rafmacalaba/datause-encoder-data huggingface dataset:
INTRODUCTnON liil Table 10 Annual Population Increase and Growth Hate by Geographic Region and by Income Group, 1985-2100

||Annual<br>p|<br>opulat|ion<br>incre|ase<br>(mil|lions)|<br>An|nual<br>gro|wth<br>rat|e (perce|nt)|
|---|---|---|---|---|---|---|---|---|---|---|
|Region<br>or|||||||||||
|income<br>group<br>|1985-90<br>2|<br>000-05<br>2|<br>025-30<br>2|<br>050-75<br>2|<br>100-25|1985-90<br>2|<br>000-05<br>2|<br>025-30<br>|<br>2050-75<br>|<br>2100-25|
|World|88.2|91.7|78.5|37.2|<br>4.9|<br>1.74|1.43|0.91|0.35|<br>0.04|
|Loss developed<br>(a)|81.7|87.4|78.2|37.7|<br>4.8|<br>2.11|1.70|1.08|0.41|0.05|
|More developed (a)|6.5|4.3|<br>0.3|<br>-0.5|<br>0.1|<br>0.54|<br>0.34|0.02|-0.04|0.01|
|Africa|18.2|26.0|30.0|19.4|2.3|3.02|2.79|1.81|0.77|0.08|
|East Africa|7.9|11.6|14.6|9.7|1.1|3.15|2.94|1.98|0.84|0.08|
|West Africa|6.8|<br>10.1|11.7|<br>7.9|1.0|<br>3.10|2.93|1.86|0.81|0.09|
|North<br>Africa|3.5|<br>4.3|<br>3.7|<br>1.8|0.2|<br>2.65|2.21|<br>1.26|0.47|0.05|
|America|10.9|9.4|6.0|1.4|0.2|1.57|1.10|0.57|0.12|0.02|
|Latin America andCaribbe|an 8.5|<br>7.6|<br>5.7|1.5|0.2|2.02|1.40|0.80|0.19|0.02|
|Northern America|2.4|1.8|0.2|-0.1|0.0|0.87|0.59|0.07|-0.04|0.00|
|Asia|55.4|53.9|41.9|16.5|2.2|1.87|1.42|0.84|0.28|0.04|
|East and Southeast Asia|26.3|21.8|13.5|3.0|0.5|1.53|1.04|0.53|0.11|0.02|
|South Asia|25.6|27.7|23.7|10.7|1.5|2.29|1.82|1.09|0.39|0.05|
|Southwest Asia|3.5|4.4|4.7|2.7|0.2|2.81|2.39|1.57|0.64|0.05|
|Europe and U.S.S.R.|3.3|2.0|0.4|-0.1|0.1|0.42|0.25|0.05|-0.02|0.01|
|Oceania|0.4|0.4|0.2|0.1|0.0|1.63|1.21|0.49|0.16|0.02|
|Income group(b)|||||||||||
|Low|58.7|63.1|56.4|27.8|3.8|2.05|1.67|1.06|0.42|0.05|
|Lower-middle|13.6|14.4|12.9|5.7|0.6|2.23|1.74|1.09|0.38|0.04|
|Ur.er-middle|8.1|8.2|7.0|3.0|0.3|1.84|1.45|0.91|0.32|0.01|
|High|4.4|3.3|0.2|-0.2|0.0|0.57|0.39|0.02|-0.02|0.01|
|Nonreporting no'menmber|3.3|2.7|2.1|0.9|0.1|0.89|0.65|0.44|0.17|0.03|

a. "More developed" comprises Europe, U.S.S.R., Northern America (United States and Canada), Australia, New Zealand, and Japan. ULess develtped comprises the rest of the world.

b. Criteria for income groups ari given in World Bank 1989. See listing in Exhibit 1.
<!-- Start of picture text --> Figure 2. Import Shares<br>%<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>1991/92 2001/02 2007/08<br>Import Substituting Other Developing Industrial<br><!-- End of picture text --> While the import substituting group shows large fluctuations driven primarily by China, the other two groups show steady increases in import shares. Other developing country group was slightly less open than industrial countries in 1991/92; this difference has continued during the second period and industrial countries continue to be more open. Again, one can assume that the other developing countries will continue to increase their import share rates along with the industrial countries generating some trade growth even in the absence of demand growth.

# **D.  Market Shares of Developing Country Exports**

# **D.1. Industrial Countries**

One of the important developments during the last few decades has been the rapid expansion of exports from developing countries both to the industrial countries and to other developing countries. This has led to fears that industrial countries were being de-industrialized and most of manufacturing production would be taken over by the developing countries. There are also arguments that recent deceleration of growth in industrial countries would lower the export and output growth of developing countries and the developments of last two decades might not be replicated. Tables 4 and 5 show behavior of manufacturing imports from developing countries for our sample of industrial and developing countries.

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# **Appendix B3:  De facto differences in the intensity of entry regulation**

One potential concern with our regulation data is that they measure _de jure_ , rather than _de facto_ regulation; amongst sectors that are subject to authorization there might be meaningful differences in the ease with which one can secure permission to operate a business. For instance, it is feasible that some sectors are de facto free entry sectors in spite of being nominally subject to authorization. To account for these differences in the ease with which authorization can be obtained, we hired a leading lawfirm to codify in which sectors obtaining authorization is particularly difficult. Their ranking which is listed in Table R2.1 below, is inevitably subjective, and thus has to be interpreted with caution.

Telecommunications, air transport (both regular and non-regular), manufacturing of tobacco, cement, flat glass, arms and weapons, as well as security, radio and TV, recycling, taxi and bus operators are among the sectors in which obtaining authorization is especially difficult. The majority of these sectors (7 out of 13) are also subject to restrictions on FDI.

Interestingly, connected firms tend to be overrepresented in these sectors, accounting for 60% of output and 73% of net profits and 15% of all jobs. These numbers are driven by a few influential sectors, since connected firms are active in 6 of the 13 sectors<sup>18</sup> that are considered especially difficult to enter (four of which are subject to restrictions on FDI). However, these sectors are among the sectors with the highest level of turnover (both economy-wide and in the list of sectors subject to difficult authorization).

> 18 Arms and weapons manufacturing (sector code 29600) was also considered to be subject to difficult authorization, but we did not find any private firms active in this sector and therefore excluded it form the analysis.

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# **5. Summary and Conclusions**

This paper estimated the global aggregate of the economic impact of major disasters during 1960 to 2007 using SAM methodology and examined the trends of estimated disaster impact. The results indicate, in total, the global aggregate of damages is about US$742 billion, losses are US$360 billion, and total impacts are estimated close to US$680 billion, in 2007 value. While geophysical disasters are most costly in terms of absolute value for damages, losses, and total impacts, meteorological disasters have the highest impact multiplier of 2.02, indicating that damages and losses can spread to a wider extent through interdependency of economic activities. Moreover, the analysis indicates a growing trend of economic impacts, such as damages, losses, and total impacts, over time using all the 184 events, whereas the trends of damages and losses are statistically significant with linear regression lines. The impact multiplier of the globally aggregated results over the period becomes nearly two, implying that on average the losses caused by a disaster can become doubled through the interdependencies of an economy. Furthermore, the investigation of economic impacts and development level was carried out to see whether or not an inverted ‘U’ curve relationship between total impacts and income level can be observed. The statistical analyses, however, found that inverted U curve relationship, or more generally quadratic relationship, is not statistically significant for total and each type, while climatological, geophysical, and hydrological disasters show a negative linear relationship, confirming the traditional disaster theory, indicating lower-income countries are more vulnerable to higher-order effects than in middle- or higher- income countries. These results conflict with Kellenberg and Mobarak’s (2008) study, in which they use the number of casualties as the disaster impact.

The results in this paper were derived from the damage data of EM-DAT and NatCat database and using SAM for each country in each decade. These damage data and SAMs are highly aggregated without having any sector-level information. For an analysis of historical trends and for international comparison, the aggregation level in this study is acceptable due to the data availability. On the other hand, further detailed analysis, based on disaggregated sectors and/or space, can reveal a more thorough and comprehensive figure of disaster impacts, as presented in a companion paper ‘Impact 28
implemented). This improvement is broad-based, with improvements in just over half of a detailed set of 141 practices measured. We then track firms for 1.5 to 2.5 years post-implementation with survey data, and use administrative data on employment to measure impacts up to 3 to 4 years post-implementation. We find evidence that the group-based intervention has grown the treated firms, with a statistically significant 6 to 7 worker (10 to 12 percent) increase in employment relative to the control group; a 8 to 9 percent growth in sales which is not statistically significant when compared to the control (p=0.12), but is statistically greater than the impact of the individual treatment; and higher energy input usage. In contrast, we find smaller and statistically insignificant impacts of the individual-based treatment. Neither treatment has a significant increase in productivity, with a 95% confidence interval of [-13%, +8%]. The group-based intervention clearly dominates the individual intervention on a cost-benefit basis, and, although there is considerable uncertainty associated with this estimate, we estimate that the group-based intervention is likely to pay for itself in terms of higher firm profits within the first year.

This work contributes to at least three literatures. The first is a general literature on improving business practices and management in firms. Most of this literature has focused on short training courses and microenterprises (see McKenzie and Woodruff, 2014 for a review). However, several studies show the potential of more intensive individualized consulting to improve management in small and medium enterprises. In addition to Bloom et al.’s (2013) work in India, this includes Bruhn et al. (2018) with firms averaging 14 workers in Mexico, and Higuchi et al. (2017) with firms averaging 20 workers in Vietnam.<sup>2</sup> Secondly, while we are not aware of other studies that directly test group-based versus individual consulting, a recent literature has highlighted the ability of firms to improve their business practices when formed into groups or paired with other firms that can serve as role models (e.g. Cai and Szeidl 2018, Chatterji et al. 2018, Dalton et al. 2018, Lafortune et al. 2018). Finally, this paper contributes to a broader literature on how to scale up policies from promising researcher pilot studies (e.g. Banerjee et al, 2017, Bold et al, 2018). Our results show the promise of group-based consulting as a pathway to greater scale.

> 2 A related quasi-experiment provided evidence of the long-term impact of participating in the Productivity Program, which allowed Italian firms to participate in study trips in U.S. plants followed by consulting sessions of U.S. experts at Italian firms (Giorcelli, 2019).

3
in relaxing credit constraints. In fact, findings from the Gallup survey confirm this reasoning (Figure 4). Asked about the most important reason why people save money, 29 percent provide a precautionary motive behind their most important reason to save, stating saving for either “a rainy day” or “in case we get sick”. The second most important reason that people report saving is “to start a business” (almost 20%). These numbers suggest that almost half of the people surveyed are actually using savings for purposes that credit was either supposed to, or is billed to, serve. What is more, enabling savings neither creates the burden of debt, nor the resource diversion that credit does.

# **Figure 4: Reasons for Saving (average 2009-10 over SSA countries)**

<!-- Start of picture text --> What is the most important reason people, like you, save money?<br><!-- End of picture text --> <!-- Start of picture text --> Just in case I might need money / for a rainy day<br>To start a business<br>To send children/grand-children to schoool<br>To buy a house / to build a house<br>In case we get sick / for medical treatments<br>To buy livestock<br>To buy a plot<br>To get interest<br>To pay for my own education<br>To immigrate<br>For special events (burial/parties/holidays)<br>Other<br>To get married<br>For when I'll retire<br>To buy a car<br>To secure a loan / as guarantee for loan<br>0% 5% 10% 15% 20% 25%<br><!-- End of picture text --> <u>Source: Gallup World Poll 2009 and 2010.</u> 18
# Tolerance towards Neighbors

|Tolerance for poor/different lifecycle stage|||
|---|---|---|
|Willing to tolerate all||e|
|Intolerant towards 1|e||
|Intolerant towards 2|e||
|Intolerant towards 3|e||
|Tolerance toward stigmatized attributes/behaviors|||
|Willing to tolerate all|*||
|Intolerants towards 1|*||
|Intolerants towards 2|*||
|Intolerants towards 3|*||
|Intolerants towards 4|*||
|Tolerance to membersofothergroups|||
|Willing to tolerate all||a|
|Intolerant towards 1|4||
|Intolerant towards 2|a||
|Intolerant towards 3|a||
|Intolerant towards 4|a|Cluster2|
|Intoleranttowards5|4|<br>Cluster 3|
||0<br>20<br>40|60<br>80<br>100|
||Percentageof|Respondents|
The rest of the paper is organized as follows. Section 2 presents the Global Findex data and some descriptive statistics on the differential use of financial services by gender across income groups and regions across the globe. Section 3 describes the additional data used in the regression analysis, including our data on legal discrimination against women and gender norms. Section 4 discusses the regression methodology used in the paper and section 5 presents the results. Section 6 concludes.

# **2. Measuring Financial Inclusion**

# **_2.1 Global Findex Data_**

Our data on the use of financial services come from the 2011 Global Findex database.<sup>5</sup> The Global Findex data was collected in conjunction with the annual Gallup World Poll Survey. The 2011 Gallup World Poll surveyed at least 1,000 individuals in 148 economies, using randomly selected, nationally representative samples.<sup>6</sup> The target population is the entire civilian, non-institutionalized population, age 15 and older. In our descriptive analyses, we focus on 140 countries<sup>7</sup> (see Appendix A for the list of countries in the sample).

The Global Findex dataset includes 41 indicators on the use of financial services around the world. In this paper, we focus on three main dimensions of financial inclusion: (1) ownership – individual or joint – of an account at a formal financial institution; (2) savings in the past 12 months; and (3) credit in the past 12 months. For savings and credit, we distinguish between cases when individuals save at or borrow from a formal financial institution (such as a bank) or if they use only informal means.

> 5 See Demirguc-Kunt and Klapper (2012) for a more description of the database.

> 6 The complete individual-level database, as well as detailed country-level information about the data collection dates, sample sizes, excluded populations and margins of error can be found at: www.worldbank.org/globalfindex **.** 7 We drop seven countries from our sample because more than 20% of the population is not sampled in these economies and we exclude Iran because of irregular surveying methods.

8
depending on spatial resolution, may indeed be covered by the same precipitation data point. Predictors such as the density of buildings or building patterns would likely improve the model.

Further, two different sets of night‐time lights data were used to improve the predictive power of the urban model, but these turned out to be poorly correlated with survey poverty estimates and did not improve the urban model’s predictive power.<sup>28</sup> This failure to improve the model is likely due to the night‐ time lights data’s coarse resolution of 1km and 500m, respectively. In rural areas, EAs are highly dispersed and poverty levels somewhat more spatially homogenous. Hence, the rural model was more successful at explaining variation in poverty in rural areas, with an adjusted R‐ squared of 94 percent (Table 10).

The uncertainty from using spatial covariates as explanatory variables was not considered in the estimation of standard errors. However, the data points used in the model were randomly selected to ensure they were taken from places far from each other. The resulting weighted average coefficient of variation (CV) from estimates for urban districts is 0.19 and 0.73 for rural districts. Moreover, EAs were randomly selected for the survey with the multi‐stage stratified process described above, which combined with a random selection of data points to estimate the model, aims to derive a sample of EAs with different values within the range of each explanatory variable, similar to the range from the overall EA population.

**Table 10: Final model to predict rural poverty.**

|**Coefficients**|**Coefficient estimate**|**Standard error**|**p‐value**|
|---|---|---|---|
|(Intercept)|2.075|0.320|0.000|
|Conflicts density|0.000|0.000|0.003|
|Distance to cultivated areas|0.040|0.008|0.000|
|Distance to food insecure areas|0.020|0.008|0.018|
|Distance to major roads|‐0.019|0.005|0.001|
|Distance to medical sites|‐0.026|0.004|0.000|
|Distance to schools|0.034|0.005|0.000|
|Distance to unsafe areas|‐0.009|0.004|0.027|
|Distance to urban areas|0.011|0.002|0.000|
|Distance to water sources|0.007|0.002|0.000|
|Distance to waterways|0.000|0.001|0.830|
|Precipitations|0.001|0.000|0.001|
|Temperature|‐0.089|0.012|0.000|
|Conflicts density x Distance to cultivated areas|0.000|0.000|0.005|
|Distance to cultivated areas x Distance to major roads|0.001|0.000|0.002|
|Distance to cultivated areas x Distance to medical sites|‐0.001|0.000|0.001|
|Distance to cultivated areas x Distance to schools|‐0.002|0.000|0.000|
|Distance to food insecure areas x Distance to schools|0.000|0.000|0.002|
|Distance to food insecure areasx Distance to urban areas|0.000|0.000|0.001|
|Distance to food insecure areas x Distance to water sources|0.000|0.000|0.000|
|Distance to food insecure areas x Temperature|‐0.001|0.000|0.000|
|Distance to medical sites x Distance to urban areas|0.001|0.000|0.000|
|Distance to unsafe areas x Distance to water sources|0.000|0.000|0.025|
|Distance to urban areas x Distance to water sources|0.000|0.000|0.000|

28 The two data sets are from ‘Visible Infrared Imaging Radiometer Suite’ (VIIRS) and Defense Meteorological Satellite Program (DMSP).

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# **1. Introduction**

Climate change is anthropogenic - the product of billions of acts of daily consumption. That solutions need to be anthropogenic too is well accepted. Normally cast in the realms of finance and technology, suggested solutions often neglect the primal root of the problem: individual behavior. A broad amount of social-psychology scholarship has examined the barriers and drivers of individual behavior in relation to both adaptation and mitigation. This paper reviews some of its conclusions, and suggests policy areas that should be considered in devising appropriate interventions. Let’s start from what separates awareness, concern and action.

# **2. Climate change and behavior change**

There are three main reasons why the drivers of human behavior are important for climate policy. First, myriad private acts of consumption are at the root of the climate change challenge. As consumers, individuals hold a reservoir of mitigation capacity. Roughly 40 percent of OECD emissions result from decisions by individuals—travel, heating, and food purchases. U.S. households directly account for roughly 35 percent of national CO2 emissions - more in absolute terms than the entire U.S. industrial sector and any other country bar China. If adopted, existing efficiency measures for households and motor vehicles can allow energy savings of almost 30 percent— 11 percent of total U.S. consumption.<sup>2</sup> Second, individuals are the drivers of larger processes of change involving organizations and political systems. Particularly in democratic countries, government action is the result of public pressures to act by citizens and voters. Third, policy decisions are taken by individuals subject to standard mental processes.

So far, the debate about changing individuals’ behavior has focused on market mechanisms. Better pricing of energy and costing of scarce resources can indeed steer individuals away from carbon-intensive consumption, encourage them to preserve endangered habitats, and better manage eco-systems. But the drivers of consumption behavior by individuals and groups go beyond prices. Many energy efficient technologies have been available for years. “No-regret” investment decisions that yield benefits irrespective of climate change are available for both mitigation and adaptation. Improving building insulation, addressing water leakages, and limiting urbanization in flood-prone areas are good examples. The trick is to grasp why adoption has not taken place.

# **3. Concern does not mean understanding**

Global awareness of climate change has constantly increased without translating into individuals’ actions.<sup>34</sup> Indeed, together with “awareness, flying, driving, holidaying abroad and using household appliances have also increased globally.<sup>5</sup> What explains the

> 2 Gardner and Stern 2008. 3 Bannon and others 2007; New Scientist 2007. 4 Leiserowitz 2007; Brechin 2008; Sterman and Sweeney 2007. 5

> IPPR 2008; Retallack, Lawrence, and Lockwood 2007 .

2