Silviase/jgov_v1.4_full_ann huggingface dataset:
equation (2), which is not intuitively persuasive. However, it is not inconsistent with our view that the primary purpose of overseas innovative R&D is to exploit foreign advanced knowledge.

The third row shows that overseas adaptive R&D, $ R\&D^{sA}/Y^{s} $ , has a positive and significant impact. The effect is similar in size to the effect of home R&D on home TFP growth. Therefore, contrary to overseas innovative R&D, overseas adaptive R&D has impacts on the host country but not on home, confirming that its primary role is adaptation for the local market.

Next, we include the interaction terms between home and overseas R&D as regressors, reporting the results in columns (5)-(8) of Table 7. The results from the system GMM, which is again the most preferred, indicate that the interaction term between home and overseas innovative R&D has no significant effect. This confirms the absence of complementarity between home and overseas innovative R&D, which has been found in the previous estimation results. Thus, we conclude that parent firms in Japan and their overseas subsidiaries are performing innovative R&D independently, without close interaction with one another.

To the contrary, the interaction term between home R&D and overseas adaptive R&D is positive and significant. Thus, it is suggested that home R&D raises the marginal effect of overseas adaptive R&D. This is consistent with our previous finding from the determinant regression that knowledge of parent firms is the primary input to overseas adaptive R&D. $ ^{17} $

### 5.4 Comparison with Existing Studies

Our results are related to those in a number of existing studies. First, Iwasa and Odagiri (2004) find that the magnitude of innovation by each Japanese MNE in the United States is positively associated with local technological strength of the state in which the MNE is located, if the MNE performs overseas innovative R&D (type R in their notation). However, there is no such correlation if the MNE performs overseas adaptive R&D (type S). Thus, they conclude that knowledge sourcing is the primary purpose of overseas innovative R&D but not
of overseas adaptive R&D. $ ^{18} $ This conclusion is consistent with our findings.

Second, Fors (1997) finds no evidence of positive effects of overseas R&D on home productivity growth, using Swedish firm-level data. We suspect that the inconsistency between his result and ours comes from the fact that Fors does not distinguish between innovative and adaptive R&D nor does he correct for endogeneity of R&D variables, since we also find no significant impact unless we distinguish between the two and correct for endogeneity.

Third, Odagiri and Yasuda (1996), Belderbos (2001), and Kumar (2001) find a significant effect of the market size of the host country on the size of overseas R&D, as this paper does. They also find positive effects of variables related to knowledge, such as R&D expenditures in parent firms (Odagiri and Yasuda, 1996) and aggregate R&D expenditures in the host country (Kumar, 2001). However, since R&D expenditures represent flows of knowledge, TFP levels used in this paper may be a better proxy for levels of knowledge stocks. Another advantage of this paper is that we use the ratio of R&D expenditures to value added as the dependent variable in the determinant regression, while Odagiri and Yasuda (1996) and Belderbos (2001) use the number of overseas R&D units due to lack of data.

Finally, Branstetter (2000) finds that foreign direct investment leads to international knowledge flows from and to the host country, using patent citation data for Japanese MNEs in the United States. $ ^{19} $ The presence of knowledge diffusion from the host country to parent firms through overseas innovative R&D is also suggested by the positive effect of overseas innovative R&D on home TFP growth found in this paper. However, we should note that the relation between TFP growth and the size of R&D does not provide direct evidence on knowledge diffusion, although patent citations present clear evidence on where knowledge used in each patent was originated from, as Jaffe et al. (1993) argue. In addition, since we also find that overseas R&D does not raise the marginal effect of home R&D, the magnitude of the knowledge diffusion may not be large.
## 6 Conclusions

This paper investigates determinants of R&D activities in overseas subsidiaries and their impacts on productivity growth in parent firms and subsidiaries, using firm-level panel data for Japanese MNEs during the period 1996-2001. We distinguish between overseas innovative R&D (basic research, applied research, or development for the world market) and adaptive R&D (R&D for other purposes), finding substantial differences between the two types of R&D. Most importantly, the estimation results suggest that overseas innovative R&D targets the exploitation of foreign advanced knowledge, successfully takes it to parent firms, and raises home productivity. By contrast, the primary role of overseas adaptive R&D is to contribute to productivity in overseas subsidiaries by adapting existing technologies and products to local conditions of the host country with the use of knowledge of parent firms. In addition, we find no complementarity between home and overseas innovative R&D, i.e., no evidence that overseas innovative R&D raises the marginal effect of home R&D on home productivity.

The estimated impacts of overseas innovative R&D activities are not as large as we first expect. Its effect on productivity growth in parent firms is similar in size to the effect of R&D by parent firms, while having no significant impact on productivity growth in overseas subsidiaries. Therefore, the overall impact of overseas innovative R&D is smaller than that of home R&D. In addition, there is no complementarity between home and overseas innovative R&D. These results may explain why overseas innovative R&D by Japanese MNEs is substantially smaller in magnitude than that by US or European MNEs, although it has an increasing trend in recent years (Belderbos, 2001). Therefore, it is suggested that Japanese MNEs promote interaction between home and overseas R&D in order to maximize benefits from overseas R&D.
## Appendix: Data and Variables

Some parent firms have more than one subsidiaries in a host country. For example, an automobile firm may have a subsidiary for production of engines, one for assembly, one for sales, and one for R&D. We aggregate all subsidiaries of the same parent firm in the same country and redefine the aggregate subsidiaries as one subsidiary. We use the industry code of each parent firm to define the industry code for its overseas subsidiaries.

The real value of capital stocks for each firm in equation (13) is computed according to the perpetual inventory method. Since our original data set starts from 1995 and book values of fixed capital for subsidiaries are available only in 1995, 1998, and 2001, we use as the initial capital stock in 1995 the book value of the fixed capital in 1995 divided by the price level for investment goods in country j where the firm is located (Japan or a host country), $ P_{t}^{j} $ , which is taken from the PWT 6.1. Capital stocks in the subsequent years are computed from $ K_{it}^{k} = (1 - \tilde{\delta}) K_{i,t-1}^{k} + I_{it}^{k} / P_{t}^{j} $ for k = p, s, where the depreciation rate, $ \tilde{\delta} $ , is 0.0792 taken from Masuda (2000). $ I_{it}^{p} $ is the reported value of purchases of fixed capital of parent firm i in year t less the reported value of its sales. However, data for overseas subsidiaries do not include sales of fixed capital, we estimate it from fixed capital stocks and the average ratio of sales to the stock of fixed capital for Japanese firms.

The aggregate capital stock for each country is also computed according to the perpetual inventory method, following Hall and Jones (1999) and Caselli (2005). Initial capital stocks in 1970 are calculated according to $ K_{j,1970}^{host} = I_{j,1970}^{host} / (\delta^{host} + g_{j}) $ where $ \delta^{host} $ , the depreciation rate, is 0.06, $ I_{jt}^{host} $ is the value of nominal investment in country j in year t, and $ g_{j} $ is the average growth rate of investment between the first year with available data and 1970 for country j. Capital stocks in the subsequent years are computed from $ K_{jt}^{host} = (1 - \delta^{host}) K_{i,t-1}^{host} + I_{jt}^{host} / P_{jt} $ .

We eliminate from our sample firms with non-positive value added or capital stocks. Then, to alleviate biases due to outliners, we drop firms of which either the growth rate of TFP or the ratio of the estimated value of capital stock to its deflated book value is among the top 1 percent or bottom 99 percent. Additionally, we eliminate firms whose ratio of R&D expenditures to value added
is among the top 1 percent.

To create the sample for overseas subsidiaries, we integrate subsidiaries of the same parent firm in the same industry in the same country into one subsidiary. The sample for parent firms is created by aggregating data for overseas subsidiaries. The number of parent firms used in estimation is substantially small compared with the number of firms included in the original surveys, because we chose firms that reported R&D expenditures of their own and their subsidiaries in any year during the period 1995-2001.
## References

Acemoglu, Daron, Philippe Aghion, and Fabrizio Zilibotti, “Vertical Integration and Distance to Frontier,” NBER Working Paper, 2002, 9191.

Aghion, Philippe and Peter Howitt, “Growth with Quality-Improving Innovations: An Integrated Framework,” in Philippe Aghion and Steven N. Durlauf, eds., Handbook of Economic Growth, Amsterdam: North Holland, 2005. forthcoming.

Aiyar, Shekhar and Carl-Johan Dalgaard, “Total Factor Productivity Revisited: A Dual Approach to Development Accounting,” EPRU Working Paper Series, 2004, 04-07.

Amemiya, Takashi, “The Estimation of a Simultaneous Equation Tobit Model,” International Economic Review, 1974, 20 (1), 169–181.

Arellano, Manuel and Stephen Bond, “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,” Review of Economic Studies, 1991, 58 (2), 277–297.

Belderbos, Rene, “Overseas Innovations by Japanese firms: an Analysis of Patent and Subsidiary Data,” Research Policy, 2001, 30, 313–332.

Biesebroeck, Johannes Van, “Robustness of Productivity Estimates,” NBER Working Paper, 2004, 10303.

Blundell, Richard and Stephen Bond, “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models,” Journal of Econometrics, 1998, 87, 115–143.

Branstetter, Lee, “Is Foreign Direct Investment a Channel of Knowledge Spillovers? Evidence from Japan’s FDI in the United States,” NBER Working Paper, 2000, 8015.

Caselli, Francesco, “The Missing Input: Accounting for Cross-Country Income Differences,” in Peter Aghion and Steven N. Durlauf, eds., Handbook of Economic Growth, Amsterdam: North Holland, 2005. forthcoming.
Fors, Gunnar, “Utilization of R&D Results in the Home and Foreign Plants of Multinationals,” Journal of Industrial Economics, 1997, 45 (3), 341–358.

Goto, Akira and Kazuyuki Suzuki, "R&D Capital, Rate of Return on R&D Investment and Spillover of R&D in Japanese Manufacturing Industries," Review of Economics and Statistics, 1989, 71 (4), 555–564.

Granstrand, Ove, “Internationalization of Corporate R&D: a Study of Japanese and Swedish Corporations,” Research Policy, 1999, 28, 275–302.

Griliches, Zvi, “Returns to Research and Development Expenditures in the Private Sector,” in J. W. Kendrick and B. N. Vaccara, eds., New Developments in Productivity Measurement and Analysis, Chicago: University of Chicago Press, 1980.

– , R&D and Productivity, Chicago: University of Chicago Press, 1998.

Hall, Robert E. and Charles I. Jones, “Why Do Some Countries Produce So Much More Output per Worker than Others?,” Quarterly Journal of Economics, 1999, 114, 83–116.

Iwasa, Tomoko and Hiroyuki Odagiri, “Overseas R&D, Knowledge Sourcing, and Patenting: An Empirical Study of Japanese R&D Investment in the US,” Research Policy, 2004, 33, 807–828.

Jaffe, Adam B., Manuel Trajtenberg, and Rebecca Henderson, “Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations,” Quarterly Journal of Economics, 1993, 108(3), 577–598.

Kiba, Takao, “Nihon Kigyo no Kaigai ni okeru Kenkyu Kaihatsu no Pafomansu ni kansuru Chosa (in Japanese: Research on the Performance of the Overseas R&D Activities of Japanese Firms),” NISTEP Report, 1996, 43.

Kuemmerle, Walter, “Foreign Direct Investment in Industrial Research in The Pharmaceutical and Electronics-Results From a Survey of Multinational Firms,” Research Policy, 1999, 28, 179–193.

Kumar, Nagesh, “Determinants of Location of Overseas R&D Activity of Multinational Enterprise: The Case of US and Japanese Corporation,” Research Policy, 2001, 30, 159–174.
Le Bas, Christian and Christophe Sierra, “Location Versus Home Country Advantages” in R&D Activities: Some Further Results on Multinational’s Locational Strategy,” Research Policy, 2002, 31, 589–609.

Mairesse, Jacques and Mohamed Sassenou, “R&D Productivity: A Survey of Econometric Studies at the Firm Level,” Science-Technology-Industry Review, 1991, 8, 9–43.

Odagiri, Hiroyuki and Hideo Yasuda, “The Determinants of Overseas R&D by Japanese Firms: An Empirical Study at The Industry and Company Levels,” Research Policy, 1996, 25, 1059–1079.

and Hitoshi Iwata, “The Impact of R&D on Productivity Increase in Japanese Manufacturing Companies,” Research Policy, 1986, 15 (1), 13–19.

Olley, Steven G. and Ariel Pakes, “The Dynamics of Productivity in the Telecommunications Equipment Industry,” Econometrica, 1996, 64 (6), 1263–1297.

Patel, Pari and Modesto Vega, “Patterns of Internationalisation of Corporate Technology: Location vs. Home Country Advantages,” Research Policy, 1999, 28, 145–155.

Pearce, Robert D., “Decentralised R&D and Strategic Competitiveness: Globalised Approaches to Generation and Use of Technology in Multinationl Enterprises (MNEs),” Research Policy, 1999, 28, 157–178.

– and Marina Papanastassiou, “Overseas R&D and The Strategic Evolution of MNEs: Evidence From Laboratories in the UK,” Research Policy, 1999, 28, 23–41.

Romer, Paul M., “Endogenous Technological Change,” Journal of Political Economy, 1990, 98(5), 71–102.

Summers, Robert and Alan Heston, “The Penn World Table (Mark 5): An Expanded Set of International Comparisons: 1950-88,” Quarterly Journal of Economics, 1991, 106, 327–368.
Tanaka, Shigeru, Hirokazu Negishi, and Kiyonori Sakakibara, “Kokuritsu Shiken Kenkyu Kikan, Tokushu Hojin Kenkyu Kaihatsu Kikan oyobi Nihon Kigyo no Kenkyu Kaihatsu Kokusaika ni kansuru Chosa Kenkyu: Uchi heno Kenkyu Kaihatsu Kokusaika ni Mukete (in Japanese: On Internationalization of R&D in National Research Institutes and Private Companies in Japan),” Research Material, National Institute of Science and Technology Policy, 2000, 67.

Windmeijer, Frank, “A Finite Sample Correction for the Variance of Linear Two-Step GMM Estimators,” Institute for Fiscal Studies Working Paper, 2000, 00/19.
Table 1: Description of Variables and Summary Statistics: Data for Overseas Subsidiaries in the Determinants Regression

Variable	Description	Mean (Standard Deviation)
R & Ds	R&D expenditures of each overseas subsidiary (as a percentage of its value added)	1.163 (3.549)
R & DSl	Innovative R&D expenditures of each overseas subsidiary (as a percentage of its value added)	0.703 (2.787)
R & DSA	Adaptive R&D expenditures of each overseas subsidiary (as a percentage of its value added)	0.460 (2.273)
ln Ap	Log of the TFP level of each parent firm	2.430 (0.884)
ln As	Log of the TFP level of each subsidiary firm	2.420 (0.417)
ln Ahost	Log of the aggregate TFP level of the host country	6.162 (0.441)
ln Yhost	Log of aggregate GDP of the host country	13.725 (1.461)
Table 2: Description of Variables and Summary Statistics: Data for Parent Firms

Variable	Description	Mean (Standard Deviation)
$ \Delta \ln A^{p} $	Growth rate of TFP of each parent firm (%)	2.588 (56.891)
$ \frac{R \& D^{p}}{Y^{p}} $	R&D expenditures of each parent firm (as a percentage of its own value added)	10.724 (6.914)
$ \sum R \& D^{s} $	Total R&D expenditures in overseas subsidiaries (as a percentage of value added of each parent firm)	0.476 (1.441)
$ \frac{\sum R \& D^{sI}}{Y^{p}} $	Total innovative R&D expenditures in overseas subsidiaries (as a percentage of value added of each parent firm)	0.331 (1.279)
$ \frac{\sum R \& D^{sA}}{Y^{p}} $	Total adaptive R&D expenditures in overseas subsidiaries (as a percentage of value added of each parent firm)	0.144 (0.672)

Table 3: Means of Home and Overseas R&D Activities by Year

R & D^{p}	$ \sum R \& D^{s} $	$ \sum R \& D^{sl} $	$ \sum R \& D^{sA} $
Y^{p}	Y^{p}	Y^{p}	Y^{p}
1996	9.24	0.18	0.14	0.04
1997	9.88	0.35	0.21	0.13
1998	11.06	0.46	0.26	0.20
1999	10.41	0.39	0.34	0.05
2000	10.81	0.55	0.42	0.13
2001	11.49	0.63	0.43	0.20
Table 4: Description of Variables and Summary Statistics: Data for Overseas Subsidiaries in the Impacts Regression

Variable	Description	Mean (Standard Deviation)
$ \Delta \ln A^{s} $	Growth rate of TFP of each overseas subsidiary (%)	4.199 (50.178)
$ \frac{R \& D^{p}}{Y^{s}} $	R&D expenditures of each parent firm (as a percentage of value added of each subsidiary)	934.446 (2329.244)
$ \frac{R \& D^{s}}{Y^{s}} $	R&D expenditures of each overseas subsidiary (as a percentage of its own value added)	1.946 (4.476)
$ \frac{R \& D^{sl}}{Y^{s}} $	Innovative R&D expenditures of each overseas subsidiary (as a percentage of its own value added)	1.210 (3.635)
$ \frac{R \& D^{sA}}{Y^{s}} $	Adaptive R&D expenditures of each overseas subsidiary (as a percentage of its own value added)	0.736 (2.785)
Table 5: Determinants of Overseas R&D Activities

(1)	(2)	(3)	(4)	(5)	(6)
Dependent variable	R & DsI	R & DsI	R & DsA	R & DsA	R & Ds	R & Ds
Estimation method	Ys	Ys	Ys	Ys	Ys	Ys
Tobit	AGLS	Tobit	AGLS	Tobit	AGLS
ln Ap	-0.535	-0.201	3.534	4.619	1.629	2.431
(0.826)	(1.077)	(0.669)**	(1.334)**	(0.584)**	(1.027)*
ln As	-1.958	-2.483	-1.003	-1.063	-1.710	-1.949
(0.395)**	(0.561)**	(0.291)**	(0.635)	(0.269)**	(0.504)**
ln Ahost	4.276	4.541	1.262	1.437	3.056	3.220
(0.777)**	(0.836)**	(0.558)*	(0.942)	(0.514)**	(0.744)**
ln Yhost	1.111	1.124	0.692	0.676	1.038	1.030
(0.212)**	(0.220)**	(0.169)**	(0.265)*	(0.150)**	(0.206)**
Observations	2017	2017	2017	2017	2017	2017
Log likelihood	-1683.90	-1614.40	-2736.39

Note: Standard errors are in parentheses. $ * $ and $ \*\* $ denote statistical significance at the 5 and 1 percent levels, respectively. In all specifications, year dummies are included, but the results are not reported. AGLS denotes the Amemiya Generalized Least Squares estimation. See Table 1 for the description of the variables used in this table.
Table 6: Impacts of Home and Overseas R&D Activities on Home TFP Growth

(1)	(2)	(3)	(4)
OLS	Fixed-effects	Differenced GMM	System GMM
R & Dp	0.506	1.812	1.304	1.292
Yp	(0.137)**	(0.289)**	(0.718)	(0.315)**
∑R & Dsl	0.812	1.533	2.688	1.450
Yp	(0.825)	(1.436)	(1.390)	(0.627)*
∑R & DsA	1.073	2.515	1.264	-0.405
Yp	(1.814)	(2.281)	(1.309)	(1.461)
Observations	665	665	532	665
R²	0.05	0.10
Hansen J statistic	0.71	0.88
Difference between	0.88
Hansen statistics

(5)	(6)	(7)	(8)
OLS	Fixed-effects	Differenced GMM	System GMM
R & Dp	0.542	1.913	0.775	1.236
Yp	(0.142)**	(0.297)**	(0.750)	(0.342)**
∑R & Dsl	2.416	4.912	0.517	3.278
Yp	(2.614)	(4.067)	(5.059)	(2.173)
R & Dp × ∑R & Dsl	-0.107	-0.217	0.022	-0.136
Yp	(0.164)	(0.238)	(0.250)	(0.130)
Observations	665	665	532	665
R²	0.05	0.10
Hansen J statistic	0.74	0.78
Difference between	0.60
Hansen statistics

Note: The dependent variable is the growth rate of TFP in each parent firm. Standard errors are in parentheses. $ * $ and $ \*\* $ denote statistical significance at the 5 and 1 percent levels, respectively. In all specifications, year dummies are included, but the results are not reported. P values are reported for Hansen J statistics and the difference between the two Hansen statistics. See Table 2 for the description of the variables used in this table.
Table 7: Impacts of Home and Overseas R&D Activities on Overseas TFP Growth

(1)	(2)	(3)	(4)
OLS	Fixed-effects	Differenced GMM	System GMM
R&Dp	0.0022	0.0203	0.0182	0.0066
Ys	(0.0011)*	(0.0028)**	(0.0029)**	(0.0022)**
R&Dsl	0.0733	0.8602	-1.0223	0.5490
Ys	(0.6815)	(1.2364)	(0.9107)	(0.9609)
R&DsA	0.9242	2.3405	2.2092	1.6918
Ys	(0.8875)	(1.2442)	(1.6566)	(0.6959)*
Observations	410	410	328	410
R²	0.04	0.17
Hansen	0.85	0.67
Differenced	0.22
Hansen

(5)	(6)	(7)	(8)
OLS	Fixed-effects	Differenced GMM	System GMM
R&Dp	0.0020	0.0201	0.0182	0.0058
Ys	(0.0011)	(0.0028)**	(0.0025)**	(0.0017)**
R&Dsl	-0.5479	-2.3711	1.7149	-0.2186
Ys	(1.0044)	(2.2595)	(3.1646)	(2.5799)
R&DsA	-2.2370	-2.4728	-1.9395	-2.1994
Ys	(1.6238)	(2.3625)	(1.6613)	(1.7293)
R&Dp×R&Dsl	0.0009	0.0033	-0.0008	0.0008
Ys	(0.0013)	(0.0022)	(0.0025)	(0.0021)
R&Dp×R&DsA	0.0037	0.0044	0.0042	0.0039
Ys	(0.0016)*	(0.0019)*	(0.0012)**	(0.0012)**
Observations	410	410	328	410
R²	0.05	0.19
Hansen J statistic	0.89	0.70
Difference between	0.20
Hansen statistics

Note: The dependent variable is the growth rate of TFP in each overseas subsidiary. Standard errors are in parentheses. * and ** denote statistical significance at the 5 and 1 percent levels, respectively. In all specifications, year dummies are included, but the results are not reported. P values are reported for Hansen J statistics and the difference between the two Hansen statistics. See Table 4 for the description of the variables used in this table.
Appendix Table 1: Determinants of Overseas R&D Activities: All Manufacturing Industries

Dependent variable	(1)	(2)	(3)	(4)	(5)	(6)
R & Dsl	R & Dsl	R & DsA	R & DsA	R & Ds	R & Ds
Estimation method	Ys	Ys	Ys	Ys	Ys	Ys
Tobit	AGLS	Tobit	AGLS	Tobit	AGLS
ln Ap	0.780	1.388	2.352	2.699	1.588	2.082
(0.512)	(0.632)*	(0.447)**	(0.609)**	(0.372)**	(0.472)**
ln As	-1.646	-2.001	-0.644	-0.568	-1.335	-1.420
(0.281)**	(0.394)**	(0.219)**	(0.339)	(0.195)**	(0.279)**
ln Ahost	3.702	3.860	1.063	1.041	2.636	2.660
(0.546)**	(0.572)**	(0.415)*	(0.482)*	(0.369)**	(0.397)**
ln Yhost	1.129	1.140	0.641	0.634	1.005	1.007
(0.151)**	(0.153)**	(0.125)**	(0.138)**	(0.108)**	(0.111)**
Observations	3281	3281	3281	3281	3281	3281
Log likelihood	-2624.07	-2321.52	-4143.04

Note: Standard errors are in parentheses. * and ** denote statistical significance at the 5 and 1 percent levels, respectively. In all specifications, year dummies are included, but the results are not reported. AGLS denotes the Amemiya Generalized Least Squares estimation. See Table 1 for the description of the variables used in this table.
Appendix Table 2: Impacts of Home and Overseas R&D Activities on Home TFP Growth: All Manufacturing Industries

(1)	(2)	(3)	(4)
OLS	Fixed-effects	Differenced GMM	System GMM
R & Dp	0.317	1.996	1.363	1.065
Yp	(0.106)**	(0.268)**	(0.756)	(0.345)**
∑R & DsI	1.275	1.889	4.001	1.505
Yp	(0.702)	(1.237)	(3.030)	(1.100)
∑R & DsA	1.384	2.617	2.796	0.280
Yp	(1.727)	(2.155)	(0.576)**	(1.208)
Observations	1255	1255	1004	1255
R²	0.04	0.09
Hansen J statistic	0.66	0.72
Difference between	0.62
Hansen statistics

(5)	(6)	(7)	(8)
OLS	Fixed-effects	Differenced GMM	System GMM
R & Dp	0.319	1.992	1.134	1.152
Yp	(0.108)**	(0.273)**	(0.726)	(0.332)**
∑R & DsI	0.888	0.332	2.924	1.907
Yp	(1.392)	(2.549)	(2.783)	(2.442)
R & Dp × ∑R & DsI	0.032	0.100	0.057	-0.012
Yp	(0.096)	(0.159)	(0.160)	(0.142)
Observations	1255	1255	1004	1255
R²	0.04	0.09
Hansen J statistic	0.56	0.33
Difference between	0.14
Hansen statistics

Note: The dependent variable is the growth rate of TFP in each parent firm. Standard errors are in parentheses. * and ** denote statistical significance at the 5 and 1 percent levels, respectively. In all specifications, year dummies are included, but the results are not reported. P values are reported for Hansen J statistics and the difference between the two Hansen statistics. See Table 2 for the description of the variables used in this table.