rafmacalaba/data-use-mentions-extended huggingface dataset:
# Abbreviations and Definitions

DTM Displacement Tracking Matrix IOM International Organization for Migration IDP Internally Displaced Person NTL <mark>Nighttime Lights</mark> Author Contributions. _Anais Dahmani-Scuitti_ : Conceptualization, methodology, formal analysis, data curation, writing – original draft, writing – review & editing. _Erwin Knippenberg:_ Conceptualization, formal analysis, writing – original draft, writing – review & editing, supervision. _Walker Kosmidou-Bradley:_ Conceptualization, methodology, software, data curation. _Johanna Lee Belanger:_ Conceptualization, methodology, software, data curation, writing – original draft.

Acknowledgments. Authors are thankful to the International Organization for Migration (IOM) for sharing the Displacement Tracking Matrix (DTM). The team would also like to thank IOM staff members of the Afghanistan office, namely Michael Speir, Henry Kwesi Kwenin, Sebastian Boonstra, Caleb Ikyernum and Saboor Sultani for their technical support in handling the DTM data. This paper benefited from input from Razilya Shakirova, and from guidance of Cesar Cancho, Andrew Dabalen, Andrea Mario Dall’Olio and Ghazala Mansuri. Finally, the team is grateful for comments received at the Data for Policy 2021 conference.

Conflicts of Interest. We declare that we have no relevant or material financial interests that relate to the research described in this paper. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank or any affiliated organizations, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work.

Funding. This research was supported through the World Bank Bank Budget (BB) for the Afghanistan Poverty and Equity Program FY 21-22 as part of its core analytical activities.

<mark>Data availability statement. The data that support the findings of this study are based on two main sources, for satellite imagery and displacement data respectively. The Nighttime Lights (NTL) data obtained from the NASA/NOAA partnerships through the Visible Infrared Imaging Radiometer Suite (VIIRS)
number has risen considerably, though how high depends on which sample of journals is examined.<sup>6</sup> Galiani, Gertler, and 5 Dewald, Thursby, and Anderson (1986) found that replication datasets were twice as likely to be available if the concerned papers were in the process of being published at the time of the replication attempt, compared to papers that had been published years before the attempt.

6 Datasets and programs failed to combine to produce viable and successful replications for a list of reasons that is, by turns, tragic, comic, incredible, and instructive. Dewald, Thursby, and Anderson (1986) describe a common situation: a regularly updated government dataset is used in analysis, but neither a copy of the relevant vintage of
Table 3. Description of World Bank Enterprise Surveys on GVC Participation: Selected Countries in Sub Sharan Africa

||**Re**<br>|**source Ric**<br>|**h Group of**<br>|**Countries**<br>|<br>||**Non-Res**<br>|**ource Rich**<br>|**Countri**<br>|**es**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||**Oil Expo **<br>|**rters**<br>|**Non-Oi**<br>|**Resourc**<br>|**e Rich**<br>|**Middle**<br>|**Income**<br>|**L**<br>|**ow Inco**<br>|**me**<br>|
||Cameroon|Nigeria|Congo,<br>Dem. Rep.|<br>Ghana|Zambia|Côte<br>d’Ivoire|<br>Kenya|Ethiopia|Malawi|Tanzania|
|**_Full sample:_**|||||||||||
|# Survey waves<br>available|3|3|3|2|3|2|2|2|3|2|
|# Survey waves used|3|3|2*|2|2*|2|2|2|2*|2|
|# Observations|931|8220|888|1214|1204|887|1438|1492|673|
We employed two data sources. First, thanks to the collaboration with the Tunisian National Institute of Statistics (INS), we rely on the _Enquête Nationale sur la Population et l’Emploi_ (ENPE), which provides nationally representative socioeconomic information at the individual level from 2000 to 2019. The ENPE is conducted by the INS and consists of demographics and labor market data. The survey consists of repeated cross-sections and does not allow to track individuals over time. However, it includes rich information and represents a valuable source of data to analyze the labor market in Tunisia. We started with an initial sample at the individual level across 24 governorates. After restricting the sample to working age individuals (between 15 and 65 years old), we collapsed the data into governorate x year cells to obtain a balanced panel of yearly outcomes of the labor markets.

Exploiting the details of employment status and the economic sector codes (NACE classification) for employed individuals, we focused on four labor market outcomes at the governorate level: the share of employment in the agriculture sector, the share of employment in less climate sensitive sectors (namely, manufacturing and construction), the share of employment in all the other economic sectors (excluding the public sector), and the unemployment rate.

Other information includes the shares of the population across five age groups; the gender of the workforce; the share of educational levels across the population; and the share of the workforce population living in urban areas. We aligned ENPE socioeconomic data with detailed weather information that we obtained from the Agri-4-Cast database. These data are provided by the Food Security Unit of the Joint Research Center (JRC.D.5) and were specifically employed for identifying the climate change impacts in the agriculture sector. The data consists of gridded meteorological observations from weather stations interpolated on a 25 × 25 km grid. They are available on a daily basis from 1979 in the European Union and its neighboring countries, including Tunisia. We selected variables for minimum and maximum air temperatures (in
the state (GoM, 2012). This is exemplified by the 2011 Human Development Index, which averaged 0.752 for the state, different districts however ranging from 0.604 to 0.841, with Latur and Nanded falling among the low-ranked districts with an HDI of 0.663 and 0.657, respectively (GoM, 2018). Other indicators confirm that Latur and Nanded are among the most deprived parts of Maharashtra. The total literacy rate in 2011 was 76.9 percent in Nanded and 79 percent in Latur, both below the state-level average of 82.9. Agriculture is the main economic activity in this area. In 2011, the percentage of people engaged in the primary sector was 72.3 percent in Nanded (GoI, 2011c) and 71.5 percent in Latur (GoI, 2011b).

We show in Online Appendix TableOA-A.1 some key statistics comparing our study sample, i.e. the districts of Latur and Nanded, to rural Maharashtra and rural India using data from the most recent District Level Household survey 4 (DLHS-4), collected in 2012-13. We show that the study sample compares well to the study districts (and rural India in general) in terms of caste composition and religion (with a slightly larger percentage of muslims), but should otherwise be considered a selecion of the poorer part of the population. Households in our sample are more likely to own a BPL card, be female headed, and are less likely to own land – compared to the population of the study districts, rural Maharashtra and rural India.

session that relies on shame and disgust.

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# **Figure 23. Rule of Law in Latin America**

<!-- Start of picture text --> (a) Crime Statistics (b) Institutional Quality<br>600 3.0 0.7<br>500 2.5 0.6<br>0.6<br>400 2.0<br>0.5<br>300 1.5<br>0.5<br>200 1.0<br>0.4<br>100 0.5<br>0.4<br>0 0.0 0.3<br>SCCEs MCCEs & SCCIs MCCIs & SCCEs MCCEs & SCCIs MCCIs & WCCIs<br>WCCEs WCCIs WCCEs<br>Homicides Kidnappings (right axis) Absence of corruption Police and Justice Quality<br><!-- End of picture text --> Note: Country groupings follow Table 2. Homicides are based on reported cases; kidnappings are probability estimates based on surveys; data on corruption and institutional quality are also survey-based (a higher number reflects better quality). _Source: World Justice Project, United Nations Office on Drugs and Crime (UNDOC) and NYA International._
key summary statistics show PPP investment highly concentrated in few regions.** As Figure 6 illustrates of all the PPP investment reported in the PPI data the large majority benefitted the LAC and EAP region (where investments are respectively just below 40% and above 30%). About 15% and 12% of investment in renewable took place respectively in the SAR and ECA region. Very little . investments have been recorded in the MNA and SSA regions

> 4 The six regions are East Asia Pacific (EAP), East Europe and Central Asia (ECA), Latin America (LAC), Middle East and North Africa (MNA), South Asia Region (SAR), and Sub-Sahara Africa (SSA).

> 5 The primary sector is energy, and the subsectors are electricity, natural gas, and other (road, telecom, or treatment plant). For the electricity subsector, the segment information tells whether it is a project for power generation, transmission, or distribution, or a combination of these. For power generation project, the technology (fuel) indicates what fuel (coal, oil, or natural gas etc.) or technology (hydro, wind, nuclear etc.) is used for the generation.

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distributional changes—that is, how distributional changes that accompany growth interact with this measurement issue in a dynamic sense.

# 5 Discussion and conclusions

In summary, we have laid out a puzzle in the relationship among growth, inequality, and poverty in SSA and suggested that an inequality measurement problem lies at its heart. Consumption-based inequality measures miss important information at the top end of the welfare distribution, leading to an underestimation of inequality. Besides identifying the problem, we propose an economically viable solution to reduce this underestimation; we suggest that using information from income distributions collected under the FAO-RuLIS project to recalibrate the top end of the consumption distribution may provide a practical solution to this measurement problem. Overall, we calculate that by using consumption as a welfare measure, inequality is biased downward by 16 percent in SSA when using Gini as the preferred inequality measure and by about 20 percent when using the 90th percentile over the 50th percentile ratio. When correcting the inequality measures with the proposed method, we almost eliminate this downward bias. The evidence is certainly not conclusive, since we can apply the correction to only a small set of countries, but it sheds new light on the distributional changes in SSA in the past two decades. The value added of our work compared to similar contributions (among others, Chancel et al. 2019) lies in the fact that we use the original distributions as much as possible (consumption but also income), only in-sample information, and limit the amount of theoretical assumptions by “letting the data speak.” We conclude, however, on a more speculative note, posing some open questions that we hope will contribute to framing a research agenda centered on the revaluation of distributional issues in light of our new findings. What appears on the surface as a somewhat technical measurement issue has, in fact, implications that go far deeper. In this paper we have argued that the standard empirical toolkit available to development economists working on SSA has limited our ability to appreciate the magnitude and persistence of inequalities in the
Garbacik (2016) verify empirically that governments facing fiscal constraints tend to increase their levels of PPP investments as a percentage of GDP without proper institutional mechanisms to deal with the liabilities they create. Using data from French municipalities, Buso, Marty, and Tran (2017) show that municipalities facing budget constraints tend to use PPPs more.

Engel, Fischer, and Galetovic (2013) argue that by itself, private financing does not reduce the fiscal burden on the government, because, through future availability payments or forgone right to charge user fees, the government ends up funding the provision of the infrastructure service over the lifetime of the project. In contrast, Auriol and Picard (2013) argue that there is a shadow cost of public funds at a given time because of the benefits of the alternative uses or distortionary taxation. Iossa and Martimort (2012, 2015) and de Bettignies and Ross (2009) emphasize the role of private finance in resolving uncertainty and achieving a better valuation and monitoring of the project. Iossa and Martimort (2012) show that private financing can allow more infrastructure investments, by yielding efficiency gains that would allow implementation of some projects that are not feasible under public provision.

In contrast to the theoretical literature on PPPs, empirical work has been sparse. One strand looks at the determinants of PPP investments. Moszoro and others (2014) and Percoco (2014) find that institutional factors, such as rule of law, better regulatory quality, and lower corruption significantly increase PPP investments in developing countries. Using data from US states, Albalate, Bel, and Geddes (2020) show that enacting various PPP–enabling laws increases private investment in transportation infrastructure.

This paper contributes to a growing strand of the literature on the determinants of PPP cancellations. Harris and Pratap (2009) find that macroeconomic shocks, investment value, and the involvement of a foreign sponsor increase project cancellations and that local government PPPs are less likely to be canceled than national ones. Marcelo and House (2016) find that multilateral
Figure 5: Trust dividends of integrity, credibility and equity <!-- Start of picture text --> Integrity and institutional trust Policy credibility and institutional trust<br>1 - Low 2 3 4 - High Never Sometimes Often Always<br>Perceptions of public integrity (index) Frequency at which policy announcements are followed by actions<br>(a) Integrity (b) Credibility<br>Equity and institutional trust<br>Often Sometimes Rarely Never<br>Inequitable treatment by the public administration<br>(c) Equity<br>100<br>70<br>80 65<br>60<br>60<br>55<br>Probability of trust (%) Probability of trust (%)<br>40<br>50<br>20 45<br>60<br>55<br>50<br>Probability of trust (%)<br>45<br><!-- End of picture text --> Data source: World Bank survey on trust (2021). Note: the estimated predictions are derived from a logit model regressing trust in the executive (coded as a binary variable) on the range of independent variables presented in our model and controlling for relevant geographic and socio-demographic variables.

# **3.5 Robustness Checks**

The previous results are robust to various model specifications, including operationalizing the dependent variable as legislative institutions; or an index of representative institutions (combining executive and legislative). They are also robust to the inclusion of additional controls, including a control for individuals’ intrinsic positive attitude. Robustness checks are presented in the tables below.

Table 5 shows the results of di↵erent model specifications of an ordered logit regression of institutional trust on relevant drivers, and reveals that the regression coefficients are broadly similar whether the dependent variable is defined as trust in the executive 18
# **1. Introduction**

Agriculture plays a key role in the economies of Sub-Saharan Africa. Across the continent, the sector contributes approximately 18 percent to GDP and accounts for 56 percent of the employed population (World Bank 2017, ILO 2016). Agriculture serves as a key livelihood strategy for many poor families in rural areas, where labor-intensive, smallholder farming is the predominant source of income. At the macrolevel, agricultural growth has been found to be more effective than non-agricultural growth in reducing extreme poverty (Christiaensen, Demery and Kuhl 2011).

Designing policies to improve living conditions of smallholder farmers requires data on outputs and inputs. One of the most important inputs into agriculture in developing countries is the labor provided by family members on their own farms, denoted in this paper as _farm labo_ r and commonly measured in hours per person or hours per person per plot over an extended reference period, such as the last season or the last year. Data on farm labor are important for a range of literature strands in development economics – such as analyses of agricultural productivity (e.g. Restuccia and Santaeulalia-Llopis 2017; Gollin and Udry 2017), agricultural household models (e.g. LaFave and Thomas 2016), rural labor markets (Dillon et al 2017) and gender differences in agriculture (O’Sullivan et al 2014; McCarthy et al 2016).

Measuring farm labor is, however, fraught with empirical difficulties. The most common approach is to ask survey respondents to recall the amount of time each member of the household spent on farm activities during the previous agricultural season ( _end-of-season recall_ ).<sup>1</sup> While this extended reference period minimizes the impact of seasonality, it can lead to difficulties for respondents to remember how much time various members of the household worked on the farm over the entire season. Recall is further hindered by the informal nature of smallholder agriculture, where working hours are highly irregular, with periods of lower labor activity after planting and prior to harvest
extent to which water regulation is decentralized based on a survey produced by the OECD (2016). Because we are interested in the supply of private capital, it seems reasonable to include a price variable. We approximate this driver of supply by the inflation rate. Finally, we considered the possibility of patterns of differences across regions in their ability to attract PPP since the World Bank PPP database clearly points to a very strong comparative advantage of Latin America and Asia in the ability to do so. This is included as a regional dummy variable in the model discussed later. Finally, we account for the income per capita in the country since it provides a sense of the extent to which the ability to pay may influence the 6
in the same product. The same is not true for competition within a destination, since the average number of firms exporting to a given destination increases overtime.

The dataset of Tanzanian firms includes exports of a wide range of products. For this reason it is useful to look at the variation in export prices. The first row of Table 5 reports some statistics on the price of each firm-product-destination combination by year. The standard deviation of the logarithm of prices is very high (2.4), indicating that – not surprisingly - variation in export prices is considerable. The variation in the price of prices by firm-product pairs across destinations is also high, which can already be a preliminary sign of price differentiation across destinations. Furthermore, prices for a product-destination pair vary considerably across firms, which indicate heterogeneity across firms. This first statistical description of the data predicts that results may be in line with models assuming variable mark-ups, i.e. the variation in prices across firms indicates that firms charge different mark-ups. Table 5 also predicts that results should be in line with quality competition models, because the variation across destinations indicates that firms charge different prices (and maybe sell different qualities) in different destinations.

The products exported are classified according to the Rauch Classification<sup>20</sup> (1999): (1) homogeneous goods are defined as products whose price is set on organized exchanges; (2) differentiated goods are products whose price is not set on organized exchanges and which lack a reference price because of their intrinsic features; (3) while goods not traded on organized exchanges that possess a benchmark price are defined as reference goods<sup>21</sup> . The dummy variable for product differentiation equals one if the product falls into category (2) and zero otherwise. Of the 3542 products exported over the sample period, more than 60 percent are differentiated products (as per column 2 of Table 6). Nevertheless, a look at the number of total shipments indicates that
|Sierra Leone|6.2|2.9|6.7|2.9|6.7|50|90|43|90|43|
|Swaziland|2.4|0.9|3.1|—|—|89|100|84|—|—|
|Tanzania|4.5|1.1|5.2|1.1|5.3|78|100|73|100|73|
|Togo|8.8|3.7|12.0|3.8|12.0|46|79|25|79|26|
|Uganda|7.9|1.9|8.5|2.2|8.6|51|97|47|94|46|
|Zambia|8.0|1.0|9.1|1.3|9.9|52|99|44|99|39|
|Median|3.9|1.3|5.1|1.6|5.3|81|98|74|96|72|

_Source_ : World Bank staff calculations based on household survey data. _Note_ : — = Grid connection status not available in the survey.

# Affordability at higher levels of consumption

Corresponding results for 50, 100, and 250 kWh are shown in Table A.9–Table A.12, Table A.13–Table A.14, and Table A.15–Table A.16, respectively, in the annex. Although this paper uses monthly consumption of 30 kWh to define affordability, higher consumption levels were also tested to see how many households could “afford” (that is, pay no more than 5 percent of total household expenditures to purchase) more electricity. The number of countries in which electricity consumption becomes unaffordable (exceeding the 5‐percent threshold) to the poor, based on total household
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