rafmacalaba/data-use-mentions-extended huggingface dataset:
ranges, especially when disaggregating the data further by gender, education or residence.<sup>7</sup> We chose therefore to not implement this adjustment in the analysis for this paper, with the understanding that our estimates for the very high mortality events are likely to be somewhat biased downwards.

A last potential source of bias has to do with the recall nature of the data. Respondents are asked to remember the date at which one (or more) of their siblings died, and relate the siblings’ age at that date. Both of those pieces of information are subject to recall error (as discussed in Obermeyer and others 2010). To assess whether there is any systematic recall bias, we use the fact that many countries have several rounds of data and, therefore, some of the sibling mortality rates from earlier periods can be derived from more than one round of the survey.<sup>8</sup> If the estimated sibling mortality rates were systematically lower or higher the more recent the survey, then this would suggest a problem. Figure 2 illustrates the mortality rates among siblings for the 1975-1979 (top panel) and 1980-1984 (lower panel). The left column shows the sibling mortality estimate based on the first DHS survey conducted after the relevant period (where recall bias should be smallest) and the subsequent DHS survey. The right column compares the first survey to the third survey (if it exists). While in most cases the mortality estimates are not identical, it is hard to discern any systematic pattern.<sup>9</sup> Multivariate analysis confirms this result: a regression of the adult mortality rate on dummy variables for survey round, controlling for country fixed effects and period dummies, yields coefficients that are small and not even close to statistically significant at conventional levels for second and third round estimates of a mortality rate.<sup>10</sup> The mortality estimates we generate based on this approach are derived from the sibling history reports of
3 seasonal fluctuations in the availability and costs of individual foods and help establish a lower bound on seasonality. It is an open empirical question, however, whether such substitution is possible or even sufficient to ensure households’ access to nutritious diets throughout the year. Linking micro-level household demographic and food consumption data from nationally representative household surveys with sub-national monthly food prices and local food composition data, this study asks whether markets across Malawi’s rural districts can supply diets meeting scientifically established nutrient requirements for all household members at an affordable cost throughout the year. Two possible ways to estimate the minimum cost for the whole household are considered, which could essentially be seen as establishing a lower and upper cost bound. It could be done by 1) summation of the cost of individually optimized diets across all household members (henceforth “individualized diets”); or 2) optimization for the combined nutrient needs of everyone in the household, accounting for the nutrient needs of the neediest member given the total energy intake needed by each member (henceforth “shared diets” or “household sharing”). While the former approach essentially assumes perfect intrahousehold targeting of individual foods according to individual nutrient needs, the latter assumes that meals are fully shared with individual quantities proportional to energy needs (Schneider et al. 2021).

If the only consideration were market prices for each food item, the lowest-cost method for a family to secure a nutritionally adequate diet would be for each person to eat a tailored diet meeting their own minimum needs and without exceeding the upper limits for the subset of essential nutrients where excess consumption causes illness (toxicity). Costing individual diets for each person in a household and adding them up over all members therefore provides a lower bound on the cost of an adequate diet for the entire family. Preparing separate meals for each
on output quantities than recall-based surveys, a key argument against their more widespread use has been the supposedly high cost of conducting them. The Uganda experience reported here suggests otherwise: local monitors were paid some US$ 10 per month to assist in keeping of diaries in each ten-household cluster, something that would translate to approximately US$12 per household per year.<sup>16</sup> Even if this cost were doubled to provide adequate training for monitors and account for other expenses, the cost would only amount to slightly more than a tenth of the cost (US$ 200 per household) for the socio-economic survey.<sup>17</sup> This suggests not only that a focus on “low-hanging fruit” could have great benefits at relatively low cost but also that there may be scope for using harvest diaries more broadly to obtain reliable information on agricultural production in a more participatory way.

> 14 We lack detailed information on enumerator characteristics except gender which can be inferred from their name. Interestingly, however, the difference is higher for female enumerators than for male ones (not reported), a result that is consistent with the hypothesis that female enumerators exert more care and thus obtain higher figures for home consumption. Unfortunately, no information on respondents, which would have allowed further exploration of this line of reasoning, is available.

> 15 The weakly significant coefficient for the share of plots managed by females in column 3 provides some support for this interpretation. 16 A full costing will have to add expenses for training and supervision of ”diary monitors” and adding these is important because, as noted earlier, the experience reported here implies that failure to invest in these items can dramatically reduce the quality of the information obtained. 17 Although somewhat high by international standards, this cost is in line with past household surveys in Uganda. Costs seem to be increased by the need to visit households twice and the requirement to measure plots in the field.

13
level, and therefore term limits will not affect the outcome. For example, consider the 1988 US Presidential Election. George H.W. Bush was elected president, after Ronald Reagan served two terms. Since the Republican party remained in power, we treat that episode as the incumbent staying in power.

In cases of political coalitions, where multiple parties form the government, our measure of incumbency considers the political party of the chief executive. Second, we use data on the name of the political party incumbent, and the political party which wins the election. With that data, the political orientation of the incumbents and election winners are calculated based on party ideological orientation.

For the majority of elections and parties, data on left-right orientation are taken from the ParlGov Party Database (D ̈oring and Manow, 2019). This database contains information for over 1,600 political parties in 37 democracies in the EU and OECD. For each party, a time-invariant score between 0 and 10 is provided, classifying their political position from left-wing to right-wing, respectively. This score, in turn, is based on data from Castles and Mair (1984), Huber and Inglehart (1995), Benoit and Laver (2006), and Bakker et al. (2015). For example, the UK Conservative Party has a score of 7.4, while the French Socialist Party has a score of 3.2. In our analysis, we do not use the continuous index, but rather split parties into left-wing and rightwing using the threshold value of 5. Overall, this dataset provides information for most countries in our sample. For countries outside the ParlGov sample (primarily South America), we also classify incumbents and election winners as left-wing or right-wing using various online sources. For example, Jair Bolsonaro, the winner of the 2018 Brazilian general election is classified as right-wing, based on his thenparty, Partido Social Liberal. In sum, of the 207 elections in our main sample, there are 91 left-wing incumbents
The size of the training data does not significantly affect the accuracy of the predictions (Table 8). Using a sample of 1,178 villages in the 55 sub-districts, the out-of-sample _R_<sup>2</sup> using all the available satellite data is 0.842, as seen earlier in Table 5. The accuracy increases marginally to 0.843 if we reduce the training sample by half, with a mild reduction to 0.832 if we further reduce the sample size by another one-half. There is only a small increase in the MAE and RMSE with the reduced sample sizes. Overall, these results indicate that changing the training sample size does not significantly change the prediction accuracy.

# **4.3 Survey-Based Predictions and Validation with the Census**

While it is encouraging that satellite data accurately predict census population density, they do not conclusively show that survey data can be used to accurately approximate population density. We now turn to examining this by testing the accuracy of a survey-based model in the 55 sub-districts sample where we estimate a Poisson model of HIES population density in HIES-villages, and obtain out-of-sample density predictions in the non-HIES villages.

The results from a model using both public and proprietary satellite imagery-based indicators are provided in panel A, columns (1)-(3), in Table 9. The _R_<sup>2</sup> , Spearman Rank Correlation (SRC), and Mean Absolute Error (MAE) between the density predictions and the census density are 0.79, 0.91, and 664, respectively. This model uses the inverse of village population as weights. If the weights are corrected based on probability of the village being selected into the HIES, we obtain similar results: 0.78, 0.92, and 665 for the same measures. Considering the _R_<sup>2</sup> and SRC, our model performs equally well compared to the census based model, and better than all “top-down” estimates except Facebook
Industrial production refers to the output of industrial establishments and covers sectors such as mining, manufacturing, electricity, gas and steam and air-conditioning. We use IPI data in other exporting countries ( _ipiot_ ) which are weighted using sectoral export shares obtained from 2018 UN Comtrade data to compute the average competition shock in third countries ( _competition shockijkt_ ). We also rely on IPI <mark>data to capture</mark> supply shocks in source countries ( _ipist_ ). Both allow for the inclusion of China for which mobility measures are unavailable. In robustness checks, we use IPI as alternative supply shocks in exporting countries ( _ipiit_ ) and alternative demand shocks in partner countries ( _ipijt_ ).

To capture the potential heterogeneous impact of reduced mobility across sectors, we construct a variable that measures the percent of occupations within an ISIC Rev. 3 4-digit sector that can be performed remotely based on U.S. 2017 O*NET data.<sup>17</sup> In order to obtain a remote labor measure that varies across exporting countries ( _remoteik_ ), we multiply this percentage with a country’s internet density defined as individuals using the Internet (as % of population) from the World Development Indicators for 2017. These measures are then indexed to range from 0 to 1. It seems counterintuitive that production-related activities like assembly can be performed remotely. However, the trade data and remote labor index are classified by sectors and not tasks. That is, services tasks that are embodied in goods such as research and development, design or marketing are also classified under goods sectors. Appendix 1 ranks ISIC Rev.3-2 digit sectors by their average remote labor index and shows that remoteness is highest in publishing, printing and reproduction of recorded media, followed by electronics and machinery sectors, while it is lowest in labor-intensive forestry, fishing, agriculture and food production.

To assess the durability of products we calculate the sector’s share of durable consumer products, semidurable consumer products and cars and transport equipment ( _durablek_ ).
4 2. The influence of mortality on fertility, at first debated widely, has cown to be accepted, based both on micro level household studies and macro studies using aggregate datal. Similarly the direct influence of fertility 2 on mortality is equally well established. Recognition of the simultaneous interaction came only later but is now also accepted although statistical estimates of the simultaneous effects, using either household or aggregate data, have been few<sup>3</sup> , and there have been no ap.plications of the empirical 4 analyses to policy assessment. The present paper fills a gap by providing lIn their survey of the determinants of fertility McGreevey and Birdsall (1974) cite studies estimating elasticities of .2 to .4 for the .1sponse of completed fertility to changes in infant mortality (1974, p41, Cchrane and Zachariah (1983) survey a number of studies and also reexa. Torld Fertility Survey data using linear regression. They conclude, base-d on an average for 25 countries, that the effect of child mortality on stusequent fertility is about .5. Some other studies establishing the effect of infant or child mortality on fertility in individual countries are Zachariah and Patel (1984), Schultz (1978), and Preston (ed., 1978).

2Much of the literature looking at the effects of fertility on mortality is focussed on the relationship between birth spacing and child survival. In a cross country study by Pebley and Millman (1986) it is found that the risk of dying for a child born with less than a two year birth interval is approximately 60% greater in the neonatal period and 100% greater in the remainder of the infant period than for a child born after more than a two year blrth interval. Also see Cleland and Sathar (1983), and Hobcraft (1984). There have also been studies looking directly at the effects of fertility on infant mortality. See, for example, Scrimshaw (1979), and Taucher (1982).

3The earliest estimates of the interaction
program mean school size of 252 students in phase-3 and phase-4 schools based on data from program applications and a per-student subsidy of Rs. 300, the bonus amount represents 6% of the expected mean annual subsidy payment to program schools. Again, the bonuses are transferred electronically to the bank accounts of the school awardees. To date, three rounds of school bonuses have been awarded.

Once schools qualify and join the FAS program, the partnership contract stipulates several conditions for maintaining benefit eligibility. The conditions that are stringently applied by PEF are (1) schooling is offered to students without charging them any tuition or fees (and displaying this status prominently on a PEF-issued signboard outside the school gate) and (2) participation of the program school in the QAT and that at least 66.67% of the tested students score 40% or higher on the QAT. A one-time violation of these conditions typically results in a warning and the capping of enrollment figures for the subsidy payment until the next QAT round. A second violation results in the permanent disqualification of the school with immediate effect. With the QAT-related condition, the school is permanently disqualified if it fails to achieve the minimum pass rate in the QAT in two consecutive attempts. Since the start of the program, there have been 51 schools that have been disqualified from the program due to double failure on the QAT.

There are also other conditions for maintaining benefit eligibility. These include (1) registering the school with the District Registration Authority within one year of joining the program; (2) conducting only one class in a classroom in any period; (3) maintaining or upgrading the quality of the school’s physical infrastructure (e.g., adequate classroom space, properly-constructed rooms and buildings, sufficient ventilation, and sufficient artificial and natural light); (4) adequate furniture and teaching tools (e.g., benches, desks, and blackboards); (5) monthly reporting on enrollment figures; (6) maximum student-teacher and studentclassroom
transmission grid operator, and boost market openness and competition.<sup>76</sup> ANRE is in the initial stages of setting up its regulations and policies. Even when it begins operations fully, ANRE’s scope will be defined by the power generation regime under the RE law with limited oversight of the transmission and distribution grids. Besides, as research shows, without a pre-requisite step of fully unbundling ONEE, the establishment of a regulator could end up not having the expected effect on the performance of the utilities.<sup>77</sup>

# 3 CONCLUSION

This paper analyzed Morocco’s experience with power sector reforms from 1990 to 2017. It draws on technical and financial data, stakeholder interviews and published studies. It described the chronological timeline of reform and examined the sector’s performance along the security of supply, electricity access and affordability, utility efficiency and financial viability, and tariffs and cost-recovery.

Morocco charted its own distinctive path of power sector reform. It selectively introduced private sector participation for generation capacity expansion and electricity distribution in certain cities, while retaining a strong, state-owned and vertically-integrated national power utility operating as a single buyer at the core of the sector. Until recently, the country eschewed an independent regulatory entity. The power sector has been guided by strong top-down policy mandates that have served to align the disparate actions of political parties and sector institutions. Ambitious targets for electricity access, liberalization, and renewable energy investments were conceived as an integrated approach to contribute to economic development by relieving fiscal pressures, reducing external dependence on fossil fuels, and positioning the country as a leader in renewable energy. The results have been impressive. Since 1990, Morocco has more than tripled its power supply, while growing renewable energy to account for one-third of the total and relying on the private sector to supply just over half of the electricity generated. Rural electrification has accelerated rapidly from 18 percent in 1995 to virtually 100 percent in 2017. While operational efficiency has been broadly adequate, performance has
105 countries are included in the standardized consumption database)

|**Category**|**Number of countries**|
|---|---|
|Motor cars|94|
|Motorcycles|89|
|Bicycles|97|
|Animal-drawn vehicles|29|
|Fuels and lubricants forpersonal transport equipment|105|
|Maintenance and repair ofpersonal transport equipment|103|
|Other services in respect ofpersonal transport equipment|87|
|Passenger transport byrailway|78|
|Passenger transport byroad|105|
|Passenger transport byair|100|
|Passenger transport bysea and inland waterway|80|
|Combinedpassenger transport|52|
|Otherpurchase transport services|85|

> 7 Quintiles were generated using <mark>weighted per capita expenditure, which was calculated</mark> by taking household total expenditure divided by household size.

18
beginning of the year was 15.3 thousand kilometers. Railways that traverse the country's territory cover 14.5 thousand kilometers, while 257 km pass through other countries." In Russian. Cites Kazakhstan Ministry of Transport web portal as data source..

20. Chu, Daye. Moscow-Kazan high-speed railway ‘may be built in stages’ for financial reasons . s.l. : Global Times, October 22, 2017. GlobalTimes.cn. Quotes Russian Railways (RZD)..

21. Railway Pro. Eurasian Development Bank to finance Moscow-Kazan HSR. RailwayPro.com. [Online] Railway Pro, May 30, 2018. https://www.railwaypro.com/wp/eurasian-development-bank-to-finance-moscow-kazan-hsr/.

22. `霍` 尔 `果斯往` 乌鲁 `木` 齐 `方向首趟客运列` 车 `正式开通` . Tianshan Network. December 26, 2013, Huochepiao. "Horgos to Urumqi, the first passenger train was officially opened." In Chinese.

23. Hulley-Jones, Frank, Blight, Garry and Gutierrez, Pablo. Follow the Silk Road. TheGuardian.com. [Online] The Guardian, July 30, 2018. https://www.theguardian.com/cities/ng-interactive/2018/jul/30/follow-new-silk-road-china-belt.

24. Ping, Chong Koh. Horgas - China's gateway to the West. s.l. : Straits Times, August 7, 2016. StraitsTimes.com.

25. Ministry for Investment and Development of Kazakhstan. _Реализация государственной программы_ « _Нурлы Жол_ ». s.l. : Government of Kazakhstan, 2017. mid.gov.kz. Slides, including maps, depicting extent of the Nurly Zhol "Golden Road" highway program. In Russian. Available at: http://mid.gov.kz/ru/infografic/1191.

26. Kushkumbayev, Sanat. Kazakhstan’s Nurly Zhol and China’s Economic Belt of the Silk Road: Confluence of Goals. s.l. : The
|Panel C: 2014||||
|Social Security Benefts|0.48|0.12|-0.17|
|Minimum Wage Compliance|0.58|-0.10|0.06|
|Child Labor|0.47|0.17|0.02|
|Relative underpayment|0.46|-0.16|0.07|
|Compensatory moonlighting|0.00|0.02|0.96|
|Labor stability|-0.01|0.71|0.15|
|Incidence of overqualifcation|-0.01|0.65|-0.14|
|Panel D: 2021||||
|Social Security Benefts|0.49|0.04|-0.22|
|Minimum Wage Compliance|0.60|-0.03|0.07|
|Child Labor|0.40|0.36|0.03|
|Relative underpayment|0.50|-0.19|0.07|
|Compensatory moonlighting|-0.02|0.33|0.71|
|Labor stability|-0.04|-0.39|-0.66|
|Incidence of overqualifcation|-0.04|0.75|0.04|

_Notes:_ Based on data from EH 2007-2021.

18
household consumption per capita together, using both the cross sections and the balanced panel in Figure 1.3 (Appendix 1). This figure shows similar V-shaped trends over time for both variables, although the income line is somewhat lower than the consumption line in earlier years (up to 2001).<sup>17</sup> We further plot in Figure 1.4 (Appendix 1) other variables including household labor income per adult, household pension per capita, and individuals’ labor earnings; these different definitions of incomes show qualitatively similar trends over time.<sup>18</sup> These results are consistent with estimates for the Gini coefficient using labor income by Calvo _et al._ (2015), who also found it to decrease by 18 percent between 2002 and 2012. Finally, we also re-estimate the mobility regressions using individuals’ labor earnings and plot these results in Figures 1.6 and 1.7 (Appendix 1), which show qualitatively similar trends to Figures 7 and 8. But the magnitudes of the estimated coefficients are unsurprisingly slightly larger; for example, moving from a part-time job to a full-time job was associated with a 12-percent increase in one’s labor income in the 199498 period.

# _Top Incomes_

A typical issue with household survey data, including the RLMS, is that such data may not capture individuals with the top incomes. In that case, the survey data may offer a downward biased estimate of income inequality due to the survey underreporting the higher end of the income distribution (see, for example, Picketty _et al._ (2018) and Novokmet _et al._ (2018)). Yet, researchers

> 17 A recent study by Kim, Gibson, and Chung (2017) suggests that income might be underreported during this period because of the higher share of informal economy. However, we also calculated the Gini coefficient using the per capita consumption variable, which displays a similar downward trend over the period 1994-2015 (Figure 1.1