PROVINCIAL PRODUCTIVITY IN CHINA TABLE ESTIMATES OF TFP AND TFP GROWTH Dependent variable log difference real GDP per capita(1978-1993) Regression coefficients Independent variable (absolute /-statisticsp In(n) 0.152(9.62) Employment growth rate 0.37(2.70 0.74(154) Jilin, Guangxi, Hainan, Qinghai, and Tibet are excluded because of insufficient data. The TFP and linear trend coefficients(not shown here, but illustrated in Fig. I)are all highly significant, as are most of the quadratic trend coefficients investment are very difficult to construct. Doing so for China would be an extremely time- and resource-expensive project(see Jorgenson and Fraumeni, 1992). We therefore have elected to estimate the impact of human capital by the flow of graduates in the second stage of our re search. We measure infrastructure by the aggregate length of waterways paved highway, and trunk railway per square kilometer of area. Foreign agement technology. 4 ably embodies the latest in production and man- 3. ECONOMETRIC RESULTS The estimates of the coefficients of investment share, employment growth and lagged percapita GDP for Eq. (3)are shown in Table 1, and the estimates Despite lack of data on human-capital investment as such, Mankiw et al. (1992)do include a proxy in their well-known study io A referee and others who have commented on earlier drafts of this paper correctly point out that the flow of graduates from universities in a province is only a proxy for the change in the province s population or labor force with university degrees, as there is a significant migration of university graduates toward the"bright lights" in coastal provinces, especially the major cities. We would, of course, have used information on the population of educated workers had annual data been available. Commentators have also noted that any correlation between university education and TFP may reflect the impact of lower levels of educational attainment or even the attainment of literacy. Our attempts to deal with these comments are indicated below I It has also been pointed out to us that our measure of transportation infrastructure is onl a crude approximation and may well be poorly correlated with an interior province's access to the coast, which is critical for export-oriented industries 12See Shang-Jin Wei(1993)for a similar view
PROVINCIAL PRODUCTIVITY IN CHINA 225 TABLE 1 ESTIMATES OF TFP AND TFP GROWTHa Dependent variable log difference real GDP per capita (1978–1993) Regression coefficients Independent variable (absolute t-statistics)b ln(I/Y) 0.152 (9.62) Employment growth rate 00.37 (2.70) ln yt01 00.74 (15.4) a Jilin, Guangxi, Hainan, Qinghai, and Tibet are excluded because of insufficient data. b The TFP and linear trend coefficients (not shown here, but illustrated in Fig. 1) are all highly significant, as are most of the quadratic trend coefficients. investment are very difficult to construct. Doing so for China would be an extremely time- and resource-expensive project (see Jorgenson and Fraumeni, 1992).9 We therefore have elected to estimate the impact of human capital by the flow of graduates in the second stage of our research.10 We measure infrastructure by the aggregate length of waterways, paved highway, and trunk railway per square kilometer of area.11 Foreign direct investment presumably embodies the latest in production and management technology.12 3. ECONOMETRIC RESULTS The estimates of the coefficients of investment share, employment growth, and lagged percapita GDP for Eq. (3) are shown in Table 1, and the estimates 9 Despite lack of data on human-capital investment as such, Mankiw et al. (1992) do include a proxy in their well-known study. 10 A referee and others who have commented on earlier drafts of this paper correctly point out that the flow of graduates from universities in a province is only a proxy for the change in the province’s population or labor force with university degrees, as there is a significant migration of university graduates toward the ‘‘bright lights’’ in coastal provinces, especially the major cities. We would, of course, have used information on the population of educated workers had annual data been available. Commentators have also noted that any correlation between university education and TFP may reflect the impact of lower levels of educational attainment or even the attainment of literacy. Our attempts to deal with these comments are indicated below. 11 It has also been pointed out to us that our measure of transportation infrastructure is only a crude approximation and may well be poorly correlated with an interior province’s access to the coast, which is critical for export-oriented industries. 12 See Shang-Jin Wei (1993) for a similar view. AID JCE 1462 / 6w10$$$123 09-30-97 14:16:24 cea
FLEISHER AND CHEN YN◆HEc)◆Js[C) -P 1978 Yuan FIG. 1. Provincial TFP and TFP growth, 1988 of TFP and TFP growth for the year 1988 are depicted in Fig. l, with coastal provinces indicated by the( C)notation. Our estimates of the determinants of TFP and TFP growth are containe in Table 2. As can be seen by comparing the second and fourth columns with the first and third columns, respectively, the variables other than trend and the coast-noncoast dummy can account for virtually all of the coast-noncoast productivity gap. The coefficient of capital vintage, while of the hypothesized The empirical formulation of Eq. (3)uses the arithmetic form, rather than the log form, of the employment-change variable, n, because annual employment growth in some provinces is occasionally negative. This specification is approximately equivalent to using the log of n+ I Thus, it is impossible to impose the constraint on the estimated factor elasticities implied by the constant-returns-to-scale assumption implicit in Eqs. (1)-(3). It is apparent I that the three city provinces, Beijing, Tianjin, and Shanghai, appear as"in the sense that they exhibit much higher than average TFP, One of our referees suggested that inclusion of these urban outliers" may have had a substantial effect on our econometric results. However, when Eqs. 3)and (4)are estimated without Beijing, Tianjin, and Shanghai, the estimated coefficients and their significance are changed very little I4 Based on the estimated coefficient of In(/n), the elasticity of capital is approximately 0. 2, implying a labor elasticity of approximately 0.8. This is at the low end of estimates of the Chen and Fleisher(1996), Chow (1994), and Chen et al. (1988). We suspect that one reason for this relatively low estimate is omission of a human-capital variable from Eq ( 3)
226 FLEISHER AND CHEN FIG. 1. Provincial TFP and TFP growth, 1988. of TFP and TFP growth for the year 1988 are depicted in Fig. 1, with coastal provinces indicated by the (C) notation.13,14 Our estimates of the determinants of TFP and TFP growth are contained in Table 2. As can be seen by comparing the second and fourth columns with the first and third columns, respectively, the variables other than trend and the coast–noncoast dummy can account for virtually all of the coast–noncoast productivity gap. The coefficient of capital vintage, while of the hypothesized 13 The empirical formulation of Eq. (3) uses the arithmetic form, rather than the log form, of the employment-change variable, n, because annual employment growth in some provinces is occasionally negative. This specification is approximately equivalent to using the log of n / 1. Thus, it is impossible to impose the constraint on the estimated factor elasticities implied by the constant-returns-to-scale assumption implicit in Eqs. (1)–(3). It is apparent in Fig. 1 that the three city provinces, Beijing, Tianjin, and Shanghai, appear as ‘‘outliers’’ in the sense that they exhibit much higher than average TFP. One of our referees suggested that inclusion of these ‘‘urban outliers’’ may have had a substantial effect on our econometric results. However, when Eqs. (3) and (4) are estimated without Beijing, Tianjin, and Shanghai, the estimated coefficients and their significance are changed very little. 14 Based on the estimated coefficient of ln(I/Y), the elasticity of capital is approximately 0.2, implying a labor elasticity of approximately 0.8. This is at the low end of estimates of the elasticity of production with respect to physical capital reported in the literature. See, for example, Chen and Fleisher (1996), Chow (1994), and Chen et al. (1988). We suspect that one reason for this relatively low estimate is omission of a human-capital variable from Eq. (3). AID JCE 1462 / 6w10$$$123 09-30-97 14:16:24 cea