rafmacalaba/datause-encoder-data huggingface dataset:
10 from simple to more sophisticated products. The third element involves the relocation of industries across countries, from advanced to developing countries as the latter undergo the process of convergence.

Are the Asian geese described by Akamatsu still flying? This pattern does appear to have persisted in Asia over the past two decades. For example, in the early 1990s China was already a dominant player in some light manufactures such as footwear and toys (table 1). Japan continued to be a dominant player in toys but was clearly moving up the technology ladder to more sophisticated games, such as Nintendo and Sony PlayStation. China, a low-income country in the 1990s, also still exported live animals on a large scale. In the 2000s it was able to move up the product ladder to more sophisticated manufactures and overtake Japan in world export shares in plastics, electrical machinery and parts, and television receivers. Korea was a major player in exports of live animals in the early 1990s but has now moved out of that primary sector. India lags in market shares but has gradually moved up in footwear.

**Table 1. Geese Still Flying in Asia:** **<u>Country Rankings in Selected Industries, 1992 and 2008</u>**

||**Live an**<br>|**imals**<br>|**Pharma**<br>|**ceuticals**<br>|**Foot**<br>|**wear **<br>|**Iron **<br>|**& steel **<br>|
|---|---|---|---|---|---|---|---|---|
|**Country**|**1992**|**2008**|**1992**|**2008**|**1992**|**2008**|**1992**|**2008**|
|China|1|1|2|3|1|1|3|1|
|India|5|4|3|1|4|2|4|4|
|Japan|3|3|1|2|5|5|1|2|
|Korea, Rep.|2|5|4|4|2|4|2|3|
|Thailand|4|2|5|5|3|3|5|5|
||**Plas**|**tics**|**Elect**<br>**machine**|**rical**<br>**ry, parts**|**Tele**<br>**rece**|**vision**<br>**ivers**|**T**|**oys**|
|**Country**|**1992**|**2008**|**1992**|**2008**|**1992**|**2008**|**1992**|**2008**|
|China|3|1|3|1|3|1|1|1|
|India|5|5|5|5|5|5|5|5|
|Japan|1|2|1|2|1|2|2|2|
|Korea, Rep.|2|3|2|3|2|3|3|4|
|Thailand|4|4|4|4|4|4|4|3|

Source: World Bank, WITS database. Note: Rankings established from data at the two-digit level for exports in the WITS database.

The international division of labor and production offers another angle for examining the flyinggeese pattern. Table 2, based on historical trade statistics for footwear, offers credibility to the flying-geese hypothesis. It is constructed using revealed comparative advantage indexes for lead countries and latecomers in that industry. The table shows that Japan had a revealed comparative advantage in the early 1960s, during the earlier light manufacturing phase of its development. Later on other countries moved into the picture and began to take over larger shares of global production. The pattern displayed does not conform precisely with a ―pure‖ dynamics of ―flying geese.‖ Real-world data always involve some ―noise‖ because products with different sophistication and different capital and technology intensities may be grouped in the same category. And government interventions may cause some deviation of industrial structure away from the optimal one determined by the country‘s comparative advantage. Still, the general picture is consistent with the theory.
# **Table A7.1.  Mean differences**

||Left the sample vs.<br>Control Group|Realiz<br>|ed Sample<br>|
|---|---|---|---|
|||Sequence vs.<br>Control|Coaches vs.<br>Control|
|_Student-level variables_||||
|Percent female|-0.001|0.026|0.021|
|Age|0.081*|-0.07***|-0.023|
|Percent of Argentines|-0.141***|0.015|0.022|
|Mother or father education (secondary)|-0.082*|0.024|0.051*|
|Have internet in their home|-0.02|0.028*|0.025|
|Have air conditioning in their home|-0.03|0.007|0.023|
|Have at least one car in their home|-0.025|-0.015|0.02|
|Took holidays in the last two years|-0.016|-0.024|-0.02|
|At most, missed one class per month|-0.01|0.018|0.024|
|Scored "Very Good" in Language|-0.015|0.015|0.027|
|Scored "Very Good" in Mathematics|0.023|0.017|0.024|
|_Teacher-level variables_||||
|Percent female|0.172|0.061|0.069|
|Age|-3.503|2.304|2.647|
|Percent with Post-Graduate Certificate|-0.179|0.14|0.069|
|Percent with Universitary degree|-1.572|1.604|1.607|
|Seniority in teaching (in years)|0.166|0.003|0.138*|
|Seniority in teaching 7th grade (in years)|-2.222|-0.424|-0.676|
|Seniority in teaching Science (in years)|1.27|1.656|1.137|
|Seniority in teaching at the current school (in<br>years)|0.434|0.416|-0.224|
|Percent of teachers that used a teaching<br>sequence|-0.021|-0.14|-0.069|
|_School-level variables_||||

21
cooperative spirit that should have prevailed between policy and regulation did not develop because of a narrow reading of the legislation. Nevertheless, the divergence in interpretation never developed into a crisis, and pragmatic compromises were always found. As shown in the paper, the connectivity to fixed and mobile telephony and the Internet has significantly improved since the reform was launched, and will continue to improve as competition consolidates in the market. The main uncertainties facing the development of the sector are mostly political in nature and entail two policy recommendations: (a) the government must at least maintain or increase its political support to the sector reform by building the capacity of newly established institutions including developing new skills required; and (b) the delays affecting the incorporation of AT require more effective measures and actions (operational, organizational, financial, etc.) from the shareholders. Unless AT’s weaknesses are effectively addressed, the long-run sustainability of the reform will remain at risk.

Overall, the telecommunications reform not only seeded the conditions for sustainable growth and development in the sector, but has also impacted reforms in at least two other sectors. First, the government launched a broad reform program in the energy sector involving restructuring of the oil sector as well as reform of the electricity sector. In the latter, the enactment of new legislation led to the unbundling of the electricity market (generation, transmission, distribution) and the establishment of an electricity regulatory board. Second, a tender was organized to privatize the international airport in Algiers, though unsuccessfully.

# **7 REFERENCES**

Pallast, G; Oppenheim, J; and McGregor, T (2003), _Democracy and regulation – how the public can govern essential services_ , Pluto Press Gomez-Ibanez, J (2003), _Regulating infrastructure – Monopoly, Contracts, and Discretion_ , Harvard University Press.

- Levy, Brian and Spiller, Pablo (1993), “Regulation, Institution, and Commitment in Telecommunications – a comparative analysis of five country studies”, in Proceedings of the World Bank annual conference on development economics, pp. 215-52.

- World Bank (2003), Foundations for the development of ICT in Algeria, ESW Report No 25841, Washington DC

World Bank (2002), A medium term macro-economic strategy for Algeria, mimeo, Washington DC World Bank (2000), Appraisal report No 20509 AL, Post and Telecommunications sector reform loan, mimeo, Washington DC 22
Aguayo-Tellez, E., J. Airola, C. Juhn and C. Villegas-Sanchez. 2013. Did Trade Liberalization Help Women? The Case of Mexico in the 1990s. Research in Labor Economics 38: 1-35.

Albrecht, J., A. Björklund, and S. Vroman. 2003. Is There a Glass Ceiling in Sweden? Journal of Labor Economics, 21(1): 145-177.

Ardagna, S., and A. Lusardi. 2008. Explaining International Differences in Entrepreneurship: The Role of Individual Characteristics and Regulatory Constraints. NBER Working Paper # 14012 Ashenfelter, O. and T. Hannan. 1986. Sex Discrimination and Product Market Competition: The Case of the Banking Industry. Quarterly Journal of Economics, 101(1): 149-174.

Banerjee, A., E. Duflo and N. Qian. 2012. On the Road: Access to Transportation Infrastructure and Economic Growth in China. NBER Working Papers # 17897, National Bureau of Economic Research Banerjee, A. and S. Raju. 2009. Gendered Mobility: Women Migrants and Work in Urban India. Economic and Political Weekly, 44(28): 115-123 Banerjee, P. and C. Veermani. 2015. Trade Liberalisation and Women's Employment Intensity: Analysis of India's Manufacturing Industries. Indira Gandhi Institute of Development Research (IGIDR) Working Paper #018 Becker, Gary S. 1957. The Economics of Discrimination. Chicago: Chicago University Press.

Bernard, A., S. Redding, and P. Schott. 2006. Multi-Product Firms and Trade Liberalization,’’ NBER working paper # 12782. National Bureau of Economic Research.

Besley, T. and R. Burgess. 2004. Can Labor Regulation Hinder Economic Performance? Evidence from India. Quarterly Journal of Economics 119(1): 91-134.

Bhalla, S. S., and Kaur, R. 2011. Labour Force Participation of Women in India: Some facts, some queries. LSE Asia Research Centre, Working Paper # 40.

Black, S., and E. Brainerd 2004. Importing Equality? The Impact of Globalization on Gender Discrimination. Industrial and Labor Relations Review, 57(4): 540-559.

Black, S. and P. Strahan 2001. The Division of Spoils: Rent-Sharing and Discrimination in a Regulated Industry. American Economic Review, 91(4): 814-831.

Bolaky, B., and Caroline Freund. 2004. Trade, Regulations and Growth,’’ World Bank Policy Research working paper # 3255.

Bollard, A., P. Klenow and G. Sharma. 2013. India's Mysterious Manufacturing Miracle. Review of Economic Dynamics, 16(1): 59-85.

35
The specific ASI variables we use are described below: **Firm Age** is defined as the year of the census - year of initial production reported by the firms. We define 9 age bins as follows : <5, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, and 40+. **Firm Size** is the total number of workers which includes workers employed directly, workers employed through contractors, supervisory and managerial staff, other employees, working proprietors, unpaid family workers, and unpaid working members if cooperative factory. As a measure of growth rates, we construct **Firm Size Ratio** which is the ratio of each firm’s firm size scaled by the average firm size of all firms in its birth cohort. Thus, we scale the employment of the each firm in youngest age bin (<5) in 2004 by the average size of that age bin in 2004, of the firms in the age bin (5-9) in 2004 by the average size in the youngest age bin (<5) in 1999, of the firms in the age bin (10-14) in 2004 by the average size in the youngest age bin (<5) in 1994, of the firms in the age bin (15-19) in 2004 by the average size in the youngest age bin (<5) in 1989, of the firms in the age bin (2024) in 2004 by the average size in the youngest age bin (<5) in 1983, and so on. Since our sample period extends from 1983/84 to 2004/05 we are able to construct this ratio for only the following age bins - <5, 5-9, 10-14, 15-19, and 20-24.

To deal with outliers, we winsorize the bottom and top 1% of all plant-level variables and drop firms that stated initial year of production to be before 1800 and clear data errors where the year of initial production is given to be after the year of the survey. The data also provides National Industry Classification (NIC) codes that map onto different revisions of the International Standard Industry Classification (ISIC) codes.<sup>9</sup> Using this we construct three digit

> 9 <mark>The census years 1983/84 uses NIC-70 which maps onto ISIC-Revision 2 at the 3-digit level. The 1989/90 and 1994/95 censuses use NIC-87 which maps onto ISIC-Revision 3 at the 3-digit level. The 1999/00 and 2000/01 censuses use NIC-98 which maps onto ISIC-Revision 3 at the 3-digit level. The 2004/05 census uses NIC-04 which maps onto ISIC-Revision 3.1 at the 3-digit level.</mark>

9
3 serve as a measure of protection actually delivered to any particular industry. Indeed, the marginal rate is likely to vary though time as market circumstances change even when the various set rates themselves are not changed.

The continuing importance also of agricultural nontariff import barriers, especially in the light of the emerging food regulations discussed in Josling, Roberts and Orden (2004), and the continuing use of agricultural production, consumption and export subsidies and taxes, the availability of the special safeguard mechanism, and the use of occasional quantitative export restrictions and prohibitions (as used for food staples in 2008 by numerous developing countries seeking to insulate their food consumers from a major rise in food prices in international markets), mean that the only sure way to measure the actual distortions to farmer and consumer prices in any country is through careful domestic-to-international price comparisons. This is indeed what the OECD Secretariat has been doing systematically for the past two decades in generating producer support estimates and consumer subsidy equivalents (PSEs and CSEs) for the key farm products of high-income countries (OECD 2007a, 2007c) and five non-EU developing countries (OECD 2007b). But there has been no such similar comprehensive pricecomparison exercise undertaken for developing countries. The seminal multi-country study by Krueger, Schiff and Valdes (1988, 1991), for the period from around 1960 to 1984, was for just 15 of today’s non-OECD countries plus Korea, Portugal and Turkey. That K/S/V study had the virtue of also estimating the indirect impact on agricultural incentives of distortions to non-agricultural prices (e.g., through manufacturing tariffs or NTBs) and to the domestic prices of foreign currencies via fixed and multiple exchange rates. It found that, at least up to the mid-1980s, incentives facing farmers in developing countries typically were depressed by government policies, in contrast to the situation in most OECD countries. It is unclear how much that situation has changed over the past two decades in which many developing countries have begun to reform their agricultural and trade policies.

This suggests the need for new empirical analysis aimed at systematic estimation of overall actual delivered rates of distortion to domestic prices for agricultural and food products from policy interventions affecting both agricultural and non-agricultural markets. The core of this paper addresses the methodological issues that need to be faced
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|Table 3(Continued)<br>|||
|---|---|---|
|_Pooled Sample_|_Male Manager_|_Female Manager_|
|**_Household Characteristics_**<br>|||
|Log [Consumption (Birr)]<br>0.1197**<br>|0.1353**<br>|0.0241<br>|
|(0.0516)|(0.0549)|(0.1046)|
|Distance to Closest Market (KM)<br>-0.0041***|-0.0043***|-0.0050**|
|(0.0012)|(0.0012)|(0.0023)|
|Household Size<br>0.0313**|0.0383**|0.0044|
|(0.0151)|(0.0161)|(0.0497)|
|Dependency Ratio<br>-0.1416***|-0.1769***|-0.0616|
|(0.0486)|(0.0590)|(0.0909)|
|More than Half of the Household Production Sold †<br>-0.4102|-0.3439|-0.3692|
|(0.2607)|(0.2878)|(0.4014)|
|Non-agricultural Labor Income †<br>0.0302|0.0412|-0.0342|
|(0.0988)|(0.1069)|(0.1960)|
|**_Household Agro-Ecological Zone Classification_**|||
|Tropic-Warm/Semiarid †<br>2.2569***|2.2417***|3.0512***|
|(0.3752)|(0.3868)|(0.5237)|
|Tropic-Cool/Semiarid †<br>1.2338***|1.0605***|3.2374***|
|(0.3369)|(0.3072)|(0.4988)|
|Tropic-Cool/Subhumid †<br>1.2831***|1.1364***|3.2379***|
|(0.3348)|(0.3076)|(0.4309)|
|Tropic-Cool/Humid †<br>0.5064|0.3326|2.4622***|
|(0.3527)|(0.3276)|(0.4387)|
|**_Shocks_**|||
|Crop Damage †<br>0.0087|-0.0256|-0.0043|
|(0.0634)|(0.0681)|(0.1073)|
|**Observations**<br>1,518|1,277|241|
|**R-Squared**<br>0.339|0.344|0.525|

Note: Clustered Standard Errors at EA (Enumeration Area) Level in Parentheses. *** p<0.01, ** p<0.05, * p<0.1 † Denotes a Dummy Variable 25
<!-- Start of picture text --> 550<br>500<br>450<br>400<br>350 Correlation 0.984<br>350 400 450 500 550<br>HLO Score<br>IRT Score<br><!-- End of picture text --> <!-- Start of picture text --> SUPPLEMENT FIGURE III<br>Comparison to Item Response Theory (IRT) Linking (2000-2010)<br><!-- End of picture text --> _Notes:_ We compare our data to the _Linking International Comparative Student Assessment_ (LINCS) project which uses Item-Response Theory (IRT) methods to link test score data. IRT methods are used to create scores for the underlying international and regional assessments used. However, to compare across assessments, IRT would require enough overlap in the test items. This is not true for a significant enough set of tests and time intervals to create a globally comparable panel data set. However, for a subset of tests, this is the case, such as a series of international studies focused on reading at primary school. The LINCS project produces scores on a globally comparable scale using this subset of data (Steinmann, Strietholt **,** and Bos 2014). We compare results on the HLO as well as the LINCS data for primary school reading on average between 2000-2010.

Supplement Figure III compares scores for the same subject (reading), schooling level (primary) and time period (2000-2010), with a correlation of .984. This comparison indicates that as we expand coverage to 164 countries, we maintain high consistency with alternative measures where there is overlap.

Finally, we compare our primary linking approach using regression and linear linking with two alternative approaches and compare robustness across them. First, we use simple mean linking which introduces a constant adjustment between tests matched with rounds, and which we average across testing rounds. This approach assumes constant standard deviations across tests. Second, we use a ratio between test means and also take an average across rounds. This approach assumes a constant scalar adjustment λ between means and standard deviations across tests. The ratio approach is salient and intuitive for policymakers. However, a potential challenge in applying ratios is that they are in principle sensitive to the scale of the test. For example, given score scales have no absolute zero, in theory we can add 300 points to the mean of each test and preserve the interval properties of the scale, but will alter the conversion ratios (i.e. exchange rates). We address this potential issue by having strict inclusion criteria for the underlying tests: they have a uniform scale with a mean of 500 and standard deviation of 100. “Exchange rates” are derived using the same scale and applied on the same scale. Thus, while in theory changing score scales might bias results, by design this is not the case. This increases the likelihood we capture differences in test difficulty rather than arbitrary scaling variation.

29
# Table 3. Enrollment P'Anning Hubel Province 7th 5-Year Plan for Vocational and Technical Educational Development 1986-1990

||1985<br>EnrolIment|1990<br>Target|Need|
|---|---|---|---|
|1. Rural Agricultural-||||
|Vocational Schools|122.4|200.0|77.8|
|of which:||||
|In urban locatlons|26.0|56.0|30.0|
|In town/country locs.|17.7|32.0|14.3|
|In rural locations|78.7|112.0|33.3|
|2. SWS|34.2|65.0|30.8|
|3. STS<br>of which:|60.1|90.0|29.9|
|In secondary<br>normal schools|23.2|27.8|3.8|
|TOTAL: Voc.Tech. Schools|218.7|355.0|138.3|
|Senior Secondary General||||
|Education Schools|366.0|360.0|-6.0|
|TOTAL: Secondary<br>of which:|582.7|715.0|132.3|
|Voc./Tech.|37.18%|49.65%||
|General|62.81%|50.35%||

(in thousands) Source: Provincial Education Commilssion, Hubel ProvInce The Provincial Education Commission will then allocate quotas for admission to the schools, except for municipally-operated schools (where the municipality receives the overall numbers and makes the allocations to the schools). SVS receive "indicative' quotas$ they are expected to exercise a good deal of discretion in adjusting the indicative quotas to local constraints and potentials. STS and SWS, on the other hand, are given "imperativel quotas, which they are expected to fill, but not exceed unless they can find supplementary funds from enterprises who are willing to pay for some extra training 'on contract." In Hubei province, the Manpower Planning Office is not expected to be a permanent establishment; it will be dissolved for the time being when its work on forecasting for the 7th Five-Year Plan (1985-1990) is done.

- 19 -
include according to the IPCC classification. This information builds the vector of emissions multipliers. Emissions from Land-use changes and International Bunkers are not considered since they do not clearly align with economic activities in the other classifications.

**Table 4 – CAIT Sector Data**

|**Sector/ Subsector**|**Content**|**IPCC Category**|**Gas**|
|---|---|---|---|
|Energy||1|C<br>2|
|-<br>Electricity & Heat|Electricity & Heat plants (fossil fuels)<br>-<br>Public plants (electricity, heat, CHP)<br>-<br>Autoproducers (electricity, heat, CHP)<br>Other Energy industries (fossil fuels)|1 A 1 a<br>1 A<br>1 A 1 b,c|C<br>2<br>C<br>2<br>C<br>2|
|-<br>Manufacturing<br> <br>Construction|&<br>Manufacturing & Construction (fossil fuels)|1 A 2|C<br>2|
|-<br>Transportation|Transportation(fossil fuels)|1 A 3|C<br>2<br>|
|-<br>Other Fuel Combustion|Other sectors (fossil fuels)<br>Biomass combustion<br>Stationaryand Mobile Sources|1 A 4<br>1 A 5<br>1 A 5|C<br>2<br>C<br>4<sup>,N</sup>2<sup>C</sup><br>C<br>4<sup>,N</sup>2<sup>C</sup>|
|-<br>Fugitive Emissions|Gas Venting/Flaring<br>Oil & Natural Gas Systems<br>Coal Mining<br>Other EnergySources|1 B 2c<br>1 B 2<br>1 B 1<br>1 B 1,2|C<br>2<br>C<br>4<br>C<br>4<br>C<br>4<sup>,N</sup>2<sup>C</sup><br>|
|Industrial Processes|Cement<br>Adipic and Nitric Acid Production<br>Other Industrial non-Agriculture<br>All Fluorinated Gases|2 A 1<br>2 B 2,3<br>2<br>2|C<br>2<br>N2C<br>C<br>4<sup>,N</sup>2<sup>C</sup><br>CHC5, PHC5, SH6<br>|
|Agriculture|Enteric Fermentation<br>Manure Management<br>Rice Cultivation<br>Agricultural Soils<br>Other Agricultural Sources|4 A<br>4 B<br>4 C<br>4 D<br>4|<br>C<br>4<br>C<br>4<sup>,N</sup>2<sup>C</sup><br>C<br>4<br>N2C<br>C<br>4<sup>,N</sup>2<sup>C</sup><br>|
|Land-Use Change & Forestry|Land Use Total (Forestry land, cropland,<br>grassland,and biomass burning)|5|C<br>2<sup>, C</sup><br>4<sup>,N</sup>2<sup>C</sup>|
|Waste|Landfills (Solid Waste)<br>Wastewater Treatment<br>Human Sewage<br>Other Non-Agricultural Sources (Waste and<br>Other)|6 A<br>6 B<br>6 B<br>6 D|C<br>4<br>C<br>4<br>N2C<br>C<br>4<sup>,N</sup>2<sup>C</sup>|
|International Bunkers|Aviation Bunkers<br>Marine Bunkers|1 A 3ai<br>1 A 3di|C<br>2<br>C<br>2|

Source: CAIT-UNFCCC.

These three sources of information provide different sector classifications that cannot always be mapped to each other immediately. To properly identify sectors that can be used for the analysis, a new sector classification is created by first assigning each CAIT sector to a corresponding OECD sector and then grouping the data to match the SEDLAC’s sector classification. This process produces a 14-sector classification. Table 5 presents the new cross-sector classification and the CAIT, OECD, and SEDLAC sectors included in each case.

10