rafmacalaba/datause-encoder-data huggingface dataset:
average compared to the next best policy of using information on incidence from the year before. Zooming in on the part of the distribution up to $400,000, the amount spent on this type of program, the cell phone data targeted policy performs over 300% better on average (Panel b, Figure 6).

The second type of targeting focuses on travelers from particular origin areas. This is more dicult in terms of implementation because it requires the ability to identify travelers coming from specic areas, but has the potential of being more cost-eective since resources are focused on the highest risk travelers. I base the targeting strategies on the district the individuals are coming from and the month in which they are traveling. Therefore, if a district-month is chosen as receiving targeting, then every person traveling from that district to the pre-elimination area in that month would be treated with the policy. I assume again that 94% of targeted travelers are tested based on the experience in Richard Toll. Four strategies are again compared: (1) the implausible one of the government randomly choosing district-months (non-targeting); (2) the government choosing district-months randomly within the malarial season and then during the rest of the year; (3) the government using monthly malaria incidence in the prior year to order the district-months from those with the highest to the lowest incidence; and (4) a cost-benet value is calculated for each district-month based on the number of travelers and the variable cost and uses incidence from the previous year to calculate the total impact of the imported cases (the benet).

Panel a of Figure 7 shows the cost curves for these four strategies zoomed in on the relevant budget of $400,000. Note that the cost starts at $63,928, which is the total xed cost for all the facilities in the ve districts, since it is assumed that training and preparations are done across all health facility areas but only particular travelers are targeted across these areas. Using the cell phone data to inform targeting leads to higher cost eectiveness than the next best strategy based on the government using incidence from the previous year to target travelers from specic locations. In particular, at a budget of $400,000 it performs 27% better on average. Panel b compares targeting of specic travelers entering all ve 31
Table 7: Frequency tabulation of hypotheses tests, by outcome type

# (a) _Height outcomes_ :

||Betw|een?||
|---|---|---|---|
|Within?|Yes|No|Total|
|Yes|5|2|7|
|No|4|9|13|
|Total|9|11|20|

# (b) _BMI outcomes_ :

Between?

|Within?|Yes|No|Total|
|---|---|---|---|
|Yes|1|3|4|
|No|0|16|16|
|Total|1|19|20|

Note: table reports counts of between-group and within-group null hypothesis test results, derived from Tables 5 and 6, covering all countries. Source: own estimates.

we assume a linear specification, the individual growth trajectories are simply defined by child mean size: _α_ ̃ _i_ = _α_ ˆ 0 + _α_ ˆ _i_ ; and linear growth velocity: _β_<sup> ̃</sup> _i_ = _β_<sup>ˆ</sup> 0 + _β_<sup>ˆ</sup> _i_ . So, rather than considering variation in _β_ , alone, we use the FEIS estimates for both variables and treat them as dependent variables. Concretely, this stage of analysis is based on regressions of the following form where children are the unit of analysis (in cross-section): Estimates for _δ_ 1 indicate the impact of the mean values of the vector _x_ , defined as before, on average child size. Estimates for _θ_ 1 give the corresponding impacts on growth velocity, indicating how the permanent elements of _x_ modify the pace of child development. Recall the estimated latent variables absorb the effects of all time-invariant child characteristics. So, this analysis seeks to quantify the extent to which these are accounted for by observed child characteristics. In addition, community-level fixed effects can be added to the regression to capture how differential access to public services and other features of the broad local environment (outside the family) systematically affect child growth trajectories. These are captured by the parameters _δ_ 2 _c, θ_ 2 _c_ , where _c_ is an index for the community.<sup>21</sup> This kind of second stage analysis has been applied in other contexts. Acemoglu et al. (2009), for

> 21 When treated as fixed effects, we cannot identify which specific features of the local environment (community) alter child growth trajectories. Nonetheless, as we show, this approach helps quantify the overall explanatory contribution of variables at this level.

25
Table 2: Differences between exporters and non-exporters

|||Non-exporter||Exp|orter|
|---|---|---|---|---|---|
||Mean|Std. Dev.|Mean|Std. Dev.|Diference|
|GBF EXT|1.10|0.26|1.30|0.19|0.18***|
|GBF INT|0.84|0.30|1.10|0.24|0.22***|
|SBF EXT|1.00|0.34|1.20|0.37|0.19***|
|SBF INT|0.66|0.37|0.77|0.41|0.11***|
|Number of employees|108.33|334.58|674.35|1480.48|566.01***|
|Multinational|0.03|0.17|0.15|0.36|0.13***|
|Interaction with MNEs|0.51|0.50|0.86|0.35|0.35***|
|Government support|0.14|0.35|0.32|0.47|0.17***|
|Financial constraints|0.19|0.39|0.20|0.40|0.01|
|Family company|0.097|0.30|0.15|0.36|0.05*|
|Formal incentives|0.54|0.50|0.62|0.49|0.08*|
|Performance indicators|0.40|0.37|0.66|0.36|0.26***|
|Manager’s with college|0.56|0.50|0.74|0.44|0.17***|
|Manager’s experience (years)|24.36|11.51|27.02|14.50|2.66**|
|Experience in large company|0.30|0.46|0.49|0.50|0.19***|
|Studied abroad|0.12|0.32|0.28|0.45|0.16***|
|Share of college-educated employees|0.12|0.15|0.18|0.23|0.07***|
|Share of R&D employees|0.002|0.01|0.007|0.01|0.01***|
|Innovation|0.26|0.44|0.60|0.49|0.34***|

Note: Table shows descriptive statistics and differences by exporter status. First rows present the logarithm of the technology indexes including GBF (EXT and INT) and SBF (EXT and INT). The last column is the coefficient of a simple regression of trade status on the variable. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01.

# **3 Descriptive statistics**

To begin exploring the relationship between exporting and the adoption of advanced technologies, we perform some correlations between trade status and the technology index. Figure 1 shows the coefficient estimates of regressing the different aggregate technology indices on dummies to capture trading status, controlling for sector, dummies for firms’ size and age, and additional control variables. The indices include the extensive measure (EXT) and the intensive measure (INT) for both general business functions (GBFs) and sector specific business function (SBF). The estimates show a positive correlation between exporter status and technology sophistication for all indices.

7
# **SKILLS AND INDUSTRIAL EMISSIONS**

To test whether higher cognitive skills are associated with production technology that relies less on emissions for production, several linear regression models were estimated using our individuallevel skills and industrial emissions data sets. Two outcome variables were examined: emissions per output in 2012 and annualized growth in emissions per output between 2010 and 2019. These models were estimated using pooled data for all countries (see descriptive statistics above); models were estimated following OECD (2013) using plausible value estimates for cognitive skills and the Jackknife 2 method. Jackknife replicate weights were provided for each country by the OECD based on either the “delete-one” Jackknife or “paired” Jackknife method; however, the paired Jackknife method was applied because the data was pooled (using the paired Jackknife aggregation formula with delete-one jackknife weights results in an overestimate of the standard error). Stata routines by Macdonald (2008) were used in implementing the estimation method.

Table 3 presents estimates from the first set of models, looking at the association between cognitive skills and emissions per output in 2012 and annualized growth in emissions per output from 2010 to 2019 within a Mincerian type model. All three measures of cognitive skills, literacy, numeracy, and problem solving, are negatively associated with the level of emissions per output in 2012 as well as growth between 2010 and 2019. Country fixed effects were added to this model and presented in Table 4 showing that cognitive skills negatively associate with emissions and growth in emissions per output within countries, across industries. Table 5 presents the same model with highest level of education added; higher levels of education generally associate with lower emissions and growth in emissions per output. Finally, Table 6 introduces an interaction term between whether the individual was employed in a managerial or professional occupation (ISCO08 categories 1 and 2). The association between cognitive skills and lower emissions per output and lower growth in emission per output is strong for individuals employed in these two occupational categories<sup>1</sup> ; for the other occupations, no statistically significant association is found except for a positive association between emissions per output and numeracy skills.

> 1 The association between cognitive skills and the dependent variable for individuals in managerial or professional occupations is measured as the coefficient for _Manager or professional occupation + Manager or professional occupation x skill_ .

14
<!-- Start of picture text --> .007626<br>0<br>0 250 500 750 1000<br>reservation wage<br>density<br><!-- End of picture text --> Figure 3 - Reservation wage. Source: Own calculations based on World Bank, Fortaleza Survey 2003. Note: the vertical line indicates the national minimum wage.

Stated more formally, we model the probability that the teenager will be out of school: where _Y_ 2 _i_ is a vector of endogenous variables—work and early parenthood—, _X_ 1 _i_ is a vector of exogenous variables, which includes gender, age, race, having suffered hunger at some point in life, and mother's schooling level. _X_ 2 _i_ includes our set of identifying instruments, the declared ideal age for first having sexual relationships (coded as one if that age is below or at 17 years old) and the reservation wage. We observe the child actually out of school if Table 2 reports results of the estimation of the instrumental variables probit model. Column 1 includes a restricted set of control variables and column 2 reports a wider set. Results indicate, first of all, that the presence of own children drives youngsters away from school. Early parenthood increases by 0.46 the probability that the teenager will drop out of school (that is, the propensity to be out of school is 46 percentage points higher than for those who do not have children). Similarly, extreme poverty detracts from school attendance. Teenagers who declare they have suffered hunger are more prone to be out of school, with the probability increasing in this case by 0.11.

12
**Appendix Table 2. Impact of Gas Price Increase: Assuming Base Consumption Levels Are Unchanged (No Substitution)** (By expenditure quintile and location in percent of total household consumption)

||Scenari|o 1: Impac|t byEnergyPr|oduct|Scen|ario 2: Impac|t byEnergyPr|oduct|
|---|---|---|---|---|---|---|---|---|
||Central Gas|LPG|Electricity|Central Heat|Central Gas|LPG|Electricity|Central Heat|
|Magnitude of price increase/<br>Quintile|37.5%<br>(1)|37.5%<br>(2)|30%<br>(3)|37.5%<br>(4)|100.0%<br>(5)|100.0%<br>(6)|80%<br>(7)|100.0%<br>(8)|
|**_A. All households _**<sup>**_1_**</sup>|||||||||
|Poorest|0.4|0.2|1.6|0.0|1.0|0.4|5.4|0.1|
|Second|0.7|0.1|1.5|0.2|1.8|0.4|4.9|0.4|
|Third|0.6|0.2|1.5|0.4|1.7|0.4|4.9|1.0|
|Fourth|0.8|0.2|1.4|0.6|2.1|0.5|4.5|1.5|
|Richest|0.7|0.1|1.1|0.7|1.9|0.4|3.6|1.8|
|**_B. Connected households with positi_**|**_ve expenditures _**<sup>**_2_**</sup>||||||||
|Poorest|2.4|4.2|1.9|...|6.4|11.3|6.5|...|
|Second|2.7|3.3|1.7|5.4|7.1|8.7|5.6|14.4|
|Third|2.5|2.7|1.6|6.1|6.6|7.1|5.5|16.1|
|Fourth|2.4|2.3|1.5|5.9|6.3|6.3|5.0|15.8|
|Richest|1.7|1.7|1.3|4.1|4.6|4.4|4.3|11.0|
|Of which:|||||||||
|**_B1. Large Cities_**|||||||||
|Poorest|2.1|...|2.3|...|5.5|...|7.8|...|
|Second|1.3|...|2.4|5.4|3.6|...|8.0|14.3|
|Third|1.2|...|2.4|6.1|3.2|...|7.9|16.1|
|Fourth|1.0|...|2.1|5.9|2.6|...|6.9|15.8|
|Richest|0.8|...|1.6|4.1|2.1|...|5.3|11.0|
|**_B2. Small towns_**|||||||||
|Poorest|2.8|5.0|2.4|...|7.3|13.2|7.9|...|
|Second|3.2|4.5|2.0|...|8.4|12.0|6.8|...|
|Third|3.2|3.9|1.9|...|8.6|10.5|6.3|...|
|Fourth|4.0|3.3|1.7|...|10.6|8.8|5.6|...|
|Richest|3.9|2.4|1.4|...|10.4|6.3|4.8|...|
|**_B3. Rural areas_**|||||||||
|Poorest|1.9|3.8|1.8|...|5.0|10.0|6.1|...|
|Second|3.0|3.0|1.5|...|7.9|8.0|5.0|...|
|Third|3.1|2.4|1.4|...|8.3|6.4|4.7|...|
|Fourth|3.8|2.2|1.2|...|10.2|5.8|4.2|...|
|Richest|2.8|1.6|1.0|...|7.6|4.2|3.3|...|

Source: HBS and Bank staff calculations.

Note: "..." indicate insufficient observations. Based on a arithmetic mean of the relative price change. 1Unconnected households as well as households that are connected but have missing expenditure data are assumed to have zero expenditures on energy. 2Includes only households that are connected and are reporting expenditure information. Implicitly, the calculations include households that are connected but have missing expenditure data, and these households are assumed to have expenditure levels equal to average expenditures by quintile and location.

19
**Figure 2: Mean Profits for Untreated Firms by Tsunami Exposure**

<!-- Start of picture text --> 180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Aug-04 Feb-05 Sep-05 Mar-06 Oct-06 Apr-07 Nov-07 Jun-08<br>Directly Affected Indirectly Affected Unaffected<br>Index (November 2004=100)<br><!-- End of picture text --> 38
|Table 3.5: Education Level<br>(percent),Argentina2005|Completed or Attending|
|---|---|
||Female<br>Male|
|None|0.14<br>0.00|
|Primary|14.22<br>16.61|
|Secondary|56.90<br>59.44|
|Tertiary|28.73<br>23.95|
|Proportion(percent)|55.1<br>44.9|

_Source_ : Own calculations on ESCVJA (four urban areas) Intergenerational linkages matter for education level and earnings. Generally, Argentines from richer households are more educated than those from low-income households. A plausible reason for this is that richer parents can pay for private schools or tutors for their children; nearly one in four youth attend or has attended a private school. Also, youth from poor households are more likely to have to leave school to work to add to the household income. In addition, education is extremely important for the level of hourly wages received; higher education leads to far higher hourly real wages (Justesen and Verner 2007b). Thus, youth are to a large extent locked in an income bracket, because of intergenerational linkages. Table 3.6 reveals that of youth in the sample with parents without any completed level of education, 36 percent have only completed primary education, and 9 percent have completed tertiary education. In contrast, of youth with a tertiary educated parent, 7 percent have completed primary education, while 48 percent have completed tertiary education. Thus, lack of parent’s education is a serious risk factor of youth leaving school early and ending up in a low paid job.

Table 3.6: Intergenerational Education Linkages in Argentina (percent)

|||None|Education<br>Primary|Level of Yout<br>Secondary|h<br>Tertiary|Total|
|---|---|---|---|---|---|---|
||None|0.0|35.7|55.6|8.7|100.0|
|Education level|Primary|0.0|18.3|62.5|19.2|100.0|
|of parents|Secondary|0.2|9.3|58.6|31.9|100.0|
||Tertiary|0.0|7.3|45.2|47.5|100.0|

_Note_ : Parents' education level defined as highest educated parent. _Source_ : Own calculations on ESCVJA (four urban areas) Financial means in the household may also increase youth development, since in addition to schooling financial means allow access to computers, internet, mobile phones, etc. Some 55 percent of young Argentines in the four cities in the sample have a mobile phone and 42 percent have a computer. The internet is used by 70 percent and practically all of these have an email account. Thus, a large proportion of youth are able to communicate and interact using technical equipment, which is needed to work productively, especially in the growing service sector.

Most youth attend school, but a third are either idle, unemployed, or working. Figures 3.3 and 3.4 show that two-thirds of both female and male youth attend school. While 26 percent of male youth work, just 16 percent of female youth work. Thus, the proportion left idle or unemployed among females is more than double the proportion of 10
case for less than half (45 percent) of the _status quo_ poor.

# **_[Insert Table 1 approximately here]_**

Any time the poverty rate for Jordan is compared to the poverty rate from some other country that measures poverty based on collecting consumption data at one point in time, the comparison will not be valid simply due to the decision to average the consumption spells. An approach that would result in a poverty estimate that would be comparable with the vast majority of countries (i.e. countries that base their estimate on the single-visit approach) would be to treat each of the four visits as four single-visit interviews to separate households. Conceptually, this is to say that the HIES sample of size _n_ with four visits can be treated as a random sample spread out over the year with sample size _4n_ and a single visit to each household. The fact that the household is the same household has some implications for the standard error of the poverty estimate, but the estimated prevalence from this approach is conceptually identical to estimating poverty from a random sample of single-visit data of size _4n_ . In this case, poverty is estimated for each household _i_ in each quarter _q_ , which we refer to as the _cross-section_ approach, because we are handling the panel data as if it were cross-sectional data.

To understand whether the conceptual difference in these two approaches (status quo and cross-section) has any empirical implications, we consider three widely used measures of poverty—the headcount, poverty-gap, and squared poverty-gap indices. These measures belong to the Foster-Greer-Thorbecke (1984, hereafter referred to as FGT) family of poverty indices and 8
poor asset quality and bubbles, a worsening of credit conditions, intensified financial disruptions and dislocations, and a major equity market sell-off. Confidence evaporated, the currency depreciated sharply and the exchange rate market ceased to operate, while other assets prices were in freefall. During 2009-10, real GDP and domestic demand declined cumulatively by a 10 1⁄2 and 23 percent, respectively. Following a deterioration of the budget deficit to 8.6 percent of GDP in 2009 from a surplus of 5.4 percent of GDP in 2007, the government‘s fiscal position improved to a deficit of 4.6 percent of GDP in 2011. In contrast to Ireland, where the deterioration of the fiscal position reflected mostly the size of the bail-out package, Iceland‘s fiscal position was primarily the result of the collapse in economic activity. Iceland allowed its banking system to collapse.

# **Spain**

39. The evolution of the crisis in Spain followed similar patterns to that of Ireland. As in Ireland, the lowering of interest rates accompanying the introduction of the euro plus the growing financial integration in Europe led to a sharp increase in lending which primarily fuelled the real estate market. While in Ireland, German banks were second to British banks in providing funding, in Spain (like in Iceland) German banks played the most important role as lender (Figure 16). When funds from German banks ceased to flow in the context of the global financial crisis and economic growth stagnated, the bubble burst, leading to an avalanche of nonperforming loans in the banking system (Figure 17), especially the ̳ _cajas_ ‘ where political meddling and profiteering from local politicians allowed loans to be extended to a segment of borrowers with questionable credit worthiness, somewhat similar to the subprime mortgage market in the United States.<sup>16</sup> <!-- Start of picture text --> 160 Figure 16: Foreign claims on Spain, December 2011<br>Non-bank private sector<br>140<br>Banks<br>120 Public sector<br>100<br>80<br>60<br>40<br>20<br>0<br>US$ (billions)<br><!-- End of picture text --> Source: Bank for International Settlements.

> 16 Deutsche Bank Research (2004).

22
Miller, M., Reichelstein, J., and Zia, B. (2015). "Can You Help Someone Become Financially Capable? A Meta-Analysis of the Literature," _World Bank Research Observer_ , Vol. 30, Issue 2, August 2015.

Muralidharan, K., and Sundararaman, V. (2011). "Teacher Performance Pay: Experimental Evidence from India." _Journal of Political Economy,_ 2011, Vol. 119, No. 1, pp. 39-77.

Glewwe, P., and Kremer, M. (2006). "Schools, Teachers, and Educational Outcomes in Developing Countries." In _Handbook of the Economics of Education,_ edited by Eric A. Hanushek and Finis Welch. Amsterdam: North Holland:943-1017.

Keefer, P. and Khemani, S. (2005). "Democracy, Public Expenditures, and the Poor," _World Bank Research Observer,_ Spring 2005, 20:1-27.

World Bank. (2017). “Global Financial Inclusion and Consumer Protection Survey.” The World Bank, 2017.

19
2003 and 2004 (IMF 2005 and World Bank 2008). Nigeria maintains an import prohibition list that contained 46 categories, in 2007—see Appendix C. The list is long and covers a wide range of products (1086 (HS8) tariff lines, 185 of which are partial bans (with some exceptions to the ban)), whose only common trait is that they compete with domestic Nigerian manufacturing or agricultural industries.

**Table 2.  Sectoral prevalence of import bans in Nigeria**

|hscodes|Description|fully<br>banned<br>tariff lines|partially<br>banned<br>tariff lines|total tariff<br>lines|Banned<br>(fully &<br>partially) /<br>total tariff<br>lines|
|---|---|---|---|---|---|
|01-05|Animal&Animal Products|53|0|233|22.7%|
|06-15|VegetableProducts|103|0|335|30.7%|
|16-24|Foodstuffs|56|8|217|29.5%|
|25-27|Mineral Products|2|1|160|1.9%|
|28-38|Chemicals &AlliedIndustries|5|32|836|4.4%|
|39-40|Plastics / Rubbers|12|4|255|6.3%|
|41-43|Raw Hides, Skins,Leather, &Furs|0|29|92|31.5%|
|44-49|Wood &WoodProducts|15|1|254|6.3%|
|50-63|Textiles|542|1|874|62.1%|
|64-67|Footwear/Headgear|2|75|105|73.3%|
|68-71|Stone / Glass|1|0|211|0.5%|
|72-83|Metals|0|7|619|1.1%|
|84-85|Machinery /Electrical|45|0|897|5.0%|
|86-89|Transportation|61|0|236|25.8%|
|90-97|Miscellaneous|4|27|389|8.0%|
||Total|901|185|5713|19.0%|

Source: Computed by authors The extent to which these bans are enforced varies and exemptions sometimes are granted. In fact, it is estimated that 8 percent (US$1.8 billion) of official total imports in 2006 were in tariff lines that carry a complete ban and another 1 percent in tariff lines with partial bans. The tariff lines with a complete ban account for 12.4 percent (14 percent with partial bans) of total tariff revenue. At the sector level, bans are most prominent for the textile and footwear sectors, where 62% and 73% respectively of tariff lines are banned (Table 2). A number of other sectors (animal products, food, hides and skins, vegetable products, transportation) also have a high prevalence of banned tariff lines, typically around 30%. It is important to note that many of the products subject to import bans, such as food and clothing, are substantial components of the consumption 8