rafmacalaba/datause-encoder-data huggingface dataset:
the United States mentioned earlier show that the percentage of people who “personally worry a great deal about global warming” dropped from 35% in 1989 to 28% in 2001, while the percentage who worry “not at all” rose from 12 to 17% during the same time period (Saad 2002). Again, biannual national level survey research in Norway finds a significant and steady downward trend in public interest and concern about global warming, with the percentage of respondents who replied that they were “very much worried” declining steadily from 40% in 1989 to less than 10% in 2001 (Hellevik 2002, Barstad and Hellevik 2004). Helevik writes, “A decline from such a high level of anxiety is to be expected. There are limits to how long it is possible for individuals to live with the extremely pessimistic environmental perspectives reflected in the 1989 results. Anxiety reduction mechanisms make people look for brighter aspects of development.” We see evidence of the same pattern in the United States. This finding parallels a study by the American Geophysical Union in the United States which reported that the percentage of people who worry a great deal about global warming dropped from 35% to 24% between 1989 and 1997 (Immerwahr, 1999). The AGU study links declining concern to a sense of hopelessness and frustration rather than greed or lack of information. The AGU study emphasized the public’s feelings of powerlessness and frustration connected to the issue of climate change rather than lack of information. This project conducted focus groups to explore public understanding of climate change (Immerwahr, 1999).<sup>2</sup> Their work concluded that rather than lack of information or concern, the public experienced frustration and paralysis with respect to the issue of climate change.

They said they care deeply about global warming, but their concern did not translate into any forward motion. As they thought about the problem, they seemed to run into brick walls, characterized by lack of clear knowledge, seemingly irreversible causes, and a problem with no real solution. As a result they were frustrated and eager for a solution but unsure of which way to go. The symptoms of this frustration are clear. The first is that people literally don't like to think or talk about the subject. Our respondents always seemed to want to move

> 2Their report, “Waiting for A Signal: Public Attitudes toward Global Warming, the Environment and Geophysical Research” is available of the web at http://www.agu.org/sci_soc/attitude_study.html

20
So far, we have looked at two channels by which the trainings could have changed profits. This section has shown that practices did not meaningfully move. However, the previous section showed that the psychological outcomes moved for participants in the DOT training but not at all for those in the PI and BSED trainings - both of which are aimed at psychology. We now turn to the likely main culprit for the lack of impact in the PI/BSED cohorts: the quality of the trainers.

# **4.3.4 Role of the trainer**

Using detailed trainer characteristics and student data from the PI/BSED study we attempt to match which teacher attributes may have mattered for student outcomes.

The following analysis focuses on the sample of students who were trained (619 students; 17 primary trainers). The question we address: among the women entrepreneurs who participate in the trainings, what characteristics of the trainer are correlated with changes in their personal initiative, self-efficacy, error competence, entrepreneurial identity and locus of control scores, as measured at follow-up? The analysis incorporates various hypotheses by sequentially adding explanatory factors grouped into trainer attribute themes: demographic characteristics, business ownership, cognitive and noncognitive skills, job and career satisfaction and experience as a trainer. These themes were formulated based on various hypotheses by which we believe the trainer could influence the effectiveness of a training. For example, perhaps younger trainers are more dynamic and open towards a new training approach or those trainers with a higher error competence may be more likely to encourage entrepreneurs to act and learn from their errors and are therefore better trainers. The full set of hypotheses are discussed in more detail with the results presented below.

Table 8 first presents summary statistics of the 17 trainers included in the analysis. The average age of the trainers was 35 years which is similar to the average age of the WEDP entrepreneurs. There was an even split of female and male trainers and the likelihood that the trainer had ever been a business owner themselves was 41%. The average scores of the noncognitive skills measures for the trainers are relatively high compared to the scores among the entrepreneurs that chose to be trained.
|**Table A2. Full specification of the m**<br>Dependent variable: Log GDP per capita|**odel including i**<br>|**nequality **|||
|---|---|---|---|---|
|<br>Item<br>|<br>(1)|(2)|(3)|(4)|
|Intergenerational persistence|||||
|<br>× High Income Countries|4.6918<sup>***</sup>||||
||(1.4411)||||
|× Eastern Europe and Central Asia|1.4544<br>||||
||(1.3379)||||
|× Latin America and the Caribbean|1.4330||||
||(1.8857)||||
|× Other Developing Countries|0.3689<br>||||
||(1.5603)||||
|Intergenerational correlation|||||
|× High Income Countries||3.3693|||
|||(2.3986)|||
|× Eastern Europe and Central Asia||1.0608<br>|||
|||(0.8183)|||
|× Latin America and the Caribbean||4.3107<sup>*</sup>|||
|||(2.3758)<br>|||
|× Other Developing Countries||7.5284<sup>**</sup>|||
|||(3.5323)|||
|Upward mobility gap|||||
|× High Income Countries|||0.0093||
||||(0.0248)||
|× Eastern Europe and Central Asia|||-0.1552||
||||(0.1074)||
|× Latin America and the Caribbean|||0.2957<sup>***</sup>||
||||(0.1095)||
|× Other Developing Countries|||0.5665<sup>**</sup>||
||||(0.1671)||
|Upward mobility in higher education|||||
|× High Income Countries||||-0.4609<br>|
|||||(0.3205)|
|× Eastern Europe and Central Asia||||5.8365<sup>***</sup>|
|||||(1.0608)|
|× Latin America and the Caribbean||||5.3322<sup>***</sup><br>|
|||||(1.8470)|
|× Other Developing Countries||||12.5791<sup>***</sup>|
|||||(3.8729)|
|_Year-level controls_|||||
|Gini index (income)|-0.0077|-0.0094<sup>**</sup>|-0.0081<sup>**</sup>|-0.0065|
||(0.0047)|(0.0045)|(0.0035)|(0.0042)|
|Log population|-1.9417|-0.6051|1.1437|1.7808|
||(2.0193)|(1.8841)|(1.8868)|(1.7730)|
|Log population (squared)|0.0495|0.0041|-0.0426|-0.0634|
||(0.0635)|(0.0580)|(0.0616)|(0.0566)|
|_Cohort-level controls_|||||
|Average years of education (cohort|-0.0881|-0.1912<sup>***</sup>|-0.1570<sup>***</sup>|-0.2127<sup>***</sup>|
|weighted)|(0.0688)|(0.0555)|(0.0466)|(0.0393)|
|Standard deviation of years of|-0.0874|-0.1097|0.1214|0.0584|
|education (cohort weighted)|(0.2349)|(0.1892)|(0.1213)|(0.1038)|
|_Cohort-specific initial conditions_|||||
in terms of avoiding deforestation in extensive pastures, with net costs around US$ 5 -10/t C, whereas areas that are under pressure for conversion to soybeans would have significantly higher costs, whose compensation would not be justified by the carbon market. On the other hand, a moratorium on direct supply of soybeans from deforested areas, in force since 2005, has created other disincentives to such conversion. Aside from an initial spurt in the first half of the past decade, soybean expansion into the Amazon has not been significant. Yet, scholars warn, the effectiveness of the moratorium could suffer, should commodity prices increase again substantially.

Note finally that there are indications that opportunity costs of forest preservation are particularly high in the Amazon region, viewed in a global perspective. A meta study by Phan et al (2012), where many of the currently available studies worldwide providing numbers for such opportunity costs were pooled, indicated that these costs may be particularly high in Latin America as compared to other regions (although admittedly the difference was not statistically significant as the sample of studies from the region was small). The study at least supports the notion that REDD implementation in the Amazon region is “hard”, and that much work lies ahead before a robust and clear path to effective and efficient REDD implementation has been found for the region.

<!-- Start of picture text --> US$ Opportunity costs for reducing deforestation<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180<br>Gigatons of CO2eq<br><!-- End of picture text -->

**Fig. 3.** Carbon supply curve for Brazilian Amazon forests. This curve begins with carbon contained in forested parcels with the lowest opportunity costs on the left, building cumulatively to the right. (Nepstad et al., 2009).

25 | P a g e
# _Agricultural extension_

48. The main functions of agricultural extension services are to inform farmers of new techniques and to transfer information from farmers to other participants. Public sources, on average, provide about 80 percent of total extension services. Studies show that extension services substantially improve technology adoption rates, awareness and productivity.<sup>33</sup> . However, many public extension services have been inefficient and poorly targeted, and have been unsustainable. World Bank supported agricultural extension projects have generally not produced good results. An OED review of World Bank agricultural research and extension investments that were operational between 1977 and 1992 shows that, of the 33 projects studied, only 10 were rated as satisfactory and the rest as unsatisfactory.<sup>34</sup> All projects, irrespective of their ratings, had major design and implementation problems. The most common problems were recurrent cost funding problems during and subsequent to the project (28 projects), inadequate researchextension linkage (26 projects), under-qualified front-line extension staff having insufficient practical knowledge of production systems and relevant technology (17 projects), top-down approaches to developing recommendations (17 projects) and ineffective contact farmer systems (16 projects) . Other problems include insufficient availability of technology, little consideration of production economics and risks, and differential access by farmers to resources, insensitivity of projects to specific fiscal, institutional, and farming system conditions, weak commitment of implementing agencies and weak monitoring systems.

49. More recent World Bank projects appear to be only marginally better. OED evaluations of six agricultural extension projects that were completed more recently, rate two as having a satisfactory performance, one being marginally satisfactory and three unsatisfactory.<sup>35</sup> 50. A program involving private sector provision of extension services in Argentina has produced good results. Faced with declining productivity of Argentine dairy farmers, the two largest dairy processors of the country initiated extension services for their suppliers. By 1990, 120 farmers’ groups were participating in the program. The results were positive. Although the number of dairy farms supplying one of the processing companies decreased by 24 percent, milk production increased by 15 % between 1976 and 1985. Milk production at the other processing company jumped by almost 50 percent despite a 6% decrease in dairy farm areas of suppliers.<sup>36</sup> 51. In some countries, decentralization of public extension institutions has generated benefits in terms of enhancing efficiency, service quality and outreach. There are also

> 33 Evenson (1977) quoted in World Bank (2002), chapter 2.

> 34 Purcell and Anderson (1997).

> 35 The satisfactory projects were in Guinea,  Mali and Madagascar while the unsatisfactory projects were in Burkina Faso, Chile and Sri Lanka.

> 36 Umali-Deininger (1997), quoted in World Bank (2002), chapter 2.

15
To the extent that 3G catalyzed competitive dynamics, we expect that the induced acceleration in productivity growth would increase labor demand. This expansion in job creation in manufacturing would be reflected in higher employment. † In Table 6, we find moderately significant employment effect across the specifications, suggesting that firms in the woredas with enhanced internet access tend to hire 23%-26% more than firms in the untreated woredas. the results suggest firms exposed to heavier competition by better mobile communication access expand the size of the firm by hiring more employees. This pattern is consistent with higher labor productivity due to the intensification of competition in woredas with more improvement in telecommunications access.

**Table 6 – DID Results on Log of Employment**

|VARIABLES|(1)<br>Base|(2)<br>Firm-level|
|---|---|---|
|MobileAccess*Post|0.230**|0.267**|
||(0.115)|(0.106)|
|Observations|2,597|2,545|
|R-squared|0.129|0.276|
|Year FE|NO|YES|

Note: Column (1) is the baseline specification. Column (2) adds firm-level controls, including firm size, firm age, and capital intensity. Robust standard errors are clustered on Woreda. Robust standard errors in parentheses (*** p<0.01, ** p<0.05, * p<0.1)

# **4.1.3 Triple Differences: Non-Exporting Firms**

In case there is some time-varying confounder that changes across woredas violating the parallel trend assumption (Wing et al. 2018), this section identifies within-woreda comparison groups that are not exposed to treatment but are exposed to the time-varying confounder — namely, exportoriented firms.

> † As the manufacturing firms operate largely in the formal sector, it is likely that formal employment is boosted through the expansion of mobile communication. This could be important evidence in the context of a JET policy agenda.

23
Table B7: Variance contributions for Kenya, by sub-group

||||Absolu|te (s.d.<br>|units)|||Rela<br>|tive (in|%)||
|---|---|---|---|---|---|---|---|---|---|---|---|
|Strata|Level|σ�<br>�|σ��<br>�|σ�<br>�|σ�<sup>�</sup>|2Σ��|σ��<br>�|σ�<br>�|σ�<sup>�</sup>|2Σ��|σ�<sup>�</sup>|
|Female|0|1.02|0.21|0.39|0.38|0.25|4.21|14.49|13.92|5.79|61.59|
||1|0.98|0.20|0.38|0.37|0.23|4.28|14.76|14.46|5.43|61.06|
|Age|6|0.98|0.29|0.38|0.39|0.21|9.08|15.38|15.66|4.83|55.05|
||9|1.01|0.16|0.38|0.39|0.28|2.58|14.24|15.12|7.47|60.59|
||12|1.01|0.15|0.38|0.33|0.26|2.36|14.15|10.80|6.68|66.01|
||15|1.01|0.18|0.38|0.30|0.24|3.08|14.38|8.96|5.63|67.95|
|Grade level|0|1.34|0.15|0.54|0.38|0.37|1.25|16.38|8.14|7.62|66.61|
||1|1.07|0.10|0.41|0.39|0.23|0.87|14.76|13.29|4.47|66.61|
||3|1.07|0.11|0.39|0.40|0.25|1.01|13.58|13.83|5.26|66.32|
||5|0.65|0.11|0.32|0.24|0.08|3.01|23.93|13.55|1.42|58.09|
|SES tercile|1|0.91|0.10|0.36|0.38|0.21|1.25|15.97|17.34|5.35|60.08|
||2|0.82|0.08|0.33|0.30|0.14|0.92|16.67|13.50|2.90|66.01|
||3|1.17|0.25|0.44|0.43|0.29|4.47|14.17|13.11|6.16|62.09|
|Mother primary|0|0.97|0.20|0.37|0.38|0.21|4.22|14.98|15.46|4.75|60.58|
||1|0.85|0.18|0.33|0.33|0.16|4.74|15.15|14.90|3.62|61.60|
|All|.|1.19|0.25|0.44|0.42|0.31|4.54|13.61|12.39|6.54|62.92|

Note: for each stratifying variable, indicated in the first column, sub-groups (second column) are mutually exclusive and span the entire dataset; female is a dummy variable (i.e., 0 = male, 1 = female); age and grade levels are grouped (e.g., age level 6 indicates children aged 6-8; age level 15 is children 15 and above; grade level 0 contains never enrolled children; grade level 5 is all those with highest grade 5 and above); for SES tercile, level 1 is the poorest group; mother edu. takes a value of 1 if the mother has attended primary school; variance components are as per equations (3b)-(3c) and the 2 individual effects are aggregated for simplicity (denoted, σa�); absolute and relative contributions are as per earlier tables, reported in standard deviation units and percentages respectively. Source: own calculations.

52
**Figure 7: Producer support by type in the EU and in Turkey, 2002-2011**

<!-- Start of picture text --> Percent of EU Percentage of Turkey<br>farmgate value farmgate value<br>40 Single commodity support 40<br>Input subsidies<br>Other support linked to production<br>30 De-coupled support 30<br>20 20<br>10 10<br>0 0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text -->

**Source: OECD (2012)**

**Figure 8: Simple average applied MFN duties for Turkey and the EU, 2011**

<!-- Start of picture text --> Fish & fish products EU<br>Turkey<br>Other agricultural products<br>Cotton<br>Beverages & tobacco<br>Sugars and confectionery<br>Oilseeds, fats & oils<br>Cereals & preparations<br>Coffee, tea<br>Fruit, vegetables, plants<br>Dairy products<br>Animal products<br>0 50 100 150<br><!-- End of picture text -->

**Source: WTO (2012)**

23
provided by MPDF/IFC, a list of medium and large businesses provided by MPDF/IFC, as well as a census of firms with at most 50 employees done for the Provincial Business Environment Survey (PBES) in 2006.<sup>2</sup> The total number of firms interviewed in the ICS 2007/2008 survey was 502. For the CRBS 2009 survey, all 502 firms of the 2007/2008 survey were re-contacted and information was collected on their activity status. A total of 89% of these firms were still found to be active while 10% of the firms were no longer active, for reasons of bankruptcy, seasonal inactivity, personal reasons (such as illness) or other.<sup>3</sup> Next, a random sample of 410 active firms was contacted to be interviewed again, creating a panel data set of 242 firms (positive response rate of 59%) with the 2007/2008 survey. In addition, 28 new (non-panel) firms were included in the sample to increase the number of large and exporting firms.<sup>4</sup> While the first survey used a standard investment climate survey instrument collecting information on firm characteristics, firm productivity and investment climate constraints, the second survey was especially designed to capture the impact of the global crisis on Cambodia-based firms as well as their risk coping strategies.

Table 1 summarizes the main results with respect to the impact of the financial crisis on firm performance, input and output prices, employment and worker remuneration. The impact is wide-spread, with the vast majority of firms (80 to 90 percent) reporting a decrease in domestic sales and profits in the first half of 2009 compared to their situation in the first half of 2008. The mean decrease in domestic sales and profits was around 30 percent. The impact of the shock on exports was also significant, although slightly less. This shows that the financial crisis affected firms widely, and not only exporting firms were affected. The wide impact is also reflected in the macroeconomic assessment of a no-growth in 2009 (compared to rapid growth of almost 10 percent in 2000-2007, see Figure 1 above).

The profit squeeze is reflected in a decline in output prices of 6% on average without a corresponding decline in input prices (2% increase) over the same period. In terms of employment, the most affected were the unskilled production workers and non-production/service workers with declines of 3-4% on

> 2 Both surveys were conducted by Indochina Research Ltd in Cambodia. The 2007 survey was financed by the World Bank and the 2009 survey by resources from DANIDA, the European Commission, and UNIDO under the Multi-Donor Trust Fund on Trade Related Assistance.

> 3 2% of the firms could not be found at the known address and may or may no longer be active.

> 4 Because the 2009 survey was focused on the impact of the economic crisis, the sample was designed to include at least 100 exporting firms.

5
# **5. Conclusion**

Scientific literature to date emphasizes that mangroves can substantially reduce the vulnerability of adjacent coastal lands from storm surges as the flow of water through the mangrove forest is obstructed by the matrix of roots/trunks and leaves of the mangrove trees. As a result, the protective role of mangroves and other coastal forests and trees against cyclonic storm surges has received considerable attention in disaster management in recent years. Even though experts and scientists agree that coastal forest belts, if well designed and managed, have the potential to act as bioshields for the protection of people and other assets against storm surges, most of the studies conclude that additional location-specific information is needed to define the specific details and limits of this protective function (FAO, 2007; Das and Vincent, 2009; Arkema et al. 2013).

This paper evaluates the effects of different species of mangroves, different widths of mangrove forests and densities of planting on the cyclone-induced surge height and water flow velocity for seven sites in cyclone-prone coastal region of Bangladesh. For each study site, suitable mangrove species were short-listed upon consultation with ecologists considering the survival potential of the tree species in the local environment, and taking into account the familiarity and interest of the local inhabitants in preserving the species. During field visits, roots, trunk systems and optimal spacing between trees were recorded. For understanding the resistance of water flow from mangroves, Manning’s coefficients were then estimated from field measurements of the root and trunk systems of mangrove species.

A hydrodynamic model for the Bay of Bengal based on the MIKE21FM modeling system was set up, calibrated and run multiple times to simulate the surge of cyclone Sidr that made landfall in Bangladesh in 2007 with a maximum wind speed of 248 kph, maximum wind radius of 64km, central pressure of 928 hPa and normal pressure of 1009 hPa. Estimates of surge height and water flow velocity were <u>first</u> recorded without mangroves and <u>then with different widths of</u> mangrove forests under different densities of planting using specific information on local topography, bathymetry and Manning coefficients estimated from the root and trunk systems of relevant mangrove species.

25
Table 4: Impacts on sanitation loan uptake and toilet uptake (survey data 2017)

||(1)<br>Sanitation loan|(2)<br>Sanitation loan|(3)<br>Toilet|(4)<br>Toilet|
|---|---|---|---|---|
|SL|0.194<sup>_∗∗∗_</sup>||0.124<sup>_∗∗∗_</sup>||
||(0.0476)||(0.0432)||
|SL - Subsidy eligible||0.176<sup>_∗∗∗_</sup>||0.0796|
|||(0.0504)||(0.0515)|
|SL - Subsidy ineligible||0.220<sup>_∗∗∗_</sup>||0.198<sup>_∗∗∗_</sup>|
|||(0.0566)||(0.0581)|
|Subsidy eligible||0.0542<sup>_∗∗_</sup>||0.0592|
|||(0.0270)||(0.0513)|
|Strata FE|Yes|Yes|Yes|Yes|
|Household covariates|Yes|Yes|Yes|Yes|
|~~F~~<br>~~i~~nteraction||0.388||0.102|
|controlmean|0.00907||0.254||
|controlmea~~n~~<br>~~n~~otel||0||0.233|
|controlmea~~n~~<br>~~e~~l||0.0145||0.267|
|N|959|959|959|959|

_Notes_ : SL stands for sanitation loan treatment arm. Sample restricted to matched client households not having a toilet at survey baseline. Standard errors clustered at the village level are shown in parentheses. *, **, *** indicate significance at the 10, 5 and 1 percent level. Dependent variables: (1)-(2) Indicator equal to 1 if MF client took a sanitation loan between Feb 2015 and August 2017, as per MFI admin data; (3)-(4) Indicator equal to 1 if household owns a toilet as observed by interviewer during endline survey in August-Sept 2017; Covariates: Toilet ownership at baseline, indicator for presence of a child aged 0 - 2 at baseline, ratio of number of sampled clients to village size, indicator for whether there is any uncertainty around the matching quality; Strata and interviewer fixed effects included.

32