rafmacalaba/data-use-mentions-extended huggingface dataset:
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during the 1950s **and** 1960s, demand estimates generally were more accurate. A survey of 75 Bank loans to 37 countries In the 19506 and 19606 found that, on average, sales forecasts were only 2% more than actual sales, but that with certain loban there was a large discrepancy between the forecast and actual amounts. For projects approved In the 1950s and 19608, the standard deviation of actual energy sales from that forecast was 38%, which Is the same as was found In a similar study by OED.

On a Regional basis, sales for East Asia utilities were almost the same as forecast during the 1970s and for East Africa were only 6% below that forecast. LAC actually exceeded the forecast level of sales by 5%, but has since had considerable overestimates. Other regions showed significant overoptimism by as much as 23% In South Asia, 18% In EMENA, and 17% In West Africa.

# 2.7 Financial Performance of Power Prolects and the Sector

An Integral part of any Bank-financed project Is to ensure the financial health and stability of the Borrower. The eventual aim Is to make the utility financially Independent so that It Is able to fund a substantial portion of its Investment program from Internal sources and Is sufficiently credit-worthy to ralse the remaining funds from commerclal sources (both local and forelgn). The fundamental flnancial soundness of a utility can be measured by five key ratios - (I) rate of return on assets, (II) operating ratio, (Ill) self-financing ratio, (lv) debt service ratio and (v) days receivables. The debt/equity ratio Is also an Indicator of financial performance where the debt Is not owed to government. However, many utilities In developing countries borrow heavily from govemment, but when In flnancial straits this debt Is often waived, Interest payments rescheduled, or the debt converted to equity. Given the problems of definition of debt, revaluation and conversion of debt, and revaluation of assets, It was
on contemporaneously observed pre-intervention differences that might influence subsequent trajectories.

> 9 For example, suppose that the true impacts of a project are greater for poor people, for whom recall is less reliable. (There is supportive evidence for this conjecture in Das et al., 2011, who found health-status recall to be worse for poor people, using data for India.) Then we will obtain a biased estimate of the difference in impacts between poor and non-poor people even with randomized assignment of the intervention.

5
education by 10-year-age bands. These variables are only available for the years that correspond to a population census. Thus, for the years with missing information, we assign the value of the previous closest year.

# **2.2 Descriptive Statistics**

Table A2 summarizes the characteristics of the with and without con- resulting sample trols, which differ in size due to the availability of marriage and education variables. The sample without controls, which results from merging the WBL dataset with the labor indicators from ILOSTAT, covers 80 countries and is an unbalanced panel of 918 country-year observations, where each country is observed for 11.5 years on average. Among these countries, 79 percent had enacted a law regulating childcare by 2022, and we observe 60 percent of them both before and after the law enactment in our data. The full estimation sample, which includes controls related to marriage and education, has 54 countries and 760 country-year observations. This smaller panel is more balanced, however, and countries appear, on average, for 14 years in the panel. Tables A3-A5 detail the countries included and show some characteristics of the sample by country.

As explained in the next section, we also construct cohort-specific datasets where we include countries that enact a childcare law in a given year and its appropriate control units. The resulting stacked dataset has 11,899 and 9,497 country-year-stack observations for the baseline (without socio-demographic controls) and the full specification (with socio-demographic controls), respectively.

Summary statistics of key variables used in our analysis are reported in Table 1. The 9
, extensive and sustainability margins

<!-- Start of picture text --> Intensive margin: Higher volumes of existing products and destinations<br>New products<br>Export growth Extensive margin:<br>New destinations<br>Sustainability margin: Survival of new products and destinations<br><!-- End of picture text --> Given that traditional export promotion policy has tended to focus primarily on the extensive margin (i.e. helping would-be exporters find new markets and develop export-ready products), this literature suggests that such efforts may not yield significant results if flows of new products and destinations are not surviving in sufficient numbers. Thus, such information can be very helpful in the search for robust and policy-related determinants of export growth, in particular related to this third margin: survival and sustainability. Firm-level data, even if harder to obtain (Gorg 2008), has produced more detailed findings that complement the macro-level results, based on aggregated data, by identifying ways in which export-promotion agencies can overcome market failures and leverage synergies between national exporters in order to achieve a brand name for the country (Molina 2009). For example, Volpe and Carballo (2008) study the _Exporting from a small landlocked economy_ _2_
They assume private returns to a year of schooling of 8%, schooling time lost to be 0.33 years, implying 2.67 percent lower future earnings. After considering the assumed mitigating effects of distance learning, the average annual loss per student is (2011 PPP) $458. Finally, Hanushek and Woessmann (2020: 8–9) use the OECD’s Survey of Adult Skills predominantly conducted in high-income countries and estimate that a loss of 0.25 school years predicts a 1.9 percent decrease of income over one’s career, while 1 lost year predicts a 7.7 percent decrease.

These simulations are certainly useful. However, they also have inherent weaknesses when assessing long-term impacts because the underlying parameters are not directly estimated from a school-disruption event. Instead, the parameters are the best guesses of what the overall disruption might look like, by necessity simplifying and omitting the complex system of response feedback loops initiated by the disruption. Among the key omissions are the mitigating bottom-up activities of parents and students in response to school closures, such as private tutoring and mentoring. Moreover, the simulations are by design assessing the global effects of the schooling disruption, setting the issue of more disaggregate effects aside.

This paper estimates the present value of lost income for a future Kuwaiti civil servant whose studies are disrupted by the COVID-19 pandemic. We do so by building on Bilo et al. (2021), who use a unique data set and the natural experiment of the Gulf War induced school disruption, to estimate the private costs of the pandemic. We argue that the Gulf War induced a school disruption that is similar to the schooling disruption caused by the COVID-19 pandemic and therefore, the methodology is well suited to simulating the long-term impacts of schooling disruptions in the context of the pandemic. Equally importantly, the Gulf War disruption happened almost 30 years ago, allowing us to capture the mentioned bottom-up feedback 3
children who enroll and the proportion who drop-out of school in the first 5 years."<sup>1</sup> The pattern is again consistent. Ghana is the only Western and Central African country which does not have a large gender gap, and Bangladesh is the only South Asian one which does not. Among countries outside of Western and Central Africa, North Africa, and South Asia, the same set of countries who performed poorly with respect to gender equality reappear. An exception is Malawi where there is a relatively large gap. Malawi has two surveys separated by four years and a comparison of enrollment rates of 12 to 14 year olds in school between the survey dates reveals that although there was a gender disadvantage in 1992, it had vanished by 1996. The gap in the percentage of 15 to 19 year olds who have completed grade 5 is therefore most likely a reflection of a gender disadvantage which existed some time ago.

Although we are focused here in female _disadvantages_ in education, it should not go unnoticed that in several countries there is a female _advantage._ Of the 41 countries analyzed (counting only the most recent survey in countries where there are two) 16 have a female advantage in the enrollment of 6 to I I year olds, 10 have a female advantage in the enrollment of 12 to 14 year olds, and I 1 have a female advantage in the completion of grade 5. The fact that the countries for which these data are drawn were not randomly selected makes it hard to draw strong conclusions, however it is indicative that a large disadvantage of girls in education may not be a worldwide problem, but is quite localized in certain regions or countries.<sup>12</sup> _Comparison with other data sources_ At this point it might be useful to digress and compare the findings based on these (generally) nationally representative household surveys to those reported in standard crosscountry tables. Table 4 reports the primary net enrollment rate for girls as derived from the DHS

> " In
# **Gender-differentiated impacts of tenure insecurity on agricultural performance in Malawi’s customary tenure systems**

# **1. Introduction**

In most African countries, land access continues to be governed by customary tenure systems. These have long been argued to provide high levels of tenure security and flexibility at a cost well below that of ‘modern’ arrangements (Bruce and Migot-Adholla 1994). Yet, studies also highlight that, with population pressure, these systems face increasing pressure: Unclear rules on how to deal with outsiders –whether migrants or foreign investors– can trigger disputes, undermining investment (Berry 2009; Fenske 2010) and productivity (Linkow 2016). Bias against women may further compromise tenure security and productive resource use (Fenske 2011; Goldstein and Udry 2006), with women more likely to be affected by disputes and to suffer productivity losses than men (Deininger and Castagnini 2006). Unclear institutional responsibilities may lead to proliferation and persistence of conflict (Eck 2014). To respond to this challenge, countries aim to either comprehensively formalize tenure or to carve out land deemed to be unutilized from customary arrangements sporadically. Yet, the limited sustainability of past efforts at systematic formalization is well documented (Atwood 1990; Pinckney and Kimuyu 1994) and although more recent efforts imply that lower-cost approaches may provide a viable option (Ali _et al._ 2014a), the verdict on their sustainability is still out (Ali _et al._ 2016a). Yet, the alternative of carving out land from the customary domain for ‘commercial’ development on a case by case basis raises equity and productivity issues (Sitko _et al._ 2014).

To contribute to this discussion, this paper assesses estates’ impact on productivity and neighbors’ tenure security decades after their establishment in Malawi, a country that underwent a burst of estate creation in the 1980s and is thus ideal for such an exploration. We use the 2006/07 National Census of Agriculture and Livestock (NACAL) that contains data on smallholders and
Measures of the monetary value of human capital based on the present value of future earnings of individuals, analogous to estimates of the value of physical capital as the present value of future returns, also exist. Naturally, these measures are conceptually closely related. Suppose for example that log wages of individual i at some future time t are given by a health‐augmented Mincer equation like lnw�� �φs� �γz� �g�t , where g� represents future trend growth in wages for the individual. Treating the unskilled wage as the numeraire, human capital measured as the present value of future wages is simply �� ~~,~~ where δ represents the discount rate, and h� is the measure of individual human ���� capital in Equation (1). Human capital measures along these lines have a long history (see for example Jorgenson and Fraumeni (1998)), and are extensively discussed in the context of satellite national accounts in UN (2016). Measures of human capital along these lines in a cross‐country setting have been developed since 2012 in the United Nations University “Inclusive Wealth Index” study (UNU (2012)), as well as in the latest edition of the World Bank’s “Changing Wealth of Nations” report (World Bank (2018)). The key incremental difficulty in constructing these measures relative to measures of h� is coming up with plausible measures of future earnings growth, g� . Because the difference between the growth rate and the discount rate is small and enters in the denominator of this measure, small changes in assumed growth rates are magnified into large changes in measured human capital.<sup>5</sup>

> 5 For example, if the discount rate is five percent, changing the assumed growth rate from three to four percent per year has the effect of doubling the measured human capital stock.

24
//econ.worldbank.org ._

> *Schipper is with the Vrije Universiteit Amsterdam and consultant to the World Bank. Hoogeveen is with the World Bank. Please send correspondence to both yschipper@feweb.vu.nl and jhoogeveen@worldbank.org. We are grateful to the Uganda Bureau of Statistics, Entebbe, for their help with the provision of the survey and census data. For useful comments and other forms of help we would like to thank Chris Elbers and seminar participants at the Vrije Universiteit.

1
, 2019; Morris, 2020; Evans et al., 2020; Brockmeyer et al., forthcoming), but to the best of our knowledge they have not been used for country case studies.

> 4Using available information on Daesh recruit age, education, occupation before joining, marital status, previous experience with jihad, and self-reported knowledge of Islam, Appendix Table A2 presents tests examining whether there are systematic differences in the characteristics of fighters who were included in the analysis and those who are not. For each variable grouping, the F-test of joint significance of the difference in means between our sample and the data universe fails to reject that these are equal to zero. Our analysis sample thus does not appear to suffer from selection bias. In addition, Brockmeyer et al. (forthcoming) show that the larger database exhibits high correlation with alternative sources of information on Daesh foreign recruits; they look at country aggregates and conclude that the data are representative at the country level. We conduct a similar exercise and compare the reported delegations of last residence for observations in our dataset with the ones obtained by Zelin (2018) on 121 Tunisian recruits who died in Syria and Iraq. The correlation between our data on recruitment per delegation and Zelin’s measure of deceased foreign fighters per delegation is 0.71. Our data thus appear to accurately represent the spatial distribution of Daesh recruitment in Tunisia.

6
|**Honduras**|4,000(est.)|550|61%|98%|10%|15%|6%|
|**Vietnam**|16,175|188|30%|41%|29%|24%|35%|
|**Ghana**<br>**(Tema)**|281|12|33%|590%|29%|56%|31%|
|**Ghana (single**<br>**units)**|1,019|44||||||
|**Kenya (EPZs)**|145|4||||||
|**Kenya (single**<br>**units)**|265|7|9%|25%|31%|57%|10%|
|**Lesotho**|425|211|64%|64%|1%|19%|‐7%|
|**Nigeria**<br>**(Calabar‐ est)**|100|1|4%|16%|N/A|N/A|N/A|
|**Senegal**<br>**(DIFZ)**<br>**Senegal**<br>**(single units)**|50<br>350|4<br>29|16%|42%|26%|144%|‐3%|
|**Tanzania**|59|1|3%|14%|N/A|N/A|N/A|

Source: UN COMTRADE via WITS and author’s calculations based on data from individual country EPZ authorities Given that many of the African zone programs were established only recently, could the low levels of exports simply be a function
# **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
|0.46|

_Sources_ : Own calculations using individual-level administrative data for Brazil. For more details on data sources and variable definitions, see Section 3.3 and Appendix 1.

_Notes_ : In this table, the unit of observation is a student. Statistics are weighted using WBSCPS sampling weights. For a given student, her peers are the other students who started her program in 2014. Means of PCA scores are different than zero due to the use of sampling weights. Number of observations is 29,453.

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participants from the same lotteries who were still in Tonga, and (iv) a group of nonapplicants in Tonga.

The initial sample frame for groups (i) and (ii) was a list of the names and addresses of the 278 (out of almost 3000 applicants) successful participants in the 2002/03 and 2003/04 migration lotteries.<sup>10</sup> Approximately 100 of these successful ballots had been approved for residence in New Zealand by the time of the survey, although some of those families had not yet moved to New Zealand. We managed to locate 65 of the families that had migrated, giving a sampling rate of over 70%. A variety of tracking methods were used to locate these families including contacting their family back in Tonga and using key informants in churches and other community groups. It was easier to draw a random sample of 55 of the successful ballots that had not yet migrated, because the DoL records included postal and home addresses and telephone numbers in Tonga. This non-migrant group includes those whose applications were rejected and those whose applications were still being processed. We use the actual number of accepted and rejected applications to weight our sample.

The initial sample frame for the unsuccessful ballots in the 2002/03 and 2003/04 lotteries (group (iii)) was a list of names and addresses provided by the DoL. The details for this group were less informative than those for the successful ballots. Only a postal address was supplied and there were no telephone numbers. Thus, it was not possible to determine whereabouts in Tonga those with unsuccessful ballots lived. Moreover, many

> 10 This was supplied under a contractual arrangement with the New Zealand Department of Labour, with strict procedures used to maintain the confidentiality of participants.

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interested in leveraging existing household survey data to shed light on the climate-migration nexus. These can serve as a starting point to evaluate the preliminary feasibility of a research study as well as to orient identification strategy and sort out issues related to variable definition and construction. This empirical checklist is reported in Box 1 and encompasses the key points we have touched upon throughout the paper. Note that the checklist refers to the case in which a researcher intends to use only existing household surveys as the migration data source.

# **Box 1: Empirical checklist for using household surveys to study climate migration**

# **1) Do you have repeated cross-sections or panel data?**

If you have repeated cross-sections, you can consider the adoption of a pseudo-panel approach (Deaton, 1985). If the data are representative at the national and subnational level, you can consider the aggregation of micro information at the spatial level to obtain panel data at that level of aggregation.

> 38 Similarly, not all the raw agricultural variables included in the construction of the score have necessarily to be strictly related to climate change impacts, as their purpose is to proxy the willingness and capacity of households to invest in and implement modern agricultural practices.

27
Republic, Gaza and the West Bank. UNRWA defines “Palestine refugees” as individuals and their patrilineal descendants whose normal place of residence was the British Mandate for Palestine during the period 1 June 1946 to 15 May 1948, and who lost their homes and livelihoods as a result of the 1948 conflict.<sup>23</sup> Palestinian refugees living outside UNRWA’s five areas of operation fall under the responsibility of UNHCR and are included in UNHCR statistics. There are other groups that are entitled to receive UNRWA services but are not identified as refugees in UNRWA’s registration system including those displaced by the 1967 and 1982 Arab-Israeli conflicts. Some Palestinian refugees may be also reflected in IDP statistics, for example it is estimated that of 560,000 Palestinian refugees registered in Syria, 280,000 were internally displaced as of December 2015 (UNRWA 2016).

The number of registered Palestinian refugees has grown steadily from 0.9 million in 1952 to over 5.2 million in 2015 (see Figure 2). This substantial growth in the numbers of Palestinian refugees is a consequence of: (a) the uniqueness of Palestinian refugee status which, unlike refugee status granted under the 1951 Convention, is maintained even in the event of naturalization in a country of asylum (e.g. the granting of citizenship to Palestinian refugees in Jordan) or resettlement in a third country (e.g. 200,000 Palestinian refugees are thought to have left Lebanon but are still reflected in registration data); (b) registration of descendants of male Palestinian refugees; (c) high fertility rates (Khawaja 2003); (d) a voluntary system of registration and deregistration linked to the provision of UNRWA services creating an incentive to register or maintain registration in areas of UNRWA operations; and (e) an imperfect process of verification and updating of registration data which may not immediately reflect all deaths.

> 21 While refugees in high-income countries are beyond the reach of development assistance provided by development actors, they may