rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 3</u>** <u>: Determinants of VUP participation (2011 and/or 2014)</u>

||<br>VUP Bene<br>|<br>ficiary|<br>||DS Benefici<br>|ary|||PW Benef<br>|iciary|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)||(2)||(3)||(4)||(5)||(6)||
||Coef.|ME|Coef.|ME|Coef.|ME|Coef.|ME|Coef.|ME|Coef.|ME|
|_Characteristics of the HH hea_|_d_||||||||||||
|Head is male (=1)|-0.323*|-0.065|-0.284*|-0.056|-0.825***|-0.064|-0.772***|-0.068|-0.139|-0.017|-0.123|-0.015|
||(0.152)||(0.154)||(0.255)||(0.287)||(0.193)||(0.192)||
|Age of head (yrs.)|-0.006|-0.001|-0.008|-0.002|-0.017|-0.001|-0.018|-0.002|-0.012|-0.001|-0.012|-0.001|
||(0.007)||(0.007)||(0.010)||(0.010)||(0.010)||(0.010)||
|No Schooling|Ref.||Ref.||Ref.||Ref.||Ref.||Ref.||
|Primary started (=1)|0.137|0.028|0.192|0.038|||||0.271|0.033|0.315|0.038|
||(0.321)||(0.332)||||||(0.317)||(0.319)||
|Primary completed (=1)|0.059|0.012|0.123|0.024|0.145|0.011|0.190|0.017|-0.051|-0.006|0.007|0.001|
||(0.157)||(0.158)||(0.250)||(0.264)||(0.181)||(0.178)||
|Secondary or more (=1)|-0.034|-0.007|0.020|0.004|0.299|0.023|0.267|0.023|-0.139|-0.017|-0.039|-0.005|
||(0.331)||(0.353)||(0.567)||(0.622)||(0.362)||(0.374)||
|Disabled (=1)|-0.004|-0.001|-0.044|-0.009|0.349|0.027|0.296|0.026|-0.147|-0.018|-0.177|-0.021|
||(0.141)||(0.141)||(0.254)||(0.259)||(0.164)||(0.162)||
|_HH composition_|||||||||||||
|# of HH members|-0.068|-0.014|-0.052|-0.010|-0.166*|-0.013|-0.158*|-0.014|0.039|0.005|0.054|0.006|
||(0.043)||(0.044)||(0.087)||(0.094)||(0.046)||(0.046)||
|Share of adults (18-65 yrs.)|-0.624*|-0.126|-0.589*|-0.116|-0.365|-0.028|-0.375|-0.033|0.124|0.015|0.141|0.017|
||(0.369)||(0.348)||(0.260)||(0.247)||(0.236)||(0.230)||
|Share of elders (65+ yrs.)|0.347|0.070|0.336|0.066|1.397***|0.108|1.407***|0.124|-3.227*|-0.394|-3.333*|-0.401|
||(0.492)||(0.480)||(0.528)||(0.531)||(1.604)||(1.628)||
|Share of disabled (=1)|0.463*|0.094|0.451*|0.089|0.985***|0.076|0.983***|0.086|-0.352|-0.043|-0.393|-0.047|
||(0.216)||(0.216)||(0.294)||(0.305)||(0.405)||(0.394)||

Table continues next page.

21
growth. Both studies identify the crucial positive impacts of bank branch and ATM penetration on access to finance for firms and households. In addition, Allen et al. (2012) consider various pro-access policies<sup>50</sup> that lower the negative perceptions associated with opening and using a bank account. For instance, they find that the probability of using bank accounts is three times higher in countries where the government makes payments through bank accounts as well as in countries where savings schemes and tax incentive programs are established.

# • **Banking Structure and Ownership**

- **Concentration:** Beck, Demirguc-Kunt, and Maksimovic (2004) find that higher concentration in the banking sector raises the probability that firms report financing as a major obstacle, especially for small firms. Yet they emphasize that this relationship is subject to various nonlinearities. For instance, the negative effect of concentration on access to finance is more prominent in low-income countries and in countries with a weak institutional environment. They also suggest that the existence of credit registries lowers the negative effect of concentration on firms’ access to finance. Barth et al. (2009) finds a complementary result in that as banking concentration increases, the probability that a firm rates bank corruption as a major obstacle to its growth increases significantly.

- **Government versus Foreign Ownership:** La Porta, Lopez de Silanes, and Shleifer (2002) find that countries with a higher government stake in the banking sector provide more restrictive access to finance (i.e., exclusive access to top-ranking firms). Detragiache, Gupta, and Tressel (2008), however, suggest that in low-income countries, the larger the share of foreign bank assets, the lower the number of bank branches and ATMs, loans, and deposits per capita. Gormley (2007) confirms this result and suggests that because of information costs the clientele of foreign banks is restricted to firms that are located at the top of the distribution of firms in terms of their profitability. From a different perspective, Clarke, Cull, and Martinez Peria (2006) find that increasing the share of foreign banks in a country leads to reductions in the negative perceptions of managers about firms’ access to finance (e.g., interest rate and long-term financing opportunities) and that this result applies to firms of all sizes. Barth et al. (2009) also argue that greater private or foreign ownership reduces bank corruption, which is a key constraint for firms.

> 50 Among others, they consider policies that require banks to offer basic or low-fee accounts, exempt some depositors from costly documentation requirements, and use bank accounts to make government payments.

24
- ———. 2018a. "Human Capital Project." In.

- ———. 2018b. _World Development Report 2018: Learning to Realize Education's Promise_ (World Bank: Washington, DC).

- Yoshikawa, Hirokazu, Diana Leyva, Catherine E Snow, Ernesto Treviño, M Barata, Christina Weiland, Celia J Gomez, Lorenzo Moreno, Andrea Rolla, and Nikhit D'Sa. 2015. 'Experimental impacts of a teacher professional development program in Chile on preschool classroom quality and child outcomes', _Developmental psychology_ , 51: 309.

# Figures and tables

19
from rigorous econometric studies based on micro-level data suggest that average consumer beliefs are indistinguishable from a no-change forecast (Allcott et al. 2011, Anderson et al. 2013), and “researchers are likely to be justified in assuming that average consumers employ a no-change forecast in most circumstances” (Anderson et al. 2013, p.401). Finally, lagged energy prices could matter over the very long-term, as new technologies penetrate the market, which is not the focus of this study.

Once the investment is made, the new energy-using capital stock remains operational until it depreciates away or is replaced. The replacement of existing capital stock is, however, costly in the short-run. Our vintage capital model thus fits in the framework introduced by Fuss (1977), in which the substitution of factors of production is easier ex-ante than ex-post. An important characteristic of such a framework, as noted by Koopmans and te Velde (2001, p. 58), is that “following an unexpected energy price increase, the energy efficiency of new vintages will improve more than the energy efficiency of existing vintages. A price change achieves its full impact only after the capital stock has been completely renewed. Hence, price elasticities are larger in the long run than in the short run.” In the extreme case of infinitely large capital adjustment costs our model becomes similar to the Atkeson and Kehoe (1999) “putty-clay” model of energy use.

We then consider production decisions based on the dynamic factor demand model of Pindyck and Rotemberg (1983), where firms minimize expected input costs (including capital adjustment costs) to produce the optimal time path of labor, capital, energy, and materials consumption to achieve the desired output level, given the level of input efficiency of installed production technology. The resulting equations are subsequently used to form stochastic specifications and estimate the input price elasticities of factor substitution and capital stock efficiency.

# **2.1 Investment in Input Efficiency**

We assume that the manufacturing industries in OECD countries operate in perfectly competitive product and factor markets, that is, take input prices as given.<sup>5</sup> We can therefore view each manufacturing sector as consisting of a

> 5This assumption follows the theoretical literature discussed above and is useful for simplifying the derivations. Of course both input and product markets of many industries, including those studied in this paper, are characterized by some degree of strategic behavior of their market participants. The strategic behaviour will have limited impact on both theoretical

7
Table B3: Robustness check using the sample of the aged 60 and below—Unobservable abilities

|||Math|(z-score)|||Language|(z-score)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.328<sup>_∗∗∗_</sup>|0.069|0.057|0.060|-0.433<sup>_∗∗∗_</sup>|0.102|0.109|0.044|
||(0.084)|(0.070)|(0.062|)<br>(0.070)|(0.088)|(0.078)|(0.067)|(0.079)|
|Schooing in years||0.103<sup>_∗∗∗_</sup>|0.101<sup>_∗∗_</sup>|<sup>_∗_</sup><br>0.111<sup>_∗∗∗_</sup>||0.127<sup>_∗∗∗_</sup>|0.116<sup>_∗∗∗_</sup>|0.143<sup>_∗∗∗_</sup>|
|||(0.010)|(0.010|)<br>(0.016)||(0.010)|(0.012)|(0.014)|
|Age at hire||0.004<br>|0.002<br>|0.002<br><br>||-0.002<br>|-0.002<br>|0.001<br>|
|||(0.005)|(0.005|)<br>(0.006)||(0.003)|(0.004)|(0.004)|
|Father’s pri.school||0.096|0.075|0.081||0.266<sup>_∗∗∗_</sup>|0.161<sup>_∗∗∗_</sup>|0.103<sup>_∗_</sup>|
|||(0.078)|(0.055|)<br>(0.058)||(0.070)|(0.059)|(0.059)|
|Mother’s pri.school||0.043|0.022|0.005||0.133|0.109|0.145|
|||(0.085)|(0.110|)<br>(0.122)||(0.084)|(0.090)|(0.110)|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.138|0.298|0.226|0.166|0.127|0.431|0.318|0.288|
|N|3763|3763|3763|3763|3763|3763|3763|3763|
|||Big 5 (z-|score)||||||
||(9)|(10)|(11)|(12)|||||
|Social networks|0.063<sup>_∗_</sup>|0.123<sup>_∗∗_</sup>|0.030|-0.005|||||
||(0.038)|(0.051)|(0.042)<br>|(0.043)|||||
|Schooing in years||0.011|0.007|0.002|||||
|||(0.008)|(0.006)<br>|(0.004)|||||
|Age at hire||0.003|-0.001|-0.000|||||
|||(0.004)|(0.004)<br>|(0.004)|||||
|Father’s pri.school||0.077|-0.054<sup>_∗_</sup>|-0.044|||||
|||(0.057)|(0.030)<br>|(0.032)|||||
|Mother’s pri.school||0.006|0.021|-0.002|||||
|||(0.068)|(0.037)<br>|(0.038)|||||
|Firm fxed efect|||X||||||
|Firm-occ fxed efect||||X|||||
|R-squared|0.107|0.121|0.046|0.054|||||
|N|3757|3757|3757|3757|||||

_Note_ . All the dependent variables are normalized to have the zero means and the one standard deviations. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

47
practices at the school, as reported by a range of actors. In addition, the samples are usually large (often census-based) and contain school identifiers, thereby enabling researchers to explore heterogeneity and answer a wide range of policy-relevant questions. We illustrate how our approach can be applied widely to other national surveys using the example of _Prova Brasil_ . This national survey plays a significant role in Brazil’s education policy because its test results, along with promotion, dropout, and retention rates, are the main inputs to the _Índice de Desenvolvimento da Educação Básica_ (IDEB), a national index representing educational quality at the school, municipality, and state levels.

We followed the same steps to create a Prova Brasil-based management index: we classified 33 questions (14 from the principal questionnaire and 19 from the teacher questionnaire) into 8 WMS topics, coded responses following the same rubric, and used the Anderson [2008] method to build a school management index.

**Potential concerns.** One of the key differences between the WMS survey and PISA or school census surveys is that the WMS is administered and analyzed by an independent interviewer, while the latter surveys are self-reported. There are a number of issues with self-reported data: for example, problems with translation and interpretation, and/or measurement equivalence. To address measurement error of cross-cultural understandings and norms on answering questions in our PISA index, we standardize our PISA-based management index _within countries_ . This has an important implication: since all 65 countries have a mean score of zero, our index cannot be used to construct cross-country rankings of school management. Instead, the value of our PISA-based index lies in enabling academics and practitioners to study the (within-country) correlation between management and other variables for a far wider set of countries than was previously possible.<sup>11</sup> This is not a concern for country-specific national surveys.

Another concern with self-reported data is that it is difficult to assess whether respondents are being accurate and truthful. In the WMS there are several strategies to elicit truthful information

> 11In the 2012 PISA the dataset included a PISA-built ‘leadership and management’ measure. This is distinctively different from ours, as it was based off a section of the questionnaire that was titled ‘management’ and contained only a small subset of questions. This index fails to take advantage of the full questionnaire and the information available elsewhere that also speaks to managerial practices used in the schools. More pertinently, PISA’s measure does not compare well to the (empirically robust) management index derived from the World Management Survey (see Liberto et al. [2015]).

8
of measures is used, the Gini coefficient can be reduced by 2.8 points (B.1). If only the CCT program is used the combined effect would be a 1.5 Gini points reduction (B.2).

**<u>Table 8. Illustrative Policy Alternatives</u>**

|**Policy**<br>**Reform**|**Source of additional tax revenues**|**Initial impact**|**Use of additional**<br>**tax revenues**|**Revenue**<br>**neutral impact**<br>**on Gini**<br>**coefficient**|
|---|---|---|---|---|
|**A.1.**<br>**Personal**<br>**income**<br>**tax**|(1) eliminate the exemption of tax of<br>the first 25 percent of income;<br>(2) eliminate deduction for voluntary<br>savings accounts;<br>(3) introduce taxation of pension<br>income.|Tax revenues:<br>+0.9% of GDP<br>Gini coefficient:<br>-0.7|(1) Income<br>declaration<br>independent<br>workers (IW)<br>(2) Expand CCT<br>program|-0.7 (initial)<br>-1.9 (IW)<br>-0.5 (CCT)<br>**= -3.1 (total)**|
|**A.2.**<br>**Personal**<br>**income**<br>**tax**|(1) eliminate the exemption of tax of<br>the first 25 percent of income;<br>(2) eliminate deduction for voluntary<br>savings accounts;<br>(3) introduce taxation of pension<br>income.|Tax revenues:<br>+0.9% of GDP<br>Gini coefficient:<br>-0.7|(1) Expand CCT<br>program only|-0.7 (initial)<br>-1.2 (CCT)<br>**= -1.9 (total)**|
|**B.1**<br>**VAT**|(1) eliminate all VAT exemptions<br>(except education, health, transport and<br>leasing);<br>(2) eliminate exemptions on real estate<br>transactions;<br>(3) convert all VAT exemptions to<br>exclusions;<br>(4) convert all VAT rates below the<br>general rate to 16percent.|Tax revenues:<br>+1.5% of GDP<br>Gini coefficient:<br>+0.5|(1) Income<br>declaration<br>independent<br>workers (IW)<br>(2) Expand CCT<br>program|+0.5 (initial)<br>-1.9 (IW)<br>-1.4 (CCT)<br>**= -2.8 (total)**|
|**B.2**<br>**VAT**|(1) eliminate all VAT exemptions<br>(except education, health, transport and<br>leasing);<br>(2) eliminate exemptions on real estate<br>transactions;<br>(3) convert all VAT exemptions to<br>exclusions;<br>(4) convert all VAT rates below the<br>general rate to 16percent.|Tax revenues:<br>+1.5% of GDP<br>Gini coefficient:<br>+0.5|(1) Expand CCT<br>program only|+0.5 (initial)<br>-2.0 (CCT)<br>**= -1.5 (total)**|
|Most<br>effective<br>Combined<br>proposal|Personal income tax (A.1) and<br>VAT (B.2)|Tax revenues:<br>+2.4% of GDP<br>Gini coefficient:<br>-0.2|(1) Income<br>declaration<br>independent<br>workers<br>(2) Expand CCT<br>program|**-3.1**<br>**-1.5**<br>**= -4.6**|
|Least<br>effective<br>Combined<br>proposal|Personal income (A.2) and<br>VAT (B.2).|Tax revenues:<br>+2.4% of GDP<br>Gini coefficient:<br>-0.2|(1) Expand CCT<br>program only|**-1.9**<br>**-1.5**<br>**=- 3.4**|

Note: IW: Introduce tax declaration for independent workers. CCT: Conditional Cash Transfer program ( _Familias en Accion_ ). Most effective combined proposal takes into account that the IW spending option is only available for the first 0.5 percent of GDP of fiscal expenditure.

23
decisions. Financial education cannot substitute for regulation but can complement an effective legal and regulatory framework for financial consumer protection.

**However building financial literacy requires a sustained long-term effort.** Addressing key weaknesses in financial consumer protection can be done quickly with immediate impact, but improving financial capability is a long-term investment. The experience of industrialized countries over the last thirty years—and more recently in developing countries—has identified lessons of “what works and what does not” in consumer protection. By contrast, little is clearly understood as to what works (and what does not) in improving financial behavior over the long term.<sup>36</sup>

**None of the nine countries has a national financial education strategy in place.** As shown in Table 10, some countries have conducted consumer awareness campaigns, especially in the initial stage of pension reform. In countries like Slovakia, Bulgaria and Croatia, the supervisory agency for the pensions sector organized campaigns to educate the public on the new private pension system, with mixed results. In its April 2009 meeting, the EU Expert Group on Financial Education suggested that the European Commission recommend that all new member states of the European Union develop national strategies on financial education<sup>37</sup> . None of the nine countries with Diagnostic Reviews has to date, however, adopted any such strategy, although several are considering their future development. Following the 2006 G-8 Meeting in St. Petersburg, the Government of the Russian Federation asked the World Bank to assist in developing a national program of financial education and financial literacy.

**Table 10: National Financial Education Programs**

|**Country**|**Financial Education Program**|
|---|---|
|Azerbaijan|National financial educationprogram underpreparation|
|Bulgaria|Awareness campaign forpensions only|
|Croatia|Awareness campaign for pensions only<br>National financial educationprogram underpreparation|
|Czech Republic|None|
|Latvia|None|
|Lithuania|Financial educationprogram in schools only|
|Romania|Awareness campaign forpensions only|
|Russia|National financial educationprogram underpreparation|
|Slovakia|Awareness campaign for pensions only|

_Source_ : Diagnostic Reviews

> 36 For a summary of academic research on the limited effectiveness of financial education in the US, see Shawn Cole and Gauri Kartini Shastry, _If You Are So Smart, Why Aren’t You Rich? The Effects of Education, Financial Literacy, and Cognitive Ability on Financial Market Participation_ , October 2007. Other analysts go further and argue that financial education fails to improve consumer decision-making and may even be harmful by developing consumer over-confidence. See Lauren E. Willis, "Against Financial-Literacy Education", _Iowa Law Review_ , Issue 94(1): 197-285, November 2008

> 37 European Commission, _Second meeting of the Expert Group on Financial Education: The financial crisis and financial education Report_ , April 28, 2009

32
**Table 1:GDP per capita of the Chinese provinces**

||**Re**<br>(hun<br>|**al GDP**<br>dreds o<br>|**per ca**<br>fyuan 1<br>|**pita**<br> 978)<br>|**Average a**<br>**gr**<br>|**nnual GDP**<br>**owth rate (**<br>|**per capita**<br>**%)**<br>|
|---|---|---|---|---|---|---|---|
||1978|1985|1992|1999|79-89|90-99|79-99|
|**Coastal Region**<br>|**4.9**<br>|**9.1**|**16.1**<br>|**36.3**<br>|**8.5**|**10.9**|**9.7**|
|1 Beijing|12.5|21.1|33.6|62.2|7.0|8.4|7.7|
|2 Tianjin|11.4|19.4|27.1|56.6|6.1|9.3|7.6|
|3 Hebei|3.6|5.9|9.9|22.4|7.0|10.5|8.7|
|6 Liaoning|6.8|11.1|17.6|31.6|7.2|7.5|7.4|
|9 Shanghai|24.8|40.3|61.6|140.7|6.7|10.6|8.7|
|10 Jiangsu|4.3|8.7|17.2|40.8|9.9|11.6|10.8|
|11 Zhejiang|3.3|8.1|15.3|38.0|10.7|12.7|11.6|
|13 Fujian|2.7|5.7|11.0|29.0|9.7|13.0|11.3|
|15 Shandong|3.2|6.1|11.0|26.1|8.7|11.6|10.1|
|19 Guangdong|3.7|7.3|16.4|36.0|10.1|11.8|10.9|
|**Central Region**|**3.1**|**5.6**|**8.4**|**16.9**|**7.3**|**8.8**|**8.1**|
|4 Shanxi|3.6|6.4|9.1|16.1|6.7|7.5|7.1|
|5 Nei Mongol|3.2|6.3|9.6|17.5|8.2|8.0|8.1|
|7 Jilin|3.8|6.7|11.0|21.2|8.0|8.3|8.2|
|8 Heilongjiang|5.6|8.4|12.2|21.0|5.6|7.1|6.3|
|12 Anhui|2.4|4.8|6.6|15.5|8.1|9.7|8.8|
|14 Jiangxi|2.7|4.9|7.8|16.2|7.5|9.6|8.5|
|16 Henan|2.3|4.5|7.0|14.8|8.3|9.4|8.9|
|17 Hubei|3.3|6.6|9.7|21.1|8.1|9.6|8.8|
|18 Hunan|2.9|4.6|7.0|13.6|6.4|8.7|7.5|
|**Western Region**|**2.5**|**4.5**|**7.0**|**12.6**|**7.3**|**7.9**|**7.6**|
|20 Guangxi|2.2|3.3|5.3|10.6|5.2|10.0|7.4|
|21 Sichuan|2.5|4.8|7.4|13.6|7.7|8.4|8.0|
|22 Guizhou|1.7|3.3|4.7|7.7|7.7|6.3|7.1|
|23 Yunnan|2.2|4.0|6.5|11.4|7.9|7.6|7.8|
|24 Shaanxi|2.9|5.0|7.8|14.2|7.8|7.2|7.5|
|25 Gansu|3.5|5.2|8.4|14.9|6.7|7.2|6.9|
|26 Qinghai|4.3|6.3|8.1|13.6|4.8|6.3|5.5|
|27 Ningxia|3.7|6.2|8.9|15.3|7.1|6.5|6.8|
|28 Xinjiang|3.1|6.2|11.0|17.7|8.7|7.6|8.2|
|**Total China**|**3.6**|**6.6**|**10.9**|**22.9**|**7.9**|**9.8**|**8.9**|

Data source : _China Statistical Yearbooks_ and authors’ calculations _._ The data of Chongqing are included in Sichuan for a better coherence; Hainan (an island and a Special Economic Zone) and Xizang are excluded for their special characteristics.

As argued by Léon (1998), the development in China is polarized in the coastal region. The infrastructure investment has not kept pace with the demand, which hinders the spillover of the economic development to the entire territory. The difference in the cumulative regional growth results in the widening of the intra-national disparities.

6
# Table 2

Public Expenditures on Active and Passive Labor<sup>Market Programs</sup> In Selected OECD Countries, as a Percentage of GDP,<sup>1988</sup>

|CoP ntry|Employ.<br>services|Adult training|Active<br>Job creation/<br>employ.<br>subsidies|Other"|Total|Passive|Unemploy.<br>rate|
|---|---|---|---|---|---|---|---|
|Australia|0.10|0.05|0.06|0.09|0.30|1.03|72|
|Canada|0.20|0.20|0.02|0.10|0.52|1.59|7.7|
|Japan|0.03|0.03|0.13|0.01|020|0.39|2.5|
|Sweden|020|0.51|0.20|0.88|1.79|0.80|1.6|
|U.K.|0.14|0.14|0.20|0.28|0.77|1.74|9.0|
|U.S.|0.06|0.11|0.01|0.06|025|r 44|_SA_|

Source: Public expenditures on labor market programs:<sup>OECD Emcobvment Outlook, July 1989, Table A.1. Unemployment rates: OECD</sup> EmDRoment Outlook, June 1989, Table R17.

# "Includes special youth measures and programs<sup>for the disabled.</sup>
Table 16: **Value added shares of exports by sector – Korea**

|||1992|1995|1997|2004|2007|
|---|---|---|---|---|---|---|
|Korea|Primary|0.415|0.223|0.203|0.119|0.084|
|Direct VA share|Textiles and Clothing|0.208|0.206|0.275|0.270|0.265|
||Machinery|0.265|0.269|0.270|0.262|0.260|
||other Manufacturing|0.247|0.251|0.260|0.223|0.194|
||Trade and Transport Services|0.600|0.587|0.588|0.456|0.436|
||Commercial Services|0.491|0.476|0.532|0.515|0.498|
||Public Services|0.695|0.673|0.675|0.686|0.678|
|||1992|1995|1997|2004|2007|
|Korea|Primary|1.431|1.014|0.701|0.563|0.393|
|Total VA share|Textiles and Clothing|0.315|0.326|0.354|0.375|0.380|
||Machinery|0.359|0.348|0.373|0.358|0.356|
||other Manufacturing|0.627|0.673|0.634|0.582|0.473|
||Trade and Transport services|0.997|0.957|0.921|0.800|0.741|
||Commercial Services|3.522|3.510|2.564|3.193|2.108|
||Public Services|3.257|1.221|2.361|2.973|3.324|

Table 17: **Relative intensity index based on forward linkages – Korea**

|||1992|1995|1997|2001|2004|2007|
|---|---|---|---|---|---|---|---|
|Korea:|Primary|0.099|0.049|0.046|0.037|0.037|0.038|
|RII|Textiles and Clothing|0.116|0.075|0.065|0.058|0.034|0.021|
||Machinery|0.184|0.226|0.259|0.282|0.342|0.348|
||other Manufacturing|0.227|0.208|0.192|0.188|0.179|0.165|
||Trade and Transport Services|0.185|0.212|0.175|0.153|0.149|0.148|
||Commercial Services|0.166|0.187|0.225|0.242|0.212|0.234|
||Public Services|0.023|0.044|0.038|0.041|0.048|0.046|

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