rafmacalaba/data-use-mentions-extended huggingface dataset:
Mechanically, the village-level equation (A1.1) can be estimated using the household-level data and specification (A1.4) by 2SLS using as instruments either a vector of village dummies, or identically a vector of village means, ln _y vt_ −1<sup>,</sup><sup>_MLD_</sup> _vt_ −1<sup>(see, for example, Angrist and Pischke (2009) and “Visual</sup> Instrumental Variables”). As long as the weights are correct (i.e., use the number of households in a village as weights in the village-level specification), then the household (2SLS) and village-level (WLS) specifications will yield identical coefficients for α _v_ ,β _v_ . This exercise is conducted Column (9) of Table 2, which is compared to Column (8) of Table 2.

While it is conventional to estimate the village-level (or country-level) regression using village sample averages and measures of inequality (e.g., ln _y vt_ −1<sup>,</sup><sup>_MLD_</sup> _vt_ −1<sup>), there are small-sample and</sup> measurement error issues with using sample averages as “proxies” for population moments (Deaton, 1985). Devereux (2007) shows, however, that the Deaton measurement-error estimator is identical to a Jack-Knife Instrumental Variable (JIVE) estimator, where Jack-knifed sample means serve as instruments for individual-level variables. The intent in that exercise, it should be noted, is NOT to estimate or identify the individual-level specification, but to correct for measurement error in the village-level (aggregate) specification. Adapting Devereux’s framework, the implied “structural” equation at the household-level is: Correcting for measurement error (implementing Deaton’s estimator using Devereux’s Jack-knife result)   simply involves using ln _y vt_ −1( _i_ )<sup>and</sup><sup>
capture those revenues, manage them prudently, and ultimately invest them judiciously to promote sustained long-term prosperity for the country.

This chain of decision is made all the harder for impediments at each stage - investors may be deterred by weak governance and business climate (Cust and Harding 2013), or from time-inconsistency challenges leading to hold-up. The negotiating of fair deals is hampered by severe informational asymmetries and uncertainty regarding the future value of resources and cost of extraction. Capturing revenues requires effective tax administrations who must carefully monitor complex operations in the face of sophisticated tax minimization techniques by multinational corporations. The management of revenues is challenged by price volatility, lumpiness and difficult distributions considerations. Finally, long term sustainable development can be elusive where countries struggle to diversify, where Dutch disease crowds out other sectors and where governments and citizens alike become addicted to the additional consumption short-term resource windfalls can sustain.

To judge how successfully resource-rich Africa diversified its economic activity away from resource dependence we consider three key criteria: 1. Economic concentration: measured in resource-sector GDP versus non-resource sector GDP; 2. Concentration of exports: measured using indexes of product and value diversification in export categories; 3. Concentration of assets: measured using Hartwick’s Rule, adjusted net savings and the diversity of asset classes across natural capital, human capital and productive assets.

# **Economic concentration**

We examine the impact of the boom on the economic concentration in resource-related GDP between the pre-boom and post-boom periods. Figure 19 shows how each country fared – those lying to the left of the 45-degree line saw a higher resource to non-resource GDP ratio after the boom, meaning economic concentration in resources increased by the end of the boom.

Figure 19 shows that 11 of 23 countries of resource-rich Africa with sectoral GDP data saw increasing concentration. Countries such as Chad, Zambia, and DRC saw a higher resource to non-resource GDP ratio by the end of the boom. In contrast Nigeria, Botswana and 10 other resource-rich countries managed to reduce economic concentration.

29
movement of natural persons (MNP) or Mode 4 trade in GATS parlance, which inter alia explains the low share of Mode 4 trade in total trade in services.<sup>2</sup> Barriers to trade in services in general do not take the form of border measures such as tariffs, but are rather embedded in regulatory frameworks. However, barriers to Mode 4 trade are often border measures in the form of visa requirements and quotas and are therefore more distinct. This said, regulatory measures in general affect cross-border trade and investment in services by increasing both the fixed cost of entering a market and the variable cost of servicing it. The importance and potentially trade- and investment-inhibiting impact of domestic regulation on service sector performance has received some attention in the services literature (for instance see Kox and Nordås, 2007, 2009; Nordås, 2016). At the same time, it is not just the incidence of restrictive regulation but also regulatory heterogeneity that imposes costs on services trade, including Mode 4. In fact, regulatory heterogeneity has also been shown to exert a significantly negative impact on bilateral services trade delivered via commercial presence or Mode 3 (Kox and Nordås, 2009; Nordås, 2016).

Against this background, this paper focuses on regulatory measures and compliance issues that can be included in an agenda to facilitate trade in services delivered by MNP. To do so, it uses OECD data on services trade restrictiveness indices (STRI) to differentiate between regulatory measures that may improve transparency or provide market access to service providers (e.g. the requirement to apply for a business visa) and those measures that just impede MNP (for

> 2 Estimates by the WTO Secretariat suggest, for instance, that this share could be as low as 5% (Maurer et al., 2016).

2
UL-2/2-1-88

-9-

reduction in inflation with relatively small costsY! followed the pattern of the classical European hyperinflationa, as documented in numerous studies [see e.g. Sargent (1986)).

The success of this program stands in sharp oppomition to the mixed results obtained by orthodox programs in the so-called "chronic inflation" countries. Stopping inflation in these countries is usually full of obstacles and strict fizcal discipline has not been a sufficient condition to ensure success. In order to deal with this fundamental problem we will first consider the reasons for Bolivia's success in bringing about an abrupt stqp to inflation and examine whether there are underlying structural differences in the price mechanism between hyperinflation and chronic inflation thus requiring different stabilization strategies. 2. Credibility and Stabilization In explaining the difficulties encountered by stabilization programs in Latin America great stress is put on the credibility of the policies. Did the Bolivian program enjoy greater credibility? Sachs (1986) argues convincingly that the program did not seem very credible from the point of view of sustainability in the light of the shaky political setting. He claims, however, that long run credibility [of the type required by Sargent (1986)] is not necessary for short run stabilization as long as the exchange rate is stabilized.

_l/_ The costs may perhaps have been larger than those suggested by the data on unemployment and growth in table 1. The cuts in government expenditure had a significant effect on government provided social services and public investment, which could affect growth in the future. For a discussion of this aspect, see Morales (1987.b).
, C., & Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely sensed and ancillary data. _PLOS ONE_ , _10_ (2), e0107042.

- Uchida, H., & Nelson, A. (2009). _Agglomeration index: Towards a new measure of urban concentration_ . Washington, D.C.: World Bank.

- World Bank. (1990). _World development report: Poverty_ . Oxford Press.

- World Bank. (2009). _World Development Report 2009: Reshaping Economic Geography_ , Washington, D.C.: World Bank.

- World Bank. (2022). _Poverty and Shared Prosperity 2022: Correcting Course_ , Washington, D.C.: World Bank.

- Zhang, Q., & Seto, K. C. (2011). Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. _Remote Sensing of Environment_ , _115_ (9), 2320–2329.

- Zhou, N., Hubacek, K., & Roberts, M. (2015). Analysis of spatial patterns of urban growth across South Asia using DMSP-OLS nighttime lights data. _Applied Geography_ , _63_ , 292–303.

27
quality, undiversified asset base), or a lack the capacity to cope with the shock ( _e.g._ financial resources).

# **2.2 Quantifying the welfare impacts of disasters**

As discussed in the previous section, there is a plethora of _ex-post_ analysis of the impacts of natural disasters on human welfare, such as on education, income, health, and inequality, primarily based on longitudinal household surveys and econometric methods. However, quantifying the welfare impacts of natural disasters _ex-ante_ , using a risk modeling framework, has received considerably less attention. As such, most risk assessment of natural disasters or climate extremes use asset damages as their main metric of impact, obscuring how the poor might experience disproportionate impacts beyond asset damages (Markhvida et al., 2020; Verschuur et al., 2020).

A number of studies have quantified the exposure of poor households to disasters on a national or global scale (Bangalore et al., 2016; Winsemius et al., 2018). Less information is available on the vulnerability and resilience of poor households exposed to disaster. The extent of vulnerability is often related to the assets that households own, the employment they are engaged in, the type of infrastructure they depend on, and the health and socio-economic characteristics of households. A number of studies have developed regional or national-scale social vulnerability indices of hazard-prone areas using survey data (using subnational statistics), providing insights where the most social vulnerable regions are exposed to hazards (Marzi et al., 2019; Lee et al., 2021). However, these social vulnerability indices are difficult to construct on an individual household level while covering a large spatial scale. The resilience 6
We have that the simple or un-weighted average tariff in year t is: The weighted average tariff is:

# **Tariffs for the the Years 2011-2020—Intuition for the Mathematics**

We have to calculate the ad valorem equivalents of the specific tariffs in future time periods and also calculate trade weights of tariff lines for the purpose of the trade weighted average. We need trade data for both of these requirements. Since trade data are unavailable after June 2011, we make two assumptions. First, we assume that the average of the trade weights from 2009, 2010 and the first half of 2011 apply in future time periods. Rather than use the most recent year of available data, we call this 2.5 year period our base period for the calculation of tariffs in later periods. We take the import statistics for 2009, 2010, and the first half of 2011 in order to reduce yearspecific shocks when computing the tariff projections for 2011-2020.

Second, we must also deal with the complication that new tariff lines appear in the years 2011-2020 that did not exist in the base period. For example, comparing the tariff codes of 2010 with those of January 1, 2012 (our classification for 2011) , 8,907 tariff lines were identical, but there were 2218 new tariff lines in the tariff classification of January 1, 2012 that did not exist in 2010. For 2020, 10,351 tariff lines were identical to those in 2010, but 1,206 did not match any tariff line in 2010 due to changes in classification.

For ten-digit tariff lines in the years 2011-2020 that are identical to tariff lines in our base period, calculation of the AVE of the specific tariff is relatively straightforward. We call these ―matched‖ tariff lines. We compute the tariff rate on tariff line h by averaging over all countries from which there are positive imports in all three time periods, 2009, 2010 and 2011*. This is analogous to equations 1 and 2.

For tariff lines h in the years
with the support of the City of Harare’s (CoH) Water and GIS departments. Complaints data were digitized for 7,179 water network breakage reports and 24,713 sewer burst location reports from around the city with the support of Harare’s GIS department. Water quality samples for 23 sampling points, administrative boundaries for water supply (DMAs) (165 suburbs), and the location of 362 markets and public facilities were also collected and digitized by City of Harare’s GIS department. Data on 1,848 private borehole locations was provided by Manyame, Upper Mazowe and Nyagui Sub-catchment area operators, for which 1,721 were inside Harare. Finally, a groundwater vulnerability index was developed by Ziva, based on a combination of geological factors (fault index, soil permeability, depth to groundwater, topographic wetness index and fissure flow).

# Population Mobility Data

We use anonymous and aggregated call detail record (CDR) data provided by two mobile network operators in Zimbabwe covering the period of the outbreak (September 2018-February 2019). Anonymous CDR data contain information on the location of the nearest cellphone tower through which any calls or texts made or received are routed. By comparing the locations of where these

> 2 It is standard practice in epidemic response that ‘Once an outbreak is declared, there is no need to confirm all suspected cases. The clinical case definition is sufficient to monitor epidemiological trends’. [26]

4
# **V.   Estimating TFP**

The first step in the analysis is to estimate the production function (6). The variables _Y, K_ and _L_ are derived from the survey data as follows:

- Value added ( _Y_ ) is calculated by subtracting materials and energy costs from the total value of sales;

- Capital _(K)_ is defined as the total book value of assets; and,

- Labor _(L)_ is defined as the total number of employees (including contractual employees) working at the firm’s main production facility at a given time.

Since the data set includes observations for each firm in each of three years, the subscript _t_ is added to indicate the observation year: This data structure has an implication for the choice of estimator. Ordinary least squares (OLS) estimates are efficient only under the assumption of zero covariance in error terms across residual terms. Since we are likely to observe covariance across observations for the same firm in different years, we need an estimator that decomposes the residual into two components where the first component is the same for all observations for firm _i_ and the second component obeys the assumption of zero covariance across observations. The generalized least squares (GLS) random effect model<sup>10</sup> achieves this decomposition and is therefore more efficient than OLS. Tables 9 and 10 present the GLS parameter estimates for (9) for China and Brazil respectively:

> 10 This estimator is implemented using the _xtreg re_ command in STATA.

14
database.

# 4.3 Descriptive Statistics

Appendix 8:F reports some statistics at the ...rm level for each exporting country. "Risky" products account for an important share of ...rms total exports in all ...ve countries. Additionally, "risky" ...rms–exporting at least one "risky" product–represent around half of the total ...rm population in all ...ve countries. Non "risky" ...rms are ...rms that export no "risky" products at all. Appendices 8:G and 8:H report some statistics for our survival data. Considering ...rms in all countries the average spell duration is about one year and four months and the median duration is only one year. Almost 40% of the spells are right censored, and 17% are left censored. Considering each country individually, Senegal exhibits the highest average spell duration, and Ghana the lowest. A large proportion of spells, 56%, start with trade values lower than 10’000 dollars, 13% are initiated with trade values higher than 100’000 dollars and only 3% start with initial trade values greater than 1’000’000 dollars. Dropping all spells with initial trade value inferior to 10’000 dollars (100’000, or 1’000’000 dollars) increase (even more) the average and median spell duration. The higher the initial trade value the higher the probability to survive. These results are in line with ...ndings in previous empirical studies (Besedes and Prusa, 2006a, 2006b).

We now move on to characterize the duration of trade relationships non paramet14
Policy Research Working Paper 10345

# **Abstract**

This paper examines global data on unbanked and underbanked consumers to highlight the role that improved financial literacy and capability could play in motivating and enabling the safe and beneficial use of financial services. The paper uses data from Global Findex, a demand-side survey on ownership and use of accounts at formal financial institutions. The paper reviews the self-reported barriers to account ownership and use cited by unbanked adults, and identifies the challenges faced by account owners who could not use an account without help. Together, these issues point to the importance of financial education to improve digital and financial literacy skills, in addition to product design that considers customer abilities, and strong consumer safeguards to ensure that customers benefit from financial access.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sansar1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
><!-- End of picture text -->

**Figure 4** ITT Effects of _Boys_ Treatment

_Notes._ This figure presents estimates of _β_ 1 from equation 1 for separate regressions with the outcome indices specified on the y-axis. Indices are centered around females (males) in ELA only communities who were not assigned (whose connected female was not assigned) to the _Goal_ treatment. Bolded markers are statistically significant at _p <_ 0 _._ 1 _. p_ -values and coefficient estimates are displayed beside each marker.

_Source._ Female and male baseline and endline data, balanced panel.

45
may have been some amount of untapped revenues for government during the boom, proxied by gap between these estimated rents values and collected revenues.<sup>10</sup> Figure 9. Cumulative natural resource rents and revenues per capita <!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Resource rents Resource revenues<br>Thousand US$<br>12,787.9 9,924.0<br>7,090.6<br>42,852.5 12,851.2 3,150.9 1,396.2 838.8 796.8 676.6 656.1 200.2 189.0 101.3 67.2 41.8 39.9 28.3 27.2 17.3<br>Equatorial Guinea Gabon Angola Botswana Congo, Rep Nigeria Sudan Chad Sao Tome and Principe Cameroon Mauritania Zambia Guinea Cote d'Ivoire Niger Congo, Dem Rep Sierra Leone Liberia Mali Uganda<br><!-- End of picture text --> Notes: Sum of natural resource rents (orange bars) and natural resource revenues (blue bars) per person between 2004 and 2014 by country in thousand US dollars. Natural resource rents include rents from oil, natural gas, coal, and principal mineral commodities (bauxite, copper, gold, iron ore, lead, nickel, phosphate, silver, tin, and zinc), but exclude rents from other important commodities – such as diamonds and other valuable minerals – which could underestimate the real value of total natural resource rents. No resource rents data for Sao Tome and Principe. Source: World Bank staff estimates based on UNU-WIDER (2021) and The Changing Wealth of Nations 2021.

10 Two important caveats are necessary to interpret this finding. First, the
Working Paper, (7412).</mark>

- <mark>Chibber, K. S., & Krishnan, S. (2011). Confronting intimate partner violence: A global health priority. Mount Sinai Journal of Medicine: A Journal of Translational and Personalized Medicine, 78(3), 449-457.</mark>

- <mark>Cislaghi, B., & Heise, L. (2020). Gender norms and social norms: differences, similarities and why they matter in prevention science. Sociology of Health & Illness, 42(2), 407-422.</mark>

- <mark>Dhar, D., Jain, T., & Jayachandran, S. (2018). Reshaping adolescents' gender attitudes: Evidence from a school-based experiment in India (No. w25331). National Bureau of Economic Research.</mark>

- <mark>De Hoop, T., van Kempen, L., Linssen, R., & van Eerdewijk, A. (2014). Women's autonomy and subjective well-being: how gender norms shape the impact of self-help groups in Odisha, India. Feminist Economics, 20(3), 103-135.</mark>

- <mark>Delprato, M., Akyeampong, K., Sabates, R., & Hernandez-Fernandez, J. (2015). On the impact of early marriage on schooling outcomes in Sub-Saharan Africa and South West Asia. International Journal of Educational Development, 44, 42-55.</mark>

28
A, Ca, Iodine, Iron, Zn, and Folate (Dizon et al. 2019, Ahmed et al. 2016). According to comprehensive nutrient gap assessment (GAIN 2021), except Folate, the other five micronutrients have high to moderate burden gap with high or moderate level of evidence certainty. Folate and Vit. B12 deficiencies are more concerned among non-pregnant non-lactating women, with all strata sharing similar deficiency levels at around 23 percent (25 percent in slums). Iodine’s data is unavailable, so we focused on the other six micronutrients.

Macro- and micronutrient values used for dietary patterns’ analysis are mostly from FCT. FoodData Central portal<sup>8</sup> was also used because FCT does not contain data for Vitamin B12. For measuring the sufficiency of nutrient intake from our modeled diets, we created minimum requirement for macronutrient intake (Annex 6a) using total energy per AME (2,430 kcal)<sup>9</sup> provided by the FBDG and shares of energy that come from protein, fat, and carbohydrate recommended by FAO. According to the recommendations of FAO (2008), for an adult male aged 19-50, 55 to 75 percent of total energy should come from carbohydrates; 20-35 percent of total energy from fat; and 12 percent of the total energy from protein. With the aim to benchmark dietary patterns’ nutrients intake, we used the minimum requirement of energy provision from protein (12 percent), 15 percent from fat and the rest, 73 percent from carbohydrates to create minimum amount of nutrients intake suggested. Since carbohydrates, fat, and protein provide 4 calories, 9 calories, and 4 calories per gram respectively (USDA 2022), applying macronutrients’ shares of energy and their unit energy provision to 2,430 kcal per AME produced the desirable macronutrient intake used in Figure 2.

Desirable micronutrient intake (Annex 6b) for benchmarking in Figure 3 is from the study Desirable Dietary Pattern for Bangladesh, which adapted recommended nutrient intake