rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7: Number of workers in presumed comparative advantage industries, UBR 2001 and UBR 2010**

||**_A. All comparat_**|**_ive advantag _**|**_e industries_**<br>% share in s|ector||**_E. Commercia_**|**_lfarming_**|% share i|n sector||
|---|---|---|---|---|---|---|---|---|---|---|
|**Size Group**|Number of wo|rkers|total||Growth|Number of|workers|total||Growth|
|**(number of**|||||rate (%|||||rate (%|
|<br>**workers)**|2001|2010|2001|2010|<br>pa)|2001|2010|2001|2010|<br>pa)|
|less than 5|10,419|39,228|11.4|31.5|30.7|250|6,726|1.0|21.8|223.9|
|5‐20 workers|11,132|31,029|12.1|24.9|19.9|2,573|12,866|10.3|41.7|34.6|
|21‐100 workers|7,558|20,191|8.2|16.2|18.6|1,991|3,396|8.0|11.0|6.1|
|More than 100|62,688|34,073|68.3|27.4|‐5.1|20,209|7,861|80.8|25.5|‐5.3|
|Total|91,797|124,521|100.0|100.0|4.0|25,023|30,849|100.0|100.0|1.8|
||**_B. Comparative_**<br>**_industries_**|**_advantage pr_**|**_imary_**|||**_F. Food and b_**|**_everages ind_**|**_ustry_**|||
||||% share in s|ector||||% share i|n sector||
|**Size Group**|Number of wo|rkers|total||Growth|Number of|workers|total||Growth|
|**(number of**|||||rate (%|||||rate (%|
|**workers)**|2001|2010|2001|2010|pa)|2001|2010|2001|2010|pa)|
|less than 5|503|12,188|1.1|27.3|258.1|4,463|8,265|9.6|17.6|7.4|
|5‐20 workers|3,432|18,011|12.6|40.4|47.2|5,657|8,265|12.2|17.6|4.0|
|21‐100 workers|2,259|6,318|8.3|14.2|20.0|3,317|9,127|7.2|19.4|15.1|
|More than 100|20,948|8,069|77.2|18.1|‐6.8|32,879|21,387|71.0|45.5|‐3.0|
|Total|27,142|44,586|100.0|100.0|7.1|46,316|47,044|100.0|100.0|0.1|
||**_C. Comparative_**<br>**_industries_**|**_advantage m_**|**_anufacturing_**<br>|||**G. Thegarme**|**nts industry**||||
||||% share in s|ector|||||||
|**Size Group**|Number of wo|rkers|total||Growth|Number of|workers|% share in|sector total|Growth|
|**(number of**|||||rate (%|||||rate (%|
|**workers)**|2001|2010|2001|2010|pa)|2001|2010|2001|2010|pa)|
|less than 5|9,362|25,866|16.1|35.0|19.6|4,653|16,990|81.0|84.2|22.9|
|5‐20 workers|6,879|11,266|11.9|15.3|7.1|884|2,164|15.4|10.7|12.5|
|21‐100 workers|4,282|11,394|7.4|15.4|18.5|205|584|3.6|2.9|16.0|
|More than 100|37,466|25,276|64.6|34.2|‐3.6|0|442||2.2||
|Total|57,989|73,802|100.0|100.0|3.0|5,742|20,180|100.0|100.0|19.6|
||**_D. Comparative_**|**_advantage se_**|**_rvices_**|||**H. Land tran**|**sport services**||||
||||% share in s|ector||||% share i|n sector||
|**Size Group**|Number of wo|rkers|total||Growth|Number of|workers|total||Growth|
|**(number of**|||||rate (%|||||rate (%|
|**workers)**|2001|2010|2001|2010|pa)|2001|2010|2001|2010|pa)|
|less than 5|554|1,174|8.3|19.1|12.4|477|605|10.1|12.8|2.3|
|5‐20 workers|821|1,752|12.3|28.6|12.6|703|1,431|14.9|30.4|8.9|
|21‐100 workers|1,017|2,479|15.3|40.4|16.0|915|2,069|19.4|43.9|10.9|
|More than 100|4,274|728|64.1|11.9|‐9.2|2,613|607|55.5|12.9|‐6.6|
|Total|6,666|6,133|100.0|100.0|‐0.9|4,708|4,712|100.0|100.0|0.0|

41
# Panel A: Unconditional Mobility

# Panel B: Conditional Mobility

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indicates the high relevance of the excluded exogenous variables for all specifications. In addition, Sargan’s overidentification test shows that the combination of the two IVs is valid for all specifications. Although qualitatively similar to the OLS estimates, the IV coefficients are larger in general, thus suggesting a more substantial gap between male- and femalemanaged establishments. For instance, in columns (3) and (4), we observe a gap of 43% and 44%, respectively. In Appendix Tables C1 and C2, we control sector-by-region fixed effects to compare firms hiring different genders of managers in the same economic environment. The results are qualitatively similar to those in Tables 6 and 7. However, given the smaller number of observations in some sector-by-region cells, we use this specification as a robustness check for our main results.<sup>13</sup> Table 7: Two-Stages Least Squares

||(1)<br>Ln(VAPW)|(2)<br>Ln(VAPW)|(3)<br>Ln(VAPW)|(4)<br>Ln(VAPW)|(5)<br>Ln(VAPW)|(6)<br>Ln(VAPW)|(7)<br>Ln(VAPW)|
|---|---|---|---|---|---|---|---|
|Female Top Manager|-1.081***<br>(0.216)|-1.074***<br>(0.217)|-0.567***<br>(0.210)|-0.587***<br>(0.210)|-0.502**<br>(0.210)|-0.595**<br>(0.275)|-0.561**<br>(0.278)|
|Log(Capital per worker)|||0.274***|0.253***|0.264***|0.253***|0.255***|
||||(0.0269)|(0.0259)|(0.0268)|(0.0382)|(0.0386)|
|Log(Employment)|||-0.0450|-0.115***|-0.0917**|-0.0843*|-0.0572|
||||(0.0364)|(0.0376)|(0.0382)|(0.0493)|(0.0476)|
|GBFs - Extensive margin||||0.286***<br>(0.0560)||||
|GBFs - Intensive margin|||||0.380***<br>(0.0773)|||
|SBFs - Extensive margin||||||0.138**<br>(0.0602)||
|SBFs - Intensive margin|||||||0.103<br>(0.0806)|
|Formality|2.258***|2.074***|1.766***|1.706***|1.660***|1.810***|1.826***|
||(0.152)|(0.166)|(0.173)|(0.174)|(0.174)|(0.213)|(0.213)|
|Management human capital index||0.562***|0.488**|0.226|0.309|0.159|0.201|
|||(0.197)|(0.214)|(0.215)|(0.211)|(0.303)|(0.307)|
|Sector|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|8423|8423|6627|6627|6627|4317|4309|
|_R_<sup>2</sup>|0.333|0.335|0.486|0.493|0.493|0.497|0.493|
|_F_|163.2|163.5|124.2|124.7|125.6|68.76|66.35|
|_J_|0.0273|0.0569|0.00182|0.00860|0.00625|0.0312|0.0272|

Note: This table presents the 2SLS regression results. Exclusion restrictions are the regional share of FTM and female ownership. FTM = Female Top Manager, GBF EXT = General Business Function Extensive Margin, GBF INT = General Business Function Intensive Margin, SBF EXT = General Business Function Extensive Margin, SBF INT = General Business Function Intensive Margin. Estimates were performed using sample weights. Robust standard errors are in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

> 13One concern in these linear regression analyses is that female top managers are not randomly allocated across the firms. To reduce some bias from self-selection, we also estimate the same specifications using a propensity score matching method in Appendix D. Overall, the results are qualitatively similar.

18
Although recent improvement of CV questionnaires has to some extent accommodated the uncertain nature of respondents’ WTP distribution, most of such studies assume the variance of WTP distribution is homoskedastistic with only a few exceptions (Alberini et al, 1997; Wang and Whtington, 2005). For example, Welsh and Poe (1998) adapted the “return potential” format and employed MBDC strategy that asked respondents to express both the choice and voting certainty for the referendum at each bid value. They found MBDC questions significantly reduced confidence intervals around the estimated WTP mean. However, as pointed out by Wang and Whittington (2005), the underlying assumption in Welsh and Poe model is that all respondents share same distribution, and heterogeneity in WTP variance was not considered. Alberini et al (2003) built upon the random valuation threshold model of Wang (1997) to model WTP thresholds be functions of respondent characteristics. While the uncertainty in thresholds was acknowledged, the variance estimated in their model was still based on homoskedasticity assumptions. Alberini et al (1997) also noticed the heteroskedasticity possibility in doubled bounded DC strategies and modeled the WTP distance to price bids to capture the heterogeneity in variance. Although the results were mixed depending on model specifications, the assumption of heteroskedasticity was not rejected.

This study considers the role of heteroskedastic variance in WTP estimation based on the payment card approach, in which respondents are asked to present their WTPs as intervals.

# **3. NMT Project in Pune**

Pune, located near the west coast of India, is the eighth largest metropolitan city in the country. According to 2001 Census, Pune has about 244 square kilometers in municipal area and population density - 10,403 per square kilometers (World Bank, 2008). The area in the center of the city is densely populated. The main driver of the economy of Pune is auto industry and its educational, research and development institutions.

6
_Source_ : author's green dictionary

# Step 2: Application to the ISCO-08 task database

The green dictionary is then applied to a task database, created from the occupational manual of the International Standard Classification of Occupations 2008 (ISCO-08). ISCO is a hierarchically structured system that classifies and aggregates occupational information for all jobs in the world into 433 4-digit level occupations (referred to as occupations here on) to be used for the collection of occupational data in statistical census, surveys, and administrative databases (See appendix D for more details on ISCO).<sup>50</sup> For each occupation in ISCO-08 there is an occupational title, a job description that delimits the scope of the occupation, a list of up to 14 tasks performed,<sup>51</sup> and examples of job titles included within the occupation.<sup>52</sup> Hence, the created occupational database contains 433 occupations; 3,245 task statements; and 46,184 words (equivalent to 8,010 unique words). Since ISCO-08 has been widely adopted in several countries, the results of the analysis described here can be applied to any country that uses ISCO-08.

The green dictionary captured 78 unique terms and 329 tasks from the ISCO-08 occupational database. Out of the 347 unique terms in the green dictionary, 78 terms were also present in the occupational database (figure 3.1 panel a).<sup>53</sup> Many of the captured terms were related to greenhouse gas reduction, pollution reduction and removal, and natural resource conservation. Out of the 3,245 task statements, the green dictionary captured 329 green tasks (or 10 percent of the total tasks) (figure 3.1 panel b – examples in table 3.3), of which 83 are strictly green.<sup>54</sup> These tasks are related to natural resource conservation (16%), climate change (14%), recycling and reuse of waste and materials (13%), and 36 percent fall into multiple green categories.

The sensitivity analysis can also be thought of as providing an upper bound. While the broad dictionary only contains 39 additional terms (or 11 percent additional terms), these terms serve to capture 246 additional tasks (or 7.6 percent of the total number of tasks). These additional tasks will be referred to as having green potential, hereafter. The vast majority of green potential tasks relate to either agriculture, forestry, and fish production (50%) or repair activities (44%). Random quality checks confirmed that the matches were appropriate for the identification of green tasks.

> 50 ISCO-08 has a total of 436 4-digit occupations, but 3 are Armed forces occupations, which do not include a list of tasks performed.

> 51 Min=1, p25=6, p50=7, p75=9, max=14.

> 52 The classification can be downloaded online from the ESCO European Commission website: https://esco.ec.europa.eu/en/use-esco/download.

> 53 Of the 347 unique terms, 308 are strictly green and 39 are green potential. Of the 58 captured terms, 56 are strictly green and 22 are green potential.

> 54 Strictly green refers to terms that match the narrow dictionary.

19
arguing that firms were better able to adapt to the new regime in regions where regulations were more pro-industry. Harrison et al. (2011) find that market-share reallocations played an important role in aggregate productivity gains immediately following the start of India‟s trade reforms in 1991. However, aggregate productivity gains during the overall 20-year period from 1985 to 2004 were driven largely by improvements in average productivity, which can be attributed to India‟s trade liberalization and FDI reforms. Goldberg et al. (2011b) investigate the impact of liberalization on Indian firms‟ product choice and find little evidence of “creative destruction” in the 1990s, i.e. Indian firms infrequently discontinued product lines even during a period of trade and structural reform. They argue that remnants of industrial licensing and rigid labor market regulation in the Indian economy prevented firms from adjusting fully to reforms.

The emphasis on attributing changes in manufacturing performance to changes in trade, investment and labor market policies in goods characterizes much of the existing empirical work on liberalization in developing countries. For instance, Pavcnik (2002) uses plant level data from Chile to find that trade liberalization forces exit of the least productive firms while increasing productivity of the remaining firms in the import competing sectors. Amiti and Konings (2007) delve deeper into the channels through which liberalization affects productivity by separately identifying the impact on Indonesian manufacturing of input and output tariff reductions, and find that the positive effect from increased availability of inputs to production is twice as strong as the effect from import competition. Halpern et al. (2009) estimate a structural model of importers using product-level data for all Hungarian manufacturing firms and reach a similar conclusion.

Empirical research on liberalization of foreign direct investment has produced mixed results. Aitken and Harrison (1999) find what they term „the market stealing effect‟ of foreign direct investment which swamps the positive effect of technology transfer on firm productivity in Venezuela. Javorcik (2004) explicitly distinguishes between intra- and inter-industry effects of foreign direct investment using firm level data from Lithuania and finds that foreign direct investment has a positive productivity effect on supplier industries but no significant effect on local competitors in the same industry. Javorcik and Li (2008) show that entry of foreign retail chains boosts the productivity of the supplying industries in Romania.

6
Finoff (2004) introduces a bio-economic model based on general equilibrium approach to analyze the effects of both stochastic changes in salinity levels and initial shock to population levels of species on the ecological and economic variables. The model is interesting since it is the first model that integrates biological aspects of water policy with a CGE framework. Agents in the model are energy maximizers. Water determines the salinization levels and biomass production in the model.

The Boccanfuso et al. (2005) extend the EXTER model of Decaluwé et al. (2001) which is an integrated multi-household (IMH) model. IMH models consist of large numbers of households by using household survey data to assess the effects of different policies on households in a detailed way. Boccanfuso _et. al._ (2005) is the first example of this approach focusing on water issues. They investigate the distributional impact of privatization of the water utility and isolate winners and losers of privatization in Senegal. They model water market with two water utilities that sell water to water suppliers with an exogenously determined price. They conclude that price changes have different general equilibrium effects and winners and losers depend on these effects.

Diao _et. al._ (2005) present a detailed intertemporal CGE model for Morocco. 88 activity types produce 49 final products in 20 regions. Their simulations show that water markets are likely to increase the agricultural output significantly in Morocco.

Roe _et. al._ (2005) represent a combined model that takes both micro and macro aspects of policy interventions for improving irrigation water allocation in a n analytical CGE framework for analyzing irrigation related issues in Morocco. The model allows for analysis of both top-down (trade reform) and bottom-up (farm water assignments and the possibility of water trading) linkages since it consists of both macro and micro aspects of the problem. The model consists of two modules, a farm level model and a macro level model. The farm level model solves the monthly irrigation water allocation for crops. Prices are exogenous for the farm model. On the other hand The CGE model accounts for the whole economy and prices are determined endogenously. CGE model consists of 82 production activities producing 44 commodities by employing 8 primary input in the agricultural sector. There are further 6 nonagricultural sectors spanning the rest of the economy. The country is separated into 20 perimeters. There are 5 types of households and the public sector at the demand side. International trade is differentiated among EU and rest of the world (Roe _et. al._ , 2005). Roe _et. al._ concludes that trade reform (top-bottom or macro to micro linkage) has a higher effect 36
**Table 5-3: The agricultural sector aggregation employed (sectors in bold type are subject to the simulated increases in productivity in scenarios 1b and 2b)**

|1.|Rice paddy and processed|14.|Animalproducts,miscellaneous|
|---|---|---|---|
|2.|Wheat|15.|Wool& silk|
|3.|Othergrains|16.|Vegetable oils &fats|
|4.|Vegetables,fruit,nuts|17.|Sugar|
|5.|Oilseeds|18.|Otherprocessedfood products|
|6.|Sugarbeet and cane|19.|Beverages & tobacco|
|7.|Plant-basedfibers|20.|Forestry|
|8.|Othercrops|21.|Fisheries|
|9.|Livestock|||
|10.|Raw milk|In ad|dition: 36 sectors industry and services.|
|11.|Meat: cattle,sheep, goats,horses|Resul|ts for these sectors are not discussed in|
|12.|Meat:pork &poultry|detail<br>|in this paper but can be found in<br>|
|13.|Dairy products|Appe|ndix D.|

# **V.3. Results: WTO accession and agriculture in Ukraine**

The GTAP model generates a huge range of outputs including simulated changes in production and trade for each combination of products and country/regions, and simulated changes in input use for each product in each country/regions. The scenarios simulated here produce literally hundreds of thousands of numbers, far too many to be discussed exhaustively in the available space. In the following we present and interpret a number of key results that provide policy makers with a sound quantitative overview of key strategic issues. Appendix D contains tables that present a selection of more detailed results. These tables and others that can be extracted from GTAP can provide a basis for in-depth analysis of specific WTO accession impacts (i.e. employment and input use changes in individual sectors, changes in trade flows between specific countries for individual products).

## **_V.3.1. Regional and global effects_**

Table 5-4 presents the sources of simulated aggregate changes in economic welfare as a result of WTO accession (figures are measured in million US$ unless stated otherwise). As outlined in chapter V, Table 5-4 breaks the total welfare changes down into changes due to allocative <u>efficiency</u> (benefits due to a better use of domestic resources following the removal of tariff distortions), technical change (benefits due to any additional changes in productivity that are assumed to take place), and terms of trade (benefits due to higher prices received by exporter and lower prices paid by importers following tariff reduction). The following key results are of note: <u>First, Table 5-4 only contains results from Scenarios 1a and 1b (impact of immediate</u> accession commitments) because the corresponding results for Scenarios 2a and 2b (final accession commitments, not shown) are almost identical.

36
# **R&D and Aggregate Fluctuations**<sup>_⇤_</sup>

**Erhan Artu ̧c**<sup>_†_</sup> **Panayiotis M. Pourpourides**<sup>_‡_</sup> World Bank Cardi↵Business School & Central Bank of Cyprus

March 28, 2012

**Abstract**

The research and development (R&D) sector is considered one of the main driving forces of sustainable growth in the long run. The sector, however, also shows excessive volatility and is one of the important sources of macroeconomic fluctuations. Using data from the United States Bureau of Economic Analysis and National Science Foundation, we show how significant technology innovations’ contributions are to improve sector productivity and the efficiency of physical capital. After taking nominal innovations into consideration, such as shocks in monetary policy and inflation, capital innovations explain 70 percent of fluctuations of real investment in R&D, while productivity innovations in the R&D sector explain 30 percent of the variation in the output of the non-R&D sectors. Technology innovations explain most of the variation of output in the R&D sector and 78 percent of the variation of output in the rest of the economy. Although the R&D sector is relatively small, it has a significant impact on the fluctuations of aggregate output.

**JEL Classification Codes** : C13; C32; C68; E32; O3

**Keywords** : Cycles; Productivity Shocks; Investment-specific Shocks; R&D; VAR

> _⇤_ The views in this paper are the authors and not those of the Central Bank of Cyprus, the Eurosystem, the World Bank Group, its Executive Directors or the governments they represent. We would like to thank Raymond Wolfe (National Science Foundation) for information and help with the data and Jane Zhang for her editorial comments. All errors are our own.

> _†_ The World Bank, Development Economics Research Group (Economic Policy), 1818 H Street, NW, Washington DC 20433, USA, Email: eartuc@worldbank.org.

> _‡_ Cardi↵Business School, Cardi↵University, Aberconway Building, Colum Drive, Cardi↵, CF10 3EU, UK, E-mail: pourpouridesp@cardi↵.ac.uk and Economic Research Department, Central Bank of Cyprus, 80 Kennedy Avenue, P.0. Box 25529, 1395 Nicosia, Cyprus, E-mail: PanayiotisPourpourides@centralbank.gov.cy.

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