rafmacalaba/data-use-mentions-extended huggingface dataset:
Hevia and Loayza 2012), which we refer to as the Standard LTGM. However, in the Solow‐Swan model (and Standard LTGM), capital is simply an aggregate, and so those models cannot simulate the specific effect of an increase in public investment. In contrast, in the LTGM‐PC, total capital is split into public and private portions. The LTGM‐PC retains many other realistic growth drivers and features of the Standard LTGM, including other growth fundamentals (human capital, TFP, demographics, labor market participation by gender), and also implications for poverty rates. Section 2 presents the model and Section 3 describes how it is implemented in the Excel‐based tool.

Despite being theoretical, the paper draws extensively on the empirical literature to guide the choice of parameters. The most important parameter is the elasticity of output to public capital, φ , which we call the _usefulness_ of public capital. In Section 5 we review the evidence from two meta‐analyses and other literature, which suggests an elasticity of φ =0.17 for essential infrastructure and φ =0.10 for generic public capital like buildings (though users can also specify its value). We also calculate the country‐specific scarcity of public capital using a new public capital database from the IMF’s Fiscal Affairs Department.

However, we could not find a suitable measure of the fraction of public capital that is of high quality (we use “efficiency” and “quality” interchangeably).<sup>4</sup> So in Section 4 we develop a new cardinal Infrastructure Efficiency Index (IEI) to quantify the extent to which public capital is of high quality in different countries. The IEI is based on estimates of the fraction of roads that are in poor condition, water that never reaches its final customers, and electricity that is lost through transmission and distribution.

Our second contribution is to document how the quantity and quality of public capital vary across countries with different levels of development, and
- (a)  Graduates will demonstrate knowledge of mathematics, science and engineering.

- (b)  Graduates will demonstrate an ability to identify, formulate and solve engineering problems.

- (c)  Graduates will demonstrate an ability to design and conduct experiments, analyze and interpret data.

- (d)  Graduates will demonstrate an ability to design a system, component or process as per needs and specifications.

- (e)  Graduates will demonstrate an ability to visualize and work on laboratory and multidisciplinary tasks.

- (f)  Graduate will demonstrate skills to use modern engineering tools, techware and equipment to analyze problems.

- (g)  Graduates will demonstrate knowledge of professional and ethical responsibilities.

- (h)  Graduate will be able to communicate effectively in both verbal and written form.

- (i)  Graduate will show the understanding of impact of engineering solutions on the society and also will be aware of contemporary issues.

- (j)  Graduate will develop confidence for self-education and ability for life-long learning.

- (k)  Graduate who can participate and succeed in competitive examinations.

Ten out of the 11 NBA Program Outcomes were included in the questions (some in an abbreviated form). Thirteen skills from previous employer surveys, notably from (Kleinke, 2006) were added. These were in particular skills often referred to as soft skills or core skills or employability skills, such as integrity, self-motivation, team skills etc. Further three specific skills were added, namely ―Basic computer‖, ―Advanced Computer‖, and ―Customer Service Skills‖. Lastly, another three skills ―Technical Skills (programming)‖ ―Communication in English‖ and ―Entrepreneurship Skills‖, were included as per request of employers.

Definition of skills and a common understanding of what a skill is, poses a problem for comparability and interpretation. Given the survey has to be relatively short to ensure an acceptable response rate, the questionnaire did not define each skill. In most instances, an additional explanation of example is provided in parenthesis.
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|Gmtry,<br>L<br>economy, cr<br>ritory|ast rublibh<br>P<br>Date|ed censu<br>opulation<br>(1000)*|s<br>Source for population<br>(mid-1985)|Source for fertility<br>(1985-90, unless irdicated)|Source for mortality<br>(1985-90, unless lndicated)|
|---|---|---|---|---|---|
|People's|Mar 85|3585 F|Bank projection from census|Bank est.|Bank est.|
|am.Rep.||||||
|Lebanon|Nov 70|2126 F|U.N. 1984 assessment|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Lesotho|Apr 76|127F|Bank projection from<br>|Based on 1977 World<br> <br>|Based on 1977 World<br> <br>|
||||urpxbished 4/86 census|Fertility<br>Survey|Fertility<br>Survey|
|Liberia|Feb84|2102 F|U.N. 1988 revision (prelim.)|1986 DHS|U.N. 1988 revision (prelim.)|
|Libya|Jul 84|3637 J|Bank projection from census|Based on U.N. 1984<br>assessment|Based on U.N. 1984<br>assessment|
|Liechtenstein|Dec 81|26 F|UN*PVSR<br>7/86|-|--|
|.uxembourg|Mar 81|365 J|UNPVSR<br>7/86 (official<br>est.)|Eurostat 1987|Eurostat 1987|
|1acao|Mar 81|242 J|UNPVSR<br>7
formal financial institutions are available in only 31 percent of the communities, and formal insurance institutions do not exist in any of the communities surveyed. Survey data also revealed that 49 percent of farm entrepreneurs and 60 percent of nonfarm entrepreneurs did not even apply for loans even though they needed access to financial resources. This low application rate for loans suggests limited formal literacy among rural entrepreneurs. The reasons that participating rural entrepreneurs gave for declining to apply for loans varied. The complexity of the procedures required to apply was given as the reason by 15 percent of both farm and nonfarm respondents. Low expectations over the likelihood of their loan applications being approved was also very similar, with 12 percent of farm entrepreneurs and 13 percent of nonfarm entrepreneurs reporting this as their reason for not applying. Doubt as to their ability to repay a loan was given as the reason for not applying by 4.8 percent of farm entrepreneurs and 8 percent of nonfarm entrepreneurs. These findings suggest that in addition to increasing the number of financial institutions and targeting financial literacy through training, credit guarantee schemes introduced by small business promotion agencies to support rural entrepreneurs and improve their ability to negotiate and repay loans could be instrumental in improving the success of their business operations.

# **5.4 Business Services**

Good business management is critical for rural entrepreneurs who typically have little knowledge and resources of business practices. As Sawada and Harishchandra (2011) identified in rural Ethiopia, rural entrepreneurs can easily improve their productivity and profitability by learning the fundamentals of business management. Markley (2006) states that providing technical assistance, training, and networking are essential to rural economic development strategy. Management knowledge can enable rural entrepreneurs to identify the appropriate levels of financing, technology, skills, processes, quality control, and marketing for a business to be profitable. The following issues related to business services needed to be improved and addressed.

First, the availability and utilization of business services are to be improved for rural entrepreneurs. With appropriate business management knowledge, unprofitable operations
.3|6.7|
|Education by Stages|||||||
|College (%)|4.8|7.3|2.9|33.2|4.6|1.5|
|Upper secondary (%)|15.8|25.3|31.3|41.6|26.3|8.2|
|Lower secondary (%)|32.7|36.5|45.3|19.8|40.6|30.2|
|Primary (%)|28.4|22.1|17.7|4.5|23.2|35.7|
|No primary (%)|18.4|8.5|2.7|.9|5.2|24.4|
|**Statistics only for migrants**|||||||
|Urban origin (%)||21.7|20.2|41.8|23.9|6.0|
|Unemployed in origin (%)||2.6|1.6|2.0|3.8|.5|
|Distance (km)||563.2|771.4|226.4|419.1|874.4|
|Income change before vs. after mig|ration (%)||||||
|Better||84.7|87.9|81.0|89.2|74.1|
|Worse||3.5|1.4|1.3|1.9|9.7|

Sources: The statistics of migrants and non-migrants are computed from the VMS 2004 and the VHLSS 2004 respectively. Notes:<sup>*1</sup> See the text for the definition of “State”, “Foreign”, “Private
# 3 Data

One of the major impediments to analysis of conict is the lack of conict data at suciently granular geographic scale. Most of the literature on the legacies of war therefore is focused on assessing the eects of conict intensity at relatively high geographical levels. For example, previous studies exploring the determinants of Nepal's civil conict (Murshed and Gates, 2005; Bohara et al., 2006; Do and Iyer, 2010; Macours, 2011) and the consequences of the conict (Valente, 2014; Pivovarova and Swee, 2015; Menon and van der Meulen Rodgers, 2015) exploit variation in intensity of the insurgency at district level or even higher geographical units. Dening conict variables at a broader geographical level measures individuals' exposure to conict less precisely and can create measurement error. Moreover, signicant heterogeneity in geography, socioeconomic status and development among areas within districts can create diculty in addressing the association between the determinants of war at teh district level and explained variables of interest, likely leading to omitted variable bias.

In contrast, this analysis uses detailed and geographically granular conict intensity data; i.e. village-level insurgency. As households within a village tend to be highly homogeneous in socioeconomic status and ethnicity and live in the same geographical terrain, this paper is able to avoid many of the concerns induced by the imprecise measurement of individuals' exposure to conict. Additionally, combining these high-resolution conict data with the 2016 Nepal Demographic Health Survey (NDHS 2016) allows me to explore the impacts of the conict on the children of individuals who were exposed to the war in their own childhoods.

## 3.1 Conict Data

I use the Informal Sector Service Center's (INSEC) records of the conict victims to create conict intensity variables. INSEC is an active Nepalese non-governmental human rights organization. Throughout the war, INSEC documented human rights violations and abuses extensively and its archive of the casualties provides detailed information
|---|---|
|(1)|(2)|(3)|(4)|
|Local Shallow|60.77|1698302|4530|
|Karstic|4.97|138873|370|
|Major Alluvial|8.16|228088|608|
|Complex|26.10|729357|1945|

Notes. This table presents descriptive statistics on the four aquifer types in Sub-Saharan Africa. Column (1) specifies the aquifer type. Column (2) is the mean share of the aquifer grid area that is covered by the aquifer type. Column (3) presents the corresponding total groundwater volume. Column (4) presents the total groundwater volume available per person per day. To compute the volume per capita per day, we divided the volume by the total population of the country that each groundwater aquifer grid lies in and then summed up the volume per-capita measure. Subsequently, we divided the measure by 365 days to obtain an estimate of groundwater availability per person per day. We use the World Bank’s curated groundwater aquifer dataset to construct this table.

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|
|Tax morale|2.3|0.5|6.1|-18.0|0.0|3.0|36.0|62|
|Low risk|6.3|1.0|9.4|-5.0|0.0|8.0|38.0|61|
|Medium-low risk|6.1|4.0|8.0|-7.0|0.0|8.0|28.0|61|
|Medium risk|7.0|4.0|8.8|-3.0|0.0|11.0|50.0|61|
|Medium-high|8.8|7.0|10.6|-3.0|1.0|12.0|50.0|61|
|High risk|9.1|6.0|11.5|-5.0|0.0|15.0|51.0|61|
|_Taxable income (%)_|||||||||
|Pooled|5.7|4.0|5.7|-2.0|2.0|10.0|30.0|64|
|Sanctions|8.6|6.0|9.2|-4.0|3.0|12.0|50.0|63|
|Procedures|6.5|4.0|10.0|-2.0|1.0|8.0|57.0|63|
|Tax morale|1.4|0.0|4.7|-12.0|0.0|2.0|21.0|63|

_Note_ : This table presents descriptive statistics for the expert forecast survey. The first panel refers to forecasts of treatment
_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _28_ history and extrapolate beyond observed intervals. Overall, the model explained holdout data with an R<sup>2</sup> of above 0 _._ 95 using 11 basic indicators. The model was applied to the IMF’s WEO database of April 2022 to project the severely food insecure population across the World Bank’s full cohort of 144 IDA-eligible and IBRD countries that together cover approximately 98% of global historical food insecure populations.

Based on the economic forecasts of the WEO of April 2022, the number of people that are severely food insecure for a sustained period of 3 years is estimated to reach over 1 billion people in 2022 globally (an IDA + IBRD wide prevalence rate of 15%). This constitutes an increase of over 172 million people (a relative increase of 21%, or a 2 point increase in the prevalence rate) over 2017-2019 prepandemic estimates from the same model. This projection concerns unmitigated impacts extrapolated from 2019 data that do not reflect the large amounts of aid and supportive policies that characterized the pandemic years, but have also not yet factored in the effects of sanctions and export restrictions imposed after March 31, 2022. Bearing these uncertainties in mind, the overall direction of the projection remains clear and paints a protracted picture in which the previously anticipated rebound in headline growth occurs unequally, leaving many behind in food insecure conditions. The projected improvement of about 0.5 point in the prevalence rate after the 2022 peak is not sufficient to offset continued population growth. Without large-scale targeted interventions, the size of the population in need of assistance is projected to remain above 1 billion through 2027.

While the projected population in need grows sharply, rising commodity prices have also increased the per capita costs of a humanitarian response. Using basic assumptions about response costs, adjusted annually for varying global food prices, the analysis estimated temporal trends in global development assistance needs. The analysis highlighted that while the prevalence
only more aware that they are or have been victims of violence but are more willing to report if it occurs. In recent years, the government, nongovernmental organizations, and feminist movements have increased their outreach campaigns to raise awareness of genderbased violence and inequality, which may have influenced the increase in reporting.

> 6 <u>https://www.bcn.cl/leychile/navegar?idNorma=1021343</u>

> 7 <u>https://www.bcn.cl/leychile/navegar?idNorma=1143040</u>

8 At the civil society level, in 2020, the Chilean Network Against Violence Against Women registered 58 completed femicides, 3 femicide-suicides and 6 murders due to violence. These figures differ from official data because of limitations in the legal definition of femicide in Chile. Source: Femicide Registry Chilean Network Against Violence Against Women (accessed - 01.11.20): https://docs.google.com/spreadsheets/d/1s_g16Ttsm0S1_9oMH2xilgDMRNaJTALjjV <u>wAdR5xVc/edit#gid=1952631340</u> 9 Source: Statistical data on femicides from SernamEG (accessed on 01.11.20): <u>https://www.SernamEG.gob.cl/?page_id=27084</u> 10 <u>https://www.latercera.com/politica/noticia/ministra-zalaquett-informa-aumento-del-166-en-las-atenciones-del-fono1455-en-5-meses-y-pide-avanzar-en-ley-de-violencia-integral-para-proteger-a-lasmujeres/LBT5HCWZNVCWFJDZXTRR5RTEXE/</u> 4
. Those statistics will be aggregated at the aggregation levels indicated in the `aggids` option. The user must have added the variable in the `sae data import` command when preparing the target dataset.

- `catvar()` : The `catvar` option indicates the categorical variables for which the user wants to estimate the two-way frequencies/distributions based on poor and non-poor groups defined from the defined poverty lines in either `plines` or `plinevar` . Those statistics will be aggregated at the aggregation levels indicated in the `aggids` option. The user must have added the variable in the `sae data import` command when preparing the target dataset.

All other options resemble those detailed for other subroutines.

# **7 Conclusions**

As we approach the 2020 round of the population census, governments and their respective national systems of statistics have renewed their international commitments towards the Sustainable Development Goals (SDGs). The SDGs will need to be operationalized at a sub-national level or reported for specific subgroups of the population. Moreover, the expansion of the availability and use of administrative, geospatial and Big Data sources for evidence based policy making is on the rise, creating a number of important opportunities for potentially better and more up-to-date measures of social well-being. Against this backdrop, to ensure 26
population. At the national level, about 20 percent of the population lived in rural areas contributing 35 percent to total poverty.<sup>3</sup> The high poverty rates in rural areas, particularly in the Northeast, are related to this region’s predominance of employment in agriculture. The Northeast has the highest share of agriculture in employment with 34 percent in 2001 compared to only 11.5 percent in the Southeast.<sup>4</sup> According to Ferreira,

> 2 Note that their analysis compares the income distribution of 1976 with the 1996 distribution. For detailed results see Ferreira and Paes de Barros (2005).

> 3 Poverty is measured by the headcount ratio. The poverty figures in this paragraph are taken from Ferreira, Lanjouw, and Neri (2001).

> 4 The figures on agricultural employment are own calculations based on the PNAD 1997 and the PNAD 2001. The PNAD (Pesquisa Nacional por Amostra de Domicílios) is a regularly conducted representative household survey. The sample had a size of about 380 000 individuals in 2001.

5
text --> _Source:_ World Bank staff calculations based on Eurostat.

The EU15 South also suffers from unfavorable domestic business environments. In comparison to rest of the EU15, the EU15 South rated consistently lower in regulatory indices and in outward FDI (Figure 6). Successful countries in Northern and Continental Europe were able not only to nurture domestic firms that invested abroad, but also to attract foreign firms to invest domestically, as reflected in their FDI stocks. This situation in turn allowed greater access to foreign markets and increased demand (Antras and Helpman, 2004). This finding implies that Southern Europe may realize productivity improvements by loosening restrictive regulatory requirements, encouraging competition, and supporting both outward and inward FDI.

**Figure 6 Policy Variables in the EU15**

<!-- Start of picture text --> Business<br>Operations Skills of Quality of Access to Inward FDI Outward FDI<br>Index workforce infrastructure credit stock stock<br>2012 Percentage with Index by World Credit to private Manufacturing and Manufacturing and<br>tertiary education, Economic Forum, sector, percentage services, services,<br>2009 2008 of GDP, 2008 percentage of percentage of<br>GDP, 2008 GDP, 2008<br>Finland 88 35 6 86 18 32<br>Sweden 84 30 6 127 38 45<br>France 82 29 7 108 28 38<br>Spain 73 32 6 203 25 21<br>Italy 75 16 4 105 7 6<br><!-- End of picture text --> 18
associated with firm’s age. Conversely, Adrjan (2018) finds that young firms pay a small premium for new hires. This positive young-firm wage premium is more recently supported by Babina et al. (2019), who show a positive and significant young-firm pay premium for the United States that only appears when one controls for the necessary worker (sorting) and firm fixed effects (employer heterogeneity) (Abowd et al., 1999).

A related literature has focused on the wage premium associated to international activity and finds that multinational enterprises and exporting firms pay higher wages than their counterparts. Lipsey and Sjoholm (2003) find that wages in domestically-owned Indonesian manufacturing plants taken over by foreign firms increased sharply after the takeover. Hijzen et al. (2013) find a large and positive wage effect following a foreign acquisition of a domestic firm in Brazil, Indonesia, Germany, Portugal and the United Kingdom. The authors also find that wage effects are higher in developing countries and that effects on wages after foreign acquisition are restricted to high-skilled workers. In the case of exporting firms, an empirical literature has focused on the impacts of exporting on workers’ wages. Brambilla et al. (2017), using comparable data for 61 developing and low-income countries, show a prevalent wage premium in exporting firms compared to their non-exporting counterparts. Schank et al. (2008), using German employer-employee data, also find a wage premium in exporting firms, but argue that it is largely explained by the self-selection of firms that are already more productive and pay higher wages.

Explaining the dynamics and causality of these wage premiums is an important gap in some of the literature, especially regarding the firm size premium, which has primarily focused on identifying the premium size but not on how the premium is created. On the one hand, rent sharing models of the labor market argue that the premium is associated with firms applying more advance technology equipment, innovation projects, or other investments; thus enabling workers to capture some of the rents
Further research could expand on this work in several ways. First, indicators extracted from pre-crisis satellite imagery could be added to the model, to see if they improve predictive power. Second, google search terms are likely affected by seasonal patterns, and accounting for these could also improve predictive power. Third and more broadly, it would be worthwhile exploring additional real-time indicators, such as satellite imagery that measures the pace and characteristics of new building construction in urban areas, and agricultural outcomes in rural areas. Call-detail records from mobile phone operators may also be very useful for this type of nowcasting. Fourth, further work can better understand which indicators and methods perform better in different contexts, as well as how model accuracy depends on the use of different training data to estimate the models. Non-parametric machine learning methods such as extreme gradient boosting may also predict better, particularly with large datasets. Finally, it would also be useful to expand the scope of this type of exercise beyond the specific COVID-19 crisis.

More research is needed to better understand the potential biases associated with different real-time indicators in different contexts. For example, Google search terms and mobility measures may not accurately reflect changes in areas where internet connectivity is low and may be more accurate in reflecting sharp downturns during a crisis than gradual progress. Google mobility measures may also suffer from selection bias since they only capture movement for a subset of mobile phone owners. Because of these concerns, we believe that these types of methods require further testing, evaluation, validation, and verification before being implemented to generate experimental official statistics. After better understanding when, where, and why nowcasts work well, these types of methods have the potential to produce extremely useful estimates, particularly in informing policy responses to unanticipated shocks. The results are therefore sufficiently promising to warrant prioritizing further investment in piloting and testing different predictors and methods to generate nowcasts for these and other related socioeconomic outcomes.

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