THE IMPORTANCE of trade and capital as determinants of trend or potential economic growth is widely acknowledged. The collapse of world trade following the passage of the Smoot-Hawley tariffs in the United States in 1930 helped to trigger the great world depression of the 1930s. By contrast, the brisk expansion of world trade in the 1950s and 1960s contributed to unusually rapid growth in the industrial countries. More recently, the fast-growing economies of Southeast and East Asia have built their success on an outward orientation and rapid increases in intraregional trade. High rates of capital accumulation also spurred growth in the industrial countries in the postwar period and in the dynamic economies of Asia more recently. The slowdown in growth after the mid-1970s in France and other industrial countries coincided with a moderation in the growth of world trade and, in some countries, with a reduced pace of capital formation, particularly by the business sector.1
Although, empirical studies based on aggregate production functions have always emphasized the importance of capital accumulation by the business sector, the importance of other types of capital accumulation has received considerably less emphasis; the role of trade has generally been emphasized only in empirical studies based on computable general equilibrium models. This paper presents new estimates of an aggregate production function for France, focusing on the role of trade and the importance of capital accumulation by government, households, and businesses, including their expenditures on research and development (R&D).
The production function is estimated with recent reintegrating techniques suggested by Johansen (1988). This methodology emphasizes the identification of long-run relationships, and hence is particularly appropriate for studying the determinants of potential output. The empirical results suggest that increased trade within the European Community has raised efficiency and productivity in France. The empirical results also indicate that in addition to the stock of business sector capital—which is the only measure of capital included in most empirical studies—the stock of government infrastructure capital, the stock of residential capital, and the stock of R&D capital have also contributed to the growth of output in France,
The estimated production function is used to calculate potential output. These calculations indicate that trade and capital—broadly defined—account for all of the growth in the French economy in the two decades 1971-91. Although labor input is also an important determinant of output, its contribution to growth has been nil over the 1971-91 period. Thus, during the past two decades, trade and capital have been the engines of growth in France. The growth of-potential output is estimated to have averaged about 2½ percent a year in 1984-91 and to continue at about 2 ½ percent a year in 1992-97. To foster more robust growth, France must encourage capital accumulation, implement labor market policies to reduce unemployment, and take steps to revitalize the trade liberalization process.
Data Sources and Definitions
Real value added, hours worked, and employment in the nonfarm business sector are from the INSEE data tape, in each case subtracting the farm sector (secteur agriculture, sylviculture, peche) from the total for the business sector (secteurs marchands). The relevant INSEE codes for real value added are PN1_V008 and PN1_U018; for hours worked, ACM_V001 and ACM_U011; and for employment, EFM_V001 and EFM_U011.
The stock of business sector capital, the stock of residential capital, labor force, and population are from the OECD Analytical Data Bank. The stock of infrastructure capital is taken from the annual estimates of Ford and Poret (1991), reported by them as INF.N (p. 80), interpolated to a quarterly frequency. The share of the labor force aged 15-24 is calculated from the OECD’s Labour Force Statistics.
The stock of R&D capital is calculated analogously to the stock of physical capital with an assumed obsolescence rate of 5 percent: kr&d =kr&d,(1 - 0.05) + (real R&D expenditures). A benchmark for kr&d was calculated using the procedure suggested by Griliches (1980). Real R&D expenditures are gross domestic expenditures on R&D deflated by an average of the GDP deflator and an index of business sector wages. R&D expenditures are from the OECD’s Main Science and Technology Indicators (1991:1, p. 16). R&D expenditures for 1991 were estimated using the same procedure reported in the text to project R&D expenditures for 1992-97. Annual data for the stock of R&D capital were interpolated to a quarterly frequency.
Data to construct the EC variable are from the IMF’s Direction of Trade Statistics. The EC variable is constructed with data for all 12 current members of the EC, even though not all 12 countries were members during the full 1971-91 period. World trade as a percent of world output was constructed from the IMF World Economic Outlook data base. Annual data were interpolated to a quarterly frequency.
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David T. Coe is in the Research Department and holds a doctorate from the University of Michigan. Reza Moghadam is in the European I Department and holds a doctorate from the University of Warwick. The authors thank Julia Darby, Robert Ford, John Ireland, Flemming Larsen, Paul Masson, Mark Taylor, Hari Vittas, and Simon Wren-Lewis as well as analysts from Directions de la Prevision and INSEE and two anonymous referees for helpful comments and suggestions, and Toh Kuan for research assistance.
This slowdown has rekindled interest in the determinants of growth and in growth theory, as evidenced by the recent emergence of a large and expanding literature on endogenous growth. See Lucas (1988), Sala-i-Martin (1990), and Helpman (1992).
The intuition behind the super-consistency result is that, for values of the parameters that do not cointegrate, the residual series will itself be nonstationary and therefore have a very large estimated variance. When the estimated parameters are close to the true cointegrating parameters, the residual becomes stationary and its variance shrinks. Since least squares and maximum likelihood methods essentially minimize the residual variance, they will be extremely good at picking out the cointegrating parameters if they exist. The super-consistency result does not hold if there are multiple cointegrating vectors.
An increase in the stock of housing, for example, may increase labor mobility.
See, for example, Romer (1987) and Sala-i-Martin (1990). There are a number of practical problems with imposing factor shares, one of which is that they are not constant over the sample period. In addition, there are a variety of ways to calculate factor shares, depending, for example, on the way that self-employment income is allocated to capital or labor.
The DF and ADF tests give the same result.
The Johansen procedure involves the simultaneous estimation of dynamic vector auto regressive (VAR) equations, for which fourth-order lags were included. It is assumed that the variables have linear deterministic trends. Estimation has been done on Microfit 3.0, see Pesaran and Pesaran (1991).
Recent studies at the aggregate level for the United States, Japan, and Germany find an elasticity of about 0.13; see Adams and Coe (1990), Citrin (forthcoming), and Coe and Krueger (1990), respectively. Griliches (1988) reports that estimated elasticities from firm- and industry-level data tend to lie between 0.06 and 0.1.
A number of other tests were performed to check the robustness of this result: (i) kr&d and EC were excluded and a trend was added; (ii) EC was dropped and a trend was included; and (iii) a trend was included along with the other variables. None of these specifications resulted in a cointegrating vector with sensible coefficients.
In theory it is possible to enter the components of the capital stock separately and test the summing restriction—for example, α ln(X + Y) [α
De Long and Summers (1991), based on cross-country data for industrial and developing countries, find equipment investment has a stronger association with growth than other components of investment. Estimates using only the stock of business equipment capital were not possible since this variable is not readily available for France
Empirical evidence of the importance of international R&D spillovers is presented in Coe and Helpman (1993).
According to one of the tests, the null hypothesis that there are no cointegrating vectors cannot be rejected.
See the references cited in the box on potential output in IMF (1991, p. 43); and Torres and Martin (1990). The constant in equation (4) has been calculated so that the average error over the sample period is zero.
Although hours and employment refer to the nonfarm business sector, labor force and population refer to the full economy.
Using an asterisk to indicate that a variable has been cyclically adjusted or that the unemployment rate (U) is at its “natural” level, the adjustment for hours worked is h—h* ≃ (U*—U)/100, which is obtained by substituting e/lf = log(l - U/100) ≃—1//100 into the equation used to decompose h and making a similar substitution for (e/lf)* into an equation for h*.
Although the prime-age male unemployment rate is often used in estimated wage equations instead of the aggregate unemployment rate (see Cotis and Loufir (1990)), we simply use this variable to capture the trend increase in the natural rate since the early 1970s. The natural rate of unemployment is estimated as a quadratic trend on the male unemployment rate for 24-50 year olds plus the differential between the aggregate and the prime-age male unemployment rates in the early 1970s. The estimate of the natural rate at the end of the sample is in line with estimates typically found in models of the French economy, which are on the order of 7-8 percent.
This may be a slight overestimate as the GDP figures for 1991 have recently been revised downward.
The regression has been estimated using OLS, and r-statistics are in parentheses. Regressions with the output gap assuming actual hours worked gave similar results.
The share of potential output growth over the past two decades attributable to closer European integration is estimated to have been less in Germany than in France, reflecting the somewhat less open French economy at the start of the 1970s.
The estimated regression is log(R&D expenditures) = 1.1 log(output) + constant, R2 = 0.8, for annual data 1970-89. The stock of R&D capital was then calculated for 1992-97 by cumulating R&D expenditures with an assumec obsolescence rate of 5 percent.