rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3.  Value Added in 2000 by Russian Regional Market and by Sector**

<u>(in billions of 2001 rubles)</u><sup>a/</sup>

|Sector<sup>b/</sup>|Central|Far East|North|Siberia|South|Urals|Moscow|St.<br>Petersb.|Tumen|North-<br>west|Sector<br>total|
|---|---|---|---|---|---|---|---|---|---|---|---|
|MAR|4.7|1.3|1.4|7.2|4.1|14.7|4.0|1.7|6.9|1.0|47.1|
|AGR|110.2|17.5|7.4|84.8|125.2|155.3|15.2|11.8|8.2|15.1|550.7|
|AIR|3.6|1.3|1.0|5.3|4.5|10.0|14.9|1.6|2.5|0.6|45.2|
|CHM|13.9|0.3|1.6|10.6|9.3|35.6|7.7|2.0|0.1|4.2|85.4|
|CNM|10.3|1.1|0.7|4.6|6.7|12.2|8.8|2.4|0.8|0.7|48.2|
|COL|0.1|5.2|3.2|32.6|1.7|1.2|||||43.9|
|CON|76.4|7.1|6.1|54.7|105.2|151.6|138.6|29.7|24.1|11.4|605.0|
|CRU||4.5|16.9|9.9|37.7|97.5|||282.9|1.0|450.3|
|ELE|26.7|7.8|7.1|34.7|22.9|62.9|42.9|10.5|19.1|5.8|240.4|
|FIN|43.5|16.4|14.3|60.7|44.0|106.8|115.7|20.8|47.0|9.0|478.0|
|FME|19.0|0.5|1.6|9.0|3.9|33.8|2.3|1.1||11.3|82.5|
|FOO|24.9|9.2|3.7|17.4|24.0|28.1|33.9|17.7|0.5|4.7|164.2|
|GAS||0.2|0.4|0.5|1.5|2.8|||52.2|0.0|57.5|
|HEA|42.3|17.5|14.6|58.8|45.0|100.6|126.3|21.8|40.6|8.9|476.4|
|HOU|22.1|8.8|6.7|29.1|24.8|50.9|68.0|11.2|13.7|4.6|239.8|
|MWO|38.7|11.2|2.6|19.4|18.0|101.8|45.4|18.9|3.0|3.6|262.7|
|NFM|2.5|8.1|9.3|113.2|4.8|46.0|7.1|4.4||0.2|195.5|
|OIL|12.2|4.7|1.5|17.4|10.1|39.2|4.4|7.2|3.9||100.6|
|OTH|7.7|2.0|2.2|8.8|4.9|18.4|6.3|3.3|7.4|1.9|62.8|
|OTI|13.6|0.6|0.7|2.5|4.0|9.2|9.9|2.6|0.1|0.9|44.0|
|PIP|1.5|0.5|0.5|2.0|1.7|5.4|1.2|0.7|5.2|0.2|18.7|
|PST|2.7|1.0|0.8|3.5|2.9|6.3|6.8|1.3|2.8|0.6|28.7|
|RLW|23.5|6.9|6.3|30.2|20.5|53.8|34.5|9.4|16.1|6.2|207.3|
|SCI|8.8|3.2|2.4|10.4|7.8|22.6|16.5|4.1|9.8|1.5|87.0|
|TMS|7.6|2.8|2.2|10.1|8.3|18.7|21.1|3.6|7.4|1.5|83.3|
|TPP|6.8|4.4|15.1|13.1|2.6|13.0|6.8|7.3|0.6|4.2|73.9|
|TRD|205.4|78.2|67.0|300.6|223.3|535.3|456.6|101.6|386.8|48.2|2,402.9|
|TRK|10.2|3.5|3.1|13.6|11.3|24.6|23.0|4.7|12.9|2.2|109.2|
|TRO|4.5|1.5|1.3|5.7|5.1|11.2|10.7|2.0|5.7|0.9|48.7|
|CLI|3.8|1.6|1.2|5.0|4.4|8.6|12.9|2.0|1.9|0.8|42.3|
|Market||||||||||||
|total|747.2|228.8|202.7|975.2|790.0|1,778.0|1,241.5|305.6|962.3|151.1|7,382.4|

a/ Value added defined here does not include taxes.

b/ Sector codes are in Table 1, oblasts in the markets are listed in Table 2.

Source: Regions of Russia, Roskomstat, and authors' calculations.

39
non‐routine cognitive occupations have more years of education, while those who move into non‐routine manual occupations have roughly the same level of education than those who stay in routine jobs. Those who move from routine occupations into unemployment have a substantially lower educational attainment. For instance, a routine worker who does not change occupations in Germany has 12 years of education, while one that moves into unemployment has only 8.9 years of education. In Germany and Spain, routine workers who move into any other status including unemployment are younger than those who stay in routine jobs. The same holds in Poland, although the share of routine workers who move into non‐routine cognitive positions tends to be older than the rest.

<u>Table 3 – Flows out of routine‐task intensive occupations, individual characteristics</u>

|||<br>|<br> <br>Observed o|<br> <br>ccupations||
|---|---|---|---|---|---|
|Country|Characteristic|Not<br>employed|Non‐routine,<br>manual|Non‐routine,<br>cognitive|Routine|
||||Movers||Stayers|
||Percentage of women|37.8|83.6|56.6|48.6|
|Germany|Years of schooling|8.9|11.1|13.3|12.0|
||Age|39.3|42.1|40.5|43.9|
||Percentage of women|64.7|49.1|41.0|25.5|
|Poland|Years of schooling|9.8|12.2|14.2|11.8|
||Age|35.4|39.0|39.0|40.2|
||Percentage of women|38.0|45.1|33.3|37.8|
|Spain|Years of schooling|11.8|12.0|14.4|12.1|
||Age|38.9|39.3|41.9|39.9|

Note: this table shows a set of descriptive characteristics of workers grouped according to their observed occupation in contrast with their simulated routine occupation. So, for example, the first column shows individuals who in the simulation would have been employed in routine occupations but that, because of the change in the parameters of the occupation model, are now not employed. The other columns are interpreted in the same way.

Occupational changes have significant implications for earnings. Figure 11 shows the simulated change in the wage of workers who in the simulation would have been in routine jobs but then move into non‐ routine manual or cognitive occupations. As expected, the change in earnings is significant. For example, a displaced German routine worker moving to a non‐routine manual occupation is expected to experience a close to 50 percent decline in wages; in Poland and Spain the corresponding losses would be of about 20 and 30 percent, respectively. Clearly, gains in wages are shown for the fewer displaced workers who move up in the occupation distribution.

27
**_Global Value Chains in the Electronics Industry                       Sturgeon and Kawakami_**

**Figure 5** Value Capture in Notebook PC GVC in Three Competitors

<!-- Start of picture text --> 70<br>Ratio of gross margin to net sales ‐ Percent<br>Intel (platform leader)<br>60<br>50<br>40<br>30<br>Dell (lead firm)<br>20<br>10<br>Quanta (contract manufacturer)<br>0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text -->

**_Source:_** Kawakami (forthcoming), based on annual reports.

Lead firms were affected by Intel’s platform strategy in different ways. American PC firms—Dell, Gateway, and Compaq—embraced the new platforms as a way to increase their market share in notebook PCs. Exploiting the increased modularity of the product, these firms stepped up their use of Taiwanese ODMs to reduce costs and speed product life cycles. The market for notebook computers expanded rapidly as a flood of relatively inexpensive machines hit the market. By contrast, Japanese firms, which had based high profitability on superior R&D and production capabilities, resisted the commoditization of the product. Toshiba, the world’s leading notebook PC company in 1997, faced not only a sharp decrease in market share, but also mounting losses from its notebook PC unit. NEC, the world’s second-largest notebook PC brand firm at the time, and other Japanese firms suffered similar fates. This led to a rapid increase in orders from Japanese lead firms to Taiwanese ODMs, but only for their lower cost models (see Sturgeon 2007). As time wore on, high-cost notebook PCs made in-house by Japanese lead firms were increasingly restricted to the Japanese market. In this way, lead firms across the notebook PC industry started to specialize in concept creation, brand marketing and the management of suppliers and to contract out mass production, product development, logistics, and after-service to Taiwanese firms.

32
composite measure of restructuring. The early literature focused on the impact on company performance of different types of mainly domestic owners – insiders, outsiders, investment funds – and was based largely on country-specific survey datasets that were frequently quite small and not necessarily representative. It did not examine in a major way the effects of foreign direct investment (FDI) as this remained relatively low until the mid-1990s in CEE (except for Hungary and the Czech Republic) and until the new millennium in the CIS (see Klaus Meyer, 1998).

D-M concluded that privatization to outside owners resulted in 50 percent more restructuring than privatization to insiders (current managers or workers). Privatization to workers had no effect in CEE and a negative effect in the CIS. Investment funds, foreign ownership and other block-holders were found to produce more than ten times as much restructuring as diffuse individual ownership. Hardening of budgets constraints (i.e. curtailing firms’ access to formal or informal state subsidies) was also found to have a positive effect on restructuring. Among other factors, import competition had a positive effect on performance in CEE, but a negative effect in CIS. Overall, D-M noted that the impact of privatization on company performance was typically positive and statistically significant in CEE, but statistically insignificant in CIS. They suggested that this could be explained by the more widespread occurrence of insider ownership after privatization and a weaker institutional environment leading to less effective governance by outside owners in the CIS countries.<sup>1</sup> Finally, D-M also pointed out that about one-half of the studies they surveyed did not take into account the endogeneity and selection issues associated with changing ownership and firm performance, and they urged future research to tackle this issue.<sup>2</sup> The present study highlights several significant shifts of emphasis in the literature in recent years. Firstly as ownership structures have evolved, research interest has shifted

> 1 This was also argued in a short survey by Sergei Guriev and William Megginson (2006) which related the mixed results on the impact of privatization in transition economies to the slow progress in microeconomic and legal reform, especially in CIS countries.

> 2 D-M’s arguments were developed in Megginson (2005). He concluded that “mass” privatization often led to disappointing outcomes, perhaps because it was frequently associated with insider ownership. Indeed, despite their massive privatization programs, because of their relatively low levels of development and the widespread use of “voucher privatization”, transition economies only generated 5% of the total global privatization proceeds between 1990 and 2000.

2
Table 4: Bilateral Tariffs and Domestic Value Added in Foreign Production: GSP Eligible vs. GSP Ineligible Country Pairs

|Panel A: No RTA|Sample||||
|---|---|---|---|---|
||O|LS|Linea|r-IV|
||(1)|(2)|(3)|(4)|
|Log DVA (GSP ineligible): _GSPij×_ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup><br>|-0.13*<br>(0.07)|-0.19**<br>(0.077)|-0.14***<br>(0.06)|-0.18**<br>(0.08)|
|Log DVA (GSP eligible): [1_−GSPij_]_×_ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup>|-0.18**<br>(0.07)|-0.26***<br>(0.09)|-0.25***<br>(0.06)|-0.32***<br>(0.09)|
|GSP eligible: _GSPijt_|-0.64***|-0.58**|-0.42**|-0.40|
||(0.24)|(0.23)|(0.17)|(0.26)|
|R-Squared|8,187|8,187|8,187|8,187|
|Observations|0.998|0.994|0.998|0.994|
|Panel B: No RTA & GSP|Eligible S|ample|||
||O|LS|Linea|r-IV|
||(5)|(6)|(7)|(8)|
|Log DVA: ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup>|-0.15|-0.22*|-0.16***|-0.23**|
||(0.11)|(0.11)|(0.06)|(0.10)|
|R-Squared|3,039|3,039|3,039|3,039|
|Observations|0.999|0.995|0.998|0.994|
|Panel C: No RTA & GSP|Ineligible <br>O|Sample<br>LS|Linea|r-IV|
||(9)|(10)|(11)|(12)|
|Log DVA: ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup>|-0.18|-0.21*|-0.22***|-0.23**|
||(0.11)|(0.12)|(0.07)|(0.11)|
|R-Squared|5,148|5,148|5,148|5,148|
|Observations|0.998|0.996|0.998|0.996|
|Column Fixed Efects (all panels)|||||
|Importer-Industry-Year-Decile|Y|N|Y|N|
|Importer-Industry-Year|N|Y|N|Y|
|Exporter-Industry-Year|Y|Y|Y|Y|

Note: The regression specification is based on Equation (16). The dependent variable in all columns is the applied bilateral tariff of country _i_ in industry _x_ against exporter _j_ at time _t_ : _t_<sup>_i_</sup> _xjt_<sup>.LogDVA(ln(</sup><sup>_DV Aj_</sup> _ijt_<sup>))</sup> is domestic value added from the importing country( _i_ ) embodied in final production in industry _x_ in the exporting country ( _j_ ). See Section 4.1 for the definition of GSP Eligibility. No RTA Sample includes only countries pairs and years with no reciprocal trade agreement in force. Standard errors (in parentheses) are clustered by importer-exporter pair. Significance levels: * _p < ._ 1 , ** _p < ._ 05, *** _p < ._ 01.

40
# **1. Introduction**

Labor market statistics are critical for assessing and understanding how an economy functions. They are paramount for identifying sources of income, especially in developing countries and particularly for poor people for whom labor is their most important asset. In practice, how labor market statistics are captured in household surveys varies widely. Recall period, question sequencing, the use of screening questions, and the respondent interviewed vary across surveys, both within and across countries. The wealth of evidence on the quality and reliability of labor statistics in household surveys comes largely from the United States (see Bound, Brown and Mathiowetz., 2001, for a thorough review). In the developing country context, little is known whether these differences have an effect on the labor market statistics produced, and whether they affect the estimates of the economic relationships estimated from the data. It is difficult to extrapolate from the U.S. studies because employment patterns are quite different in East Africa than in the U.S. which may create alternative impacts of measurement error on labor statistics and parameter estimates.<sup>1</sup> In this paper, we provide evidence from a survey experiment in which we varied respondent type (self-reporting versus proxy respondent) and the level of detail of the labor module to study variations in labor force participation, hours, earnings, sectoral distribution, and employment status.

Although the method by which employment data are collected may have a large effect on the labor market statistics, few studies in low-income countries have attempted to

> 1 An important part of the U.S. literature focuses on response ‘errors’ which impact unemployment measurement. In most of this literature (Abowd and Zellner, 1985; Poterba and Summers, 1986), the emphasis focuses on developing procedures to adjust for spurious transitions arising because of classification errors (in a panel setting) rather than on analyzing which aspects of the questionnaire or the interview process originate those errors.

2
according to societal rules or social hierarchy, not according to the officially regulated ordering of services.

# **Box 2. Enforcement mechanisms**

Rules are supported by different enforcement mechanisms. Examples are police, jail, courts, reputational sanctions, and various community/ group sanctions. The first three elements of enforcement mechanisms may be categorized as “formal”. There may be private sector formulated formal mechanisms as well, such as rules governing membership or acceptable behavior in a private club.<sup>6</sup> Formal enforcement mechanisms work properly when informal rules (e.g. social norms) are consistent with the formal rules. For example, bank loans may not be repaid by borrowers that have the ability to pay. Even if creditors go to court, they may not succeed in getting repaid or in confiscating the collateral they are owed because courts will not adjudicate according to the law’s intent; for example, if the creditor is rich and the debtor is poor, and social codes of conduct favor more equity than the current legal system provides for, <u>judges will not want to move against their community.</u> _From beginning to end, several institutions govern any single transaction._ The discussion above described how formal and informal rules work together. In each economic transaction, multiple (formal and informal) rules interact. For example, in buying a house, rules governing property sale or resale, rules for title recording, realtor’s licensing requirements, banking rules governing lending and financial disclosure, social norms governing personal indebtedness (e.g. religious rules on paying and receiving interest), and laws of inheritance, rules regarding taxation of mortgage interest or property, among other things, are relevant. Simply changing a single law may not affect outcomes as intended. It is the system of institutions governing each transaction that matters for how a transaction is done, if it is done at all, and who may benefit or lose from the transaction. For example, if a bankruptcy law is established, yet accounting regulations do not exist, or accounting rules vary from firm to firm, it will be hard to designate a firm as “bankrupt” in a consistent manner.<sup>7</sup>

# **What are the main functions of institutions (what do they do)?**

> 6 However, these would be nested in the country’s legal system.

> 7 The WDR 2002 includes various examples of institutions in different market transactions.

5
self-provision at least part of their energy needs. This self-production of energy may be quite inefficient for many firms. We need a better understanding of how these various factors add up into productivity, and such a study would be substantially more challenging to model than our current focus on unique material inputs.

Second, and related, our current focus on material inputs has been almost exclusively from the perspective of individual firms, without worrying too much about spillovers or externalities outside of the firm. We believe this is reasonable for a study of material input counts, especially since most of the results indicate a positive role for these inputs into the productivity of the plants holding fixed other inputs. Moreover, where we observe an independent role for higher input usage in the local area, the spillover effect appears positive (although unidentified econometrically). This approach, however, would be inadequate for considering the self-production of energy by plants, given the potential pollution costs and related externalities across plants and other parts of society. Self-production may be associated with greater pollution through plants choosing dirtier but cheaper techniques, being less efficient at energy production than a dedicated facility, or a combination of these two.

This relates to another important area for future consideration. Ghani, Goswami, and Kerr (2012a) describe how the long-run trend for Indian manufacturing is that organized sector firms are moving towards rural locations, while the opposite is true for unorganized sector firms. Our current study considers a cross-section from 2000, and future work should marry these two studies by studying cohorts over time. While data limitations will prevent a perfect analysis, this study would help to answer some basic questions: Is the movement of firms in the organized sector from urban to rural areas associated with productivity declines due to poorer input conditions (e.g., land-use regulations pushing firms towards non-optimal conditions)? If input counts remain high, do we see instead positive uptake by other firms in the area? The latter might occur through diffusion of managerial practice (e.g., Bloom et al. 2013) or positive spillovers in local activity (e.g., unorganized sector firms benefit from better supplier conditions that result from organized sector firms being located in the district).<sup>10</sup>

> 10 Greenstone, Hornbeck, and Morretti (2010) find substantial productivity gains to existing rural firms in locations that win bids for “million dollar plants” in the United States, while Falck et al. (2013) find adverse effects for local West German incumbents from relocating East German firms. The present Indian case may fall in between,

20
I POLICY RESEARCH WORKING PAPER 1709

# Summary findings

Comparisons of poverty rates are only rarely based on identical underlying definitions of welfare. Lanjouw and Lanjouw examine the sensitivity of poverty rates calculated from alternative definitions of consumption. They consider what theory can say about the direction of bias in comparisons and show that under certain conditions robust comparisons are possible.

Their robustness result is used as a baseline to explore the tradeoffs involved in aggregating noisy expenditure components. Although nonfood expenditures are often thought to be especially poorly measured, the authors' data indicate that the more comprehensive is the measure of consumption spending, the better it is as a measure of welfare.

Data from Ecuador, El Salvador, and Pakistan show that the magnitude of biases can bc substantial.

- - This paper a product of the Poverty and Hurnan Resources Division, Policy Research Department is part of a larger effort in the department to improve the reliability and comparability of poverty measures. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Andrea Ramirez, room N8-036, telephone 202-458-5734. fax 202-522-1 153, Internet address mramirez@worldbank.org. January 1997. (26 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the_ **_countries_** _they represent._

## Prodtuced by the Policy Research Dissemination Center
In HTGRs, the fuel particles consist of fissionable fuel kernels with tri-structural isotropic (TRISO) coating.<sup>19</sup> The TRISO coating system constitutes a miniature pressure vessel that is capable of containing the readionuclides and gases generated by fission of the nuclear material in the kernel. One of the coating layers consists of silicon carbide (a strong refractory material) which can retain radionuclides at extremely high temperatures under all accident conditions—temperatures can remain at 1600°C for several hundred hours without loss of particle coating integrity. Furthermore, the graphite holding the TRISO-coated particles together can withstand even higher temperatures without structural damage.<sup>20</sup> And the massive graphite structures in the core create an extremely large heat capacity. The combination of large thermal margins, low power density of the core, and relatively large length-to-diameter ratio of the core, allow for very slow and stable response to transients caused by initiating events and for passive heat removal (INL, 2011).

The effectiveness of passive safety features can be illustrated by comparing outcomes from probabilistic risk analysis (PRA). In 1991, a Level-2 PRA was developed for the EBR-II fast neutron spectrum experimental breeder reactor—a 21 MWe plant—to compare its operational risk to that of commercial LWR‘s for which PRA‘s were available. EBR-II employs an extensive array of passive and inherent safety measures to back up traditional active safety systems. This PRA exercise showed that for EBR-II the risk of simply violating a fuel pin technical specification (with no core damage) is less than the risk of significant core disruption for the LWRs of the time. The point of the PRA comparisons is that application of passive and inherent safety measures as incorporated in SMRs can help to overcome the increase in numbers of SMRs needed to deliver the same societal energy provided by a smaller number of large-sized LWRs. Similarly, preliminary Level-1 PRA results for the NuScale Power Reactor indicate a total single-module mean CDF of 2.8x10<sup>-8</sup> /reactor-year, well below that of existing nuclear plants. And for the VK-300, the probability of severe core damage has been estimated to be less than 2.0x10<sup>-8</sup> /reactor-year (Hill et al, 1998; Kuznetsov and Gabaraev, 2007; Modarres, 2010).

> 19 Fuel kernels are coated with successive layers. The innermost layer is porous carbon, which allows fission products to collect without creating internal pressure. The next layer is pyrolytic carbon, followed by silicon carbide and a final pyrolytic carbon layer. These outer three layers create a compound barrier against fission product release—the   silicon carbide coating plays the dominant role.

> 20 Thousands of TRICO-coated particles are held together in a carbonaceous material forming a cylindrical fuel compact (prismatic HTGR) or spherical fuel element (pebble bed HTGR).

37