rafmacalaba/datause-encoder-data huggingface dataset:
- 21 -

Table 2: Panel Data Regresions (Annual Data)

||Dependent variable: annua|l growth rate of income||
|---|---|---|---|
|Method of Esti mte|Pooled OLS|Fixed-effects|Random-effects|
|Countries:|53|53|53|
|Observations:|989|989|989|
|_Sf/Sf_|0.059<br>(0.007)|0.051<br>(0.006)|0.053<br>(0.006)|
|_SI/Sf_|0.012<br>(0.004)|0.013<br>(0.004)|0.013<br>(0.004)|
|_NIN_|0.421<br>(0.169)|0.529<br>(0.311)|0.430<br>(0.240)|
|_C_|0.033<br>(0.004)||0.032<br>(0.006)|
|_RF_|0.090|0.088|0.085|
|_F2_|0.087|0.034|0.031|
|Test of Restrictions:||F(52,933)=4.33|X<sup>2</sup>(3)= 17.00|

Note: Standard errors are in parenthesis.

we discuss empirical findings, we explain the different econometric methods used in the three regressions. The first column is the result from a simple OLS regression using pooled data. The second and third columns are results from panel data regressions using variable-intercepts method. The difference between the second column and the third cohlmn is that we use a fixed-effects model for the regression in the second column, and a random-effects model in the third. That is, in the
# **How Much Would China Gain from Power Sector Reforms? An Analysis Using TIMES and CGE Models**

## **1. Introduction**

Since the 1980s, many countries have undertaken market-oriented reforms or restructuring of the power sector, and it is continuing (Jamasb, Nepal and Timilsina, 2017). The primary objective of the reforms is to restructure the vertically integrated state-owned utilities to enhance efficiency in the electricity service industry by introducing market competition and thereby encouraging the participation of private and foreign investors (Joskow, 1998; Newbery, 1999). China started the first-round power sector reform program in 2002 by altering the role of the State Development and Planning Commission (SDPC) to manage the Chinese power sector (Yeh and Lewis, 2004). It created two power grid operators (the State Power Grid and China South Power Grid) and five electricity generation companies (China Guodian Corporation, China Huaneng Group, China Datang Corporation, China Huadian Corporation, and State Power Investment Corporation) (Xu and Chen 2006; Lei et al. 2018). It also established a State Power Regulatory Commission.

Despite the 2002 reform, the Chinese power sector continued to face several problems. For example, there was a seasonal mismatch between supply and demand, thereby causing a shortage of supply to meet the demand in some seasons, whereas there was surplus capacity in other seasons. The change in wholesale prices due to the fluctuation of coal prices did not pass through the consumers as the retail (end-use) prices were controlled by the National Development and Reform Commission (NDRC), which often kept the retail prices below the wholesale prices, thereby providing subsidies (Lei et al. 2018). In this context, China started the second-round of the electricity reform in 2015. The objectives of the 2015 power sector reform are to improve the power system reliability; to increase the use of market mechanisms for power supply; to protect residential and agricultural consumers; to facilitate energy savings, to reduce emissions of greenhouse gases (GHG) and local air pollutants; to increase deployment of renewable and distributed generation; and to improve power system governance and regulation (Dupuy et al. 2015; Lei et al. 2018). To accomplish these objectives, the reform aims to enhance competition, including in the transmission and distribution segment of the electricity industry, and also to reform the retail electricity pricing (e.g., reducing cross2
14 Sources of data feeding into the IMFDOT and into the COMTRADE datasets are different and as such, differences in annual trade values can be expected. Such differences have been observed (IMF, 1999: Table 2), although differences do not exceed 10%. We constructed separate trade variables based on both the IMF and UN datasets. We converted the trade values in these two datasets into constant US$ of 1999 (for IMFDOT) and of 2002 (for COMTRADE). We then used annual country-level GDP data from the GGDC&CB (2005) dataset, which is expressed in 1999 and 2002 US$ to construct our trade variables. Missing trade values in particular years were ignored because our trade variables are calculated as long-term averages.

The following definitions apply for the two trade variables we constructed: Let _i_ =1 and _i_ =2 be two riparian states sharing a river. Let _IMP_ 12 _t_ be import of 1 from 2 in year _t_ , [= _EXP_ 21 _t_ ]; _EXP_ 12 _t_ be export of 1 to 2 in year _t_ . [= _IMP_ 21 _t_ ]; _IMP_ 1 _wt_ be import of 1 from w in year _t_ ; _IMP_ 2 _wt_ be import of 2 from _w_ in year _t_ ; _EXP_ 1 _wt_ be export of 1 to _w_ in year _t_ ; _EXP_ 2 _wt_ be export of 2 to _w_ in year _t_ ; _GDP_ 1 _t_ be gross domestic product of country 1 in year _t_ , _GDP_ 2 _t_ be gross domestic product of country 2 in year _t_ ; and _w_ be rest of the world (not including 1 and 2).

We first constructed two annual trade variables for each trade dataset. The first variable ( _TRD_ 1) expresses total trade between 1 and 2 as a fraction of the countries’ GDP, expressing the economic importance of trade to the riparians (Sigman 2004). The second variable ( _TRD_ 2) measures trade between 1 and 2 as a fraction of their trade with the rest of the world, expressing their dependence on each other (Reuveny and Kang 1996).

In this framework, we adopt the significant finding by Arora and Athanasios (2005) that the relatively important trading partners tend not to change much over time. This finding is very important in the case of international water treaty formation because long-term impact variables such as scarcity, governance, and trade play in the relationship between the riparian countries, justifying use of long-term values.

The two trade variables that we apply to the two trade data sets are presented in equations [7] and [8].
_<mark>Table 10:</mark> Effect of CCT on Incidence of IPV (Ranking PMT)_

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Incidence of<br>physical IPV|Incidence of<br>sexual IPV|Incidence of<br>emotional IPV|Incidence of<br>economic IPV|
|RD Effect|0.035|0.011|-0.056|0.001|
||(0.60)|(0.64)|(0.36)|(0.98)|
|FDR q-val|0.99|0.99|0.95|0.99|
|Observations|563|563|563|563|
|Control mean|0.175|0.050|0.186|0.211|
|P-values in parent|heses: * p<0.1 ** p|<0.05 *** p<0.01|||

_Note:_ All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all IPV regressions in Tables 10 and 11.

_Table 11: Effect of CCT on Frequency of Exposure to Intimate Partner Violence (Ranking PMT)_

||(1)<br>Frequency of<br>physical IPV|(2)<br>Frequency of<br>sexual IPV|(3)<br>Frequency of<br>emotional IPV|(4)<br>Frequency of<br>economic IPV|
|---|---|---|---|---|
|RD Effect|0.001|0.023|-0.007|-0.067|
||(0.99)|(0.24)|(0.89)|(0.34)|
|FDR q-val|0.99|0.95|0.99|0.95|
|Observations|563|563|563|563|
|Control mean|0.075|0.025|0.125|0.164|
|P-values in parent|heses: * p<0.1 **|p<0.05 *** p<0.01|||

_Note:_ All regressions include a set of individual characteristics as control variables: age, ethnic background, and religious background. FDR q-values are computed using p-values for all IPV regressions in Tables 10 and 11.

22
2. _If we can, is this where policy should focus, or are there other market failures or distortions that are likely to be more important?_ Answering these questions requires substantial information. It is not enough to note, for instance, that a product is not being produced or a desirable outcome is not observed- policy makers need to identify and quantify a market failure driving these outcomes. But answering both questions is critical. Governments have limited bandwidth and capacity and hence the pursuit of one policy will necessary exclude another.

**Likelihood of successful government redress of the market failure** .  On the other side of the ledger is the capacity of the government to design and execute an appropriate resolution of the identified market failure.  The classic concerns here are the diagnostic, coordinative and executive capacity of governments, and the potential for distortionary rent seeking.

We broadly divide the policy space into Vertical and Horizontal market failures and hence interventions.

_Vertical (sectoral)) interventions_ : If producing a certain product is thought to generate externalities that make social returns higher than private, then policies that seek to increase the production of that good we consider “vertical.”<sup>3</sup> _Horizontal interventions_ : Market failures that do not pertain to a particular good but which are to greater or lesser degree economy wide we term “horizontal.” These tend to be related to factor markets and range from infrastructure provision, to public education, to general innovation subsidies. Though benefiting sectors that use that factor intensively disproportionately more than others (See Rybczinski 1955), they affect many sectors at once, some that do not yet exist, and hence are considered _ex ante_ horizontal compared to vertical policies specifically targeting the production of a good per se. Within this group, we include _Coordination and Cluster policies_ which we discuss separately in section V. Productivity advance may require the simultaneous provision of factors, pursuit of compatible

> 3 Unlike IADB (2014) the difference between vertical and horizontal policies lies in where the market failure inheres.  We think the real debate is about whether we should be “picking winners” and not whether we resolve the manifold human capital, infrastructure or other failures that may disproportionately affect one sector.

3
**<u>Table 3. Plot-level Data on Output and Input Use as well as Investment for Owner-cum Tenants</u>**

||All|Own land|Barga land|Difference|
|---|---|---|---|---|
|**Plot characteristics**|||||
|Land area (acre)|0.40|0.36|0.46|*|
|Distance to homestead (meter)<br>|878.8<br>|810.9<br>|979.1<br>|***|
|Gray color soil|0.853|0.854|0.853||
|Sandy soil|0.145|0.148|0.142|*|
|Loam soil|0.111|0.111|0.110||
|Light clay soil|0.457|0.458|0.455||
|Heavy clay soil|0.259|0.257|0.261||
|No salinity|0.460|0.466|0.452|***|
|Moderate salinity|0.512|0.506|0.520|***|
|Easy to drain|0.350|0.354|0.340|**|
|Moderately easy to drain|0.504|0.505|0.503|***|
|Difficult to drain|0.146|0.141|0.152||
|**Input use & productivity**|||||
|Used any fertilizer|0.970|0.969|0.972||
|Used any manure|0.596|0.632|0.544|***|
|Used any pesticides|0.866|0.885|0.837|***|
|Used any seeds|0.984|0.978|0.992|***|
|Used any draught power/transport|0.311|0.328|0.300|***|
|Used any casual labor|0.672|0.660|0.681||
|Used any family labor|0.928|0.920|0.941|***|
|Fertilizer & manure (Rs/acre)|1,942.06|2,195.05|1,569.15|***|
|Pesticides (Rs/acre)|605.61|666.13|516.40|***|
|Seeds (Rs/acre)|1,256.45|1,428.47|1,002.88|***|
|Draught power/transport (Rs/acre)|1010.87|1087.02|898.62|***|
|Casual labor cost (Rs/acre)|886.98|942.67|804.90|***|
|Family labor use (Days/acre)|70.07|74.74|63.17|***|
|Gross production value (Rs/acre)|19,892.1|22,062.2|16,693.3|***|
|Netproduction value(Rs/acre)|13,145.3|14,565.9|11,051.3|***|
|**Land-related investment**|||||
|Invested in soil & water conservation (y/n)|0.28|0.39|0.12|***|
|<br>... if yes, cost (Rs)|141.48|203.08|50.67|***|
|No. of family days invested in 2007|4.78|6.78|1.83|***|
|Access to private irrigation (y/n)|0.50|0.54|0.44|***|
|Access to public irrigation (y/n)|0.17|0.17|0.18||
|Number ofplots|9,285|5,532|3,753||

_Source:_ Household questionnaire from the 2008/9 West Bengal survey.

* significant at 10%; ** significant at 5%; *** significant at 1% (based on simple t-test for the mean difference between own land and barga land). Private irrigation includes ponds, wells, and bore wells.

18
even if the pension wealth indicator suggested a lack of incentives for delayed retirement, in reality, most probable, the RCAR does encourage retirement at higher ages – which is also consistent with the slope of the IRR function.

**Figure 4:  Normative Probability of Early Retirement**

<!-- Start of picture text --> CNSS<br>CMR<br>RCAR<br>0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45<br>Normative probability of retirement at age 55<br>Source: Authors’ calculations.<br><!-- End of picture text --> In conclusion, the utility maps and the normative estimator of the probability of early retirement provide more precise information about the incentives to retire early, given individuals with heterogeneous preferences. The slope of the IRR function and the change in pension wealth are useful to flag distortions, but one needs to be careful when deriving conclusions on actual retirement behavior based on these overly simplified measures. This is because these two indicators do not take into account the trade-off between consumption and leisure and the level of risk aversion of individuals. Another attractive feature of the proposed indicator is that the calculations are simple. Therefore, future assessments of pension institutions and international comparisons of the effects of benefit formulas and eligibility conditions on retirement decisions could benefit from the proposed calculations. Finally, the indicator is useful to assess, ex-ante, the potential impact that policy changes can have on retirement decisions. As discussed in the next section, these decisions affect the level of human capital of a given country and therefore its growth potential.

# **3. Estimating the cost of early retirement in terms of the loss of productive capacities of the economy**

As discussed in the previous section, from the individual’s point of view the decision to retire is a choice that reflects preferences between leisure and consumption. From the point of view of the country, however, retirement represents a loss of human capital, particularly when it occurs at early ages. Standard analyses in pension policy focus on the fiscal costs of early retirement. Here we argue that the economic cost of early retirement should be also taken into account. Hence, this section develops a methodology to assess the impact that a pension system can have on human capital 10
at the household level, with a special focus on agricultural statistics and the link between agriculture and other household income activities. Ethiopia’s LSMSISA data-set is a panel with three rounds collected in 2011-2012, 2013-2014, and 2015-2016. The first round collected data on 3,776 rural households, before expanding to 5,262 in the 2nd round to include households living in urban areas.<sup>2</sup> The attrition rate across rounds is 5.54%. The survey is representative at the national and regional levels with population weights to adjust for sample design.

Ethiopia’s LSMS-ISA data are characterized by its combination of detailed agricultural data with household characteristics. It contains both household and parcel-level indicators, including detailed data on the following:

- Parcel-level data detailing the origin of land tenure for each parcel of land.

- Parcel-level measures of area, crop, geophysical characteristics and location, allowing for the calculation of land fragmentation measures.<sup>3</sup>

- Household-level data on welfare outcomes specific to food security.

- Household-level data on demographic characteristics and assets held by the household.

- Household-level data on shocks experienced, such as drought.

2. A number of these households living in peri-urban areas has access to land parcels, and we include them in our analysis.

3. Land data in the LSMS-ISA are collected at three levels of aggregation: parcels; fields; and plots. Plots are the smallest unit of analysis. Multiple plots can make up a field. Multiple fields make up a parcel; parcels are the highest unit of land aggregation. For our main analysis we chose to aggregate all these measures up to parcels, weighed by area. See appendix for details.

7
Table 3: Enrollment, Attendance and Working Proportions

||||E|nrollment||||
|---|---|---|---|---|---|---|---|
||1999|2000|2001|2002|2005|2006|2007|
|Male|85.9|83.5|84.9|85.1|90.9|89.5|91.3|
|_Female_|_82.3_|_82.5_|_83.1_|_83.4_|_88.2_|_86.2_|_89.2_|
|Indigenous|81.1|82.0|84.0|84.4|89.6|88.2|89.6|
|<br>_Non-indigenous_|_88.6_|_83.8_|_83.2_|_84.8_|_89.6_|_87.7_|_90.4_|
|Urban|90.2|87.6|88.7|88.2|94.0|89.7|91.5|
|_Rural_|_77.8_|_77.9_|_78.8_|_80.1_|_84.9_|_86.2_|_88.8_|
|Spanish|84.0|84.2|85.6|86.0|90.4|88.4|90.9|
|<br>_Non-Spanish_|_42.9_|_29.5_|_61.6_|_67.9_|_54.8_|_76.8_|_79.4_|
|Ed M Head|-|-|85.8|84.7|95.6|90.7|92.8|
|_Non-ed M Head_|-|-|_82.5_|_78.9_|_87.2_|_87.0_|_88.1_|
|Ed F Head|-|-|90.8|88.6|96.9|95.5|92.5|
|_Non-ed F Head_|-|-|_84.6_|_73.1_|_83.5_|_83.3_|_91.1_|
|Extr. poor|82.7|80.4|81.9|82.2|88.0|87.2|89.3|
|_Non-extr. poor_|_89.3_|_85.9_|_86.4_|_86.8_|_92.1_|_88.9_|_91.5_|
||1999|2000|A<br>2001|ttendance<br>2002|<br>2005|2006|2007|
|Male|55.0|67.7|52.1|59.9|62.3|73.0|48.3|
|_Female_|_54.9_|_65.0_|_52.5_|_59.7_|_58.4_|_74.7_|_50.6_|
|Indigenous|62.4|73.5|55.4|66.8|74.4|81.9|58.8|
|_Non-indigenous_|_40.0_|_58.1_|_45.7_|_49.2_|_40.9_|_60.5_|_33.6_|
|Urban|43.2|60.6|55.8|67.7|54.8|66.5|42.4|
|_Rural_|_69.0_|_73.6_|_48.1_|_50.4_|_67.4_|_81.3_|_58.1_|
|Spanish|52.5|65.7|51.5|60.9|60.1|73.8|49.0|
|<br>_Non-Spanish_|_50.4_|_77.8_|_69.4_|_58.4_|_94.8_|_73.7_|_60.0_|
|Ed M Head|-|-|52.4|60.3|57.6|69.4|50.7|
|_Non-ed M Head_|_-_|_-_|_50.4_|_48.5_|_61.5_|_76.8_|_47.4_|
|Ed F Head|-|-|51.1|63.2|70.3|57.7|44.7|
|_Non-ed F Head_|_-_|_-_|_62.0_|_56.5_|_59.3_|_70.3_|_54.8_|
|Extr. poor|58.9|72.5|52.7|60.6|67.2|82.0|59.2|
|<br>_Non-extr. poor_|_41.9_|_60.1_|_51.9_|_58.9_|_54.2_|_64.7_|_38.7_|
||1999|2000|<br>2001|Working<br>2002|2005|2006|2007|
|Male|26.4|20.6|23.2|23.0|31.0|24.4|21.9|
|_Female_|_26.1_|_15.8_|_18.3_|_18.6_|_22.6_|_22.0_|_17.5_|
|Indigenous|31.0|24.1|25.4|25.9|35.3|30.0|24.6|
|<br>_Non-indigenous_|_17.2_|_11.5_|_14.4_|_14.2_|_15.2_|_15.4_|_15.6_|
|Urban|8.8|6.7|7.8|7.4|10.2|6.4|5.4|
|_Rural_|_45.1_|_31.7_|_35.9_|_36.0_|_45.2_|_40.2_|_36.8_|
|Spanish|37.0|27.2|20.1|18.8|26.3|22.3|19.0|
|<br>_Non-Spanish_|_85.8_|_75.4_|_36.8_|_45.3_|_64.7_|_60.5_|_39.7_|
|<br>Ed M Head|-|-|17.7|20.2|14.1|12.2|10.0|
|_Non-ed M Head_|-|-|_22.7_|_33.8_|_34.7_|_32.0_|_27.7_|
|Ed F Head|-|-|12.8|14.2|8.5|3.7|8.8|
|_Non-ed F Head_|-|-|_23.2_|_33.0_|_32.1_|_19.9_|_19.3_|
|Extr. poor|29.5|23.8|26.1|27.8|36.2|33.4|28.0|
|_Non-extr. poor_|_15.0_|_12.1_|_14.9_|_12.8_|_16.8_|_11.9_|_10.3_|

Source: MECOVI. – appears whenever the number of observations is below 10.

21
_15_ The U.S. Treastiry bill rate used in these calculations was 5.4 percent in 1991, down from 8.1 percent in 1989. All else equal, lower interest rates will increase the imputed stock of legitimate foreign assets, leading to lower capital flight under the Dooley measure. Reported interest earnings were $24 billion in 1991, up from $19 billion in 1989. These two effects reinforced each other, resulting in substantially higher and increasing estimated stocks of legitimate foreign assets for 1990 and 1991-up from $235 billion in 1989 to $436 billion in 1991, and thus lower capital flight for these years under Dooley (see table in Anniex 1). In general, the Dooley measure is more variable than the World Bank residual measure because it depends on a ratio of two variables (reported earnings and the international interest rate), which may have little contemporaneous correlation.

The (broadest) Hot Money and the World Bank Residual measure are ,,lotted in Figure 2. The variation between Hot Money measures and the World Bank Residual measure arises from differences between balance-of-payments figures on the flow of public and publicly guaranteed debt and World Bank data on the change in the stock of public-sector external debt (adjusted for currency-movements, resch dulings, etc.). Even so, the two measures broadly reflect the same trends, save for 1977, and 198689, when the Hot Money measure shows lower capital flight. This suggests that large increases in public-sector debt went unrecorded in t'ie balance-of-payments accounts in those years.

**Figure 2: World Bank Residual and Hot Money (Annual Flows)**

<!-- Start of picture text --> Bl11ons of USS<br>120<br>t 00<br>80<br>e0<br>40<br>20 e<br>0 -f--'..._ ... .<br>71 72 73 74 76 78 77 78 79 80 81 82 83 84 86 86 87 88 8a 90 91<br>.20<br>World Bank Residual Hot Money 1<br><!-- End of picture text --> The three misinvoicing measures are exports underinvoicing, imports overinvoicing and the sum of the two (Figure 3); positive numbers for exports indicate underinvoicing and for imports overinvoicing, while negative numbers for exports denote overinvoicing and for imports underinvoicing. Underinvoicing of imports is clear after 1981. Exports are overinvoiced up to 1985 and undervoiced thereafter.
Table 1 compares intangible expenditures in a given year (2006) for Brazil relative to other available countries.

|**Table 1: Cross-c**|**ountry Co**<br>|**mpariso**<br>|**n of Ag**<br>|**gregate**<br>|**Intangi **<br>|**ble Exp**<br>|**enditures**<br>|**(% of**<br>|**GDP)**<br>|
|---|---|---|---|---|---|---|---|---|---|
||Germany|France|Italy|Spain|UK|US|Japan|China|Brazil|
||2006|2006|2006|2006|2006|2000|2000-05|2006|2006|
|**1. Computerized info.**|**0.69**|**1.33**|**0.61**|**0.75**|**1.42**|**1.24**|**2.03**|**1.88**|**1.10**|
|**2. Innovative property**|**3.38**|**2.98**|**2.13**|**2.66**|**2.88**|**4.07**|**5.42**|**3.40**|**2.01**|
|a) R&D, including social|1.62|1.22|0.56|0.60|0.98|1.69|2.57|1.02|0.56|
|sciences and humanities||||||||||
|b) Mineral exploration and|0.01|0.04|0.09|0.04|0.04|0.78|-|0.21|0.03|
|evaluation||||||||||
|c) Copyright and license|0.20|0.29|0.10|0.17|0.20|0.55|1.01|0.08|0.11|
|costs||||||||||
|d) Development costs in|0.70|0.56|0.56|0.50|0.06|0.55|1.84|0.47|1.10|
|financial industry<br>e) New architectural and<br>engineering designs|0.85|0.87|0.82|1.35|1.60|0.50|-|1.62|0.21|
|**3. Economic competencies**|**3.14**|**3.84**|**2.63**|**2.19**|**6.44**|**5.94**|**3.39**|**2.34**|**1.61**|
|a) Brand equity|0.88|1.55|1.13|0.67|1.77|2.25|1.83|0.63|0.93|
|_Of which:- Advertising_<br>|_0.65_|_1.15_|_0.75_|_0.30_|_1.40_|_2.07_|_-_|_0.63_|_0.84_|
|_expenditure_||||||||||
|_- Market research_|_0.23_|_0.40_|_0.38_|_0.37_|_0.37_|_0.18_|_-_|_-_|_0.09_|
|b) Firm-specific human|1.21|1.41|0.99|0.78|2.33|1.05|0.46|0.29|0.34|
|capital||||||||||
|_Of which: - Continuing_<br>_vocational training_|_0.61_|_1.17_|_0.68_|_0.68_|_-_|_-_|_-_|_0.29_|_-_|
|_- Apprentice training_|_0.60_|_0.24_|_0.31_|_0.10_|_-_|_-_|_-_|_-_|_-_|
|c) Organizational capital|1.05|0.88|0.50|0.75|2.35|2.64|1.10|1.41|0.34|
|<br>_Of which: - Purchased_|_0.51_|_0.30_|_0.14_|_0.26_|_0.47_|_-_|_-_|_-_|_0.04_|
|_- Own account_|_0.54_|_0.58_|_0.36_|_0.49_|_1.88_|_-_|_-_|_1.41_|_0.30_|
|**Total Expenditures**|**7.21**|**8.15**|**5.37 **|**5.60**|**10.74**|**11.25**|**10.84 **|**7.62 **|**4.72**|

Notes: (1) US estimate is an average for 1998-2000. (2) Japan R&D excludes social science, and social science R&D is in the 2.0 percent of the residual term of innovative property. (3) Brazil firm-specific human capital excludes services.

Sources: The original data source of the US is CHS (2005), of Japan is Fukao et al. (2009), and of the UK, Germany, France, Italy and Spain is van Ark et al. (2009). The source of China estimates is Hulten and Hao (2011). This table is based on their Table 2, which contains downward revisions of the non-China data to be consistent with the results of another EU-funded project. GDP in this table is the sum of conventional GDP plus new intangibles. Estimates of investment have been converted to estimates of expenditures using CHS formulae.

17