rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Estimation Results for EV Demand

||OLS|OLS|OLS|IV|IV|
|---|---|---|---|---|---|
|Price - Incentive (1,000 USD)|-0.010<sup>_∗∗∗_</sup>|-0.022<sup>_∗∗∗_</sup>|-0.019<sup>_∗∗∗_</sup>|-0.027<sup>_∗∗∗_</sup>|-0.029<sup>_∗∗∗_</sup>|
||(0.001)|(0.001)|(0.002)|(0.005)|(0.005)|
|Log Charging Ports|0.451<sup>_∗∗∗_</sup>|0.475<sup>_∗∗∗_</sup>|0.317<sup>_∗∗_</sup>|0.290<sup>_∗∗_</sup>|0.818<sup>_∗_</sup>|
||(0.072)|(0.066)|(0.149)|(0.145)|(0.462)|
|Range (miles)|0.005<sup>_∗∗∗_</sup>|0.004<sup>_∗∗∗_</sup>|0.008<sup>_∗∗∗_</sup>|0.008<sup>_∗∗∗_</sup>|0.008<sup>_∗∗∗_</sup>|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|1(PHEV) _×_ Range|0.019<sup>_∗∗∗_</sup>|-0.012<sup>_∗∗_</sup>|0.006|0.003|0.003|
||(0.003)|(0.005)|(0.006)|(0.007)|(0.007)|
|Vehicle size (m3)|-0.040<sup>_∗∗_</sup>|-0.012|-0.017|0.007|0.013|
||(0.017)|(0.015)|(0.014)|(0.021)|(0.021)|
|Indicator of Non-fnancial Incentives|0.479<sup>_∗∗∗_</sup>|0.521<sup>_∗∗∗_</sup>|0.223|0.219|0.096|
||(0.167)|(0.141)|(0.148)|(0.149)|(0.180)|
|Brand FE||✓|✓|✓|✓|
|Fuel Type FE||✓|✓|✓|✓|
|Country FE|||✓|✓|✓|
|Year FE|||✓|✓|✓|
|First Stage F-stats for Price||||54.67|77.00|
|First Stage F-stats for Charging Station|||||36.34|
|Underidentifcation Test||||62.60|77.22|
|Weak Identifcation Test||||31.85|22.47|
|Overidentifcation Test||||55.14|62.14|
|Observations|4528|4528|4528|4528|4528|

_Notes:_ The regressions are based on data for 13 countries from 2013 to 2020. Observations for China in 2020 are excluded due to the impact of COVID19. The dependent variable is log(sales). _Price - Incentive_ variable is constructed from tax inclusive price subtracting total incentive received. Column (4) shows 2SLS estimates using instruments for consumer prices using the battery supplier dummy interacted with battery capacity as well as BLP instruments including the number of models and models’ size, battery capacity, and range for both own brand and rival brands. Column (5) in addition instruments for the number of charging ports using heavy duty EV stock, construction labor costs, and their interactions. Standard errors are clustered at the country by year level and reported in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 27
**<u>Table 3. The district fixed-effect RDD-DD regressions of individual’s health and children’s education</u>**

||||D<br>|ependent variab<br>|les<br>|||
|---|---|---|---|---|---|---|---|
||Having|The annual|The annual|The annual|The number|Currently|Log of|
|Explanatory variables|<br>health<br>insurance<br>(yes=1,<br>no=0)|<br>number of<br>healthcare<br>visits|<br>number of<br>outpatient<br>healthcare<br>visits|<br>number of<br>inpatient<br>healthcare<br>visits|<br>of completed<br>schooling<br>years<br>(children<br>aged 6-17)|<br>attending<br>school<br>(children<br>aged 6-17)|<br>education<br>subsidy for<br>students<br>(children<br>aged 6-17)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Program * Post-program period|-0.0174|0.3757**|0.3814***|-0.0059|0.0422|-0.0080|1.2841***|
||(0.0995)|(0.1483)|(0.1371)|(0.0371)|(0.3331)|(0.0448)|(0.4805)|
|(Poverty rate – 50) * Post-program|0.0162|0.0561**|0.0523**|0.0037|-0.0368|-0.0102**|0.0504|
|period|(0.0118)|(0.0229)|(0.0221)|(0.0040)|(0.0396)|(0.0051)|(0.0546)|
|Program * (Poverty rate – 50) *|-0.0066|-0.0580**|-0.0547**|-0.0032|0.0385|0.0075|-0.0467|
|Post-program period|(0.0111)|(0.0226)|(0.0218)|(0.0038)|(0.0381)|(0.0049)|(0.0517)|
|Age|-0.0020***|-0.0058|-0.0059|0.0000|1.1998***|0.1571***|0.3874***|
||(0.0007)|(0.0039)|(0.0038)|(0.0006)|(0.0410)|(0.0093)|(0.0473)|
|Age squared|0.0000***<br>(0.0000)|0.0003***<br>(0.0001)|0.0002***<br>(0.0001)|0.0000***<br>(0.0000)|-0.0212***<br>(0.0019)|-0.0081***<br>(0.0004)|-0.0197***<br>(0.0021)|
|Male|0.0133***|-0.1495***|-0.1382***|-0.0111**|0.1212**|0.0370***|0.1056***|
||(0.0029)|(0.0263)|(0.0253)|(0.0054)|(0.0599)|(0.0105)|(0.0325)|
|Ethnic minorities (yes=1, Kinh=0)|0.0153|-0.2511**|-0.2432**|-0.0078|-1.1867***|-0.1293***|0.7992***|
||(0.0211)|(0.1012)|(0.0992)|(0.0114)|(0.1724)|(0.0258)|(0.1114)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.2507***|0.7249***|0.6527***|0.0723***|-5.4798***|0.2662***|-1.1919***|
||(0.0211)|(0.0801)|(0.0780)|(0.0122)|(0.3037)|(0.0530)|(0.2757)|
|Observations|187,734|86,980|86,980|86,980|50,645|50,645|50,645|
|R-squared|0.515|0.096|0.091|0.028|0.526|0.195|0.330|
|The mean value of the dependent<br>variables before the program|0.285|0.619|0.519|0.100|4.733|0.823|1.254|

Note: This table reports district fixed-effect RDD-DD regressions of individual’s health and children’s education using individual-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

42
relatively poorly, with only 70 percent covered by a network.<sup>3</sup> Also according to the ITU, the number of mobile cellular subscriptions per 100 people equals 86 in Dominica (2021) and 64 in Haiti (2021).<sup>4</sup> In Saint Lucia we found that when 68.2 percent of individuals have a cell-phone, the share of households with a cell phone is over 95 percent. We therefore consider the share of households with access to a phone to be close to 100 percent in all countries, perhaps with the exception of Haiti where coverage may be a bit lower.

This study relies on a wave of survey data collected in the fourth quarter of 2021 jointly by the World Bank and the U.N. Development Programme (UNDP).<sup>5</sup> The response rate in this survey wave was 49.2 percent in Belize, 47.2 percent in Dominica, 43.9 percent in Haiti, 43.6 percent in St. Lucia, and 34.2 percent in Suriname. Survey weights were adjusted to account for non-response.

# **2.2 Disaster risk survey module**

The survey wave referenced above included a specific module to measure disaster risk awareness and preparedness for Belize, Dominica, Haiti, Saint Lucia, and Suriname. The survey module was adapted from a disaster risk reduction survey guide developed by the Red Cross Societies and Johns Hopkins Bloomberg School of Public Health (2013). It was further adapted for the purpose of the analysis presented in this paper and refined through the solicitation of extensive feedback from numerous development partners in the region.

The disaster risk survey module begins by asking respondents about perceived exposure to 12 types of disaster risks, that can be classified across four disaster risk categories as follows: meteorological (tropical cyclones / hurricanes, severe storms, and extreme rainfall), geophysical (earthquakes, volcanic eruptions, and tsunamis), climatological (heat waves, droughts, and wildfires) and hydrological (floods, landslides, and high swells).<sup>6</sup> In Suriname, after detailed incountry consultations and reflecting the local context, households were only asked about

> 3 - - See the ITU’s digital development dashboard: https://www.itu.int/en/ITU <u>D/Statistics/Dashboards/Pages/Digital Development.aspx</u> (consulted on February 12, 2024).

> 4 See World Development Indicators: https://data.worldbank.org/indicator/IT.CEL.SETS.P2 (consulted on February 12, 2024).

> 5 For Suriname, data were collected later, in June 2022.

> 6 These four disaster risk categories are not entirely mutually exclusive. For instance, floods (which we classify as hydrological risks) may be caused by heavy rains (which we classify as meteorological risks). See, for instance, Below et al. (2009) for discussion.

4
- This differential effect ( _τl/τh_ ) must be larger when _d_ is smaller or _p_ is larger. If baseline reporting is much lower among asymptomatic individuals and/or Ebola symptoms are common among uninfected individuals, then _τl/τh_ must be large.

# **Numerical Examples**

Suppose that individuals with no symptoms report 25 percent as often as those with symptoms ( _d_ = 0 _._ 25) and that 25 percent of individuals display flu-like symptoms over the course of several months even when uninfected ( _p_ = 0 _._ 25). If treatment has no effect on reporting among symptomatic individuals ( _τh_ = 1), then the 40 percent increase in exposure would have to be accompanied by roughly two times as much reporting by individuals with no symptoms ( _τl_ = 1 _._ 94). If treatment led to a 20 percent increase in reporting among those with symptoms ( _τh_ = 1 _._ 2), then exposure can only increase by 16 percent, and _τl_ must reach 1.67.

**Table E.25:** Implied _E_ and _τl_

|_p_|_d_|_τh_|_τl_|_E_|
|---|---|---|---|---|
|0.25|0.25|1.00|1.94|1.40|
|0.50|0.25|1.00|3.02|1.40|
|0.25|0.50|1.00|1.67|1.40|
|0.50|0.50|1.00|2.21|1.40|
|0.25|0.25|1.20|1.67|1.17|
|0.50|0.25|1.20|2.21|1.17|
|0.25|0.50|1.20|1.54|1.17|
|0.50|0.50|1.20|1.81|1.17|

For reasonable choices of _p_ and _d_ , we find these scenarios unlikely. First, in our NFA treatment, there is no plausible pathway whereby treatment increased exposure. Even in CM areas, the last planned community meeting took place a year prior to the Ebola outbreak.

Second, these imply large treatment effects and high relative rates of reporting among individuals with no symptoms to report. One cannot preemptively test for Ebola — the virus can only be detected days after symptoms begin. There is no reason for asymptomatic individuals to report, and widespread fear that deterred the use of health facilities even among those in desperate need of medical care. Elston et al. (2016, 675) report reductions in hospital attendance during the Ebola crisis in Sierra Leone, including significantly lower numbers of “women admitted during labor, urgent paediatric hospital admissions including children hospitalized with malaria and outpatient consultations.” Third, ceiling effects are unlikely. One might be concerned that _h_ is close to 1. In that case, there is less room for treatment to affect reporting among those displaying symptoms, as _hτh ≤_ 1. However, the CDC forecasts used an underreporting factor of 2.5 for Sierra Leone and Liberia based on expert opinions.<sup>A9</sup> (This would correspond to _h_ = 0 _._ 4 in our terms.) This implies that _τh_ could be as large as 2.5 before hitting any ceiling effects. We can rule out any increase in exposure when _τh ≥_ 1 _._ 4. Qualitative evidence stresses underreporting as a major concern during Sierra Leone’s Ebola crisis. This implies that many symptomatic

> A9 `https://www.cdc.gov/mmwr/pdf/other/su6303.pdf`

A60
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33
**Table 3: Corporate Vulnerability Index and Socio-economic Outcomes**

Logit Regressions, 2006-2016 Panel A: Corporate Vulnerability Index

|Variables|Unemployment rise<sup>a</sup>|Recession|Welfare reduction|Unemployment rise<sup>a</sup>|Recession|Welfare reduction|
|---|---|---|---|---|---|---|
|L. Vulnerability Index (CVI)|8.880***|9.825***|6.509***|6.835**|11.06***|5.832**|
||(2.220)|(3.229)|(1.915)|(2.673)|(3.816)|(2.307)|
|Constant|-3.661***|-3.111***|-3.738***|-3.036***|-4.770***|-4.940***|
||(1.004)|(0.899)|(0.639)|(0.979)|(1.320)|(1.059)|
|Macro Controls^|No|No|No|Yes|Yes|Yes|
|Observations|294|281|367|287|232|340|
|ROC (Area under the curve)|0.734|0.857|0.810|0.717|0.896|0.841|
|Marginal Effects at Median|1.776***|0.912***|0.780***|1.359**|1.016***|0.657***|

Panel B: Including DaR based on ICR

|Variables|Unemployment rise<sup>a</sup>|Recession|Welfare reduction|Unemployment rise<sup>a</sup>|Recession|Welfare reduction|
|---|---|---|---|---|---|---|
|L. Vulnerability Index (CVI)|12.97***|11.68**|9.496***|9.045***|10.84**|5.214|
||(4.124)|(4.922)|(3.673)|(3.448)|(5.066)|(4.147)|
|L.DaR ICR|-2.430|-0.922|-1.483|-1.352|0.107|0.308|
||(1.914)|(1.581)|(1.398)|(1.529)|(2.035)|(1.731)|
|Constant|-3.442***|-3.133***|-3.720***|-2.962***|-4.772***|-4.946***|
||(0.897)|(0.896)|(0.607)|(0.978)|(1.325)|(1.056)|
|Macro Controls^|No|No|No|Yes|Yes|Yes|
|Observations|294|281|367|287|232|340|
|ROC (Area under the curve)|0.745|0.859|0.815|0.720|0.895|0.841|
|Marginal Effects at Median|2.541***|1.072***|1.125***|1.790***|0.998**|0.590|

Unemployment rise: Dummy variable 1 if unemployment rate increases, 0 otherwise. Recession: Dummy variable 1 if GDP growth is negative, 0 otherwise. Welfare reduction: Dummy variable 1 if GDP per capita growth is negative, 0 otherwise. ROC denotes Receiver Operating Characteristic, a common indicator that captures the ability of the specified model to replicate the results of the outcome variable.

Unit of observation: country-year.

^ Macro controls: Current Account Balance (%GDP), General Government Balance (% GDP), Government Debt (%GDP), real GDP per capita, Inflation rate. a. Information for Macedonia not included Weights: Sum of value common shares for all firms in sample/Stock market capitalization. Information of Stock market capitalization available for 33 countries. Calculations per country-year. Year dummies included, errors clustered at country level. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 25
# **Figure 6 Share of Households in Largest Lineage Group (1995), Disputes over Land as the Most Important Source of Conflict (2003), and Share of Households Participating in Land Rental(2003)**

<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Share of Households in the Largest Patrilineal Clan (1995)<br>Disputes over Land Rented Land<br>.8<br>.1<br>.6 .08<br>.06<br>.4<br>.04<br>.2<br>Disputes over Agricultural Land as<br>the Most Important Cause of Conflict (2003) .02 Share of Households Rented Land (2003)<br>0 0<br><!-- End of picture text --> Source: 2004 Rural Household Social Network, Labor Allocation and Land Use Survey, and 2003 RCRE Village Survey, RCRE, Ministry of Agriculture.

_Land Tenure Insecurity and Migration, Page 43_
# **1. Introduction**

This paper is an attempt to contribute to the ongoing debate and discussion of the underlying causes of problems and risks faced by poor and excluded people. The paper has three objectives: first, to develop a survey instrument which would allow us to address possible risk factors commonly found among poor and excluded people, including youth; second, to document some of the simple findings based on the application of the survey in three poor urban neighborhoods in Fortaleza in the Northeast Brazil; and finally, to address intergenerational transfer of poverty and risk.

The motivation behind the developed survey is the growing concerns over widespread and deep poverty in broad sense and the limited knowledge of many of its elements, such as hunger, early pregnancy and fatherhood, violence, crime, drug use, low education attainment, low levels of social capital and the intergenerational transfer of each of these risks, and their causes and effects. Poverty is not only a state of existence but also a process with multiple dimensions and complexities. Usually, it is characterized by deprivation, vulnerability (low capacity to cope with risks), and powerlessness (Lipton and Ravallion 1995; Sen 1999). These characteristics impair people’s sense of well-being. Poverty can be chronic and transient, but transient poverty, if acute, can trap succeeding generations. The poor adapt all kinds of strategies to mitigate and cope with poverty. Hence, to understand poverty it is essential to examine the social and economic context.

Two groups of key factors that affect individual behavior relate to the characteristics of the household and those of the area where the home is located. Such characteristics include, for example:

- Living in highly densely populated areas that lack basic infrastructure and social services;

- Being unemployed or underemployed;

- Experiencing high levels of domestic and neighborhood violence;

2
|Summary of dispersi|**Mean**<br>**De**<br>on and central t|**Standard**<br>**Deviation**<br>**Table 2**<br>**scriptive Sta**<br>endency meas|**p25**<br>**tistics**<br>ures for the|**p50**<br>main variabl|**p75**<br>es, by coun|**Nro. Obs**<br>try.|
|---|---|---|---|---|---|---|
|**Total Sample**|||||||
|Gross Savings|2.91%|13.42%|1.66%|4.69%|7.91%|72,450|
|Excess Savings|-2.00%|15.11%|-5.91%|-0.22%|4.32%|72,450|
|Gross Capital Formation|4.93%|11.29%|0.01%|4.72%|10.19%|73,687|
|<br>Leverage|54.57%|26.28%|38.06%|55.29%|70.25%|73,687|
|Liquidity Holding|16.47%|15.71%|5.67%|11.88%|21.89%|73,687|
|<br>Acquisitions|-3.03%|9.00%|-6.15%|-2.92%|-0.06%|73,687|
|**France**|||||||
|Gross Savings|4.68%|10.44%|3.06%|6.23%|9.46%|7,460|
|<br>Excess Savings|-2.46%|13.78%|-8.29%|-1.12%|4.47%|7,460|
|Gross Capital Formation|7.22%|12.99%|1.40%|6.98%|13.60%|7,638|
|Leverage|61.06%|23.64%|48.07%|61.11%|73.38%|7,638|
|Liquidity Holding|14.41%|14.25%|4.65%|9.83%|19.13%|7,638|
|Acquisitions|-2.61%|9.15%|-6.27%|-2.93%|0.46%|7,638|
|**Germany**|||||||
|Gross Savings|3.10%|20.65%|1.72%|6.06%|9.79%|7,250|
|Excess Savings|-3.25%|22.31%|-9.03%|-1.13%|5.25%|7,250|
|Gross Capital Formation|6.42%|13.98%|0.14%|6.67%|13.50%|7,649|
|Leverage|57.93%|26.08%|41.71%|60.18%|73.57%|7,649|
|Liquidity Holding|15.48%|18.19%|3.10%|8.57%|20.62%|7,649|
|Acquisitions|-4.26%|11.05%|-8.04%|-3.97%|-0.39%|7,649|
|**Italy**|||||||
|Gross Savings|3.66%|8.88%|2.15%|4.93%|7.63%|2,701|
|Excess Savings|-2.58%|12.18%|-7.85%|-1.57%|3.62%|2,701|
|Gross Capital Formation|6.37%|11.31%|1.05%|6.17%|12.25%|2,777|
|Leverage|61.15%|19.59%|48.85%|62.71%|73.98%|2,777|
|Liquidity Holding|11.72%|12.45%|3.92%|7.71%|14.60%|2,777|
|Acquisitions|-1.87%|8.88%|-5.59%|-2.63%|1.13%|2,777|
|**Japan**|||||||
|Gross Savings|3.52%|9.29%|1.77%|4.13%|6.72%|39,666|
|Excess Savings|-0.04%|10.89%|-3.67%|0.50%|4.29%|39,666|
|Gross Capital Formation|3.57%|9.32%|-0.38%|3.69%|8.08%|40,239|
|<br>Leverage|53.18%|22.14%|36.65%|53.88%|69.76%|40,239|
|Liquidity Holding|17.58%|13.72%|7.92%|13.86%|23.01%|40,239|
|Acquisitions|-3.07%|6.88%|-5.61%|-2.81%|-0.41%|40,239|
|**United Kingdom**|||||||
|Gross Savings|0.25%|18.67%|-1.13%|5.43%|9.49%|15,373|
|Excess Savings|-6.12%|19.74%|-11.58%|-2.19%|4.07%|15,373|
|Gross Capital Formation|6.37%|12.95%|0.53%|6.09%|12.80%|15,384|
|Leverage|52.12%|35.99%|34.21%|51.10%|66.04%|15,384|
|Liquidity Holding|15.93%|19.65%|2.71%|8.46%|20.96%|15,384|
|<br>Acquisitions<br>Number of observations are firm-<br>|-2.73%<br>year observations.<br>distribution. Varia|12.09%<br>p25, p50, y p75<br>ble definitions ar|-7.09%<br>represent the 2<br>e provided in T|-2.84%<br>5, 50 (median)<br>able 1.|1.54%<br>, and 75 perce|15,384<br>ntiles of the|
**13 |** P a g e

Developments in foreign trade of the wood cluster point to the success of businesses taking advantage of BiH ample endowment in forests. The challenge for the authorities is to ensure rational management of this important resource.

# **C. Level of processing embodied in exports**

In fact, a cursory examination of imports from BiH, as reported by BiH’s trading partners, points to growing specialization in more processed goods. According to partners’ statistics, the share of BiH in total world imports of the following product groups increased: machinery excluding automobiles; automobiles and parts; and consumer goods. The increase was particularly impressive in sectors producing machinery (Table 7).

**Table 7: World imports from BiH in terms of end‐use products in 2003‐07 (in percent)**

|End‐use Product|Composition of<br>imports in 2007|Share in world<br>imports in 2007|Change in share in world<br>imports in 2007 over 2003|
|---|---|---|---|
|Agriculture, Food & Feeds|13.8|0.048|123|
|Industrial Raw Materials|16.8|0.100|139|
|Fuels|6.0|0.012|192|
|Iron and Steel|7.3|0.060|142|
|Machinery excluding automobiles|14.3|0.015|529|
|Automobiles & Parts|1.4|0.005|360|
|Other Consumer Goods|40.5|0.034|341|
|All Goods|100.0|0.028|197|

Note: Agricultural Food & Feeds (SITC 0+1+2+4‐27‐28); Industrial Raw Materials (SITC 27+28+68); Fuels (SITC 3); Iron and steel (SITC 67); Machinery excluding automobiles (SITC 7‐78); Automobiles & Parts (SITC 78); Other Consumer Goods (SITC 5+6+8+9‐67‐68); All Goods (0 to 9).

Source: Derived from data reported by BiH trading partners to the UN COMTRADE database.

Not surprisingly considering that the EU‐27 is a major export market for BiH, a more detailed examination of EU statistics corroborates observations derived from world statistics of imports from BiH rather than those implied by BiH statistics. If anything, they indicate rather a significant progress in BiH industrial restructuring as captured by changes in competitiveness of various product groups. EU mirror statistics also point to plugging‐in of BiH suppliers into global automotive networks discussed in more detail in Section 6.

The shift away from low‐processed goods can be easily inferred from gleaning data tabulated in Table 8. The aggregate share of traditional imports (foods, raw materials, and fuels) fell from 38 percent in 2003 to 29.5 percent in 2007. Except for agricultural foods and feeds, other categories from this group have experienced the loss in market share in the EU since 2005. Simultaneously, the aggregate share of machinery and automotive parts, i.e., highly processed manufactured goods increased from 11.7