rafmacalaba/datause-encoder-data huggingface dataset:
education among women relative to men is also significantly associated with lower women-tomen adult mortality rate. However, there is no significant relationship between the absolute level of adult women’s mortality rate and the number of years of education of women. What this suggests is the presence of gendered effects of education in that education matters for women more than for men as far as mortality rates are concerned. Similarly, higher population density, better quality of democracy and greater percentage of children with immunization against DPT are all significantly associated with a higher women-to-men adult mortality rate. However, like education, these variables show no significant correlation with the absolute level of women’s adult mortality rate. In contrast, higher prevalence of HIV among women is significantly associated with higher absolute women’s adult mortality rate but there is no such relationship between the prevalence of HIV among women relative to men and women-to-men adult mortality rate. One interpretation here is that while HIV prevalence among women adversely affects their mortality rate, this adverse effect spills over to men as well.

# **5. Instrumental Variables Approach**

According to World Bank (2015), over the last 25 years, there has been a precipitous rise in the number of economies adopting domestic violence legislation. The report suggest that the increase has been largely driven by international and regional human rights conventions. Thus, we instrument domestic violence legislation using three instruments. The instruments include the ratification of the optional protocol of the Convention on the Elimination of all Forms of Discrimination Against Women (CEDAW), the ratification of the convention of Bélem do Pará, and the Maputo protocol. The optional protocol for CEDAW was effective in the year 2000 and established complaint and inquiry mechanisms for the original CEDAW. It has been used for issues 17
# **Social Movements**

Jacques, Peter 2006 The Rearguard of Modernity: Environmental Skepticism as a Struggle of Citizenship <u>Global Environmental Politics</u> 6:1.

See above.

Moser, Susanne C. and Lisa Dilling. 2007. Creating a Climate for Change: Communicating <u>Climate Change and Facilitating Social Change.</u> New York: Cambridge University Press.

This edited volume tackles climate change communication and social change, see more detail under specific entries.

Norgaard, Kari Marie. 2006. "'People Want to Protect Themselves a Little Bit': Emotions, Denial, and Social Movement Nonparticipation." Sociological Inquiry 76:372-396.

Emotions can be a source of information and an impetus for social action, but the desire to avoid unpleasant emotions and the need for emotion management can also prevent social movement participation. Ethnographic and interview data from a rural Norwegian community describes how people avoided thinking about climate change in part because doing so raised fears of ontological security, emotions of helplessness and guilt, and was a threat to individual and collective senses of identity. In contrast to existing studies that focus on the public's lack of information or concern about global warming as the basis for the lack of public response, my work describes the way in which holding information at a distance was an active strategy performed by individuals as part of emotion management. Following Evitar Zerubavel, I describe this process of collective avoiding as the social organization of denial. Emotions played a key role in denial, providing much of the reason why people preferred to avoid information. Emotion management was also a central aspect of the process of denial, which in this community was carried out through the use of a cultural stock of social narratives that were invoked to achieve "perspectival selectivity" and "selective interpretation.".

# **Culture**

Bell, Alan. 1994. “Climate of Opinion: Public and Media Discourse on the Global Environment,” Discourse and Society. 5(1): 33-64.

Bell compares the public discourse on climate change in New Zealand with media coverage in the preceding year. The author identifies “considerable mismatch” between media reporting of climate science and public understanding. In general the Bell finds that the public knows little about causes of climate change. Confusion in the public is characterized by overestimates of temperature and sea 62
and sell more. They use more energy both during and post-intervention, with this increase statistically significant when measured in levels during the intervention. The log results suggest firms are using 17 percent more energy, although this is not statistically significant. In contrast, the pattern is more mixed for the individual treatment group, which has a statistically insignificant increase in energy costs when measured in levels, but statistically insignificant decrease when measured in logs.

Column 6 examines whether the improvement in management has resulted in higher labor productivity (measured as real sales per worker). The percent increase in employment for group treated firms is slightly higher than the percent increase in sales, and the result is a small, and statistically insignificant, drop in labor productivity (3 percent). The individual treatment also has a small and negative point estimate on labor productivity. These results contrast with the 17 percent improvement in productivity found in India by Bloom et al. (2013). However, since the improvement in management in our experiment is only one-third that found in India, a proportional improvement in productivity would be only 5.7 percent.<sup>15</sup> The confidence intervals of approximately [-13%, +8%] for the productivity effect found here include both the possibility of a productivity improvement of this magnitude, commensurate with what the existing literature would predict from a management improvement of the size, but also include the possibility that labor productivity fell.

Finally, columns 6 and 7 examine the extent to which any increase in sales came through exports. We use administrative data on exports, which have the advantage of being available for all firms and all months. Sixty percent of firms exported in at least one month between January 2013 and December, but on average, only 21 percent of firms export in a given month. As a result, most sales are domestic: exports are 0 percent of monthly sales for the median firm, and 3.8 percent for the mean; and even conditional on exporting in a month, exports for the median exporter are only 14 percent of that month’s sales. Column 6 shows that there a small, negative, and insignificant effect on the extensive margin of whether firms export at all in a given month. Column 7 shows

> 15 See McKenzie and Woodruff (2017) for evidence from multiple developing countries that improvements in business practices appear to be have a linear relationship with firm outcomes over large ranges, which would suggest that this assumption of proportional scaling is a reasonable approximation.

24
# **II. Previous literature**

Our analysis is inspired by three strands of the literature. The first strand compares the characteristic of entrepreneurs in developing countries to those of wage and salary employees and other workers. The second strand attempts to measure the extent to which the selfemployed are self-employed by necessity (and would rather be wage and salary employees) or are potentially successful entrepreneurs, while the third attempts to identify and measure the characteristics of those self-employed who have the potential to be successful but are constrained by lack of access to capital or other reasons.

A recent and growing literature studies the characteristics of entrepreneurs in developing countries. Djankov, Qian, Roland and Zhuravskaya (2005) collected data on the personal, family and business characteristics of approximately 1500 entrepreneurs and nonentrepreneurs in 2004 in China. Djankov, Qian, Roland and Zhuravskaya (2006) use similar data (from 2003-2004) to examine the characteristics of entrepreneurs in Russia.<sup>1</sup> They find that compared to non-entrepreneurs, entrepreneurs in China and Russia are more mobile, more willing to accept risk, have parents who are more educated, are more likely to have parents and other family members who were entrepreneurs, and are more willing to trade away leisure for more money. Djankov, Qian, Roland and Zhuravskaya (2005 and 2006) further distinguish between entrepreneurs and “failed entrepreneurs” (who at one point were entrepreneurs but are not now). Failed entrepreneurs score worse on aptitude tests compared to entrepreneurs, but have the best self-reported performance in school.

De Mel, McKenzie and Woodruff (2008) perform a similar analysis using data from surveys carried out in Sri Lanka between 2005 and 2007 of employers in small and medium sized firms, own account workers and wage and salary employees. Although they do not find that entrepreneurs are more willing to accept risk, they do confirm other patterns from China and Russia. Compared to own account workers and wage and salary employees, employers are older, more educated, have parents who are more educated, and lived in wealthier households as children. Employers and own account workers are more likely than wage and salary workers to have parents who were self-employed. Years of schooling is highest for employers, followed by wage and salary workers, and lowest for own account workers. Finally, own-account workers score lower on measures of cognitive “ability” than both employers and wage and salary employees.

In part, this literature examining the characteristics of entrepreneurs in developing countries stems from a recent debate about the extent to which self-employment reflects voluntary exit

> 1 Non-entrepreneurs are wage and salary employees.  Djankov, Qian, Roland and Zhuravskaya (2005 and 2006) do not consider own account workers.

4
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Policy Research Working Paper 5388

# **Abstract**

In this paper the authors compare indicators of development, infrastructure, and living conditions in the slums of Dakar, Nairobi, and Johannesburg using data from 2004 World Bank surveys. Contrary to the notion that most African cities face similar slum problems, they find that slums in the three cities differ dramatically from each other on nearly every indicator examined.

Particularly striking is the weak correlation of measures of income and human capital with infrastructure access and quality of living conditions. For example, residents of Dakar’s slums have low levels of education and high levels of poverty but fairly decent living conditions. By contrast, most of Nairobi’s slum residents have jobs and comparatively high levels of education, but living conditions are but extremely bad. And in Johannesburg, education and unemployment levels are high, but living conditions are not as bad as in Nairobi. These findings suggest that reduction in income poverty and improvements in human development do not automatically translate into improved infrastructure access or living conditions.

Since not all slum residents are poor, living conditions also vary within slums depending on poverty status. Compared to their non-poor neighbors, the poorest residents of Nairobi or Dakar are less likely to use water (although connection rates are similar) or have access to basic infrastructure (such as electricity or a mobile phone). Neighborhood location is also a powerful explanatory variable for electricity and water connections, even after controlling for household characteristics and poverty. Finally, tenants are less likely than homeowners to have water and electricity connections.

This paper—a product of the Sustainable Development Division, Africa Region—is part of a larger effort in the department to improve the global knowledge base on African infrastructure as part of the Africa Infrastructure Country Diagnostic. The paper has benefited from funding, at different stages, from the Norwegian Trust Fund, Public Private Infrastructure Advisory Facility (PPIAF), Water and Sanitation Program-Africa (WSP-Africa), the Africa Region of the World Bank, and the multi-donor supported Africa Infrastructure Country Diagnostic (AICD). Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at sgulyani@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
17 IV.

Safety Regulations. Productivity of R&D<sup>and Effective Patent</sup> Duration: The Case of the U.S.<sup>8</sup> This section makes essentially two points.<sup>First, there are two</sup> approaches to deal with the fact that pharmaceutical drugs can be a source of serious health risks; these approaches<sup>can be biased towards the market</sup> mechanism or towards a governmental enforced regulatory system. The history of the U.S. --and other industrial countries--shows a clear drift towards the second approach (Subsection 1). Second, the stringent<sup>regulations introduced in the</sup> U.S. during the early 1960s, has had clear<sup>effects on the costs and productivity</sup> of R&D undertaken by private pharmaceutical companies. Furthermore, increased safety and efficacy regulations have reduced<sup>the effective patent protection,</sup> thus reducing the profits per dollar invested<sup>in R&D (Subsection 2). This has</sup> been an important argument in defending this industry's request for longer patent duration; it is also a factor in explaining<sup>the industry's demand for higher</sup> protection in developing countries.

# 1. Market induced information flows vs.<sup>government safety</sup> regulations

The purpose of this section is to discuss<sup>the effects of the changing</sup> regulatory environment of pharmaceutical drugs in the United States. Safety regulations in the area of these drugs<sup>are justified with the argument of market</sup> failure. The problem is that pharmaceutical drugs can be a source of serious risks; in fact, some fatal cases associated<sup>with their consumption have shaped</sup> This section draws from Grabowski and Vernon<sup>(1983).</sup>
area; the estimates are population characteristics for the country as a whole, as well as by province, and for urban and rural areas.<sup>39</sup> As in many developing countries, poverty in Afghanistan is more prevalent in rural areas.<sup>40</sup> Rural areas often lack basic infrastructure, like roads, electricity and communications; and households often have limited access to basic services, like healthcare and education. Given the dependence on agriculture, rural households are often more susceptible to the impact of natural shocks, like drought. In this context, it is not surprising that total monthly per capita consumption in rural areas is 44% lower than that in urban areas, and that rural households spend a larger share (63%) of total consumption on food than urban households (44%). Rural and urban households also differ in their levels of food security. Although their per capita calorie intake is similar, rural households have lower levels of dietary diversity. These statistics suggest that while calorie intake may be similar across areas, micronutrient intake is not.

Following guidelines from the World Food Programme (WFP, 2008), we create three food consumption profile groups using the recommended cutoffs (of 28 and 42 for populations whose daily diets include sugar and oil) for the food consumption score (FCS). Approximately 20% of all Afghan households have poor or borderline diets, with a disproportionate number in rural areas.

Further differences between rural and urban households can be observed in the calorie and expenditure shares devoted to various food groups. (Table 2) The patterns are consistent with those seen in other low-income countries. Grain, including wheat, breads and other cereals, represent 48% of food expenditure and 70% of calorie intake for the national sample. On average, urban households devote significantly fewer resources to grains and dairy and significantly more resources to the other food groups than rural households; household calorie shares follow a similar pattern. We also present the consumption and food security statistics for the other subpopulations examined. (Table 3) Given that agricultural households and wheat producing households are largely situated in rural areas, they have consumption and dietary patterns similar to rural households.

Finally we present select national statistics by quarter (Table 4). Over the survey year, there was a small, statistically insignificant decrease in nominal monthly per capita total and

> 39   All point estimates are weighted to reflect population characteristics; all standard errors are corrected for sample stratification and clustering.

> 40 World Bank (2008)

16
Policy Research Working Paper 6789

# **Abstract**

This paper uses farm panel data from Indonesia to examine dynamic patterns of land use, capital investments, and wages in agriculture. The empirical analysis shows that an increase in real wages has induced the substitution of labor by machines among relatively large farmers. Large farmers tend to increase the scale of operation by renting in more land when real wages increase. Machines and land are complementary if the scale of operation is greater than a threshold size. In contrast, such a dynamic change was not observed among relatively small holders, which implies a divergence in the movement of the production frontier between Java and off-Java regions given that the majority of small farmers are concentrated in Java.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at fyamauchi@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Algorithm** Locate crash/event locations **Input** Text Landmark gazetteer Roads Areas (e.g., neighborhoods) List of event words (e.g., crash, accident, wreck, etc) Prepositions, grouped by tier Types, grouped by tier **Output** Coordinates of event

# **A. Clean Tweets**

1. Replace @ with “at” only when it is not proceeded by via or when it is not the last word in a tweet.<sup>7</sup>

2. Remove select stopwords<sup>8</sup> 3. Mask common phrases that contain a location but refer to something else, such as “[city] bus”<sup>9</sup> 4. Removing hyperlinks and only keeping alphanumeric characters (e.g., removing punctuation).

# **B. Extract Locations**

1. Extract exact matches of landmarks, roads and areas

2. Extract fuzzy matches of landmarks, roads and areas

- (a) Break tweets into 1-3 grams

- (b) For each n-gram, check levenstein distance to gazetteer entries. If word/phase is 0 – 4 characters, ignore; if 5-10, allow levenstein distance of 1; if above 10, allow levenstein distance of 2

3. Extract landmarks after prepositions. For each preposition in the tweet:<sup>10</sup>

- (a) Take the word after the preposition and extract all landmarks that start with that word

7 We found that @[word] often referred to a twitter handle when proceeded by via or when it was the last word in a tweet; otherwise, users were more likely to use “@” as a shorthand for “at.” Distinguishing these cases is important as we rely on preposition to prioritize landmark references.

- 8 We only remove “a” and “the”;

- other stopwords may be part of a landmark name (e.g., the stopword “and” appears in the restaurant “nice and lovely”. We remove these stopwords as we later determine whether a preposition proceeds a landmark, and we consider [preposition] [landmark name] to be equivalent to [preposition] [stopword] [landmark name].

9 In Nairobi, we found that matatu (minibuses) often were referred to by the location where they traveled to; consequently, we mask phrases such as: “githurai bus”, “rongai matatu”, machakos minibus”, etc. In masking, we replace each word in the phrase with a random sequence of characters. Doing this preserves that a word appears at a location in the tweet, which may affect procedures such as determining the landmark closest to an event word.

10 This procedure will often capture the same landmarks as captured in the preceding steps; however, it helps to capture other landmarks where the process for augmenting the gazetteers did not generate the landmark name contained in the tweet S16