rafmacalaba/datause-encoder-data huggingface dataset:
tests. Non-observable skills have direct effects on wages (given schooling), schooling, achievement tests and social outcomes. Labor market relevant observable and unobservable skills affect socioeconomic success. Abilities and skills are both acquired. They are influenced both by genes, schooling and the environment.

When a true measure of ability is an omitted variable in the earnings equation, different approaches have been used in the empirical literature to capture the “true” return to education.<sup>§</sup> This paper uses achievement test scores measuring adult literacy skills (generally measures of capability to process and apply knowledge, particularly in the workplace), and employs them as additional controls in the earnings function. One should, however, keep in mind that both schooling and the test scores are generated by the same latent ability and, therefore, be aware of the joint causality between schooling and test scores (see Hansen _et. al._ 2003; Nordin 2005).

We use the International Adult Literacy Survey (IALS) and the Adult Literacy and Life Skills (ALL) data, which contain direct measures of adult literacy. The IALS and ALL data have been used in several studies. Blau and Khan (2001) examined the role of cognitive skills in explaining higher wage inequality in the United States. Leuven _et al_ . (2004) used IALS data for 15 countries and explored the hypothesis that wage differentials between skill groups across countries are consistent with a demand and supply framework. They find that cognitive achievement is an important

> § One approach uses twins, to arrive at a measure of the causal return to education. For example Ashenfelter and Rouse (1998) and Rouse (1997) using data from the US, have compared the earnings of twins with different educational levels, and reported an estimate of the return to education that is about 30 percent smaller than the OLS estimate. Another approach uses sources for exogenous variation in educational attainment, such as institutional changes in the schooling system in the form of changes in compulsory schooling laws, abolition of fees, etc., as well as other “natural variations” (i.e., school construction projects and other “natural experiments”) affecting the schooling decision, to estimate a causal return to education effect using instrumental variable estimation.

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background ( _SE_ ) policy performance variables ( _PP_ ) measures of bilateral relations between donor and recipient ( _BR_ ) and other additional controls ( _OC_ ).

# **4.1 Disaster Impact Measures**

Our main measures for humanitarian need and disaster impact are the number of people affected and the number of people killed in each emergency. In the EM-DAT system, a person is registered as killed if confirmed dead, missing or presumed dead. A person is counted as affected if she requires immediate assistance during the emergency period, which includes displaced or evacuated people. One potential empirical concern regards the exogeneity of humanitarian impact.

If international support was quickly and effectively disbursed, it could reduce emergencies’ human impact, and thus induce a downward bias to our estimates. To minimize this problem, we restrict our sample to rapid onset emergencies (449 observations). Rapid onset emergencies usually last less than one day, so that the direct effect of aid on our disaster impact variables should be negligible. Finally, to control for potential differences in the measurement of impact, we use disaster type dummies (flood, windstorm, fire etc.) in all of our specifications.

# **4.2 Socioeconomic Background**

The socioeconomic indicators included are GDP per capita, population (in logs), and population density. While higher per capita income reduces the risk to be affected by natural disasters ex-ante, it is likely to be the most important measure for the degree to which exposed countries can cope with the damage inflicted by natural disasters<sup>15</sup> . In general, as highlighted by the UNDP’s report (United Nations, 2004), low levels of development of an economy can amplify the risk that a natural event translates into a disaster, as well as the extent of the severity of the losses incurred.

Larger countries are ex-ante more likely to have disasters, but should generally also be more able to deal with a shock of a given size. More densely populated areas may be prone to suffer more in the aftermath of natural disasters’ as evacuation possibilities can be limited and the risk of infectious diseases may be higher. On the other hand, densely populated areas may have better local networks and thus be able to recover more easily after natural hazards.

# **4.3 Policy Performance Variables**

To account for structural differences in recipients’ ability to cope with natural disasters, we include a set of basic policy variables in our empirical specifications. Poor policy settings may increase local’s population need for foreign assistance, but at the same time lower the effectiveness of financial flows and thus the potential to help from a foreign perspective.

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||(0.398)|(0.398)|
|---|---|---|
|HDD|7.732***|7.879***|
||(0.334)|(0.334)|
|Observations|40,673|40,673|
|R-squared|0.994|0.994|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

# **_4.2_** _Impact of the pandemic on generation profile_

So far, we have found that the total daily generation falls as the stringency of COVID-19 mitigation measures increases. Is the effect the same or different across energy sources? How did renewables fare during the pandemic?

Tables A.3-A.9 in the Appendix report the estimation results from fitting Equation (1) to the share of each type of electricity. The same estimation results are summarized in Table 3 for the sake of clarity. Among renewable energy types, the share of solar has increased during the pandemic, while the share of wind has dropped. Columns 1 and 3 report the coefficients of the “post” dummy and stringency index effects on the share of each energy source. A coefficient of 0.01 on “post” should be interpreted as a 1 percentage point increase of that energy source’s share of the generation mix. For solar, the coefficient of the “post” variable is 0.006, which translates to an increase of 0.6 percentage points in solar share, suggesting that solar’ share has increased by 0.6 percentage point in the total daily generation compared with the pre-pandemic world after controlling for seasonality and fixed effects. The wind share has fallen by 2.3 percentage point during the pandemic. Among fossil fuel-fired electricity, both coal and natural gas have increased their share during the pandemic. Specifically, the coal share has increased, on average, by 0.3% percentage point, and natural gas has increased, on average, by 1% percentage point during the pandemic.

**Table 3: Summary of the pandemic’s impact on generation profile**

||(1)|(2)|
|---|---|---|
|Share of Energysource|Post|StringencyIndex|
|Hydropower|-0.00181|-8.14e-06|
|Solar|0.00573***|6.52e-05***|
|Wind|-0.0234***|-0.000159***|
|Coal|0.00328**|-0.000105***|
|Natural Gas|0.0107***|0.000100***|

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||**[6]**<br>**(t)**<br>**average)**|0.208<br>(0.169)<br>-0.002<br>(0.001)<br>-0.011<br>(0.012)<br>-0.014***<br>(0.003)|549 / 80<br>0.991<br>3.733<br>2.475|
|---|---|---|---|
||**[5]**<br> <br>**(3-year**|0.048<br>(0.072)<br>-0.002<br>(0.002)<br>-0.013<br>(0.014)<br>-0.017***<br>(0.004)|485 / 76<br>0.99<br>3.733<br>2.475|
||**[3]**<br>**[4]**<br>**TCR**<br>**(t)**<br>**(2-year average)**|0.109**<br>(0.055)<br>0.262**<br>(0.120)<br>0.000<br>-0.001<br>(0.001)<br>(0.001)<br>-0.011<br>-0.007<br>(0.008)<br>(0.008)<br>-0.013***<br>-0.013***<br>(0.003)<br>(0.002)|675 / 76<br>778 / 80<br>0.99<br>0.991<br>3.733<br>3.733<br>2.489<br>2.489<br>iable_y_.<br>ch the dependent variable is measured.<br>5%; *** significant at 1%|
|**Table 1**<br>_Sample of 1974-2004_<br>_Dependent Variable: Logarithm of Real Exchange Rate_|**Variables**<br>**[1]**<br>**[2]**<br>**(t)**|int1. Index (t)<br>0.036*<br>(0.022)<br>int2. Index (t)<br>0.169**<br>(0.075)<br>_Control Variables_<br>∆Log(ToT)<br>0.000<br>0.000<br>(0.001)<br>0.000<br>Trading partners growth<br>-0.008<br>-0.007<br>(0.005)<br>(0.005)<br>Financial account to GDP<br>-0.012***<br>-0.010***<br>(0.002)<br>(0.002)|Observations / Countries<br>1350 / 76<br>1433 / 80<br>R-squared<br>0.989<br>0.99<br>Mean Dep. var.<br>3.718<br>3.733<br>St Dev Dep. var.<br>2.414<br>2.475<br>Note:_y_(t, t+s) corresponds to the average over the period t to t+s of the var<br>Except otherwise indicated, all controls are averages for the period over whi<br>All regressions included country fixed effects and Time dummies<br>Robust standard errors in parentheses / * significant at 10%; ** significant at|
**Table 4c:  Firm Characteristics in Mexico (1999), in percent**

||**Home-base**|**d Workers**|**Rest of the**<br>**Economy* **|
|---|---|---|---|
||_Informal_<br>_Entrepreneurs_|_Contract_||
|_Sample size_|_4322_|_93_|_89683_|
|Formal sector employer<sup>~~a~~</sup>|0.0|19.7|54.9|
|Firm size 0-5|100.0|80.7|44.4|
|Firm size 6-15|0.0|12.9|9.7|
|Firm size 16+|0.0|6.5|46.0|
|0-1 minimum wages|19.4|17.5|9.3|
|1-3 minimum wages|51.2|62.5|49.0|
|3-5 minimum wages|16.9|15.0|20.4|
|5-10 minimum wages|9.0|4.2|14.9|
|10+ minimum wages|3.5|0.8|6.5|
|Hours workedper week|34.8|36.2|45.9|
|Manufacturing|43.0|100.0|22.9|
|Commerce|21.7|0.0|21.7|
|Services|23.4|0.0|32.0|
|Agriculture|0.0|0.0|1.7|
|Other|11.9|0.0|21.8|

*Rest of the economy are those who are working but not at home

a Formal sector employer is identified by the receipt of benefits and a firm size with greater than five workers.

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# **Appendix 3 – Household characteristics and comparison with GLSS6**

The GLSS6 was collected in 2012/2013 while the poverty-DRM data was collected in 2017. The GLSS6 data is representative for the population of GAMA, which not only includes the city of Accra but also its surrounding areas. Conversely, the poverty-DRM survey data was collected from only specific urban slum areas in GAMA and was not designed to be representative for either GAMA or urban slum areas in GAMA. As explained previously, the objective of the poverty-DRM survey is to examine whether there are any systematic differences in behaviors and livelihoods of slum population depending on the level of flood proneness or the level of poverty.

Average household expenditures for quartile of the Poverty-DRM survey are very similar to those of the GLSS6 (GAMA) (Table 14). This result may seem surprising because the Poverty-DRM survey draws sample households from urban slum areas only, not the full city, and expenditure levels could be expected to be lower. There are two possible reasons that may explain why this is not the case. First, GDP per capita in Ghana still grew 9.5% between 2012 and 2017, and expenditures may have increased faster than the predictors used in SWIFT, weakening the model stability. Second, a preliminary assessment using GLSS6 data shows that the difference in (monetary) poverty rates between non-slum and slum areas in Accra appears quite small. This could mean that the key challenge faced by urban slum residents is not monetary poverty but concerns other dimensions of living standard (such as lack to access to services or poor housing quality).

_Table 14: Changes in distribution of household expenditures between GLSS6 (GAMA) and Poverty-DRM surveys in Ghana_

|Quartile|GLSS6 (GAMA)|Poverty-DRM|
|---|---|---|
|Q1|2,115|2,248|
|Q2|3,810|3,907|
|Q3|5,781|5,904|
|Q4|10,715|11,988|

Source: Authors' estimation using GLSS6 and Poverty-DRM surveys Household heads in the poverty-DRM sample are more likely to be women and have a lower education level than those of the GLSS6 sample. Further, in the poverty-DRM survey, households live in relatively smaller dwellings despite having the same amount of household members indicating that households are more crowded in the poverty-DRM sample. The proportion of household members employed is higher in the poverty-DRM data. To further explore potential vulnerabilities of households living in slum areas we will look closer at access to services and tenure.

37
**Figure 9. Map of difficulty of items using Rasch Scores and a dichotomic fluency variable* (N=475)**

<!-- Start of picture text --> 100 F110<br>ECE Items<br>90<br>Fluency (words per minute) F90<br>80<br>F75<br>19<br>17 18<br>70 9<br>F60 15 16<br>10 11 14<br>8<br>60<br>F45<br>6 7<br>F40<br>50 12 13<br>F30<br>40<br>30 4 3 5 2<br>1<br>20<br>10<br>0<br>Items<br>Rasch score: difficulty level<br><!-- End of picture text -->

* The dichotomic variables distinguish between students who read at certain fluency levels. The number of words read per minute is noted in the label of the variable. For example, F30 is a dichotomic variable in which students who read 30 words per minute or more score 1, and those who read less score 0.

Seven dichotomic items are created to account for these various levels of fluency. We run seven separate analyses, one for each dichotomic item of fluency, to estimate the difficulty of different levels of fluency relative to the items in the ECE test. This was done with Sample B and the map is shown in Figure 9 (see Table 36 in Annex 7.4.b for detailed results).<sup>43</sup> Results are quite suggestive. They indicate that a student that can read at least 30 words per minute (first grade level, as recommended for Peru), would be 100 percent likely to answer the first five items in the ECE test correctly:<sup>44</sup> that is, can read words and short isolated sentences. Then again, we’ve seen that any student, regardless of fluency, would be 96-97 percent likely to do so because these items are too easy.

A student who reads at least 40 words per minute can also understand a short informative poster at the literal level, with very high probability of answering items 12 and 13 correctly. This student, however, would be unlikely to answer correctly items 6 through 11, and 14 through 19. Here it is worth noting that the poster in question states prices of milk, cheese and butter and the days they are sold (see

> 43 The difficulty levels of the ECE items vary slightly from one analysis to the next (they get relatively easier as fluency level increases), but their order does not change. For illustration purposes, the difficulty levels of ECE items used in the plot come from their corresponding analyses: items 1-5, from the F30 analysis; items 12-13 from the F40 analysis; items 6-7, from the F45 analysis, items 8, 10, 11 and 14 from the F60 analysis; items 9 and 15-19 are an average of the difficulty levels estimated in the F75, F90, and F110.

> (θ−β) _e_ 44 Probability = (θ−β) , where θ is the individual score, β is the item difficulty, and _e_ is the exponential. In this 1 + _e_

43
vehicle occupant accidents and pedestrian accidents occurring annually. Section 3.2 models the likelihood that an accident results in a fatality. Implications of the models are summarized in section 3.3.

# **3.1.   Models of Motor Vehicle Accidents**

To model the number of motor vehicle accidents, we follow Edlin’s (1999) derivation of the probability that vehicle i is involved an accident with another vehicle, pi, and the probability that vehicle i is involved in a one-vehicle accident (for example, hitting a tree), ri.<sup>7</sup> The expected number of occupant accidents occurring annually is the sum over all vehicles of pi + ri.

Following Edlin, the probability that vehicle i has an accident with vehicle j is the probability that both vehicles are in the same location at the same time, and that neither driver avoids an accident. Formally, where fi = probability that vehicle i is on the road; L = number of locations at which an accident may occur; qi = probability that the driver of vehicle i does not avoid an accident.

fifjL<sup>-1</sup> is thus the probability that vehicle j is in the same location as vehicle i,<sup>8</sup> and qiqj the probability that neither driver avoids an accident.<sup>9</sup> Following Edlin, we assume that fi

> 7 Single vehicle crashes account for a substantial portion of fatal crashes and cannot be ignored in modeling.  In the U.S., over 50% of fatal crashes result from single vehicle accidents (U.S. DOT, <u>http://www-fars.nhtsa.dot.gov/report.cfm?stateid=0&year=2000&title=Trends).  Single-vehicle run-off-</u> roadway accidents account for approximately one third of all U.S. highway fatalities (Lee and Mannering, 2002).

> 8 L is not squared in this expression since it does not matter where on the road the two vehicles meet.  The probability that vehicle i is in _any_ location on the road is fi.  The probability that vehicle j will then be in the same location as i is equal to the probability that vehicle j is on the road, fj, times the probability that j is at the _same_ location as vehicle i, 1/L.

> 9 This specification makes the simplifying assumption that accident rates and vehicle locations are both uniform.

8
double-difference method described in section 7). Identifying responses through savings and investment provides a clue to understanding current impacts on living standards and the possible future welfare gains beyond the project’s current life span. Instead of focusing solely on the agreed welfare indicator, one collects and analyzes data on a potentially wide range of intermediate indicators relevant to understanding the processes determining impacts.

This also illustrates a common concern in evaluation studies, given behavioral responses, namely that the study period is rarely much longer than the period of the program’s disbursements. However, a share of the impact on peoples’ living standards may occur beyond the life of the project. This does not necessarily mean that credible evaluations will need to track welfare impacts over much longer periods than is typically the case — raising concerns about feasibility. But it does suggest that evaluations need to look carefully at impacts on partial intermediate indicators of longer-term impacts even when good measures of the welfare objective are available within the project cycle. The choice of such indicators will need to be informed by an understanding of participants’ behavioral responses to the program.

In learning from an evaluation, one often needs to draw on information external to the evaluation. Qualitative research (intensive interviews with participants and administrators) can be a useful source of information on the underlying processes determining outcomes.<sup>61</sup> One approach is to use such methods to test the assumptions made by an intervention; this has been called “theory-based evaluation,” although that is hardly an ideal term given that NX identification strategies for mean impacts are often theory-based (as discussed in the last section). Weiss (2001) illustrates this approach in the abstract in the context of evaluating the impacts of community-based anti-poverty programs. An example is found in an evaluation of social funds (SFs) by the World Bank’s Operations Evaluation Department, as summarized in Carvalho and White (2004). While the overall aim of a SF is typically to reduce poverty, the OED study was interested in seeing whether SFs worked the way that was intended by their designers. For example, did local communities participate? Who participated? Was there “capture” of the SF by local elites (as some critics have argued)? Building on Weiss (2001), the OED evaluation identified a series of key hypothesized links connecting the intervention to outcomes and tested whether each one worked. For example, in one of the country studies for the OED evaluation of SFs, Rao and Ibanez (2005) tested the assumption that a SF works by

> 61 See the discussion on “mixed methods” in Rao and Woolcock (2003).

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<!-- Start of picture text --> 100<br>90<br>80<br>70<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>50<br>40<br>30<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>0<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>=——East Asia & Pacific —— Europe & Central Asia == Latin America & Caribbean<br>———= Middle East & North Africa South Asia =—— Sub-Saharan Africa<br><!-- End of picture text -->