rafmacalaba/data-use-mentions-extended huggingface dataset:
16.0%|9.5%|16.3%|

_Source_ : World Bank staff calculations, based on WDI and CBS data The analysis further reveals the substantial sensitivity of results to the choice of the NA aggregate growth to be used in the projections, with poverty projections based on the growth rate of constant per capita GDP being sizably different compared to those in which growth is estimated using per capita GDP in current prices (LCU) deflated using CPI. In particular, the while projections based on growth in constant pc GDP show a monotonic increase in poverty over the period 2011-2020, projections based on current pc GDP deflated using CPI show a marked increase in poverty between 2011-2016 – which corresponds to the most intense phase of the Syrian conflict – a decline in poverty between 2016 and 2019 – which correspond to a subdued phase of the conflict and pick up again post 2019, reflecting the compounding impact of the Lebanon financial crisis. _Based on considerations related to the reliability of constant GDP estimates_<sup>_33_</sup> _and a better match with observational accounts regarding the evolution of welfare in Syria based on non-monetary indicators, poverty projections based on current pc GDP growth deflated using CPI are preferred._ Lastly, Figure 13 and Figure 14 show poverty projection results using preferred poverty baseline (HIES 2009) and growth estimates (per capita GDP in current prices, deflated with CPI) at different values of the pass through: unitary, “conflict-adjusted” (which varies over the conflict years from 0.7 to 0.48 - see Box 2), as well as the pass through observed between 2007 and 2009 (0.29), a period characterized by drought and regressive welfare dynamics.<sup>34</sup> As expected, employing a lower pass-through rate leads to considerably lower poverty estimates possibly reflecting household capacity to smooth consumption in face of a shock. However, the possibility for households to continue smoothing their consumption over a prolonged conflict spell is dubious. Given the amount
that integrate back‐office services and manufacturing services seem to be most productive. They also tend to be larger.<sup>13</sup> Similar results are obtained when using four alternative TFP measures (Figure A2 in Annex 1).

**Figure 3.7: Distribution of firm‐level TFP by service category (2014)**

<!-- Start of picture text --> TFP ACF<br>5 6 7 8 9 10<br>Estab Pre-man Manu Post-man<br>Post-sales Back-office Goods only<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> Source: EIS data from Turkey; author’s calculations. The vertical axis of this shows the frequency of firms that have a certain level of TFP. The level of TFP is shown on the horizontal axis.

**A higher share of post‐manufacturing services produced in‐house is correlated with lower levels of firm productivity.** Figure 3.8 shows the negative relationship between firm‐level TFP and post‐manufacturing

13 Note that manufacturing firms engaged in pre‐manufacturing services and post‐sales services are the largest in Turkey. All servicified firms are much bigger compared to the non‐servicified goods‐only firms. (The benchmark regression analysis controls for firm size.) 20
In line with the findings and recommendations from Corral et al. (2022), gradient boosting methods appear to outperform the traditional small area methods recommended for off-census years. The unit-context and Fay-Herriot models are direct competitors to the machine-learning models, which are often motivated by a lack of up-to-date census data. Also aligned to Corral et al. (2022), area-level models outperform unit-context models in terms of bias and MSE.

Now that we have established that in the context of the Mexican data used and with covariates derived from census data, gradient boosting methods can perform just as well as CensusEB methods our attention is turned to gradient boosting’s performance under geo-referenced data – Gradient Boosting (GIS-MUN).<sup>31</sup> The gradient boosting model with GIS-based covariates is our closest approximation to the standard implementation of machine learning in the context of poverty mapping, the method is contrasted to an application of an area-level model with similar data – Area-level (GIS-MUN).<sup>32</sup> The median MSE for the gradient boosting model with GIS-based covariates is 0.008 (with a root MSE of about 0.09), meaning that the median municipality’s error is roughly nine percentage points. The area-level model using geo-referenced covariates achieves a median MSE of 0.01.

The bias associated with poverty estimates is especially important due to the policy relevance of understanding absolute living standards. In panel (b) of Figure 2, we thus present box plots for the municipality-level bias estimates associated with each model (see Eq. [26] for the bias-variance decomposition of the MSE). The median bias for all models is approximately zero, with the possible exception of the unit-context model and the area-level model estimated at the PSU level.<sup>33</sup> Perhaps the more interesting result is that the bias estimates from the gradient boosting model with georeferenced covariates exhibits considerable variation, with minimum and maximum biases reaching -0.43 and 0.43, respectively.
One important community characteristic that has been shown to affect individual contributions to public goods (Habyarimana et al. 2007), and participation in community activities (Alesina and La Ferrara, 2000) is ethnic fragmentation. Albania is ethnically homogenous; however, as discussed in Section 2.2, there seems to be significant cultural and linguistic differences between the north and the south of the country. The more isolated north, which used to rely on a patriarchal tribal system oriented around the institutions of clan and blood feuds, is characterized by a lower percentages of cooperating parents as compared to the south (73% vs. 76%) although the difference is not statistically significant (p = 0.22). Comparing cooperation in the four main geographical areas of Tirana, the Coast, the Centre and the Mountain shows that parental cooperation in the game is significantly lower in the coastal area. One reason might be the recent flow of migrants from the Mountain to the Coastal area, and the consequently growing linguistic and cultural fragmentation in the region. Finally, while Albania is a prevalently Muslim country, a significant number of followers of other religions (Orthodox and Catholic Christianity) exist.<sup>29</sup> Based on the LSMS data, we constructed a district-level proxy for religious heterogeneity, i.e., a dummy equal to 1 if in the district there is no religion whose followers constitute more than 90 percent of the population, and 0 otherwise. About 60 percent of the districts are religiously heterogeneous, and the percentage of cooperating parents in these districts is 73 percent, against 78 percent in the homogenous districts; the difference is significant at the 5 percent level.

In Table 4, we conduct probit regressions on individuals‟ decisions to invest in the group account in the game. We report marginal effects for continuous explanatory variables and the effect of a change from 0 to 1 for dichotomous explanatory variables. In column 1 we control for demographics, and in column 2 we add individual measures of social
***|
||(0.0129)|(0.0129)|(0.0128)|(0.0126)|(0.0128)|
|Observations|16,880|16,880|16,880|16,880|16,880|
|R-squared|0.772|0.772|0.775|0.764|0.759|
|Treatment Obs|9684|9684|9684|9684|9684|
|Control Obs|7196|7196|7196|7196|7196|

Notes: Robust standard errors in parentheses;<sup>***</sup> p<0.01,<sup>**</sup> p<0.05,<sup>*</sup> p<0.1; avg_radBuff05 ranges from 1<sup>st</sup> to 99<sup>th</sup> percentile (0.02 to 2.7 nanoWatts/sr/cm<sup>2</sup> )

#

#

According to EM-DAT & CRED / UCLouvain (2023), Nigeria suffered flooding during the period of July 1, 2022 to October 31, 2022. The first row of Figure 9 presents the flooded pixel during each month and over the whole period, The second row shows flooded pixel during each month but not flooded during the other months. During the first couple of months of the flooding season, most flooded pixels are located in the south. During the second part of the period, the floods mainly occur along the Benue and Niger Rivers in the center part of the country. Given the length of the flooding period and the potential for adaptation and population displacement after the first flooding events, we focus on the ‘unexpected’ first floods. Therefore, we select the pixels that were flooded in July but not in August, September or October.

18
|86.1|95.1|97.0|100.0|
|1 child|100|109.3|108.2|110.7|96.9|101.9|100.9|101.8|86.8|92.4|93.9|94.8|84.6|93.4|95.3|97.1|
|2 children<br>3 and more|100|105.9|108.6|109.8|99.1|104.4|105.6|105.4|87.3|95.2|98.5|94.3|87.8|98.2|99.3|104.3|
|children|100|110.8|118.6|113.3|89.0|100.4|95.2|93.2|91.9|99.8|96.9|94.5|83.7|89.2|95.1|100.0|
|singleparent|100|99.9|98.0|102.4|93.4|97.4|93.8|100.9|83.9|92.3|84.6|86.4|83.7|93.1|95.2|98.1|

Source: Authors’ estimates based on HSS data.

39
# Table 16 – Migration Rate by Economic Activity

|Economic Activity|Non-Migrants|Migrants|Total|
|---|---|---|---|
|Agriculture, Forestry|97.3|2.7|100|
|Mining and Quarrying|84.5|15.5|100|
|Manufacturing|89.7|10.3|100|
|Electricity, Gas, Steel|78.0|22.0|100|
|Water supply, Sewage|85.7|14.3|100|
|Construction|92.9|7.1|100|
|Wholesale and Retail|89.6|10.4|100|
|Transportation and Storage|89.5|10.5|100|
|Hotels, Accommodation, Food and restaurants|90.2|9.9|100|
|Information, Telecommunications|84.3|15.7|100|
|Financial, Insurance|87.4|12.6|100|
|Real Estate|92.3|7.7|100|
|Professional, Scientific|89.1|10.9|100|
|Administrative and Support Services|84.2|15.8|100|
|Public Administration|88.4|11.7|100|
|Education|90.4|9.6|100|
|Health and Social Work|91.1|8.9|100|
|Arts, Entertainment|87.8|12.2|100|
|Total|91.8|8.2|100|

Source: Authors’ calculations using Labor Force Survey Figure 5 shows the relationship between demeaned net migration flows (to work) and demeaned agricultural employment for each governorate. Governorates with high migration rates have a lower share of agricultural employment.

15
**Information Treatments.** The Doctor and Patient Voucher treatments were cross-cut with a third within-clinic treatment. This “Patient Information” ( _PI_ ) was conveyed through a short video, which emphasized that all suspected malaria cases should be confirmed with a malaria test, noted that RDTs should be available for free at the clinic, and described recommended treatment for simple and severe malaria. The video also demonstrated an RDT test and how to recognize a positive test result. Finally, half the clinics were randomly selected to receive a more intensive training designed to increase providers’ trust in the diagnostic accuracy of RDTs. Both information treatments are analyzed in detail in a separate paper. We find limited evidence that the information treatments impacted the size of _PV_ and _DV_ treatment effects (see Appendix Tables B3 and B4 ).

The within-clinic randomization and associated data collection were conducted after the health worker trainings in November and December 2016, covering the end of the rainy season and therefore the period of highest malaria risk. We divided the 60 clinics into three cohorts of 20 based on geography. Each of the three cohorts rotated through two weeks of data collection and experimental intervention. Within each cohort, we randomly assigned each clinic to one of 20 intervention schedules depicted in Figure 2. Each clinic received two Control days, two Doctor Voucher days, and two Patient Voucher days. Although all clinics were informed of the upcoming study activities and interventions in advance, clinic staff did not receive prior notice of the actual intervention schedule – rather, our field officers informed clinic staff of the day’s intervention on the morning of an observation day.

# **3.2 Data Collection**

In order to identify the source of over-demand for malaria treatment, we require detailed data on prescription and treatment outcomes from patients at the clinic, as well as patients’ true malaria status. Our main analysis uses data from three sources.

**In-Clinic Survey.** On clinic observation days, enumerators approached all acutely ill patients at clinic intake
current account. As a result, higher consumption as well as an increase in the number of domestic producers will be observed in economies that lags in deregulation. Second, a decrease in monopoly power leads to less volatile business creation, reduced countercyclicality, and weaker substitutions in labor supply to alleviate the adverse effects to productivity shocks. Third, financial deregulation contributes to a moderation of firm-level and aggregate output volatility; financial ties between two countries therefore reduce volatility in the foreign economy. The study claims that its findings are consistent with findings drawn from data following the deregulation of banks in the United States beginning in 1977.

The model of exchange rate determination of Obstfeld and Rogoff (1995) incorporates the role of current account imbalances. However, despite its initial motivation, the paper became fundamental to further research on the relation between the exchange rate and net NFA accumulation, deemphasizing the role of the latter, due to the non-stationarity revealed within the developed model. In the same line, Cavallo and Ghironi (2002) revisited the former with emphasis on the original intent; that is, the authors develop a two-country model of exchange rate determination in which stationarity and foreign asset composition play an explicit role. They explore whether the exchange rate is determined less by the exogenous money supply and more by net foreign assets. They find that the flexible-price model delivers exchange rate appreciation under a relatively favorable productivity shock; the home economy accumulates assets rather than debt. The sticky-price model generates appreciation, net foreign debt, and stock market expansion after a permanent favorable shock to relative productivity. The study proposes further research on the implications of the developed model as well as the consequences of deviations from purchasing power parity. It also suggests exploration of the performance of policy rules.

Debelle and Galati (2007) examine the consequences of the current account dynamics for the exchange rate. They show that over the past 30 years, episodes related to current account adjustments in industrial countries were associated with major slowdowns in economic growth and large exchange rate deprecations; the effects
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UNEP-WCMC (2016). World Database on Protected Areas User Manual 1.4. UNEP-WCMC: Cambridge, UK. Available at: http://wcmc.io/WDPA_Manual

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We have to make assumptions about how to treat households for which the indicator might not apply and for households with missing indicators. For example, the indicator on school attendance does not apply to households without school-aged children. When reporting on and ranking the indicator alone, we report the share only including the subset of households that have school-aged children. But when creating the multidimensional poverty indicator, we follow the World Bank’s PIP methodology, where ineligible households are assumed deprived if a sufficiently large share of households in the geographic region were deprived (e.g., World Bank 2018). Specifically, all households without schoolaged children are assumed to be deprived in provinces that have a share deprived above the national median. And in provinces with below-median deprivation, all households without school-aged children are assumed to be not deprived.<sup>19</sup><sup>_,_20</sup> However, we omit one indicator from the comparison between the 2009 HIES and the 2016-2018 DHS- the share of the population living in a household where not all schoolaged children attend school. The 2009 HIES appears to be poorly capturing school attendance and does not appear to be comparable to the DHS or other nationally representative surveys.<sup>21</sup> However, when including the indicator, the change would indicate a significant worsening (likely artificial) over the time period and would not contradict the main empirical findings demonstrating no improvement in well-being following a period of strong resource-led growth. And we continue to report the attendance indicator in the 2016-2018 MPM and summary statistics.

indicators and two of the access to basic services indicators. An example of a household that narrowly misses out on being multidimensionally poor includes a household that is deprived in one of the two education indicators and only one of the access to basic services indicators.

> 19The indicator based only on eligible households (41.8 percent) and the indicator with the above province-level assumptions (43.6 percent) are nearly identical at the national
<!-- Start of picture text --> 45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>1st 2nd 3rd 4th 5th<br>Central Eastern Northern Western<br><!-- End of picture text --> Source: Staff calculations using UNPS 2005/06‐2013/14

# 3. Returns to input use

In this section we examine the profitability from using improved inputs, taking into account the variability of the output prices and the quality of available inputs to farmers. We focus on the use of inorganic fertilizer and improved seeds for several of the main crops in Uganda described in Table 2.2: maize, beans, coffee and matooke.<sup>6</sup> We first estimate the inputs’ profitability under optimal conditions using the best available information: agronomic evidence on the yield return to inputs from experimental fields, as well as output price data from local markets. We also briefly touch on the effects of sub‐optimal weather conditions on these financial returns. We then study the potential returns under optimal conditions. Together with yield responsiveness and the historical distribution of agricultural output prices, we estimate whether the expected return to using modern inputs is, on average, positive for farmers in Uganda. Lastly, we consider the issue of the quality of inputs (for maize) and adjust returns closer to what an average farmer might actually obtain. More than 90 percent of households engaged in agriculture report buying their inputs from markets, shops and local vendors, according to the 2015 National Service Delivery Survey,<sup>7</sup> whose inputs rarely retain the same quality as those used in agronomic field trials.

## _3.1. Yield gains from the use of authentic inputs_

It is often noted that experimental yields from controlled agronomic
in the bottom percentiles in almost all countries, due to the association between fertility and education, income, and life expectancy.

**Figure 5. Top 60 percent households tend to have lower child dependency ratios than bottom 40 percent households in countries in all income categories**

Child dependency ratios in LIC countries Child dependency ratios in LMC countries <!-- Start of picture text -->
1.6 1.6<br>B40 T60 B40 T60<br>1.4 1.4<br>1.2 1.2<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>Child dependency ratios in UMC countries  Child dependency ratios in HIC countries<br>1.6 1.6<br>B40 T60 B40 T60<br>1.4 1.4<br>1.2 1.2<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>NER BEN BFA MOZ BDI MWI ETH UGA MDG GIN ZAR TZA AFG GNB MLI TCD TGO RWA COM NPL LBR BGD HTI KHM SLE CMR SEN YEM ZMB TMP SWZ CIV PAK VUT GHA WBG GTM STP GUY SYR NGA LAO BOL FSM PHL HND SLV MAR DJI SLB PRY CPV NIC EGY LSO IND IDN BTN MNG LKA VNM UKR<br>AGO BLZ BWA IRQ GAB MHL JOR MYS PAN DOM ZAF MEX VEN PER COL ECU JAM TUR LBN PLW BRA ARG CRI ALB IRN SYC THA FJI MUS MNE ROM HUN BGR BIH CHN BHS URY ISL SWE NOR USA CHL GBR IRL NLD FRA FIN DNK BEL CHE LUX AUT MLT CZE ITA SVN CYP HRV DEU ESP POL PRT LTU EST LVA GRC SVK RUS
point occurs at 58 percent of urbanization. Finally, higher diesel prices is associated with lower per capita carbon emissions. The coefficients associated with gasoline prices are of the expected sign but are not statistically significant. Because gasoline prices are highly correlated with diesel prices, including the diesel price in the regression could absorb most of the explanatory power.

In the above analysis, we do not control for the existence of transportation policies aimed at reducing transport-related emissions. As a robustness check, we collect data from IEA transportation policy database and control for the existence of various transportation policies in the estimation. We use the principal component analysis method to create one comprehensive transport decarbonization policy variable that summarizes several transportation policies: fuel efficiency standards, tailpipe emissions regulations, driving restrictions, promotion of electric vehicles, promotion of biofuels, R&D and vehicle emission standards. Including transportation policy variable in the regression reduces the sample size by half due to limited data availability for many developing countries. The results are reported in Table 3. The existence of transportation policies has a strong impact, sharply decreasing per capita emissions. The coefficients of the other variables have the expected sign, but the statistical significance reduces due to the smaller sample.

# **6. Conclusions**

Emissions from the transportation sector – which includes travel of people and goods by road, rail, aircraft, and marine vessels; and the pipeline transportation of many fuels – represented more than 24 percent of global CO2 emissions in 2018. Worldwide, emissions from transportation are increasing faster than those from any other sector. Decarbonization of the transport sector therefore plays a crucial role in achieving the Paris Agreement target of limiting global temperature rise to 2 degrees Celsius – and preferably to below 1.5 degree Celsius – compared to pre-industrial levels.

This paper examines the status of the decoupling of transport-generated carbon emissions and economic growth of 145 countries. In addition, it studies the patterns and drivers of transport-related CO2 emissions of 145 countries during the period from 1990 to 2018. We observe considerable differences