rafmacalaba/datause-encoder-data huggingface dataset:
Carvalho, Anabela 2007. “Communicating global responsibility? Discourses on climate change and citizenship” International Journal of Media and Cultural Politics 3( 2):180-183.

Carvalho describes two “background” ways that news media contribute to the normalization of climate change. On the one hand, “the media is a culprit that instigates materialism by constantly promoting wealth and consumption while acting almost in collusion with the main forces that foment global warming for sake of advertising revenue.” Furthermore, Carvalho observes that mainstream media provides news mainly from the Northern hemisphere, Western Europe and American-based news. As a result, it steers the gaze of viewers away from parts of the world where climate impacts may be most visible.

Carvalho, Anabela 2005. “Representing the Politics of the Greenhouse Effect: Discursive Strategies in the British Media” Critical Discourse Studies 2(1): 1-29.

The author uses cricitcal discourse analysis to to identify the discursive strategies of political actors and the media in their re-constructions of climate change. The author compares representations of climate change in three British newspapers -- The Guardian, The Independent, and The Times.

Dunwoody; Sharon 2007 “The challenge of trying to make a difference using media messages” in Moser, Susanne C. and Lisa Dilling. eds Creating a Climate for Change: Communicating <u>Climate Change and Facilitating Social Change. New York: Cambridge University Press.</u> Mazur, Allan 1998. Global Environmental Change in the News: 1987-90 vs 1992-6 International <u>Sociology, 13 (4): 457-472.</u> Early work on media coverage of climate change by Alan Mazur (1998) indicates that global environmental hazards including climate change and other issues were both present in the US media in the late 1980s up until 1990, after which point it declined, even though scientific consensus increased during that time and the Clinton White House was sympathetic to environmental concerns from 1992 until 2000. Mazur also hypothesizes that attention to climate change spread from the US media to media coverage in other nations.

Sonnett, John H. 2006 “Representing the Earth: Global climate issues in popular, political, scientific, business, industry, and environmentalist news: A new old sociology of knowledge” <u>Dissertation Abstracts International, A: The Humanities and Social Sciences,</u> 67(02): 743.

John Sonnett (2006) notes differences in language representation between scientific community, news media and the oil industry: scientists speak of “climate change,” the news media of “global warming,” and the oil industry of “greenhouse gases.” He observes that each term is “embedded within risk discourses, structured primarily by scientific uncertainties and political fears.” 66
38

**Table 2. Stages in Southern Cone Disinflation**

|(1)<br>Stage|(2)<br>Dates|(3)<br>GDP Growth<br>per Capita|(4)<br>Current<br>Account<br>Export ratio|(5)<br>M1<br>Growth|(6)<br>Inflation|(7)<br>Daaitikn|
|---|---|---|---|---|---|---|
|Chile|||||||
|Historic|1950-73|1.5<br>|||||
|I|74-76<br>|-4.6<br>|-.12<br>|257<br>|370<br>|435<br>|
|II|77-81<br>|6.2<br>|-.43<br>|71<br>|50<br>|27<br>|
|III|82-83|-9.0|-.38|18|19|43|
|Uruguay|||||||
|Historic|1950-74<br>|0.6<br>|||||
|I|75-78|3.6<br>|-.21<br>|54<br>|59<br>|51<br>|
|II|79-80|5.3|-.39<br>|67<br>|65<br>|23<br>|
|III|81-83|-4.8|-.16|13|35|68|
|Argentina|||||||
|Historic|1950-75<br>|1.7<br>|||||
|I|75-78|-0.9<br>|.19<br>|239<br>|265<br>|190<br>|
|II|79-80|2.4|-.27<br>|124<br>|148<br>|53<br>|
|III|81-83|-4.3|-.33|204|204|295|
|RestcfL|A||||||
|Historic|1950-74|3.4|||||
|I|75-78<br>|2.9<br>|||||
|II|79-80|3.8|||||
|III|81-83|-2.7|||||

Stage I is the monetary-fiscal stabilization,<sup>stage II is the first (and main)</sup> part of the exchange rate based stabilization,<sup>and stage III is the collapse.</sup> Dates are annual and therefor not precise.

Columns (3), (5), (6), and (7) are<sup>annual percentages.</sup> Source: Ramos (1986), from various tables.
The data are from a combination of manufacturing censuses of firms, collected by each country’s statistical agencies, and the Amadeus database, compiled by the Bureau Van Dijk, which collects the financial information of public and private firms from 34 European countries. The manufacturing censuses correspond to Ghana, Ethiopia, Kenya, Chile, Colombia, Peru, India, Malaysia, and El Salvador. In the majority of the cases, the censuses are meant to be universal for firms of a certain size, typically ten workers, with the exceptions of Peru and El Salvador, for which there are no size constraints. The Amadeus database in turn does not impose size restrictions, but it is well known that for some countries there is a bias toward overrepresenting the largest firms. Given these differences, we apply some adjustments to maximize comparability and to minimize biases in the representation of the size distributions.

The first adjustment we are forced to implement is restricting the analysis to firms with 10 workers or more. The benefit of this choice is that it maximizes comparability across all of the data sources. Of course, the downside is the lack of information on the prevalence and behavior of the smallest firms in the size distribution. Our assumption is further supported by observing that existing cross-country estimates of the costs of doing business are in effect based on the legal costs of creating a 10-plus worker firm. For example, the World Bank’s DBI (a leading source of information for the calibration of entry barriers in the literature<sup>22</sup> ) computes the cost of starting a business as the summation of all the processes entrepreneurs must undergo to obtain the necessary approvals, licenses, and permits for operating a new firm. The prototypical firm for the measurement of these costs is precisely one that features 10 to 50 employees one month after operations start. Thus, we see our analysis as providing a characterization of the business environment that rationalizes the cross-country differences in the average size distribution of firms with 10 workers or more. Of course, the truncation to the data requires that the model is calibrated to match properties of the truncate firm size distribution in the US, our target economy, a requirement we take into account when calibrating

> 22See, for instance, Barseghyan and DiCecio [2011] and Boedo and Mukoyama [2012].

24
5 Economic Parameters of Deforestation forests). This model is used to determine the direction of the effects of policy changes when demand is totally elastic, i.e. for timber exports of a small country. The main results of this section are the comparative' statics effects of changes in the economic parameters, such as timber price or logging costs, on the profit maximizing time oflogging and the optimal effort in logging.

Section 5 contains a simulation model of profit maximizing land use in which the timber price is endogenized. In the market simulations, the price of timber rises at a decreasing rate until a steady state is reached. The simulation of several policy changes provides insights into the effccts of policy changes when demand is not fully elastic, i.e. for local fuclwood demand. Combined with site-specific geographic and economic data, the simulation model could bc used as a tool for analyzing future deforestation rates and quantitative impacts of a specific policy intervention.

# 2. Modelling Approach and Summary of Results

This paper analyzes the links between economic parameters and deforestation through theoretical models based on profit maximizing agents. The models are based on the analysis of the incentives for the use of land in a specific land class, which includes all lands with identical site-specific characteristics. While von Thiinen's work focusses on the central role of transpurtation costs, other site-specific determinants of land use include soil quality, climatic conditions, slopes, road access, factor costs, etc.. The models analyze how economic parameters determine the incentives for land use in a given land class. The ultimate interest, of course, is not in the individual land class but in aggregate land use changes. However, aggregate changes are determined from changes in land use within each class. For example, if a policy change leads to deforestation in some land clsses and no reverse change in other land classes, it can be concluded that the policy change leads to more aggregate deforestation.
measure of exposure used in the study. Alternatively, the _dioxin‐only hit count_ restricts itself to dioxin‐contaminated spray applications (Agent Orange, Purple, Pink and Green).

# [FIGURE 1 ABOUT HERE]

# _3.1.3. Combat activity files and bombing intensity information_

To control for other potential determinants of conflict intensity, further data on combat activities provided by the Information Management and Mine Action Programs (iMMAP) are included in the analysis. Records combine the Combat Activities file (CACTA), the South‐East Asia Database (SEADAB) and the Combat Naval Gunfire File (CONGA), and cover combat activities of allied forces over the period October 1965 ‐ June 1975 with the exception of few missing months. The dataset contains the following fields: aircraft type, ordnance type, quantity, load, and drop date and location. Drop location is recorded when the drop actually takes place, which might differ from impact location. For example, in the case of B52 carpet bombing, the drop location of general purpose bombs refers to the north‐west corner of the 1.5‐kilometer*1.5‐kilometer square, which was considered the most accurately recorded point of the box (for further details, see Miguel and Roland, 2006).

To be consistent with the herbicide exposure index, a measure of bombing intensity is constructed by aggregating bomb load across all categories and over a 15‐kilometer buffer zone around commune centers. A binary variable indicating whether a commune has been bombed at all within 15km of its center and the logarithm of aggregate bomb load will henceforth be the adopted measures of bombing intensity.

# _3.1.4. Other data sources_

Additional information on agricultural herbicide use, cigarette and alcohol consumption, and coal and firewood expenditures are obtained from the 2004 Vietnam Household Living Standards Survey (VHLSS) to look at household‐level confounding factors. The General Statistics Office undertook a nationally representative Vietnam Household Living Standards Survey (VHLSS) in 2004. A total of 9000 households in 3300 communes were administered a detailed food and non‐food expenditure survey module. From this dataset, herbicide and pesticide expenditures per area of agricultural land are computed, and averaged by surveyed communes. Similarly, household per capita expenditures in coal and firewood, cigarettes and alcohol are computed and averaged by communes. Exposure measures are assigned to each VHLSS 8
|_Table A1.2: Attrition ‐_|_Baseline diff_|_erence betwe_|_en attritors and_|_non‐attrito_|_rs_|
|---|---|---|---|---|---|
||Non‐attr<br>|itors<br>|Attrito<br>|rs<br>|N|
||Mean|SD|Coeff|SE||
|Indi<br>|<br>vidual and h<br>|<br>ousehold char<br>|.<br>acteristics<br>|<br>||
|Age|27.632|4.826|‐0.281|0.236|2,292|
|Married|0.661|0.473|‐0.025|0.026|2,291|
|Employment status|0.619|0.486|0.004|0.020|2,292|
|Business ownership|0.649|0.478|0.011|0.018|2,292|
|Consumption food|5.405|1.170|‐0.108**|0.051|2,292|
|Consumption nonfood|2.432|1.325|0.004|0.063|2,292|
|Formal bank account|0.397|0.489|‐0.068***|0.021|2,292|
|(Log) amount formal loans|‐0.290|1.626|‐0.183**|0.088|2,280|
|(Log) amount informal loans|‐1.360|3.323|0.275**|0.132|2,258|
|Education<br>No education|0.210|0.408|‐0.035*|0.019|2,292|
|level<br>Some Primary|0.307|0.462|0.059***|0.019|2,292|
|Some|0.379|0.485|0.019|0.022|2,292|
|Secondary||||||
|Some University<br>or Higher|0.104|0.305|‐0.042***|0.011|2,292|
|Literacy<br>No English|0.261|0.440|‐0.012|0.020|2,292|
|Some English|0.286|0.452|0.003|0.020|2,292|
|Good English|0.453|0.498|0.009|0.023|2,292|
|Numeracy<br>Low|0.252|0.434|‐0.026|0.018|2,292|
|Medium|0.173|0.378|0.028|0.017|2,292|
|High|0.575|0.494|‐0.002|0.020|2,292|
|Household size|7.384|3.342|‐0.299**|0.144|2,292|
|Number of children|3.248|2.294|‐0.211**|0.104|2,292|
|Number of elderly|0.098|0.344|‐0.002|0.014|2,292|
|Number of rooms|3.179|1.691|‐0.125|0.078|2,292|
|Number of buildings|3.611|1.989|‐0.016|0.077|2,292|
|(Log) distance to KCB branch|2.338|1.938|0.227*|0.132|2,256|
|Conflict<br>exposure<br>2011‐14|0.074|2.427|‐0.083*|0.049|2,292|
|(300km buffer)||||||
|Conflict exposure 2015‐2017|0.083|2.139|‐0.056|0.057|2,292|
|(300km buffer)||||||
||State|at baseline||||
|Central Equatoria|0.171|0.376|0.008|0.007|2,292|
|Eastern Equatoria|0.154|0.361|‐0.001|0.001|2,292|
|Lakes|0.147|0.354|0.001|0.001|2,292|
|Northern Bahr El Ghazal|0.175|0.380|‐0.005|0.004|2,292|
|Western Bahr El Ghazal|0.171|0.376|‐0.002|0.003|2,292|
|Western Equatoria|0.183|0.387|‐0.001|0.003|2,292|

_Note: Differences between attritors and non‐attritors in baseline characteristics estimated by an OLS on the attrition dummy and strata fixed effects. Standard errors are clustered at boma level. * (**, ***) indicates statistical significance at the ten‐ percent (five‐percent, one‐percent) level._ 35
# Table 62 (Con't)

||Bas<br>in|edon Value-A<br>DomesticPric|dded<br>es|Base<br>in|d onValue-Added<br>World Prices|
|---|---|---|---|---|---|
||1|2|3|4|S<br>6|
|In(Other Afrims<br>Tednidans and<br>Middle Managers)|3.BE4<br>(1.11-4)|42134!<br>(1.OE-4)||3.lE4<br>(1.E4)|_0308BE-_<br>(0.106E-3)|
|In (Non-Africans<br>Tecduiaans and<br>Middle Managas)|93E-5<br>(6.8E-5)|||4.7E-5<br>(72E-5)||
|In (SenegaleseUpper<br>Managers)|72E-S<br>(7.0E-S)|||S.11E5<br>(7.41-5)||
|In (Other Africans<br>Upper Managers)|23134<br>(I.SE,4)|||1.4E4<br>(1.6E-4)||
|In(Non-Africans<br>Upper Managers)|-3.11-5<br>(86E-5)|||-7.01-5<br>_(92E-S)_||
|In(Cotal Senegalese)|||0510<br>(O.OS7)||0580<br>(0.060)|
|bn(TotalOther Africans)|||1.6E4'<br>(S.9E-S)||4&S4<br>(62&)|
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The state of sanitation is particularly dire in the rural areas, where only 31 percent of individuals have access to improved sanitation (figure 9b). The difference is largely due to the ubiquity of flush toilets in urban areas (figure 9b). In rural areas, on the other hand, there has been a shift away from improved latrines and back toward inferior traditional latrines—and even open defecation in some cases. Indeed, up to 5 percent of rural Zimbabweans lost access to improved latrines between 1999 and 2008. This is due to the fact that the construction of the improved latrines was becoming obsolete, at a time when cement had become too scarce and expensive to allow for the necessary repairs and reconstruction of the latrine structures to take place. Despite the progress in traditional latrines and, to a much lower extent, flush toilets, the combined expansion of improved sanitation facilities fell short of rural population growth, which stood at 1.4 percent per year.

**Figure 9. Access to water sources is highly inequitable between urban and rural communities**

<!-- Start of picture text --> a. Water supply b. Sanitation<br>100 100<br>90 90<br>80 80<br>70<br>70<br>60<br>60<br>50<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>0<br>0<br>Urban<br>Rural Urban Rural<br>% population % population<br><!-- End of picture text --> _Source:_ AICD water supply and sanitation database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Access figures calculated by the AICD using data from the 2005 Demographic and Health Survey.

Water utility performance is plagued by inefficiencies and lack of investment. In the late 1990s, legislation established the Zimbabwe National Water Authority (ZINWA) to take responsibility for water supply and sanitation nationwide. But water supply and sanitation systems significantly deteriorated under the ZINWA, affecting utility performance and service quality. The lack of investment in the sector for almost a decade has led the ZINWA to have distributional losses as high as 45 percent of production (see table 7), more than double the level of a well-performing utility and one of the highest among southern African utilities (figure 10a). Water production is estimated to be 30 percent below requirements (World Bank 2008a), while bill collection is very low at around 20 percent as of April 2009. Since dollarization in 2009, collections ratios started to pick up, but are still at very low levels.

With less than 90 connections per employee, the ZINWA‘s labor productivity is one of the lowest in southern Africa (figure 10b). The recent transfer of responsibility for ensuring provision of water and 27
11 within the range of income Ginis recorded for other developing countries. For instance, the income Ginis recorded in the most recent edition of World Development Indicators suggest that Gini coefficients of per capita household income range from a low of 0.420 (Bolivia) to a high of 0.601 (Brazil).<sup>20</sup> In Table 2 the sources of rural income for Egypt and Jordan are presented by income quintile group. The results suggest that nonfarm income plays a radically different role for the poor in the two countries. While in Egypt the poor -- that is, those in the lowest quintile group - receive almost 60 percent of their total per capita income from nonfarm income, in Jordan the poor receive less than 20 percent of their total per capita income from this source. Furthermore, while in Egypt the share of nonfarm income in total income generally falls as income rises, in Jordan the share of nonfarm income in total income typically increases.

The reasons for this dramatic difference have to do with land. In Egypt the cultivated land base is 100 percent irrigated and highly productive with yields for the main field crops (wheat and rice) among the highest in the world. The large number of people living in rural Egypt therefore all seek access to agricultural land. However, in fact, the very real land constraints in rural Egypt -- 75.7 percent of the households in this sample own no land<sup>2</sup> ' - force most people (and especially the poor) to earn their livelihood from outside agriculture. In Jordan the situation is quite different. Only 30 percent of the cultivated land base is irrigated, and the main field crops (wheat and barley) are generally grown under rainfed conditions and so yields are low.<sup>22</sup> People in rural Jordan thus do not press for land access, since the really attractive economic rates of return are to be found not in agriculture, but rather in the nonfarm sector. In clear contrast to Egypt, the rich in rural Jordan thus earn less than 10 percent of their total per
we examine whether there is residual variation in unit costs once we control for potential cost drivers such as terrain ruggedness and access to markets. We focus on two dimensions of the environment a firm operates: conflict and corruption.

Our analysis yields four main findings. First, we show that there is a large dispersion in unit costs across countries for comparable road work activities. For example, the difference between countries of an asphalt overlay of 40 to 59 mm amounts to a factor of three to four. Second, we find that after accounting for environmental drivers of costs such as terrain ruggedness and proximity to markets, residual unit costs are 30% higher in conflict countries on average. This result is robust to different measures of conflict and political instability. Third, we also find evidence that costs are higher in countries with higher levels of corruption. Moving a country from the seventy-fifth percentile of corruption to the twentyfifth percentile of corruption is associated with 6.8% lower unit costs. Countries with corruption levels as measured by the Worldwide Governance Indicators above the median in the sample have about 15% higher costs. Fourth, these effects are robust to controlling for a country’s public investment management capacity and business environment.

A growing literature highlights the effects of transport infrastructure on transport costs, trade volume, market development, productivity, and poverty and consumption (Casaburi et al. 2013; Dercon et al. 2009; Donaldson 2013; Faber 2014; Gertler et al. 2014; Jacoby and Minten 2009; Limao and Venables 2001; Mu and van de Walle 2011; Shiferaw et al. 2012; Stifel et al. 2012). However, while substantial progress is being made on evaluating the benefits of infrastructure, research on the cost side is lagging behind. Our paper contributes to the recent effort in collecting and analyzing data on unit costs of different types of infrastructure investments across countries (AFRICON 2008; Alexeeva et al. 2008; Alexeeva et al. 2011). In addition to coverage of contracts of all regions worldwide, an advantage of our paper is that with a large number of contracts we can control for systematic differences in the cost of construction by including fixed effects at the very detailed work activity level.

When exploring the correlates of costs, we focus on conflict and corruption. The focus on conflict is motivated by the fact that 1.5 billion people live in conflict-affected or fragile 3