rafmacalaba/datause-encoder-data huggingface dataset:
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Table A1: **Variable**

|**_Variables_**|**_Defnition_**|
|---|---|
|Market share|Ratio of frm i’s sales to total industry j’s sales in a particular year|
|HHI|Herfndahl-Hirschman Index defned as the sum of squares of the market shares of<br>the frms within each 3-digit NAICS industry|
|Sales/IC|Sales/Invested Capital<sup>TOT</sup>|
|Capex/IC|Capital Expenditures/Invested Capital<sup>TOT</sup>|
|Tobin’s Q<sup>TOT</sup>|Q<sup>TOT </sup>= V/TOTCAP<br>where V is the market value of the frm defned as the market value of equity (=total<br>number of common shares outstanding (Compustat item CSHO) times closing stock<br>price at the end of the fscal year (Compustat item PRCC<br>~~F~~) plus the book value<br>of debt (Compustat items DLTT + DLC) minus the frms current assets (Compus-<br>tat item ACT) which includes cash, inventory, and marketable securities. TOTCAP<br>is sum of Property, Plant and Equipment (Compustat item PPENT) and Intangi-<br>ble Capital (ICAP). ICAP is defned as the sum of externally purchased intangible<br>capital (INTAN) and internally purchased intangible capital, both measured at re-<br>placement cost. Internally purchased intangible capital is in turn measured as the<br>sum of knowledge capital (K<br>~~i~~nt<br>~~k~~now) and organization capital (K<br>~~i~~nt<br>~~o~~rg).|
|CPS|Identifying complex problems and reviewing related information to develop and eval-<br>uate options and implement solutions.Source: O*NET|
|NRCOG|Mathematical Reasoning + Inductive Reasoning + Developing Objectives and Strate-<br>gies + Making Decisions and Solving Problems. Source: O*NET|
|RMAN|Spend time making repetitive motions + Pace Determined by Speed of Equipment +<br>Manual Dexterity + Finger Dexterity. Source: O*NET|

67
**<u>Table 12: Institutional Structure</u>**

||Types andTerms|
|---|---|
|Australia|Decentralized competitive structure.<br>High operating costs and fees in retail funds.<br>Weak marketing of life annuities.|
|Chile|Competitive but highly concentrated structure in<br>accumulation phase and PWs.<br>High operating fees and profit margins.<br>Competitive decentralized structure for life annuities.<br>Price competitionandhigh MWRs.|
|Denmark: ATP|Centralized administration and asset management during<br>bothaccumulationand payout phase.|
|Denmark: Other|Decentralized structure.<br>Competition for plan mandates among multi-employer<br>funds and life insurance companies.<br>Accumulation and payout phase by same institution<br>subject to pensionplan rules.|
|Sweden: PPM|Centralized administration but decentralized asset<br>management during accumulation phase.<br>Centralized administration, longevity risk-pooling and asset<br>management for traditional annuities.<br>Centralized administration and longevity risk-pooling but<br>decentralized assetmanagementforunit-linked annuities.|
|Sweden: Other|Four large multi-employer schemes with similar structure to<br>PPM.|
|Switzerland|Decentralized and fragmented structure among foundations<br>and insurance companies.<br>Accumulation and payout phase by same institution<br>governed by pension plan rules.<br>Pervasive product and priceregulations.|

Sources: Andersen and Skjodt (2007); Brunner and Thorburn (2008); Buetler and Ruesch (2007); Palmer (2008); Rocha and Rudolph (2010); Rocha and Thorburn (2007); and Vittas (2008).

Denmark follows a mixed approach. In the case of the ATP, the supplementary public fund, account administration, risk pooling and asset management are all centralized. But in occupational and personal pension plans, there is a high degree of decentralization. In occupational pension plans, much depends on the rules of collective labor agreements. Insurance companies and multi-employer pension funds compete for winning occupational mandates. Individual workers have limited choice. Most insurance companies are profit-seeking commercial entities but multi-employer pension funds and some insurance companies are organized as mutual not-for-profit institutions. In Denmark and Sweden, the determination of annual bonuses and the distribution of net investment returns between policyholders and shareholders is an important issue.

Switzerland has a highly decentralized structure and its system suffers from a high degree of fragmentation, reflecting the presence of a large number of pension funds. However, the extensive product and price regulation of the payout phase limits the scope for competition and excessive spending on marketing campaigns, while pension plan rules restrict the choice of individual workers, during both the accumulation and payout phases. The only workers in the mandatory pillar who have individual choice are those 32
were then converted into real Quetzales per pound (indexed to year 2000) using the exchange rates and Consumer Price Index from the IMF's International Financial Statistics Yearbook (2005). The graph shows that with the exception of a stable period in the late 1980s, the price of snowpeas has continuously fallen, partly reflecting the expansion in the global supply in the market. The declining price is likely to influence both adoption and withdrawal.

<!-- Start of picture text --> Figure 3: Snowpeas Prices 1978-2005<br>15<br>10<br>5<br>0<br>1980 1985 1990 1995 2000 2005<br>year<br>average price, real 2000 quetzales<br><!-- End of picture text -->

# **IV. Understanding NTX adoption and withdrawal**

# **_Empirical Specification_**

Duration modeling allows for the analysis of the decision over time to adopt and withdraw from the cultivation of snowpeas. “Risk” of adoption (and withdrawal) over time can expressed through the hazard rate high prices as Guatemalans first entered the market, followed by high prices in the 1980s and declining prices in the 1990s.

21
<!-- Start of picture text --> Risk appetite shock, macro risk premium response<br>0.5 TT<br>E —<br>o<br>a<br>x<br>2<br>Q<br>S -0.5<br>a<br>0 5 10 15 20<br>Quarters<br><!-- End of picture text --> <!-- Start of picture text --> Risk appetite shock, GDP growth response<br>10 JY<br>3 s<br>Lo)<br>Q<br>[O}<br>><br>iya3s O ll a<br>=]<br>5|<br>05 10 15 20<br>Quarters<br><!-- End of picture text -->
**Table 11: Improvements in Resilient Accessibility from Project Interventions (during flooding events)**

|Reduction in Travel<br>Time|**High Schools**<br>**(×1,000 people)**|**Referral Hospitals**<br>**(×1,000 people)**|**Value of**<br>**Agricultural**<br>**Production**<br>**(US$, millions)**|
|---|---|---|---|
|**5–20 minutes**|275|301|134|
|**20–45 minutes**|122|180|203|
|**> 45 minutes**|30|212|290|
|**Total**|**427**|**693**|**627**|

# 3.3. Prioritization of Road Segments

The prioritization uses a multicriteria approach using the outputs of the accessibility analysis and the climate vulnerability analysis. Each criterion is based on absolute gains of accessibility by each individual road segment, calculated as the sum of the reduction in travel time in minutes for every person or the value of agricultural production. The values for each of the criteria are then normalized from 0 to 100 and then averaged out using equal weights. A value of 100 would indicate that the segment is the top segment in each of the criteria. Each road segment is then given a score from 0 to 100. These criteria and approach were discussed and agreed with the main stakeholders. After prioritization is done using this analysis, additional selection is done to select the final list of road candidates for improvement. Social and environmental safeguards screening further screens the list of identified roads to ensure that the proposed investment does not cause major negative environmental and social impacts and maximizes development gains for the beneficiaries. Finally, on this, a final short list of lifeline roads can be identified which are eligible for investment with a balanced representation of the targeted provinces (Figure 8).

14
# **Table 3: Impact of Ratification, OLS**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||DPT rate<br>(log)<br>All<br>Countries|DPT rate<br>(log)<br>All<br>Countries|DPT rate<br>(log)<br>Low<br>Income|DPT rate<br>(log)<br>Lower<br>Middle<br>Income|DPT rate<br>(log)<br>Upper<br>Middle<br>Income|DPT rate<br>(log)<br>High<br>Income|Measles<br>rate (log)<br>All<br>Countries|Measles<br>rate (log)<br>All<br>Countries|Measles<br>rate (log)<br>Low<br>Income|Measles<br>rate (log)<br>Lower<br>Middle<br>Income|Measles<br>rate (log)<br>Upper<br>Middle<br>Income|Measles<br>rate (log)<br>High<br>Income|
|Convention on the<br>Rih f h Child||-0.038|-0.002|-0.086|0.011|0.013||-0.019|-0.061|-0.067|0.037|0.021|
|gts o te -<br>yes/no (CRC)|---|(3.28)**|(0.03)|(3.03)**|(0.8)|(1.27)|---|(1.71)|(0.9)|(2.58)*|(2.37)*|(1.97)*|
|GDP per capita (in<br>tt 2000 US|0.047|0.085|0.341|0.12|-0.11|0.264|-0.041|-0.016|0.179|-0.076|-0.068|-0.073|
|consan<br>dollars, log)|(1.52)|(2.78)**|(2.40)*|(1.55)|(2.76)**|(6.59)**|(1.42)|(0.55)|(1.35)|(1.06)|(1.5)|(1.72)|
|ICRG-Bureaucratic|0.007|0.01|-0.017|-0.014|0.017|0.088|0.005|0.006|-0.021|-0.008|0.033|-0.018|
|Quality|(1.05)|(1.49)|(0.52)|(1.07)|(2.54)*|(8.38)**|(0.78)|(1.03)|(0.69)|(0.66)|(4.43)**|(1.6)|
|D S|0.002|0.002|-0.002|-0.001|-0.003|-0.019|0|0|-0.004|-0.002|-0.006|-0.026|
|emocracy core|(1.6)|(1.83)|(0.32)|(0.3)|(1.57)|(3.12)**|(0.35)|(0.38)|(0.75)|(0.89)|(2.55)*|(3.86)**|
|Population Density|4.353|4.301|7.237|-2.79|0.691|0.43|4.928|4.909|8.524|-1.329|0.627|-1.002|
|(log)|(6.41)**|(6.63)**|(5.01)**|(1.5)|(0.25)|(0.42)|(7.66)**|(8.01)**|(6.30)**|(0.78)|(0.2)|(0.91)|
|Total Population|-3.899|-3.839|-4.946|3.059|-0.308|-0.522|-4.67|-4.65|-6.127|0.998|-0.317|0.878|
|(log)|(5.72)**|(5.90)**|(3.50)**|(1.64)|(0.11)|(0.51)|(7.24)**|(7.57)**|(4.63)**|(0.58)|(0.1)|(0.81)|
|YEAR|0.001<br>(0.81)|0.001<br>(0.77)|-0.043<br>(1.77)|0.007<br>(1.32)|0.005<br>(3.13)**|-0.001<br>(1.16)|0.007<br>(4.99)**|0.007<br>(4.86)**|-0.044<br>(1.95)|0.021<br>(4.24)**|0.006<br>(3.05)**|0.009<br>(6.22)**|
||48.402|47.391|140.374|-50.8|-2.522|11.364|48.292|48.127|158.44|-49.352|-3.628|-21.555|
|Constant|(5.52)**|(5.63)**|(3.15)**|(2.03)*|(0.07)|(0.9)|(5.82)**|(6.06)**|(3.80)**|(2.14)*|(0.09)|(1.6)|
|Observations|2074|2056|209|572|614|635|2074|2056|209|572|614|635|
|Number of<br>group(countryc)|127|126|19|44|52|48|127|126|19|44|52|48|
|R-squared|0.16|0.17|0.35|0.13|0.26|0.25|0.17|0.18|0.38|0.12|0.26|0.2|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1%. Country fixed effects are included but not reported.

19
# **Table A12: First stage: Log Refugees at 100km instrumented by distance-based IV with decay function represented (i.e. IV generated using 1)mean border distance,** **<u>2)square of mean border distance and 3) square root of mean border distance)</u>**

|||||||Log of Refug|ees (100km)||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of Distance IV (mean border distance)|5.45956***|5.46255***|5.44191***|5.41772***|||||||||
||(1.30920)|(1.30982)|(1.47648)|(1.52001)|||||||||
|Log of Distance IV (square of mean border<br>distance)|||||3.48184***|3.48374***|3.46234***|3.45131***|||||
||||||(0.77086)|(0.77132)|(0.87745)|(0.90773)|||||
|Log of Distance IV (square-root of mean border<br>distance)|||||||||9.62042***|9.62596***|9.73377***|9.71382***|
||||||||||(2.52952)|(2.53040)|(2.84457)|(2.92326)|
|HH head age||0.00151|0.00110|0.01251||0.00176|0.00150|0.01401||0.00131|0.00080|0.01151|
|||(0.00185)|(0.00196)|(0.01344)||(0.00185)|(0.00199)|(0.01359)||(0.00186)|(0.00194)|(0.01343)|
|Age squared||-0.00001|-0.00001|-0.00008||-0.00001|-0.00001|-0.00010||-0.00000|-0.00000|-0.00006|
|||(0.00002)|(0.00002)|(0.00012)||(0.00002)|(0.00002)|(0.00012)||(0.00002)|(0.00002)|(0.00012)|
|HH head sex (male)||0.00875|0.01494|0.05091||0.00914|0.01435|0.06007||0.00847|0.01562|0.04487|
|||(0.00653)|(0.01204)|(0.06553)||(0.00644)|(0.01152)|(0.06408)||(0.00678)|(0.01296)|(0.06955)|
|Household size|||0.00037||||0.00030||||0.00046||
||||(0.00101)||||(0.00099)||||(0.00104)||
|Married monogamously|||-0.03797*||||-0.03693*||||-0.03947**||
||||(0.01928)||||(0.02119)||||(0.01793)||
|Married polygamously|||-0.03500*||||-0.03450||||-0.03566*||
||||(0.02029)||||(0.02196)||||(0.01911)||
|Separated (divorced/widowed)|||-0.01379||||-0.01286||||-0.01427||
||||(0.01832)||||(0.02027)||||(0.01695)||
|Completed primary sch|||-0.00020||||-0.00179||||0.00075||
||||(0.01021)||||(0.01017)||||(0.01026)||
|Commercial farming|||0.01342||||0.01077||||0.01549||
||||(0.01537)||||(0.01504)||||(0.01585)||
|Wage employment|||0.00335||||0.00302||||0.00282||
||||(0.00954)||||(0.00940)||||(0.00985)||
|Non-agricultural self-employment|||-0.01334**||||-0.01278*||||-0.01397**||

15
**2**

**TYPs** acvounted for som 23 percent of total industrial output value of Chinese **industry. Some** rural areas that only a **fey** years back had relied almost entirely on agriculture have become overwhelmingly industrial communities, with agriculture continuing as a marginal activity subsidized out of industrial income.

Growth of the TVP sector has been an important component of China's reform, and has fitted into the strategy of developing an economy which is partly market-driven and partly plan-driven. Some commodities have come to be largely traded at market prices while others have seen a larger or smaller marketed component supplement trade at controlled prices. Continued state ownership of major industry (with, at the same time, some reforms in enterprise management) has been complemented by ownership reforms in agriculture and growth of a nonstate industrial sector.

What, then, does the growth of the TVP sector mean for China's overall reform? To what extent does it represent a broadening of the market mechanism outside agriculture and a potential source of competition for state industry? How coupatible with a market economy are the patterns of incentives for TVP owners, managers and workers? Is the TVP sector a proxy for a private industrial sector? Or is it just an extension of traditional state ownership to rural industry? What are the regional and distributional implications of TVP development? This paper considers these questions, drawing on the results of a recent collaborative resenrch project between the World Bank and the Institute of Economics of the Chinese Academy of Social Sciences)l The extent and characteristics of rural industrial development differ greatly among China's many counties. The project therefore involved in-depth fieldwork in four counties selected for their diversity in stage of development
Munley (1984) also estimated water pollution abatement costs based on the framework proposed by Rossi, Young, and Epp.

Goldar, Misra, and Mukherji (2001) identified problems associated with the cost function proposed by Rossi, Young, and Epp, and argue that output of abatement activity should be defined as the reduction in the pollution load. They define output of water pollution abatement as a function of the volume of waste water treated, the difference in the pollution levels of influent and effluent water, and inputs used to purify the water. Golder, Misra, and Mukherji specified a water pollution abatement cost function in which the cost of abatement is an explicit function of the quantum of abatement (i.e., the difference between water quality before and after the treatment) and factor prices. There are some similar studies that did not include factor prices in the abatement cost function (e.g., Mehta, Mundle, and Sankar, 1993).

Another set of studies considered pollution abatement as an inseparable multi-output process, and suggested that the cost of abatement might not be separable from the cost of production (see Pizer and Kopp, 2005; Maradan and Vassiliev, 2005; Boyd, Molburg, and Prince, 1996). Gollup and Roberts (1985) used observed data on utility pollution abatement and production costs to estimate a cost function that included emission control rates as a predictor of production costs. Nordhaus (1994) compared a number of published models in terms of percentage difference of carbon emissions from a baseline path and propose an aggregate formula relating cost to output and reduction of greenhouse gases. In a similar manner, Newell and Stavins (2003) explored the pollution abatement cost heterogeneity (i.e., the relative cost of uniform performance measured in terms of emissions per unit of product output) by using a second-order approximation of the costs around the baseline emissions. Their approach was based on variation in baseline emission rates, thus estimation of the cost function required data on baseline and project emissions. In contrast, Newell, Pizer, and Shih (2003) developed a quadratic abatement cost function in which the cost of pollution abatement per unit of output depends on abatement rather than emissions. Using project-level census data on compliance costs and emissions abatement in four industries, they estimated the parameters of the cost function and compute gains from emission trading.

5
_Figure 4: Estimation Results – Change in the Labor Earning (%) for Graduating from the DTS, Education, Work Experience, and HS Grade_ <!-- Start of picture text --> Years of work experience 1.6<br>Years of schooling 4.7<br>High school (HS) grade 1.1<br>Interaction term between DTS and HS grade ‐8.2<br>DTS dummy 3.9<br>DTS dummy 11.8<br>‐10 ‐5 0 5 10 15<br>TVI controlled<br>TVI not<br>controlled<br><!-- End of picture text --> Source: _DTS Tracking Survey Data, 2015, World Bank; World Bank calculations._ Note: _Wage premium (%), percentage increase or decrease to the monthly earning with one unit change to the listed controls, is computed from an OLS estimation. The estimation controls for gender, education, years of work experience and squared term, maternal education level, TVI dummies, and enrollment year dummies. (See Table 1.1 for regression results in detail)_ 20
To be able to estimate _p_<sup>_′_</sup> _k_<sup>,weneeddataonthepricesofthedomesticandthecomposite</sup> imported good, as well as consumer expenditure in sector _k_ . The relative price between the domestic good and the composite imported good is obtained using the two first order conditions of the consumer maximization problem. More precisely, we take the ratio between (4) and (5) and solve for the relative price. The quasi-linear structure of the theoretical framework implies that there are neither income nor substitution effects for non-numeraire goods. Thus, the model allows us to assume that the price of the imported good in every sector is equal to one. Consumption is readily obtained from trade and production data.<sup>21</sup> We use then equations (4) and (5) to calculate the price of the domestic varieties. One concern we have when constructing _p_<sup>_′_</sup> _k_<sup>in this fashion is measurement error, but we instrument</sup> the term _p_<sup>_′_</sup> _k_<sup>jointlywiththerestoftherighthandsidetermtoaddressthisproblem.</sup> Finally, a well-known problem with the estimation of the ‘protection for sale’ model in its traditional or extended form is the endogeneity of the right-hand-side variables. In order to correct for this we use as instruments China and India’s share in world trade by product, and the capital-labor ratio of the United States in each industry. The results of the estimation of the extended and traditional ‘protection for sale model’ as well as the first-stage regressions are discussed in the next section (4.3).

# **4.3 Results**

We start by discussing the results of the estimation of the degree of substitutability between domestically produced goods and varieties that are imported from either China, India or the rest of the world. We estimate equation (20) using data on imports from China, India, and rest of the world separately. Parameter estimates obtained for equation (20), as well as the implied _σ_ ’s are reported in Table 3. A quick look suggests that the degree of substitution between Chinese goods and goods produced domestically in Latin America – as measured by _σC_ – is higher than the degree of substitution between goods imported from the rest of the – – world and domestically produced goods as measured by _σROW_ using data on the entire

> 21Or rather apparent consumption which equals imports plus domestic production minus exports.

18
The amount of carbon emissions by each production activity will depend on the type and amount of energy being used as inputs and the carbon emissions associated with each energy input. Table 3 shows the carbon emissions for different productive sectors in South Africa based on their energy expenditures and the CO2 coefficients in Table 2.

Summarizing the information further in Figure 2, the sectors that generate the most carbon emissions in South Africa are the production activities that use the most coal or electricity, followed by public and private transportation due to the consumption of petroleum. The characteristic of the electricity sector is now apparent: although using electricity in various economic activities is fairly clean, producing it is another story. Because of its heavy reliance on coal as an input, the power sector is the largest emitter of CO2 in South Africa, accounting for 48 percent of the total CO2 emissions. Sectors that produce metallic products are also fairly dirty because of their use of coal to fire up furnaces, emitting about 22 percent of the CO2 emissions as a group. The transportation sector combined with household use of petroleum accounts for 10.5 percent of total CO2 emission. Households by themselves contribute about 9 percent. Other sectors with notable CO2 emissions include the chemical sectors, rubber, water supply, other mining, and food manufactures. Although burning petroleum as an energy input is fairly dirty, next only to coal in terms of its CO2 coefficient, the sector producing crude oil in South Africa hardly emits any carbon emissions because all crude oil is imported, its pollution being generated outside the country.<sup>13</sup>

**Figure 2: Sectors emitting the most CO2**

<!-- Start of picture text --> 14.8 Electricity<br>4.9<br>Basic metals and metal<br>products<br>47.6<br>10.5<br>Transportation, including HH<br>expenditure on petroleum<br>Basic and other chemicals,<br>22.2 rubber, and water supply<br>Rest<br><!-- End of picture text --> Taxes on the purchase of energy commodities are relatively low in South Africa suggesting some potential for using tax policy to regulate carbon emissions.<sup>14</sup> Among energy inputs, a 15.4 percent sales tax is applied to purchases of refined petroleum, but basically none is applied to coal and electricity & gas (Table 4).<sup>15</sup>

> 13 In the analysis, we ignore all possible taxes on carbon emissions originating from other countries. 14 From the Energy Détente database, the taxation of gasoline is low in South Africa when compared to other countries.

> 15 Sales taxes are ad valorem equivalent.

10