rafmacalaba/data-use-mentions-extended huggingface dataset:
country PFM systems. If donors’ decisions to use country PFM systems are influenced by the PD-endorsed measure of PFM Quality, then we should observe a stronger relationship in a sample limited to recipients with publicly-available CPIA ratings than in a sample that includes a mixture of public and non-public ratings.<sup>10</sup> Results from equations 1.1 and 1.2 are strongly consistent with this prediction.

The explanatory power of these regressions (R<sup>2</sup> = .05 in 1.1 and .08 in 1.2) suggests that donors’ decisions are influenced by many other factors, potentially including other recipient characteristics. The OECD (2011: 50, 52) report and Ellmers (2011) both emphasize the large variation in use of country systems across recipients, for a given level of PFM Quality, that is reflected in these low R<sup>2</sup> values. Nevertheless, this variation should not obscure the reality of a large, positive and significant relationship between the two variables.

Donor-specific factors such as tolerance for risk undoubtedly contribute to the large variation in use of systems at a given level of quality (Knack, 2013), but they cannot account for the bulk of variation in the data. A full set of donor-year dummy variables (in the absence of any other regressors) statistically explains only 20% of the variation in use of country PFM systems, compared to 23% for a full set of recipient-year dummies. Recipient characteristics thus appear to matter somewhat more than donor characteristics.<sup>11</sup> Henceforth, we control for donor-year fixed effects and for several additional recipient-level variables, in testing the robustness of our initial results from equations 1.1 and 1.2. Estimates are therefore informed only by cross-

> 10 We cannot entirely rule out the possibility that some donor agency officials may have obtained the confidential ratings for non-IDA countries through unofficial channels.

> 11 In some instances, particularly where
access to healthcare. On the other hand, both groups still outrightly prefer intermediate policies on family reunification and labour market access. By contrast, contact does not seem to matter for policy preferences with respect to length of residency: both groups prefer timelimited options, with the prospects of renewal not making a detectable difference. We further conduct the analysis with an alternative version of the social contact index that was less restrictive (segmenting by those who had any index value greater than zero) and with the question of whether the respondent has shared a meal with a Venezuelan and the results remain qualitatively the same (Figure A8).

Here, it is worth comparing the results from the self-reported social contact index and measures that rely on migrant density calculations as proxies for contact (included in Figure <u>A9</u> in the Appendix). As described earlier, we hypothesised that more meaningful forms of contact involving personal interaction would have greater impacts on policy preferences. While the likelihood of contact has often been measured by the proportion of migrants in a local area, it is possible that experiencing groups of migrants at ‘arms-length’ in this way might actually foster greater feelings of threat—although empirically measuring this is beyond the scope of our study. Inspecting the CMMs corresponding to each subgroup labelled as having ‘high’ or ‘low’ contact on either measure reveals that, while the direction of preferences among policy options does not substantively vary, the differences _between_ subgroups do change in magnitude for some dimensions. For example, meaningful contact (compared to mere migrant density) appears to matter more for respondents’ preferences on family reunification, health access, and location restrictions. While more work needs to be done to empirically document the mechanisms at play, our results suggest that the _quality_ of contact likely matters for migration policy preferences.

22
many fold draws). As a result, these out of time results should be viewed as only a rough check of how much one should trust the within-time results.

Models based on the bureau data, which is lower dimensional, tend to be relatively stable over time. On the other hand, standard models using phone indicators see substantial deterioration (AUC declines from 0.71 to 0.63 for Random Forest and from 0.76 to 0.60 for logistic stepwise). Our modified phone indicator models that use only within week variation are much more stable (AUC increases from 0.62 to 0.64 for Random Forest and decreases from 0.63 to 0.59 for stepwise OLS FE).<sup>13</sup> All phone indicator models continue to outperform models using credit bureau data on this cut of the data (AUC 0.55-0.58). Our performance also lies within the range of the one comparable published benchmark of out of time performance of traditional credit scoring we could find in the literature, from a developed setting (AUC 0.57-0.76, Table S4). Those and our results suggest that bureau models can face at least slight deterioration when tested out of time, with the caveat that we have tested only one time period. The out of time performance of our methods is expected to improve to improve when trained on multiple cohorts (just as credit bureaus have evolved the data they collect by observing default patterns over many cohorts).

After the results from this pilot, the telecom implemented a scoring system using data and methods similar to what we suggested, suggesting they viewed it to be profitable.

# **5. DISCUSSION**

Mobile phone data appear to quantify nuanced aspects of behavior that are typically considered soft, making these behaviors ‘hard’ and legible to formal institutions (Berger & Udell, 2006). Further, these data are already being captured. The method can assist with the provision of financial products to the
# **References**

Abbafati, Cristiana, Kaja M. Abbas, Mohsen Abbasi-Kangevari, Foad Abd-Allah, Ahmed Abdelalim, Mohammad Abdollahi, Ibrahim Abdollahpour, et al. 2020. “Global Burden of 87 Risk Factors in 204 Countries and Territories, 1990–2019: A Systematic Analysis for the Global Burden of Disease Study 2019.” _The Lancet_ 396 (10258): 1223–49. https://doi.org/10.1016/S0140-6736(20)30752-2.

Abu-Ouf, Noran M., and Mohammed M. Jan. 2015. “The Impact of Maternal Iron Deficiency and Iron Deficiency Anemia on Child’s Health.” _Saudi Medical Journal_ 36 (2): 146. https://doi.org/10.15537/SMJ.2015.2.10289.

Andreas, Nicholas J., Beate Kampmann, and Kirsty Mehring Le-Doare. 2015. “Human Breast Milk: A Review on Its Composition and Bioactivity.” _Early Human Development_ 91 (11): 629–35. https://doi.org/10.1016/J.EARLHUMDEV.2015.08.013.

Angulo, Erick, Dalia Stern, Analí Castellanos-Gutiérrez, Adriana Monge, Martín Lajous, Sabri Bromage, Teresa T. Fung, et al. 2021. “Changes in the Global Diet Quality Score, Weight, and Waist Circumference in Mexican Women.” _The Journal of Nutrition_ 151 (12 Suppl 2): 152S-161S. https://doi.org/10.1093/JN/NXAB171.

Aquino, Estela M. L., Sandhi M. Barreto, Isabela M. Benseñor, Marilia S. Carvalho, Dóra Chor, Bruce B. Duncan, et al. 2012. “Brazilian Longitudinal Study of Adult Health (ELSA-Brasil): Objectives and Design.” _American Journal of Epidemiology_ 175 (4): 315–24. https://doi.org/10.1093/aje/kwr294.

Badillo-Suárez, Pilar Amellali, Maricela
s� �1 �s� in the case with no explicit measure of natural resource rents, and s� �1 �s� �s� when such a measure does exist. s� is, in an important sense, too large when data on resource rents are lacking.

If we assume that we have no direct measure of natural resource rents then the traditional approach to TFP growth measurement has been to calculate,

# g� �g� ��s�g� �s�g��

This formalizes the statement above – TFP growth is measured as the difference between the growth rate of GDP and a weighted average of the inputs to production. In this formula the weights are, quite naturally, the share of the factors in value added, s� and s�.

If we have measures of the natural resource rents and so can measure the share of rents in value added s� and the growth rate of resource inputs g�, then s� is correspondingly lower and we can write the formula for TFP growth measurement including natural resource inputs as, g� �g� ��s�g� �s�g� �s�g�� Now the weighted average of inputs to production includes the contribution to natural resources. These two formulae are the basis for measuring TFP growth with and without natural resource inputs in this study.

Recent work at the OECD has provided an approach to treating natural resources as production factors, which they have applied to selected countries, mainly OECD member countries, using World Bank data on minerals and energy (Brandt et al. 2017; See Annex I for the OECD approach to TFP). An important finding, derived below and by Brandt et al. (2017), is that there is a systematic relationship between TFP growth measured using only factor data on capital and labor, and TFP growth that also measures natural resources as a factor of production. If we denote TFP growth excluding natural resources as g���
tively similar prices to that of higher-income countries. If tariffs are summed to these qualityadjusted import prices, along with other relevant factors needed to make these assets ready for use in production, we conclude that several African nations tend to face relatively high prices on digital goods, especially in low-income countries where the tariff burden tends to be higher.<sup>8</sup> These results are aligned with evidence from item-level price data from the World Bank’s International Comparison Program on digital machinery, equipment, and software, suggesting that prices in Africa are higher than other parts of the world, when accounting for these other factors.

We then examined the likely effects of AfCFTA on digital goods imports. AfCFTA is a groundbreaking trade agreement encompassing 54 African Union member states. It seeks to foster economic integration by progressively eliminating tariffs on 90 percent of traded goods, promoting trade in services, and implementing rules of origin to ensure preferential treatment of products. By 2030, the AfCFTA has the potential to create a single market of over 1.3 billion people with a combined GDP exceeding $3 trillion.

To estimate the likely effects of AfCFTA on digital goods imports, we used the detailed tariff schedule of the agreement, and applied the model proposed by Hoekman, Ng, and Olarreaga (2002) for the total demand for imported goods broken down to the 6-digit level of the Harmonized System. The model simulates a “counterfactual” trade flow resulting from tariff liberalization and compares it with the base year. Despite the comprehensive liberalization expected from the AfCFTA, our analysis indicates that the agreement is unlikely to have a significant impact on reducing tariff protection on digital goods in Africa. The average importweighted tariff on these products is expected to decrease by only 0.13 percentage points due to the agreement, from 3.89 percent to 3.76 percent. This result is partly due to the absence of tariff reductions on extra-zone imports of digital goods, which constitute the majority of digital goods imports, and the limited intra-regional trade
_PoLtcy_ RESiK-k1Rc i WORkUNG PAPR I 1927

# Summary **findings**

Using a household budget survey for 1992, Hassanshows the poor revenue performance and distribuLional impact of BuLgaria's personal income tax system. He explores the implications for revenue and income distribution of two alternative tax systens - a flat. tax and a progressive but simpler three-brackets tax system.

He demonstrates that simpler tax structures with lower tax rates could achieve at least equal revenue and distributional objectives and are superior in terms of efficiency anid equity. (The findings are robust when Bulgaria's significant tax evasion is included.) But tax changes since 1992 have, if anything, moved Bulgaria even further from a simple income tax system: the number of rates and brackets increased from 7 to 10, and **the** levels of exemption remain unchanged. (Complex, higher rates complicate administration and enforcement and provide inicentives for tax evasion. And in the alternative systems Hassan explores, the poor are protected with higher exemnptions.) Fortunately, the country's personal income tax structure began to move toward less nominal progressivity after Bulgaria's 1997 tax reform program. The tax rate in the top income bracket was reduced from 52 percent to 40 percent, the number of tax brackets was halved, and the exemption level was increased 20 percent (reducing tax burdens on the poor).

- - This paper a product of the Poverty Reduction and Economic Management Sector, Europe and Central Asia Region is part of a larger effort in the region to analyze the social and revenue dimensions of tax reforms in transition economies. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Alison Panton, room Hl 1-033, telephone 202-458-5433, fax 202-477-0816, Internet address apanton@ecworldbank.org. The author may be contacted at fhassan@worldbank.org. June 1998. _(25_ pages)

_The Policy, Research Working Paper Series disseminates the findinigs of work in progress to encourage the exchange
Policy Research Working Paper 10999

# **Abstract**

This study examines the effects of climate and weather shocks on Afghanistan’s agricultural economy, with an emphasis on food security, prices, and wages. By utilizing a dynamical model and a unique data set that includes monthly global and local food prices, agricultural wages, unofficial exchange rates, and local climate data, the research provides econometric estimates of the impacts of droughts and floods. The findings reveal that both flooding and drought significantly increase food insecurity, directly and indirectly. Directly, these climatic shocks are linked to heightened risks of food insecurity in the following months, even when controlling for price and wage fluctuations. Indirectly, droughts and floods drive up food prices and depress agricultural wages, further exacerbating food insecurity. The study suggests that enhancing climate resilience in the agriculture sector could mitigate these risks, stabilize local food prices and wages, and strengthen food security and the broader agricultural economy. The results also show that price data effectively capture food security shocks from various non-economic sources, and can serve as a versatile monitoring tool in situations where detailed data on food security are unavailable.

This paper is a product of the Agriculture and Food Global Department and the Development Data 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 bandree@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
# **5. Results**

## **5.1 Characteristics of the migrant population**

## _5.1.1 Demographic characteristics and spatial distribution of migrants_

We begin our analysis by looking at the total number of migrants recorded during 2017 and 2018 in each Libyan province (Figure 4a). Almagerb, Alkufra, and Bengazi had the most migrants registered, and Al Jabalal Al Akhadar, Ghat, and Ubari had the fewest.<sup>9</sup> Figure 4b shows that the number of migrants has increased in almost all provinces from 2017 to 2018, with a huge increase in Almargeb. Finally, Figure 4c documents high heterogeneity in terms of number of migrants across Libyan provinces during the period under analysis. There was a slight increase in the number of migrants between rounds 5 and 16 (January 2017–February 2018). In round 17 (March-April 2018), the data indicate a massive increase in the number of migrants in almost all provinces. After that, the last three rounds of 2018 included in our data set show a reduction in the number of migrants registered at the FMPs, except in Almagerb, where the number of migrants remained very high.

DTM data also allow us to recover some information on the demographic composition of migrants. These are represented in figure 5 at the Libyan level and in figure 6 at the provincial level. The large majority (97%) of migrants are males. In 2017, females and minors were between 3% and 5% of the migrant population, respectively. The most diverse composition of the population is found in the Western regions in 2017 (Aljufra, Ghat, and Nalut). In 2018, the shares of women and minors were significantly higher.

## _5.1.2 Top origin countries and preferred destinations_

In each survey round, for each FMP, DTM data report the three most common migrant nationalities registered and the three most preferred destination countries for the migrants located at that FMP.

Table 2
Journal_ , 118(530):1025– 1054.

- Bleakley, H. (2010). Malaria Eradication in the Americas: A Retrospective Analysis of Childhood Exposure. _American Economic Journal: Applied Economics_ , 2(2):1–45.

- Boothe, K. and Walker, R. (1997). Mother Tongue Education in Ethiopia: From Policy to Implementation. _Language Problems and Language Planning_ , 21(1):1–19.

- Card, D. (2001). Estimating the Return to Schooling: Progress on Some Persistent Econometric Poblems. _Econometrica_ , 69(5):1127–1160.

- Central Statistical Authority (Ethiopia) and ICF (2016). Ethiopia Demographic and Health Survey 2016.

- Central Statistical Authority (Ethiopia) and ICF International (2011). Ethiopia Demographic and Health Survey 2011.

- Central Statistical Authority (Ethiopia) and ORC Macro (2005). Ethiopia Demographic and Health Survey 2005.

19
-19.9891|-0.8565|-11.9873|
|Schools<br>TOWN|5.1411|-1.5651|3.5728|-3.3756|-|-|
|VILLAGE|-7.3432|-1.496|-9.3596|-2.0706|-|-|
|PRIVATESCL|2.0065|0.0116|0.1877|1.3994|1.9492|3.1875|
|STU<br>~~F~~EES|11.4805|-29.6824|0.2682|-8.9641|-0.1311|3.278|
|RATCMP15|-1.56|-11.0681|7.5765|-21.4644|-3.1063|3.4899|
|SCHAUTON|-17.2045|8.6425|-21.8484|10.7935|-7.8405|20.4172|
|EXC1<br>~~B~~AND|0.4597|-7.9357|0.6927|3.9018|24.6725|6.7965|
|**TOTAL WEALTH**|4.3397|-104.6487|-8.0073|-129.5225|23.2931|74.1877|
||3%|-75%|-11%|-177%|29%|91%|
|**SUM TOTAL (ED WEALTH & WEALTH)**||**139.9046**<br>**100%**||**73.2792**<br>**100%**||**81.2017**<br>**100%**|

Notes: The ’Mean values’ for Dev7 and Vietnam are taken from the whole data set and represent the same values
41 Appendix table 2.1: School and health facility quality indicators

|Schoo|ls||Health f|acility||
|---|---|---|---|---|---|
|Characteristic|Mean<br>Weight|Mean<br>Ranking|Characteristic|Mean<br>Weight|Mean<br>Ranking|
|Wel qualified teachers<br>who teach children well|.252|3.18|Drugs always available<br>when you visit|.261|2.29|
|Excellent headmaster who<br>manages the school well|.188|3.41|Well qualified,<br>trustworthy doctors and<br>nurses|.228|3.12|
|Enough supplies so each<br>child has a desk and<br>workbooks|.245|2.46|Close to your homes, in<br>the village or ward|.165|2.91|
|Clean building with toilets<br>and playground|.163|2.75|Clean, with toilet, safe<br>water, covered waiting<br>area|.155|2.93|
|Emphasizes academics,<br>requiring no self reliance<br>work|.149|3.15|Public services:<br>sanitation, immunization,<br>control of pests|.189|2.98|

Source: HRDS survey.
periodic breaks. Table A-5 lists the resulting set of indicators and their sources.

We retain 14 distinct environmental indicators, covering different types of pollution, habitat preservation, and forest protection. Three come from the World Bank World Development Indicators: the ratio of CO2 emissions in kg to GDP (in PPP $), forest area (% of land area) and SO2 emissions. Eleven additional outcome measures are sourced from the EPI data. These include two measures related to Climate Change mitigation: black carbon intensity and greenhouse gas emissions (measured in terms of average annual rate of increase); three indicators related to Environmental Health (PM2.5 exposure, NOx Intensity, and Ozone Exposure, all measured using the number of age-standardized disability-adjusted life-years lost per 100,000 persons due to exposure to ground-level ozone pollution); and six measures of Ecosystem Vitality - a protected areas index (the proportion of biologically scaled environmental diversity included in a country’s terrestrial protected areas, with higher values reflecting better performance), the species habitat index (the proportion of suitable habitat within a country that remains intact for each species in that country relative to a baseline set in the year 2001), the sustainable nitrogen management index measuring the efficiency in application of nitrogen fertilizer with maximum crop yields (higher values indicating worse performance), a species protection index measuring the species-level ecological representativeness of each country’s protected area network, tree cover loss (five-year moving average of the percentage of forest lost relative to forest cover in 2000), and wetland loss (quantified using a five-year moving average of the percentage of gross losses in wetland areas relative to a 1992 baseline). For EPI indices, a higher value indicates (sometimes counter-intuitively) better performance with respect to the outcome of interest.<sup>8</sup> For labor protection we use two outcome variables: the _Mosley-Uno labor rights_ indicator, a measure of collective labor rights, and the _QOG worker rights_ indicator, a composite index measuring freedom of association in the workplace
of their urban area definition. Additionally, some countries take into account factors such as the availability of urban infrastructure and services, the structure of the local economy, and/or population density (Roberts et al., 2017). However, it is worth noting that many countries do not utilize explicit criteria for delineating cities (Dijkstra et al., 2021; Roberts et al., 2017). This lack of a consistent definition of urban areas can lead to statistical artifacts, ultimately impeding policy recommendations and potentially reducing their effectiveness.<sup>12</sup> In response to the challenges posed by inaccurate and inconsistent urban delineation, recent research has highlighted the significance of developing new methodologies to accurately and consistently define urban areas across countries.<sup>13</sup> This is made possible by the increasing availability of high-resolution global gridded population datasets and satellite imagery. According to Combes et al. (2023), two leading methodologies have emerged for achieving consistent urban delineation. The first is the Degree of Urbanization (DOU) method, initially introduced by Dijkstra and Poelman (2014) and later extended globally by Dijkstra et al. (2021). The DOU method defines cities based on two primary absolute thresholds: population size and population density. This method is straightforward to implement and has been widely applied by a coalition of international organizations led by the European Commission. In fact, it was endorsed by the United Nations Statistical Commission in March 2020 as a recommended method for international comparisons of urban areas (Dijkstra et al., 2021). In contrast, the second approach, known as

> 11Albania, Brazil, Kazakhstan, Kenya, Mexico, Morocco, Thailand, and Sri Lanka.

> 12As an example, Latin American countries have significantly lower GDP per capita than expected based on their urbanization levels and are also “over-urbanized” relative to the size of their agriculture sectors. Roberts et al. (2017) demonstrate that these patterns are an illusion resulting from systematic biases in the