rafmacalaba/data-use-mentions-extended huggingface dataset:
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# **A Appendix: Proofs of Propositions**

## **A.1 Proof of Proposition 1**

We have left to find the
Development Goals (SDGs). To take it one step further, we capture the quality and performance of services using a ‘quality-adjusted’ measure of access that accounts not only for functionality but desirable levels of access. For example, in the water sector, effective access measures the number of households with a physical connection that is used as a primary source. In contrast, an appropriate quality-adjusted access indicator might be one that limits this to include only those households with connections that provide continuous 24/7 water supply. This quality-adjusted access measure can be used as a benchmarking tool for achieving not just widespread coverage, but also high-quality service.

Drawing on empirical data from 14,200 households across 15 Kenyan cities, we employ our alternative measures of access to examine how effectively households are being served, how much households are spending and how many are benefiting from high-quality service. We then apply our three measures of access to understand how service varies by settlement type and poverty level in urban Kenya. Using hedonic regression analysis, we explore the perceived value of infrastructure services using data from rental markets. First, we investigate the aggregate impact of infrastructure variables on rent. Then, we examine the relative impact of different infrastructure services on rent and whether higher-quality services translate into higher rents. Finally, we analyze our data to identify factors that systematically impact the likelihood of access for a household to each of the selected infrastructure services.

# **2 Survey Data and Methodology**

To examine questions of access, this study draws on the results of a comprehensive “Kenya State of the Cities” baseline study of over 14,200 urban households in 15 cities, commissioned by the World Bank and the Government of Kenya. The study used face-to-face household interviews to capture key indicators related to the demographic, infrastructure, and economic profile of the following 15 Kenyan

> 1 World Bank. 1994.  World Development Report 1994: Infrastructure for Development.  New York:  Oxford University Press. World Bank.  http://hdl.handle.
343 (8.36%). Korean MNCs’ global production sharing is most pronounced in emerging Asian countries such as China and Vietnam.<sup>22</sup> the division of Korean into two new Although firms groups may provide insights into the impacts of minimum wage increases, identifying the employment effects is not straightforward. There are observable (and unobservable) differences between MNCs and non-MNCs (see Table 2). MNCs tend to be larger than non-MNCs in many dimenrevision 10. Hence, we match the two different revisions based on revision 9 throughout the paper. 18If a firm produces multiple products across different industries, the survey identifies the industry of a firm as the one with the highest sales value. If a firm has multiple plants, the survey identifies the firm’s region based on its headquarters’ location.

19Note that the baseline year is set to 2016 as the sequence of events in 2017 was totally unexpected in the eyes of firms in 2016. Hence, four pre-event periods are 2013, 2014, 2015, and 2016; three post-event periods are 2017, 2018, and 2019.

> 20In our data set, there is a total of 12,151 firms, 2,786 of which had at least one foreign affiliate in 2016 (23% of the total). In the manufacturing sector, there are 5,916 firms, 1,939 of which had at least one foreign affiliate in 2016 (33% of the total).

> 21A summary of industry affiliations for Korean MNCs’ foreign affiliates is provided in Appendix Table A.1. Of the 24 manufacturing industries, "Motor Vehicles, Trailers and Semitrailers" accounts for 761 subsidiaries, or 18.55% of the 4,103 in total, followed by "Electronic Components and Computer" with 738 (17.99%) and "Other Machinery and Equipment" with 359 (8.75%).

22Ahn and Park (2021, 2022) document that Korean outward FDI in emerging Asian countries
, 2005/06 8%<br><!-- End of picture text -->

**Source** : Authors‘ calculations based on the IHS 1992/93 and UNHS 2005/06

This diversification was an _addition_ to household livelihoods, not a switch, which is why it does not show up in the primary employment data. Figure 4 shows how households combine types of incomes into livelihoods. As a result of the growth in other sources on income, the proportion of households that solely rely only on farm income (both wage and non-wage) declined by a third, from 54 percent in 1992/93 to 36 percent in 2005/06. In rural areas, the proportion of households relying on agriculture income only (usually subsistence) declined by 43 percent during this 18
# V. Conclusion and Policy Implications

**The European Union showcases remarkable progress in living standards, yet welfare inequalities persist across its regions and demographic groups.** Recent data indicates a decrease in the anchored at-risk-of-poverty (AROP) rate for EU27 countries, yet significant monetary poverty disparities endure among member states. Moreover, wide disparities in social and economic outcomes persist across diverse groups, potentially jeopardizing social cohesion and necessitating tailored policy responses.

**Despite progress, multidimensional poverty remains a challenge. Analysis of individual countries reveals stark variations, with Romania and Bulgaria experiencing notably high poverty and social exclusion rates.** Vulnerable groups such as the elderly and children are disproportionately affected, with rural-urban disparities exacerbating socio-economic deprivation. Distinguishing between temporary and persistent poverty is crucial for effective policy formulation, and systemic challenges persist, particularly among marginalized communities like the Roma. Addressing these multifaceted challenges necessitates comprehensive policy interventions tailored to different contexts and informed by accurate data collection methods that account for diverse population groups' specific needs **.**

**To comprehensively analyze multiple deprivations and the at-risk-of-poverty or social exclusion (AROPE) indicators among vulnerable groups within the European Union, this paper relies on the latest cross-sectional and longitudinal data from the European Union Statistics on Income and Living Conditions (EU-SILC).** This extensive survey spans 27 EU member states, along with Iceland, Norway, and Switzerland, collecting data on income, poverty, social exclusion, and living conditions of individuals aged 16 and above within private households, which employs a stratified multi-stage sampling approach, incorporating both cross-sectional and longitudinal data to capture individual-level changes over a four-year period. While enabling comprehensive AROPE monitoring across EU countries, the dataset lacks ethnicity information, precluding the assessment of deprivations among the Roma population. Nevertheless, to analyze poverty and multiple deprivations among the Roma community, we used the latest available national SILC data from Bulgaria (2021), leveraging the AROPE framework and the longitudinal components for a nuanced understanding of persistent socio-economic challenges among this group over time.
September to July). However, just before schools were due to re-open in April 2015, the Ministry of Education, Science and Technology announced the continuation of a pre-Ebola policy, that ‘visibly pregnant girls’ would be unable to re-enrol. Given the difficulty of correctly identifying a pregnant girl, school principals gained discretion in exactly how they enforced this ban.<sup>13</sup> Finally, the epidemic had severe consequences for health care provision. As Panel D in Figure A1 shows, health care services pre-crisis were already very low. Ebola impacted the health system in two ways: (i) the human capital of health care workers; (ii) public trust in using health facilities. On the first dimension, health workers were under-equipped and under-prepared for the epidemic. Their inability to rapidly implement infection prevention and control measures left them exposed to infection during routine contact and enabled further transmission to other health workers. Evans _et al._ [2015] document how Ebola deaths were disproportionately concentrated among health personnel. For example, by May 2015, while _._ 06% of the population had died from Ebola, 6 _._ 85% of health care workers had died from Ebola.<sup>14</sup> On the second dimension, health facilities became associated with Ebola as some were transformed into holding centers. Visits to health centres were thought to be among the largest causes of Ebola transmission. Combined with a lack of a cure and huge uncertainty, confidence in the health system was undermined, leading families to keep sick members at home, thus further spreading the virus.

The crisis impacted economic opportunities for all. However, each policy response might have had further unintended consequences on women. For example, they could have been relatively more impacted by this economic disruption because womens’ labor force participation rates are higher than for men up until age 30 (this is confirmed in our pre-intervention census). Young women are especially reliant on self-employment, and were more exposed to
using the pre-FPE data (i.e. 2010-2011), assuming that the FPE-treated first enrolled in university in 2011. Because the FPE-treated students were not at the university in those years, we do not expect to see any positive placebo FPE effect on performance because, if there is, it would imply that the observed increase in academic performance post-FPE presented in Table 4 cannot be attributed to FPE. It would imply that academic performance is simply continuing its upward trend post-FPE. The results are reported in Table 6. Looking at column (1) results, we can see that, pre-FPE, there was no statistically significant increase in academic performance. The results in column (2) indicate that, in fact, academic performance was on the decline pre-FPE, although the decline is statistically insignificant. Taken together, these results, therefore, indicate that the observed significant increase in academic performance post-FPE is indeed attributable to FPE, not some positive time trend.

# **6.3.3 Narrow age range (18-22 years old sample)**

Given the school entry age of 6 years, the 18 to 22 year olds are the age appropriate cohort for undergraduate university level. Further, narrowing the age range of the sample increases the 27
response if the _youngest_ child under 5 in the household suffered an ARI, whereas DHS and MICS ARI treatment data come from _all_ health care seeking responses of _all_ children under 5 with an ARI in a household. The HEFPI database therefore does not include ARI treatment data from the WHS.

> 29 For antenatal care, the MICS 2 antenatal care visit data only refer to visits to a _specific provider_ , whereas all later MICS waves and all DHSs do not impose this limitation. For all DHSs and all MICSs from 2002 onwards, a bed net is considered treated if it a) is a long-lasting treated net, b) a pre-treated net that was purchased or soaked in insecticides less than 12 months ago, or c) a non-pre-treated net which was soaked in insecticides less than 12 months ago. By contrast, data limitations in the MICS 2 wave (collected before 2002) restrict our definition of treated nets to those _ever_ treated. For antenatal care, the MICS 2 antenatal care visit data only refer to visits to a specific list of providers, whereas all later MICS waves and all DHSs do not impose this limitation. WHS measles immunization data are only available for the _younges_ t child in the household, whereas our DHS and MICS measles immunization data come from _all_ children under 5 in a household.
. We do not go beyond 2017 to avoid noisy effects possibly coming from more recent events (e.g., Wagner group starting to operate in 2018, leading to the cut of budget support from France and other traditional partners; the COVID-19 lockdowns, the crypto-asset law of 2022). As the main macro-socio indicators, we focused on GDP _per capita_ , industrial value added and manufacturing value added (over GDP) from the World Development Indicators (35), satellite NTL from the DMSP data (22), and a reduced version of the (harmonized) human asset index.

We use a SCM to estimate the expected levels of our key outcomes in the absence of war and compare theses to the observed actual series to quantify the war’s impact on CAR’s socioeconomic development during the period 2013-17. This method provides causal estimates and incorporates pre-intervention dynamics, especially with small-N settings. It has previously been used to estimate the effects of local conflicts and massive revolts (8,9) although these papers focused only on the evolution (and demand decomposition) of GDP _per capita_ series after the events. More recently, the SCM has also been used to assess the social and institutional impacts of the Syrian civil war (10). We used permutation (or placebo) tests to assess whether our results could be due to chance.

5
# **Appendix III. Tools and Techniques**

**I. Reduced form regressions** . These studies typically use single-country time-series data or cross-country time-series data to test the statistical relationship between the following: (1) components of public spending and economic growth (this is roughly equivalent to _Y(B,K,O_ )); and (2) components of public spending and social indicators (this is roughly _B(Y,S,K,0_ )). The empirical evidence on the relative effectiveness of expenditure components has thus far been inconclusive. The robustness of the results is often sensitive to the empirical strategy employed and the countries and time period covered by the sample. The following gives a flavor of the techniques that have been used and their findings:

- _Cross-country growth regressions_ .<sup>33</sup> This typically uses panel data for a large cross-section of countries over time. A large empirical literature on economic growth has emerged since the early 1990s, due in part to developments in growth theory and the greater availability of cross-country macroeconomic data. Empirical studies of the link between expenditure policy and growth are reviewed in Baldacci, Clements, and Gupta (2003). Dollar and Kraay (2002) and Foster and Szekely (2001) (see Table 2) use aggregate cross-country data and cross-country micro data, respectively, to estimate the link between public spending and poverty. Both find a statistically significant effect of overall government consumption on poverty. Some studies use simple bivariate regressions using cross-country to examine the link between spending and poverty.<sup>34</sup>

- _Time-series studies_ . Country studies using time-series data for fairly long time periods provide important evidence on the causal impact of intervention on economic growth. A recent study of the impact of education on growth in India over the 19661996 period, for example, suggests that it is primary education that ha a strong causal impact on growth, compared to other education levels (Self and Grabowski 2004). Researchers have
0297)|||
|gr_change x|||||
|SSA||1.487**|||
|||(0.605)|||
|initial headcount gini|||2.133||
||||(3.107)||
|Baseline: <2000|||||
|2001-2009||||-0.0335|
|||||(0.0286)|
|2010 and above||||-0.0359|
|||||(0.0271)|
|gr_change x|||||
|2001-2009||||-1.273|
|2010 and above||||(0.949)<br>-0.880|
|||||(0.882)|
|Baseline: Consumption|||||
|Income|0.00572|0.00762|0.0048|-0.00448|
||(0.0345)|(0.0374)|(0.0346)|(0.0354)|
|Constant|0.00317|0.0395|0.0281|0.0405|
||(0.0461)|(0.0480)|(0.0591)|(0.0546)|
|country fixed effect|No|No|No|No|
|Observations|574|574|574|574|
|R-squared|0.178|0.186|0.178|0.203|

Notes: Table shows results of a regression of the annualized log change in poverty headcount on the annualized log change in GDP per capita. Sample consists of all consecutive spells between 1981 and 2021, dropping spells with baseline and endline poverty rates lower than 2 percent. Robust standard errors in brackets. ***: Statistically significant at 1%; **: Statistically significant at 5%;
trained are able to collectively engage in, and productively contribute to, one of Sudan’s leading industries. Focusing on improving the quality of and access to agricultural jobs in Sudan’s rural areas should also reduce the strain on Sudan’s urban labor markets, which have not been able to absorb the influx of rural women and youth seeking employment, resulting in an increase in urban poverty. In addition to creating jobs and reducing poverty, the above-described approach would also provide a productive link between social protection and women’s and youth’s economic empowerment.

# **_Combine efforts to build human capital with activities aimed at strengthening agency_**

It is important to promote gender equality from a young age, tracking disparities in human capital accumulation by ensuring that, among other things, girls and young women complete their education. As presented in this report, enrollment rates have increased in Sudan for both boys and girls and, in fact, reached gender parity in primary and secondary education in 2017 (World Bank Gender Statistics). However, these are still below the average of Sub-Saharan Africa countries at the primary and secondary levels and girls in rural areas are less likely to be enrolled especially in primary school.

This report has identified early marriage as an important reason that girls drop out of school and there are various interventions that could be considered to delay marriage and support girls who marry early. Based on a review of what works to end child marriage, such interventions could include (a) empowering girls with information, skills, and support networks; (b) educating and mobilizing parents and community members; (c) enhancing the accessibility and quality of formal schooling for girls; (d) offering economic support and incentives for girls and their families; and (e) fostering an enabling legal and policy framework (Wodon et al. 2017). Programs to educate girls and their parents about the negative impacts of this would be useful. Curbing early marriage and pregnancy will also help reduce the fertility rate
twenty-four weeks (starting from up to four weeks prior to birth), the CCB is payable after the PLB period ends for up to two years after birth, and the CCA is payable after the PLB and the CCB periods end for up to three years after birth. The PLB and the CCB are based on prior employment and earnings, while the CCA is a fixed allowance equal to the minimum old-age pension (less than two-thirds of the PLB/CCB of minimum wage earners), available regardless of labor market history. Mothers who are not eligible for the PLB and the CCB receive the CCA for up to three years after giving birth. We focus on the PLB and the CCB which had similar conditions with the benefit amount equal to 70% of pre-birth earnings. The only major difference between the two is that while the PLB is not capped, the maximum amount of the CCB is 140% of the monthly minimum wage. Thus any social security contributions paid based on earnings above twice the minimum wage do not further increase the amount of the CCB. Since 96% of beneficiaries are women and our data does not allow us to identify fathers because we rely on records of giving birth in health data, we refer to the PLB and the CCB as “maternity benefit” and study only mothers, though, for the CCB, the same eligibility conditions apply to fathers if they are the primary caregiver.

To be eligible for maternity benefits, the mother must be insured for social security for at least 365 days during the two years before giving birth. Until December 2014, the monthly benefits were set at 70% of a woman’s average monthly earnings over the last calendar year before benefit receipt.<sup>6</sup> Since January 2015, by default, the amount of the benefit is 70% of average earnings over the 180-day period preceding the third month before giving birth. For example, for a woman who gave birth on October 1, 2014, the reference period was January
.

To put the accuracy of our ML measure in perspective in context, we also show the _R_<sup>2</sup> and _ρ_ for SR and crop cut-calibrated satellite yields, which we obtained from Lobell et al. (2019). These results are consigned in the bottom panel of Table 7. Our ML-based imputed CC yields, in comparison to SR yields, explains unambiguously more of the variation of in CC yields, and even more impressive finding is that the _R_<sup>2</sup> and _ρ_ of the are on-par with that of the CC-calibrated satellite yields.

# **4.2.3 Empirical assessment of the yields and inputs relationship using predicted yields**

We replicate bi-variate and multivariate regressions of yields as a function of key inputs, with competing measures of yields, including SR, CC and ML yields. We use our preferred model specification of ML in the first sample split simulation in the test sample. Since we are working with predicted measures, the standards errors for the regression coefficients are bootstrapped. Further, as a robustness check, we also use the method introduced by Chernozhukov et al. (2018) to compute de-biased regression coefficients and conservative standard errors when the number of control parameters is large as it is the case of the relationships of interest between yields and key inputs.

The regression results are reported in Table 8. We note that in the test sample, the observations that we previously made concerning the difference between the coefficients of the inputs when using SR or CC are still valid. For example, in the multivariate case, the quantity of seeds per hectare is positively correlated with yields but carry a stronger coefficient when the yield measure is SR, as opposed to CC. The regressions based on ML yields yield new findings. The coefficients obtained with ML yields are similar to the ones obtained with CC yields and the coefficient pairs are not statistically distinguishable, irrespective of the agricultural input in question. This is better seen in the bottom panels of