rafmacalaba/data-use-mentions-extended huggingface dataset:
# **2.1 Routing Data**

Plotting the road network, while informative about its extent, does not permit a rigorous assessment of its efficiency. Routing engines<sup>4</sup> provide more precise information on how cars can use the network to get from A to B. In my case, A and B could be anywhere on the continent. Two approaches invite themselves to choose points: (1) using a uniform spatial grid and taking the cell centroids, or (2) selecting the centers of important cities/places. In this paper, I employ both approaches, but for spatial analysis, the grid approach is more suitable. Hence, I begin with it.

Accurate spatial analysis at the continental scale warrants using an equally spaced grid. Discrete Global Grid Systems (DGGS) provide these via hierarchical tessellation of cells partitioning the globe. Sahr et al. (2003) propose the Icosahedral Snyder Equal Area Aperture 3 Hexagon (ISEA3H) as a good general-purpose Geodesic DGGS. The grid is implemented as part of the DGGRID C++ library (Sahr, 2022), accessed via the ’dggridR’ R package (Barnes, 2020). To limit complexity, I choose a resolution 9 ISEA3H grid, with a cell area of 2591.4km<sup>2</sup> and a centroid spacing of 50.28km. Partitioning the African mainland and Madagascar with this grid yields 12,016 cells.

Figure 3: Grid Cell Centroids and OSRM Route Starting and End Points Grid Centroids (Inaccessible Cells in Grey) Actua ~~l~~ Route Starting/Destination Points _<u>Notes:</u>_ <u>Figure shows centroids of 50.28km ISEA3H grid</u> (LHS) <u>and the nearest road segment to each centroid (RHS).</u> To compute a large number of routes, I configure a server with the Open Source Routing Machine (OSRM)<sup>
list of investments from the Infrastructure Development Program for Africa (PIDA) in its long-term vision (2012-2040) to create our counterfactual.

We start by mapping the current transportation network to calculate transportation times between large cities in African countries and the rest of the world. The nodes in the global network, which serve as export destinations in the analysis, are all cities (worldwide) with a population over 500,000 and the two most populous cities, data permitting, in each African country. This gives a total of 1,037 cities connected to the transportation network and 32 cities with a population of less than 50,000. The subset for Africa includes 165 connected cities that serve not only as destinations but also as export origins. The final network is solved for each origindestination pair, and routing is determined by the shortest path, adjusting for a maritime preference parameter.

The construction of the transportation network at the city level proceeds as follows.<sup>3</sup> We first build a transportation network for Africa and the rest of the world that includes land and maritime connections. For Africa, we rely primarily on major roads from Open-Street Map, including different classes of roads for which we assume different speeds (see Table A1 in the Appendix). For the rest of the world, we use the land and maritime

> 3To be compatible with trade data, the city-level network is then aggregated to the country level using a theoretically consistent approach. The aggregation procedure, following Hinz (2017), is described in detail below.

4
formal theoretical and empirical testing, the value of trust to social and economic progress is not diminished (Barrett, 1997).<sup>6</sup> As a measure of vertical trust, we use GWP’s _Trust in National Institutions Index_ (NI index). The NI index is composed of four items measuring respondents’ trust in key national institutions (Gallup, 2016). Respondents were asked whether they had “ _confidence in each of the following, or not?_ ” with the options of “(1) How about the military? (2) How about the judicial system and courts? (3) How about the national government? and (4) How about the honesty of elections?” Responses were combined into a single index score for each individual. The index is the mean of valid items multiplied by 100, and is only calculated if respondents had two or more out of four valid scores. As a sensitivity check on the NI index, we study each of the four questions independently. We also examine the effects of food insecurity on more local public institutions (as opposed to national institutions or interpersonal levels of trust), using binary measures of trust in the local police and trust in the financial system and local banks. Further, we also use a principal component approach to reduce potential measurement error (Gillen et al., 2019).

For the horizontal dimension of trust, we use respondents’ trust in friends and family. This binary measure equals one if the respondent answers positively to the question: “ _If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them?_ ” This measure allows us to test whether food insecurity influences interpersonal relationships and social capital rather than only with authority and formal institutions. People who trust friends or family are also more likely to exhibit altruistic behavior and reciprocity, boosting civic cooperation (Putnam, 2000; Uslaner, 2002; Corbacho et al., 2015)
15 and NBHS 2009.

### **The increase in urban poverty and decrease in rural poverty might be explained by the relatively high**

**prices of staples (Figure 9a).** At the time NHBPS 2014/15 was conducted, both sorghum and millet prices had risen significantly, though they have since reduced. As the urban population is a net consumer of food and the rural population is a net producer of food, this increase in food prices may have had an adverse effect on urban households while having a beneficial effect on rural households. These price increases might have exacerbated the already prevailing negative effect on urban areas from the reduction in oil revenues. As noted in the introduction, increasing agricultural production can bring down food prices, thereby benefitting urban consumers. Food prices have been a contributing factor to the recent political instability, as prices have increased rapidly since 2018 (Figure 9b). Increasing Sudan’s food production is therefore as vital for meeting Sudan’s domestic consumption needs as it is as a source of foreign revenue.

10
all six economies have laws that allow an employee to bring a claim for employment discrimination on ethnic and religious grounds in both the private and public sectors. In Bulgaria, Mexico, and the Netherlands, that holds true also for claims of employment discrimination on sexual orientation and gender identity grounds (see table 4).

> 60 Bulgaria, Labor

> Code (1992), Art. 8, Para 3; Mexico, Federal Labor Law (1970), Articles 3, 133; Morocco, Labor Code (2011), Art. 9; the Netherlands, Equal Treatment Act (1994), Sec. 5(1); Tanzania, Employment and Labour Relations Act, (2004), Art. 7(4); Vietnam, Labor Code (2012), Art. 8.

> 61 Bulgaria, Labor Code (1992), Art. 8, Para 3; Mexico, Labor Law (1970), Articles 3, 133; the Netherlands, Equal Treatment Act (1994), Sec. 1(1)(b).

> 62 Protection Against Discrimination Act (2004), Art. 12, Para. 1 and Art. 4, Para. 1

> 63 The United Nations Rule of Law Indicators is a tool for assessing the rule of law in post-conflict situations. See UNOHCHR (2011).

Anti-Discrimination Law and Shared Prosperity 20
take-up is only 14%—a 77% decline in take-up relative to Private accounts ( _p_ <0.001). This indicates that the blocked accounts are substantially less desirable when their details would be revealed to the worker’s network.

We use survey data to examine workers’ self-reported reasons for the low take-up of Nonprivate accounts (Appendix Figure A4). Among those who declined a Non-private account, a striking 96% say that an important factor in their decision was that network members’ knowledge of the account would lead to an increase in net transfer requests. In addition, 46% say that they worried this would decrease their partner’s contribution to household expenses—also a component of redistributive pressure in our setting. In contrast, only 5% of workers cite any other reason as motivating their decision not to take up a Non-private account. We return to these ideas when we examine heterogeneous effects in Section 7.3, where we also directly examine the potential role of intra-household bargaining for our results. Overall, while only suggestive, these responses indicate that concerns about redistributive pressure were prominent in workers’ minds when refusing the Non-private accounts.<sup>25</sup> Among workers who take up a blocked account, 26% choose a threshold close to the minimum (i.e., a round number within 10% above their baseline earnings), while the remaining 74% select a threshold that is higher (see Appendix Figure A5). Overall, the median threshold chosen corresponds to 139% of mean baseline earnings. This is consistent with some desire to maintain flexibility in liquid earnings among workers—leaving room for workers to adjust labor supply to respond to cash needs in a given paycycle, while still allowing them to make use of the blocked accounts to save a subset of their earnings increases.<sup>26</sup> Despite the choice of fairly high thresholds, the accounts were actively used by workers. In Phase 1, workers who took up the
Policy Research Working Paper 5671

# **Abstract**

Despite the importance of agriculture to economic development, and a vast accompanying literature on the subject, little research has been done on the quality of the underlying data. Due to survey logistics, agricultural data are usually collected by asking respondents to recall the details of events occurring during past agricultural seasons that took place a number of months prior to the interview. This gap can lead to recall bias in reported data on agricultural activities. The problem is further complicated when interviews are conducted over the course of several months, thus leading to recall of variable length. To test for such recall bias, the length of time between harvest and interview is examined for three African countries with respect to several common agricultural input and harvest measures. The analysis shows little evidence of recall bias impacting data quality. There is some indication that more salient events are less subject to recall decay. Overall, the results allay some concerns about the quality of some types of agricultural data collected through recall over lengthy periods.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kbeegle@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors
temporary jobs have a higher male profile in most of the analyzed countries. The exceptions are Armenia and Moldova in the starting point of the study where there is observed a share of males larger than 50% of part-time employees. In the case of temporary employment, we find in all cases and periods a predominantly male profile.

From a dynamic point of view, both part-time and temporary employees show in most of the countries a growing share of women. In fact, in the case of part-time employment, this growing female prevalence is observed in Russia, Kyrgyzstan, Armenia, Albania, and Moldova, while Turkey and Georgia show a very small rise on the share of men. Additionally, regarding the four cases in which we identify temporary employment, there is also a small growing female participation in Kyrgyzstan and Georgia, while the distribution by gender remains almost constant in Turkey and Armenia.

Figure 22: Part-time employment by gender. (Early 2000s/Mid-2010s) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s 2000s 2010s<br>Russia Georgia Kyrgyzstan Turkey Armenia Albania Moldova<br>Female Male<br><!-- End of picture text -->

# Source: Own calculations based on Household surveys.

Like in the case of the age profile, the growing female participation is consistent with the observed trend in overall employment. Indeed, in the considered period, there is observed a rise in the female participation in total employment of approximately 1 percentage point on average. Statistics among the age and gender profile for total employment are included in the Annex II of the paper.

Page 29 of 51
1 Related Literature**

Our paper makes two main contributions to the literature on tax policy in developing countries. First, we introduce differences in informal consumption along the income distribution as a novel channel through which consumption taxes are redistributive. This contrasts with the existing literature, which omits informal consumption and concludes that consumption taxes have, at best, limited redistributive impacts in developing countries (for recent studies, see Harris et al., 2018 and Lustig, 2018).<sup>3</sup> Our approach is thus related to recent the redistributive of differences in papers investigating implications consumption patterns across the income distribution, though these studies are in rich

> 3Two exceptions are Mu ̃noz and Cho (2003) and Jenkins et al. (2006) who use retailer information to classify expenditures as formal or informal in respectively the Dominican Republic and Ethiopia.

4
Sergio Firpo and Alysson Portella. Decline in Wage Inequality in Brazil: A Survey for white workers in these high-paying firms, which suggests discriminatory practices in hiring and retention of workers. Moreover, within firm differences in wage premiums across races account for another 5-6% of the total racial wage gap. The wage losses due to unexplained differences in sorting and wage-setting are larger among workers with higher skills, as measured by workers fixed-effects, suggesting that discrimination may be important in explaining differences at the top of the distribution. We may therefore conjecture that the observed decrease in firm heterogeneity during the 1990s and 2000s documented by Alvarez et al. (2018) may be somehow related with the reduction in the wage gap across races. However, without causal models it is not possible to pin down the exact direction of this effect: was it the reduction in firms’ heterogeneity that led to a decrease in racial wage gaps, or was it a reduction in discrimination that contributed partially to the compression of the firm-specific pay premium?

Machado et al. (2018) find that the gender earnings gap in Brazil is diminishing across generations, at least within the formal sector. If taste-based discrimination is also behind the observed gender gap, then the trade liberalization episode in the early 1990s may be also responsible for part of the reduction in the gender gap. Morchio and Moser (2019) develop an equilibrium search model that includes firm and worker heterogeneity, as well as gender-specific amenities and employer taste for discrimination. Using matched data for firms and workers in the formal Brazilian sector, they find evidence for taste-based discrimination, as it explains around 2/3 of the observed raw gender gap in earnings<sup>17</sup> of 15 log points. The gender earnings gap conditional on a richer set of dummies - years of schooling, interacted with potential experience dummies, and state, industry and occupation dummies - increases the earnings gap to 22 log points,<sup>18</sup> meaning that women are on average endowed
fees of 7% on average. Evidence from developed countries shows that even modest transfer taxes are highly distortionary: A 1.1% land transfer tax in Toronto has been shown to lead to significant welfare losses (Dachis _et al._ 2012) and, if the decision of whether to own or rent is accounted for, was estimated to incur a deadweight loss of 79% of revenue (Han _et al._ 2022). In the UK, transaction taxes caused large distortions to volume, timing, and price of property transactions (Best & Kleven 2018) and negatively affected land markets and labor mobility (Hilber & Lyytikainen 2017; Eerola _et al._ 2021). While their net contribution to public revenue is thus often modest, transfer taxes push property transactions into informality and encourage corruption and under-reporting to a point where price data become meaningless, and registries deteriorate. Replacing them with equivalent recurrent land tax on a much larger base is easy technically and can increase revenue, welfare, efficiency, and transparency.

Satellite imagery allowed cost-effective generation of urban tax maps at scale in Italy (Casaburi & Troiano 2016) and, via national programs, in India (Awasthi _et al._ 2020), and Brazil (Christensen & Garfias 2020). Computer-assisted mass appraisal (CAMA) of residential property is now standard in most developed countries (Grover et al. 2017). In the Netherlands 2.8% of GDP is raised in property tax revenue by transparently valuing millions of properties annually in a way that is equitable, trusted and perceived as fair as assessed property values are available publicly (Kuiiper & Kaathman 2015).

While these techniques have been successfully applied to African cities (Ali _et al._ 2020b), African countries overwhelmingly use formulas (i.e., charges per property or area unit, possibly for different zones or bands) that are less tightly linked to market values and thus more regressive. They often also require valuation by a licensed valuer even for residential property (Franzsen
**explored by Neumark and Wascher, 2006, among others).** This adverse employment impact is attributed to employers potentially substituting minimum wage workers with alternative resources like skilled nonminimum wage workers or capital. Economic theory also posits that heightened wages can lead to higher input costs, reduced production, and decreased labor demand. However, if the labor market operates under monopsonistic competition, a market structure where employers wield influence in determining wages,<sup>6</sup> a minimum wage hike might paradoxically lead to augmented employment. Moreover, the potential ramifications of minimum wages could also hinge upon factors like coverage and adherence to regulations (as highlighted by Posadas and Sanz-de-Galdeano, 2018).

**A potential risk of high minimum wages is an increase in the informality of the economy.** Since undeclared work is, by definition, not reported to authorities, accurately estimating its size is challenging. Due to the availability of information in the EU labor force surveys,<sup>7</sup> Eurostat does not publish statistics on informal employment. Estimates of temporary work and self-employment are available, but they are considered imperfect proxies of informality. Other global databases of informality do not include recent Romania estimates.<sup>89</sup> An EC study used an indirect approach (the Labour Input Method (LIM) to measure the size of the undeclared economy. The results indicate that Romania was one of the countries with the highest share of informal employment in the European Union. In 2013, the share of undeclared work in the private sector's gross value added was 26.2%. This was significantly more than the EU average (16.4%) and the second-highest result in the EU after Poland (27.3%). Romania also stands out when undeclared work is measured in terms of total labor input, with one of the highest shares of the EU (European Commission, 2017). The standard dual economy models of labor market segmentation suggest increasing
Policy Research Working Paper 10716

# **Abstract**

Countries worldwide are implementing GovTech reforms to modernize the public sector and achieve better performance while responding to citizens’ needs. At its core, GovTech represents a whole-of-government approach to public sector modernization, which emphasizes three critical aspects: (i) citizen-centric public services that are universally accessible; (ii) a whole-of-government approach to digital government transformation; and (iii) simple, efficient, and transparent government systems. Within this context, strengthening citizen engagement is crucial to ensure accountability, improve public policy quality, and enhance service delivery. Accordingly, this study aims to be the first cross-country empirical assessment of the impact of GovTech platforms, which can allow citizens to: (i) participate in policy decision-making and (ii) provide feedback on public service delivery. Using a large sample of 176 countries, the study assesses the impact of the implementation of national platforms that allow citizens to participate more effectively. This research employs entropy balancing as the main identification strategy, as well as propensity score matching and ordinary least squares regressions on the matching sample as alternatives. Additional robustness checks were performed using alternative GovTech Maturity Index 2022 data and by considering the possibility of a slower diffusion of the technology. A sensitivity analysis, considering the role of governance, political and institutional factors, as well as the level of development, is likewise performed. The results show a significant and positive impact of GovTech platforms on citizen engagement. Similarly, democracy and the equal distribution of political power have strong and positive effects on citizen engagement. By contrast, public sector corruption negatively and significantly impacts citizen engagement. The findings also provide evidence that GovTech platforms are more effective in fostering citizen engagement in high-income economies and in countries where the government is efficient, institutional and social fragility is low, and there is no conflict or only low-intensity conflict. The results of an Africa-focused analysis indicate that African countries that have adopted such digital platforms likewise experience an increase in citizen engagement.

This paper is a product of the
|HHs with 2 adults and at least 4 dependents|16%|19|6.4|4898|48%|
|---|---|---|---|---|---|
|HHs with 3 or more adults and at least 3 dependents|13%|25|7.2|5358|57%|
|Other HHs|2%|72|1.4|14305|4%|
|Total|100%|29|5.0|7499|31%|

**Source** : Authors’ calculations based on HIECS 2017/18. **Note** : “Dependent” is any individual who is 65 years old (or older) or 18 years old (or younger).

Table 5 complements this analysis of the profile of the poor in 2017/18. Again, children are more affected by poverty than adults. On average, poor households still have a rather large size (5.5 members) and have several dependents (2.6 dependents). Poor households are still very often headed by a person with low education. Finally, the fraction of poor households who owned a private car had barely changed since 2015, but the fraction of poor households who owned a smartphone had increased four-fold, likely reflecting the evolution of communication technologies rather than an improvement in their welfare.

_Table 5: Profile of the poor in 2017/18 (total expenditure)_

|Profiles among poor households (total expenditures)|Value for<br>poor HH|
|---|---|
|Children|36.3%|
|Fraction of<br>Elderly|16.3%|
|poor among<br>Men<br>Women|31.8%<br>30.1%|
|Average HH size|5.49|
|Average number of dependents|2.60|
|Fraction of