rafmacalaba/data-use-mentions-extended huggingface dataset:
charge of all land-related issues, i.e., to receive, validate and notarize transactions, disseminate information, and help implement land use plans at sector local level. This was expected to help decentralize land services. All SLMs received some basic training and were sworn so they could also notarize transactions. By the end of 2015, 367 of the 416 sectors in Rwanda had a SLM in place. Deploying so many staff at very short notice raised logistical and managerial challenges but also prompted a range of innovations. For example, RNRA developed a web-based interface for SLMs to directly initiate transactions in LAIS instead of relying on paper records that have to be transported to the DLO and can be altered more easily. SLMs are also equipped with GPS-enabled tablets so they can conduct subdivision surveys although clarification of legal provisions on this subject may be needed to allow these to be effectively deployed.

Table 4 provides descriptive statistics on roll-out of SLMs and land week events at cell level: coverage with SLMs increased from 18% in batch one (in the third quarter of 2014) to 73% and 82% in subsequent batches (2<sup>nd</sup> and 3<sup>rd</sup> quarter of 2015). Local land week events were conducted in a total of 1,076 cells (614 in land week I and 462 in land week II). We also note that the mean distance to the DLO ranges from some 7 km in Kigali to some 15 km in the East and that the urban population share, which ranges between 9% in the East and 19% in the West and South, increases to more than 200% in Kigali city.

# **3.3 Assessing the impacts of decentralization and awareness campaigns**

To see if measures to improve awareness and decentralize service delivery affected the volume of registered land transfers, we exploit quarterly data from 2014 and 2015 on the number of registered transactions for the country’s 2,148 cells. Locally weighted regressions
to quarter, so using employment and income measures for Q3 may be a sensible way to model the counterfactual labor market situation. However, one concern is seasonality. When we compare trends in FY19 across quarters, the indication is that Q4 employment composition and earnings are not very different from those observed in Q3 (Annex A, Table A.0). The ratio of monthly earnings between Q3 and Q4 in FY19 ranged between 1.02 and 1.06, depending on the sector, suggesting a positive and not a very large difference between the two quarters, on average.

We update workers’ labor market status (extensive margin), based on the changes observed in activity status and employment by sector. Due to sample size limitations in modeling transitions, we use six states for males (not working, which combines out of the labor force and unemployed; working in agriculture; working in industry as an informal worker; working in industry as a formal worker; working in services as an informal worker; working in services as a formal worker). For females, we consider three states (not working, formal workers, and informal workers). Table 1 shows the percentage of workers across these states by gender in HIECS 2017/18.

To produce the most accurate update, workers had to be remapped into different labor market states to reflect the changes in labor force participation and unemployment and the increases in informality observed over the period. The transition model parameters are estimated using the Egypt Labor Market Panel Survey (ELMPS) for the period 2012 to 2018. The ELMPS is a key source of information for the Egyptian labor force, and main trends and patterns align closely with the LFS. The value added 6
(50% in Kigali to 73% in Northern Province), 11.7% in residential, 8% in forest, 1.5% in commercial, and 0.3% in administrative use. The share of parcels in residential use is, with 34%, highest in Kigali, followed by the East (21%), West (13%), the South and North (7% and 6%, respectively). With an average of 0.18 ha; sizes vary between residential (900 m<sup>2</sup> in Kigali to 1,600 m<sup>2</sup> in the East) and agricultural parcels (0.11 and 0.42 ha in the North and East, respectively).

Administrative data support the gender sensitive nature of LTR as 86% of parcels owned by natural persons have a woman either as sole (25%) or co-owner (61%) and only 14% are registered exclusively to male claimants. It can help RNRA quantify and manage outstanding issues and monitor progress at high levels of disaggregation (cell or village level), e.g., the 16% of parcels for which claimants are not yet recorded. Integration of land and the mortgage registries increases ease and security of using land as collateral.<sup>6</sup> Table 2 shows that by Dec. 31 2015, US$ 2.6 billion of mortgage lending was secured by 49,694 mortgages, 65%, 30%, and 5% each secured against residential, agricultural, and commercial land, respectively. With the

> 6 Earlier, the mortgage registry was run by the Rwanda Development Board (RDB) in isolation from the land registry. In addition to increasing transaction costs, lack of a unique parcel identifier made it difficult for the lender to ensure absence of potentially competing claims.

7
211<sup>_∗∗_</sup>|0.010|-0.046|0.043<sup>_∗∗∗_</sup>|0.003|
||(0.019)|(0.015)|(0.082)|(0.015)|(0.109)|(0.012)|(0.013)|
|Urban (0/1)|-0.029<sup>_∗∗_</sup>|-0.004|-0.109|-0.013|0.136|-0.002|0.002|
||(0.014)|(0.011)|(0.105)|(0.015)|(0.130)|(0.013)|(0.010)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|Observations|30,967|30,978|2,521|30,978|4,666|30,934|30,968|
|_R_<sup>2</sup>|0.076|0.027|0.275|0.043|0.180|0.078|0.084|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

15
# **Appendix to “The Mismeasure of Weather: Using Remotely Sensed Weather in Economic Contexts”**

## **Defining Growing Season**

We define growing season following the FAO crop calendar for each country. Table A2 presents details for each country on the growing season used, as well as whether that season spans years and whether it is unimodal or bimodal. Earth observation data used in our analysis follows the defined growing season in each respective country.

It is important to note that both Malawi and Tanzania span calendar years. This means that the growing season begins in one year and extends into the year that follows. Consider Malawi. The growing season begins on 1 October and ends on 30 April. This means that it would begin 1 October 2024 and would end 30 April 2025.

Similarly, both Nigeria and Uganda have bimodal seasons. Season modality designates whether different regions within the countries have different growing seasons. In both Nigeria and Uganda, the northern part of the country has a different growing season from the southern part of the country. In these cases we designate the modality of the season, and also provide the growing season dates for both regions.

## **Weather Variables**

45
>(0.480)|-0.340**<br>(0.141)|-0.348**<br>(0.141)|-1.166***<br>(0.221)|
|Constant|-149.844***<br>(52.882)|-46.290***<br>(15.033)|-46.784***<br>(15.076)|-31.433<br>(25.185)|
|Observations|295|295|295|295|
|R<sup>2</sup>|0.172|.|.|0.198|

_Notes_ . Robust standard errors are reported in parentheses. *** (**) (*) denotes significance at 1% (5%) (10%) level. Regressions include fixed effects for week and day of the week, and controls for weather (temperature, precipitation and two measures of wind).

Given the count nature of our outcome of interest, we also estimate both Poisson and negative binomial models of crashes (Columns 2 and 3). The results similarly show large and significant reductions in crashes of around 34%, which are comparable to the magnitude from the OLS estimate. We reject the null of no over-dispersion of the dependent variable (p=.057); therefore, the model is more correctly estimated using the negative binomial compared to the Poisson. Similar to Green et al. (2016), we compare the mean squared residuals for both the negative binomial and the linear specification to determine whether to continue with the negative binomial. We find the results are very similar, but slightly better for the OLS specification; therefore, we continue with the OLS specification for the rest of the paper.

We look specifically at the curfew hours interval, from 20:00 until 5:
topics covered in the standard WBES questionnaire, it allows for investigations on a wide range of research questions. Here, we will build on the approach suggested in Chapter 6 of Taglioni and Winkler (2004). They describe how consistent indicators for three dimensions of GVC integration can be calculated based on responses to the WBES surveys and they illustrate the results for a few countries.

In Section 2.2 we calculate these three indicators for each country in sub-Saharan Africa and illustrate some patterns that emerge. We exploit both the firm, country, and time dimension of the surveys to document the range and depth of GVC integration and the rate of change over time. A table in the Appendix shows the averages and changes over time for all individual countries, averaged over all survey years. We also make a data file with the firmlevel indicators an a set of firm characteristics available to interested researchers. We show how patterns of GVC engagement differ along a few dimensions of interest, e.g. they differ between foreign-owned and domestic firms, between large and small firms, or between manufacturing and service firms. This rich source of information can be exploited to condition on several more firm characteristics of interest. For example, the data also records each firm’s location, detailed sector, capital intensity, owner characteristics, and many more dimensions of firm heterogeneity.

While it is useful to explore the extent that firms integrate in GVCs in its own right, the WBES information can also be used to construct the average extent of integration for each country. However, the representativeness of the surveyed firms for the entire economy will naturally vary by country. Freund and Pierola (2015) have shown that a few firms account for an extremely large fraction of exports in most developing countries. To have a sense of the fraction of a country’s interaction with the world economy that the sample of firms represents, we calculate for each country-year survey the ratio between the aggregate exports over all sampled firms and the aggregate exports for the country as
medicines. The survey adopts the perspective of an average patient, meaning that the focus is on indicators of provider knowledge of common conditions, and physical inputs required for commonly-used services. This information is collected through enumerator visits to a representative sample of health facilities in each country undertaking an SDI survey. The majority of the data collection is based on a survey administered to the facility manager, and also includes direct observation of the availability and functioning of infrastructure, equipment, medicines and other physical assets.

# 3. Data description and methodology

## 3.1 Sample

The SDI health surveys have been implemented for over 10 years across 13 countries in Sub-Saharan Africa and this paper presents data combined from across this period. Four surveys were excluded, either because they were pilots (Senegal 2010) or because they were not yet complete and data were not publicly available (Guinea Bissau 2018, Cameroon 2019, Malawi 2019). In countries with multiple SDI surveys, we include only the most recent survey, excluding Kenya 2012 and Tanzania 2010/2014. The resulting data set includes information on nine countries, covering 7,810 health facilities. The data include results from the following country surveys: Kenya (2018), Madagascar (2016), Mozambique (2014), Niger (2015), Nigeria (2013), Sierra Leone (2018), Tanzania (2016), Togo (2013) and Uganda (2013).

In each country, the sample of facilities is selected based on a complete listing of health facilities provided by the Ministry of Health. This includes facilities operated by private entities or non-governmental organizations (NGOs), and facilities at all levels of care, including hospitals, health clinics and health posts (or the national

> 11 Kruk et al. (2018).

10 World Bank (2004).

2
characteristics, that have been highlighted in the literature, matter in our data and whether our main results are robust to including them. First, in column (1) we include as control an index of bureaucrats’ perception on management quality<sup>36</sup> . Rasul and Rogger (2016), drawing upon Bloom and Van Reenen (2007), have documented a positive correlation between management practices and project completion in Nigeria. Including a variable on management practices does not change the positive effect of politicians’ integrity, and the coefficient on the management variable is not statistically different from zero, although the point estimate is positive and large.

We also examine whether ethnic heterogeneity among the districts’ bureaucrats and politicians play a role in service delivery in columns (2)-(3), as in the literature on the role of ethnic diversity in teams (Hjort, 2014) and in economic development more generally (Alesina and La Ferrara, 2005; Alesina et al, 2017). Our proxy for ethnic diversity is a Herfindahl Index of self-reported ethnicities. We first include as control the index for all surveyed individuals, and in column (3) we include separate herfindahl indices for bureaucrats and politicians. Once again the correlation between integrity and service delivery remains broadly unchanged. The point estimates on the herfindahl indices are small and not significant. In column (4) we include both ethnic diversity and management practices measure as controls; the coefficient on politician integrity remains significant, and with little change in size across all these different specifications.

Finally, we consider whether the integrity of different types of bureaucrats–those with greater leadership powers, such as the Chief Administrative Officer (CAO) and the Resident District Commissioner (RDC), versus those within the technical health bureacracy, such as the District Health officer– matters for service delivery, and whether this disaggregation by bureaucrat type changes the results for political integrity. We calculate the integrity index separately for the leaders of the bureaucratic organization - CAOs and RDCs
South Asia (SAS), East Asia and Pacific (EAS), Latin America (LCN), Europe and Central Asia (ECS), and Middle East and North Africa (MEA). For the number of poor, the order is similar except that the last two regions, ECS and MEA, now switch places. Second, all the regions have steadily reduced the poverty rate over time (Figure 3, Panel A), although the speed of reduction is strongest for East Asia and Pacific and South Asia. This result is qualitatively similar when we consider the decrease of the number of poor people over time (Figure 3, Panel B), except that SubSaharan Africa has in fact seen an increase in the number of poor people. Furthermore, South Asia has reduced the number of poor more slowly than it has reduced the poverty rate.

We end this subsection on a cautious note that using a different evaluation method can result in a different conclusion for a country’s progress on the SDGs; and analysis using time series data, or varying lengths of these time series, may show a quite different result from a static snapshot approach.

# **III.3. Data Challenges**

Our illustrative examples have so far assumed that the data we analyze are complete, comparable, and of good quality. Put differently, underlying our analysis are two implicit assumptions that the data are consistent and are not missing for each and every year. As it happens, these crucial assumption plays a very important role in helping us correctly interpret the data and their trends. These assumptions cannot be taken for granted. We discuss in the next example the interpretation issues that come from data inconsistencies and unavailability, before we review the current status of the UN SDG database.

# _Different Data Sources_

16
first year of index child. Selected controls are mother's age, wealth index, living in urban area, child's age, consumption of durable goods index, household size, number of children younger than 5 years in the household, and Height for age Z-score. Sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009. * and ** indicate statistical significance at 10% and 5%, respectively. Robust standard errors in parentheses

# **<mark>TABLE IV. Effect of maternal mental health on children vocabulary at age 8</mark>**

28
# **7. References**

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Mathiassen 2013; etc.). Or the higher share when using the FIES could be consistent with the fact that the measure is also extending the dimensions beyond just the quantity and quality of food consumed to include dimensions relating to anxiety over poor food consumption.

Yet it may also be the case that, in Nigeria, the FIES is poorly aligned with the dimensions of food access that it is trying to capture. The simple consumption model presented above suggests that several more severe food access deprivations, such as undernourishment, coincide with less severe deprivations, such as poor diet quality. Thus, one might expect nearly all of those households with poor food access using a measure that only captures severe food access problems to also have poor food access using a measure that better captures less severe food access problems. However, Table 2 demonstrates that this is not happening: many households have poor food access according to the FIES and adequate food access according to the FCS, as well as vice versa.

# **Section 5. Empirical Strategy**

Based on the predictions from the simple model of consumption presented above, we estimate two empirical specifications that explore the relationship between the FIES and monetary poverty. First, we estimate the difference in the prevalence of poor food access between poor and non-poor populations using the official national poverty line:

# (1) _Poor Access_ _~~F~~ IESi_ = _β_ 0 + _β_ 1 _Poori_ + _εi_

where _Poor Access_ _~~F~~ IES_ is an indicator if the household _i_ is categorized as severely food insecure by the FIES or is an indicator if household _i_ is categorized as at least moderately food insecure by the FIES; and _Poori_ is an indicator if the household is monetarily poor using the national poverty line.<sup>25</sup> The estimate of _β_ 0 represents the share of the non-poor

> 25The specification does not include any regional or month fixed effects. Although fixed effects could be added and the results are qualitatively identical, _β_ 0 in
average implications of different forms of fiscal interventions would look differently if richest households were fully represented in the data.

Figure 6: Average monthly per capita income (KHR) by income concept Figure 7: Net effect of the fiscal system and its elements on average monthly per capita income (%) <!-- Start of picture text --> Market income Full system -0.6<br>Direct<br>Disposable income -0.6<br>interventions<br>Indirect<br>Consumable income<br>-3.5<br>interventions<br>Final income In-kind<br>3.7<br>interventions<br>400,000 500,000 600,000 700,000<br>-4.0 -2.0 0.0 2.0 4.0 6.0<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

16
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