}}_{{{\text{fc}}c}}^{G}\)) and in CEN70 (\({\text{VA}}_{{{\text{fc}}i}}^{{{\text{cen}}70}}\) and \({\text{VA}}_{{{\text{fc}}c}}^{{{\text{cen}}70}}\)).

$${\text{GCF}}_{i}^{\text{add}} = \left( {{\text{GCF}}_{i}^{G} /{\text{VA}}_{{{\text{mp}}i}}^{G} } \right) \times \left( {{\text{VA}}_{{{\text{fc}}i}}^{G} {-}{\text{VA}}_{{{\text{fc}}i}}^{{{\text{cen}}70}} } \right)$$
(5)
$${\text{GCF}}_{c}^{\text{add}} = \left( {{\text{GCF}}_{c}^{{^{G} }} {\text{VA}}_{{{\text{mp}}c}}^{{^{G} }} } \right) \times \left( {{\text{VA}}_{{{\text{fc}}c}}^{G} {-}{\text{VA}}_{{{\text{fc}}c}}^{{{\text{cen}}70}} } \right)$$
(6)

So the additional investment—that is, the portion of gross capital formation not included in CNE70—was obtained. Thus,

$${\text{GCF}}^{\text{add}} = {\text{GCF}}_{i}^{\text{add}} + {\text{GCF}}_{c}^{\text{add}}$$
(7)

And the revised figure for gross capital formation was derived as,

$${\text{GCF}}^{1970R} = {\text{GCF}}^{{{\text{cen}}70}} + {\text{GCF}}^{\text{add}}$$
(8)

Then, I adjusted private consumption figures in CEN70 for the changes introduced in gross capital formation. That is, I assumed that the additional value added in industry and construction (derived by deducting CNE70 value added from Gandoy’s estimates) lessens the additional investment (GCFadd) accrued to private consumption, since the values for net exports of goods and services (NXcen70) and public consumption (GOVTcen70) provided by CEN70 were obtained from a sound statistical basis.Footnote 41 That is,

$${\text{CONS}}^{\text{add}} = \, (({\text{VA}}_{{{\text{fc}}i}}^{G} + {\text{VA}}_{{{\text{fc}}c}}^{G} ) - ({\text{VA}}_{{{\text{fc}}i}}^{{{\text{cen}}70}} + {\text{VA}}_{{{\text{fc}}c}}^{{{\text{cen}}70}} )) - {\text{GCF}}^{\text{add}}$$
(9)

And the revised figure for total private consumption was reached as,

$${\text{CONS}}^{1970R} = {\text{CONS}}^{{{\text{cen}}70}} + {\text{CONS}}^{\text{add}}$$
(10)

Lastly, the new estimates of GDP at market prices were obtained as,

$${\text{GDP}}_{mp}^{1970R} = {\text{CONS}}^{1970R} + {\text{GCF}}^{1970R} + {\text{GOVT}}^{{{\text{cen}}70}} + {\text{NX}}^{{{\text{cen}}70}}$$
(11)

Appendix 2: GDP at current and constant prices, 1954–2013: alternative splicing series

See Tables 4 and 5.

Table 4 GDP at current prices, 1954–2013: alternative splicing series (million Euro)
Table 5 GDP at 2010 prices, 1954–2013: alternative splicing series (million Euro)

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Prados de la Escosura, L. Mismeasuring long-run growth: the bias from splicing national accounts—the case of Spain. Cliometrica 10, 251–275 (2016). https://doi.org/10.1007/s11698-015-0131-4

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