rafmacalaba/data-use-mentions-extended huggingface dataset:
the Northeastern Uplands appear most exposed to cyclones, with hurricane force winds occurring once every five years. Visual comparison of Figures 4 and 8 suggests that heavy rainfall was caused by the cyclones in some cases, though not in all. There appears less correlation between coastal and riverine flooding and cyclones. Not only do the localized and riverine flood variables not capture the wind damage associated with cyclones, they also overlap only partially with the cyclone paths, underscoring the need to use an additional measure to reflect damages associated with cyclones.

# **3 Identifying Welfare Effects from Natural Disasters**

The effects of natural disasters and other shocks on household welfare are commonly assessed by augmenting a standard reduced form consumption regression with explicit measures of the disasters themselves: ln ���� ������ �������� ������������������ (1) where ���� is a measure of consumption for a household _i_ in cluster _c_ at time t, _X_ represent a series of household, cluster and regional characteristics that determine the level of consumption, _ND_ are (stochastic) measures of (covariate) natural disaster k in cluster c at time t, ε is a residual term.

> 21 The storm track dataset (UNEP/GRID‐Europe 2007) had some missing wind speeds.  These were filled in using values obtained by regressing known winds speeds on pressure, pressure squared, and dummy variables for hurricane monitoring centres.  The R‐squared was above 0.91, giving confidence in the predictive power of the data and the regression.
relative performance.

Figure 5 reveals stepwise selection to be the most effective approach in this setting, followed by the group lasso and then the variable importance measures. Whereas the performance penalty of the group lasso is moderate, the variable importance measures underperform significantly and cannot be recommended for this use case. Whereas there is a steep rise in the required number of questions to achieve the ultimate, small reductions in EER (top left), the sequencing of variables by predictive power results in a strong accuracy impact for initial questions (bottom right). The second key result is that fewer than 5 questions raise the EER strongly, and beyond 15 questions the accuracy gain becomes imperceptible for the best-performing method of stepwise selection. An intermediate range provides a moderate

> 10The full questionnaire would be selected when a gradient boosting model is used in the PMT algorithm instead of OLS on nation-wide data.

11 We use the Group Lasso python library and the LightGBM framework [Ke et al., 2017] to implement gradient boosting.

20
the average spatial resolution (ASR) of the input regional data, which is the square root of the land area divided by the number of administrative units. We find that on average the ASR decreases from high to low income groups.

# **3.2.2 Components**

Another source of uncertainty is indirect temporal inaccuracy propagated from the input datasets of the components, which are modeled. We discuss all five components of agricultural GDP: crop, livestock, forest, fish and hunting. The SPAM model (You, Wood, et al., 2014) is a result of several gridded modeled datasets including rural population density from Global Rural-Urban Mapping Project (GRUMP) Alpha version (Balk et al., 2006). Likewise, the Gridded Livestock of the World v2.0 includes rural population density in 2006 (GRUMP) along with other predictors such as precipitation (Hijmans et al., 2005) and a

> 17For a discussion on climate yield factors see Block et al. (2008).

> 18This also includes cases where administrative units at the same level are merged to match the geography of the regional accounts data.

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reforms. The data on tariffs and the share of products which have approval for foreign investment (FDI) are also from Topalova and Khandelwal (2010). Additionally, we control for the logarithm of real net state product (source: Reserve Bank of India) to account for income differences across states that might affect firm

> 5 We report the Sargan-Hansen tests of overidentification to verify the validity of our instruments.

> 6  The 2008 National Industrial Classification is based on the International Standard Industrial Classification (ISIC) Rev.4.

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# **Estimating the gains from migration**

Several approaches can be used to measure the change in remuneration associated with migration. With cross section data, it is possible to build counterfactuals and compare the earnings of migrants with the earnings they would have had, had they not moved. This can be done by computing a wage when staying and when moving, using equations (3.3’) and (3.3’’) and correcting for selection bias. The difference between the two potential wages can be said to account for the effect of migration on wages.

Observe that with panel data, it is also possible to directly estimate whether migrants gain from migration by taking advantage of the panel dimension. One approach is simply to regress the change in earnings between two dates as a function of personal characteristics (or changes in personal characteristics) and migrant status:<sup>24</sup> As mentioned in the previous section, studies usually measure a gain associated with migration but it does not have to be the case given the possibility of an income differential paradox which has workers migrate in spite of a lower wage in the area of destination (Vishwanath, 1991, Katz and Stark, 1986a). One important reason can be that workers incur a short term loss in their income but a long term gain. This justifies studies that focus on the assimilation of migrants in a dynamic perspective when longitudinal data are available.

**Estimating the extent and speed of labor-market assimilation in the place of destination**

> 24 The advantage of differencing a cross-section model (variables in the RHS of (3.5) are then expressed as differences) is that it can wipe out fixed effects potentially correlated with the error term in the crosssection regression of wages, thus removing a potential endogeneity problem of nonlongitudinal data, and justifying differenced estimations without having to control for selection. However, an endogenity problem may remain even after differencing if there is some selection on individual residuals.

33
|Gurugram|1 (mid-level official at the property tax<br>department of Municipal Corporation of<br>Gurugram)|1 (junior official in the sub-registrar<br>office of Gurugram)|
|---|---|---|
|Dehradun|1 (senior official working in the revenue<br>department of Nagar Nigam Dehradun)|1 (sub-registrar in Dehradun)|
|Pune|1 (junior official working in a technical<br>role in Pune Municipal Corporation)|1 (senior official working in the district<br>registrar’s office in Pune)|

# 5. Main thematic findings

## 5.1 Mixed approaches to record-keeping in sample cities regarding female property ownership

One of the important findings is that in most of the cities researched, with three exceptions, officials noted that they do not explicitly keep (digital) gender-based records or analyze their data in this way, even if gender was noted during registration. Quantitative data was received only for Bengaluru Municipal Corporation, South Delhi Municipal Corporation (SDMC), and Ludhiana Municipal Corporation, leading to a mixed view of the situation on the ground.

Bengaluru, as shown in Figure 1, has gender-disaggregated new home ownership registration data each year for the past four years. Bengaluru does not offer gender-based discounts on property-related taxes, and, as can be seen, no shift in the trend of female ownership has occurred over the last few years, with approximately 70 percent of new homeowners each year being male and 30 percent being female.

Figure 1: Gender-disaggregated home ownership registrations in Bengaluru over the past four years (numbers of new registrations each year) 19
available, the characteristics of the dwellings inhabited by homeowners and by tenants might nonetheless differ considerably. Attention should therefore be paid to the selection among comparison groups to avoid comparing the incomparable. This matters both in the case of national accounts and country-level CPIs where obtaining correct estimates of average housing values serves the purpose and in the case of the analysis of household living standards wherein the construction of income or consumption aggregates requires the imputation of values for each household.

Despite the existing empirical evidence on the inaccuracy of homeowner self-assessed rental values described in section 2, there is no clear methodological recommendation on how to test accuracy. In developing countries in particular, analyses are hampered by the lack of up-to-date, complete administrative data and alternative surveys on the market for dwellings. Practitioners must therefore rely on the data collected in household budget surveys to test the accuracy of homeowner self-assessed rents.

The study illustrated in this paper proposes the use of matching estimators to assess the accuracy of homeowner self-assessed rents based on information typically available in household budget surveys. Matching estimators allow the identification of groups of tenants who live in dwellings that are as similar as possible in observable characteristics to the dwellings inhabited by homeowners. Thus, the comparison between homeowner selfassessed rents and market rents paid by tenants occurs among dwellings that share similar observable characteristics.

The rest of the paper is organized as follows. Section 2 provides a short review of studies that test the accuracy of homeowner self-assessed rents. Section 3 describes recent trends in the housing market in Peru. Section 4 presents the methodology used to ascertain the accuracy of homeowner self-assessed rents. Section 5 describes the microdata used in the analysis. Section 6 presents descriptive statistics and discusses the results. Section 7 offers final remarks.

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<mark>industries include agriculture, fishing, manufacturing, mining, construction, and services. These variables are all based on respondent reporting. Moreover, anthropometric data were collected by measuring children’s height and weight during survey fieldwork. Using this information, a series of variables were computed for children that range from 0 to 5 years of age: length/height-for-age, weight-for-height, BMIfor-age and weight-for-age Z-scores. These estimates are calculated using the 2006 WHO child growth standards, and an indicator for wasting was created for children who have a weight for height values under -2 standard deviations from the WHO child growth standards (</mark> Onis et al., 2006).

# Coding steps

The construction of HP datasets was done in Stata. It consists of a series of structured Stata scripts (“dofiles”), made available in a dedicated public GitHub repository (Bentze, 2024). A consistent processing protocol was executed in each wave. As a first step, plot-crop, plot, individual, and household frames were created, using the crop harvest modules (for the plot-crop and plot frames), the cover information of the household questionnaire (for the household frame), and the household roster (for the individual frame). Following a modularized approach, each variable in the HP was then cleaned and processed separately. This approach guarantees a stable sample size and facilitates reuse by data users, who can download the code and include new variables as they see fit (see “Usage Notes” below). The coding operations described above are undertaken separately for each country-wave pair, in do files which are given the following naming structure: country ISO code, survey name, and wave number. For example, the do file for the first wave of the Ethiopia ESS is named _ETH_ESS1.do_ .For each of the plot-crop, plot, household, and individuallevel datasets, survey waves were then appended (i.e. combined) for each country, and panel unit IDs were created. During
# Panel B: 2011-2015

## Female workers

## Male workers

<!-- Start of picture text -->
-0.5 -0.5<br>0.6 0.6<br>-0.6 -0.6<br>0.4 0.4<br>-0.7 -0.7<br>0.2 0.2<br>0.0 -0.8 0.0 -0.8<br>2011 2012 2013 2014 2015 2011 2012 2013 2014 2015<br>R manual R cognitive R manual R cognitive<br>NR manual NR analytical NR manual NR analytical<br>NR interpersonal NR interpersonal<br>High-educated workers  Low-educated workers<br>-0.1 -0.1<br>0.6 0.6<br>-0.3 -0.3<br>0.4 0.4<br>-0.5 -0.5<br>0.2 0.2<br>-0.7 -0.7<br>0.0 -0.9 0.0 -0.9<br>2011 2012 2013 2014 2015 2011 2012 2013 2014 2015<br>R manual R cognitive R manual R cognitive<br>NR manual NR analytical NR manual NR analytical<br>NR interpersonal NR interpersonal<br>NR skills  NR skills<br>R skills and NR manual skills R skills and NR manual skills<br>NR skills  NR skills<br>R skills and NR manual skills R skills and NR manual skills<br><!-- End of picture text -->

Source: Authors’ calculations based on Philippines STEP survey 2015-2016 and Sakernas 2005-2010 and 20112015.

Notes: High-educated workers are workers having at least complete secondary education. Low-educated workers include workers with incomplete secondary education or less.

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widely. The maximum lump sum subsidy, available<sup>for the very poor, is R16,000. As, in most cases, this</sup> is insufficient to cover the costs of a serviced site<sup>and a 40 square meter top structure, households have to</sup> augment the subsidy with a loan (or from<sup>own savings) or embark on an incremental housing process</sup> where only a rudimentary shelter can be provided<sup>at outset. Between April 1994 and September 1999, the</sup> Department of Housing facilitated the construction<sup>of some 920,000 housing units, thus spending some</sup> 70% of the allocated budget. This notwithstanding,<sup>the services provided have, in many cases, not</sup> corresponded to beneficiary demand and failed<sup>to crowd in the private sector or transform beneficiaries</sup> into worthy subjects for the credit market (Tomlinson<sup>1999).</sup>

# 4 **Evidence on land reform implementation**

In this section we use the survey evidence to examine<sup>whether land reform was targeted to the poor; to</sup> what degree the data can provide evidence on a<sup>potential conflict between attaining equity and efficiency</sup> goals; and whether characteristics of successful<sup>projects might be used to inform the overall design of the</sup> land reform program. We start out with descriptive<sup>evidence and then move to econometric analysis.</sup> Main results are that (i) the program was well<sup>generally targeted; (ii) establishment of economically</sup> viable projects did, according to the available evidence,<sup>not conflict with the objective of reaching the</sup> poor; and (iii) successful projects were characterized<sup>by beneficiaries making an
absorb many more studen-s than originally planned for. Kuwait University has its campus in five Iccations and its present buildings were intended to be used for five years following the establishment of the University in 1966. The same buildings are still being used after twenty years. (Safi, 1986) Laboratory facilities and equipment generally leave much to be desired in the heavilypopulated poor universities while expensive equipment adorns small research laboratories adjacent to faculty offices in other universities. In the Student Survey Evaluation involving Kuwait University students conducted by Safi (1986), laboratory facilities were described as somewhat adequate but mishandled and in need of repair. Reference is made to the improper utilization and/or handling of laboratory apparatus. There is evidence of improper maintenance, and resources are ordered through bureaucratic procedures with delays of up to a year common before delivery. Szyliowicz (1973, pp. 290-291), _lS_
work permit is obtained in advance for which a recommendation from a prospective employer must be accompanied by a letter from the RAS confirming refugee status leading to in practice permits being rarely issued (UNHCR & World Bank, 2020). This results in many refugees taking lowpaying jobs in the informal sector that are more prone to short-term contracts and layoffs without much protection during times of crises (Betts, Sterck, & Omata, 2018). As many refugees flee their country without much preparation, they frequently lack official documents such as birth certificates, IDs or schooling certificates, which are necessary to access many Kenyan services or signal one’s skill level to a prospective employer. Refugee entrepreneurs are allowed to open their own business inside the camps by applying for a business license to the local county government. However, the license needs to be renewed annually with charges depending on business size, effectively making it a ‘tax’ on economic activity (Betts, Sterck, & Omata, 2018).

Given the regulatory challenges, only a fraction of refugees in camps were employed before the pandemic. Results from socioeconomic surveys show that in 2019 and in 2018 only around 20 percent of refugees in Kakuma Camp and 39 percent of those in Kalobeyei Settlement were employed in any sort of economic activity. In contrast, more than 60 percent of Kenyans at the national level and in the county-level host community were employed over the same timeframe. Wage work is the most important type of employment for refugees in Kakuma with nearly 50 percent of the 18-64 year old population employed for wages, usually in the

> 6 While the refugee camps are jointly administered by the Refugees Affairs Secretariat (RAS) of the Government of Kenya and UNCHR, shelter, food and cash assistance, health care, and education services in Kakuma and Dadaab are primarily provided by the international community (UNHCR & World Bank, 2020).

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but after a while he brought them all out of education because of his financial circumstances.” A female working youth from Khartoum said, “Maybe because she sees her family suffering in paying the education expenses, so she feels it is better to stop education in order to help her family.” A male youth from Madani who had a job said, “I know a person who was the only man in the house and he had like five sisters, so he left school for financial reasons.” Another male youth from Madani who did not work said, “...I drop out my study because of substantial reasons because of my situation, I entered the university but I dropped [out] because the expenses to register were 1500. And this amount wasn’t available, I have elder sisters who go to the college and there are younger sisters who go to the school, so I drop out my study and I help my father so the rest of my sisters can continue their study.” The FGDs also revealed it was common for a combination of financial constraints and patriarchal customs to affect daughters’ ability to study as well as work outside the home after they were married. As mentioned in preceding sections of this report, poor families with large numbers of unmarried daughters will often choose to discontinue their daughters’ educations so that they can get married, since marriage transfers the economic responsibility for the daughters to their husbands’ families while also generating some wealth for the daughters’ families in the form of ‘bride price’ and other gifts from the husband (Papps et al. 1983). A female youth from Madani who was not working said, “Girls stop their education depending on their families’ decisions, but it is different for boys. If boys want to continue their education, no one will influence them.” This comment was followed by a direct comment from another female youth from Madani who was not working either: “For girls, it is their family’s
17 customer locations _(maximum information case)._ The telephone companies could provide such a database, in which customer addresses could be geocoded using a standard GIS package, like ArcView&. Each location could then be mapped using a simple custom computer program to the nearest wire center location.

Should this kind of information not be available, the regulator could resort to Census data at any level of (dis)aggregation (as discussed above). The question remains whether this would be a good substitute for the precise individual customer locations. To show that it could be so (at least in populated areas), we considered two wire centers (A and B) of Argentina's second largest city and compared the results using the geocoded locations of the customers'<sup>6</sup> served by those wire centers (geo-A and geo-B) with the results when Census data _(manzana_ level) corresponding to the area served by them is used (census-A and census-B). The estimates are shown in Table 2.

**Table 2**

||**Total lines**|<br>**Total**<br>**Investment**<br>**(in dollars)**|**Average**<br>**Investment**<br>**(in dollars)**|
|---|---|---|---|
|Census-A|1,716|1,257,492|733|
|Geo-A||1,216,582|709|
|Census-B|5,169|3,113,898|602|
|Geo-B||2,964,068|573|

The table shows that there are no large differences between both data sources, so in principle, Census data could be used whenever geocoded customer locations are not available. Moreover, if the regulator wishes to measure the costs of serving a currently non-served
of observations|28341|2139|10959|11124|4119|
|Standard errors in parentheses * p < 0.05,|** p < 0.01, **|* p < 0.001||||

> 5 While Defense Meteorological Satellite Program/Operational Linescan System (DMSP-OLS) was originally designed for military use, DMSP-OLS has been widely used in social science because of its low-light imaging capabilities to detect nighttime artificial lighting (Elvidge et al., 1997). Henderson et al. (2012) empirically prove the concept of using an annual composite of DMSP-OLS as a proxy measurement of economic growth across the national and/or local level. Many studies extensively use nighttime lights to estimate the national and sub-national GDP. These estimates are critically useful for developing countries and/or conflict-affected areas where official statistics are unavailable or limited.

> 6 Other possible specifications were considered but not reported in this note. A dynamic panel specification was considered but 3 years of data was not sufficient to report strong results for a dynamic panel.

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Achieving economic transformation remains a high priority on the national economic agenda of African countries, with structural change and productivity convergence being pivotal components. This paper examined how imported capital and intermediate goods from the Global South and Global North drive economic transformation in Africa. The analysis relies on a panel dataset comprising 44 African countries from 2000 to 2022. Overall, we find that imported capital and intermediate goods are crucial to economic transformation of African countries. However, the contribution of imported capital and intermediates from the Global South or Global North varies with the specific channel of economic transformation. Imported capital and intermediates from the Global South are more influential in achieving structural change. Conversely, imported capital and intermediates from the Global North play a dominant role in achieving productivity convergence. These findings highlight the importance of leveraging both regional and global trade to achieve comprehensive economic transformation in Africa.

Our findings have significant policy implications. Firstly, African countries should focus on building absorptive capacities to maximize the benefits of advanced technologies and high-quality inputs from the Global North. This involves investing in education, infrastructure, and institutional frameworks that enhance the ability to assimilate and utilize new technologies. Secondly, policies should also promote South-South trade by improving regional integration and reducing trade barriers within the Global South. This can provide access to more appropriate technologies and inputs that are cost-effective and well-suited to local conditions. Finally, a nuanced trade policy that strategically leverages both North-South and South-South trade can help African countries achieve the dual goals of structural change and productivity convergence, leading to a more inclusive and sustainable economic transformation.

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