rafmacalaba/data-use-mentions-extended huggingface dataset:
30% of total net national income, the same share received in 2003. The top 0.1% received approximately 20% of income throughout the period. Broader measures of top income shares show decreases in the period – the top 10% share decreased from 60% in 2003 to 55% in 2019. These levels of income concentration place Honduras among the most unequal countries in the world. According to WID (2022), the richest 10% of the global population currently claims 52% of the world’s income. The Middle East and North Africa (MENA) region stands out as the most unequal region in the world (58%), while Latin America follows closely behind (55%). Moreover, Bergolo _et al._ (2023) show that in Latin America, the top 1% of earners receive approximately 20% of total income - this represents the highest concentration of income among regions globally. Within the region, only Mexico and Peru, where the top 1% receive 27% and 28%, respectively, register inequality levels similar to the ones we document in Honduras. As we discuss in more detail below, this might be partially driven by the lack of granular income data at the top of the distribution for other countries in the region.

The detailed tax records we use allow us to provide a granular description of levels and composition of income at the very top of the distribution. We show that, while labor income represents close to 90% of total income for individuals all the way to the top 1%, within the top 1% highest earners, capital income becomes very important: it represents half of income for those in the top 0.1% and more than 80% for those within the top 0.01% highest earners. We also document that the trends in capital vs. labor share in the top 1% fully drive total income shares - the share of labor in total income decreased from 70% in 2011-2014 to close to
**Figure 8: Estimated Probability of Being Shortlisted for BPO Jobs, by Amount of Time between the Job**

**Posting and the Submission of the Application**

Source: Authors’ analysis using Babajob database Note: The figure shows the estimated probability of being shortlisted by using a probit model, controlling for location, number of vacancies, month, and applicants’ basic characteristics.

# **5.4. Predictive Analysis of Skills Demand**

Online job-portal data can facilitate predictive analysis based on historical labor market trends and emerging dynamics. In many developing countries the institutions charged with building workforce skills often struggle to improve the employment prospects of their trainees due to skills mismatches in the labor market. While analysts and policy makers often attempt to forecast changes in skills demand in order to better target training programs, these efforts are frequently constrained by a lack of timely and accurate information on labor market conditions.

Workforce surveys may take months or years to publish, resulting in a substantial lag between when information is collected and when it is available to policy makers. Traditionally, many countries have used “manpower planning” approaches to forecast labor demand. Manpower planning focuses on headcount imbalances and uses highly subjective employer surveys to determine the number of workers required in each occupation.<sup>17</sup> The limitations of this approach have increasingly led policy makers to replace it with different types of labor market analysis (Psacharopoulos 1991).

Some international organizations conduct workforce surveys and publish information on employment trends at the regional or country level (e.g. CEDEFOP 2010 and ILO 2016). Econometric modeling can be

> 17 See Spalletti (2008) for a review of manpower planning and forecasting approaches.

20
code governorate migration, qism migration, and sheyakha migration if a respondent experiences a move across relevant geographic units between two survey waves.

Notes: ELMPS tracks international migrants but it is not possible to directly link them to the baseline survey. Consequently, we decided against including international migrants in the analysis as we could not measure their occupation type at baseline.

# **B.3 India Human Development Survey (IHDS)**

Baseline (2005): 49
Using this data, we estimate: where _αb_ again represents the matched-triplet fixed effects; _δt_ are week fixed effects; _s ∈{_ 1 _,_ 2 _,...,_ 160 _}_ indexes sections; and _t ∈{_ 1 _,_ 2 _,...,_ 34 _}_ indexes weeks. For panel models, we cluster our standard errors at the section-level, which in the Ebola sample coincides with the clinic, the level of randomization.<sup>23</sup> We amend Equation 3 to detect spillovers within our study sample — namely, the reallocation of patients from control to treated sections (or vice versa). Specifically, we interact our treatment indicators with covariates that, in the presence of such spillovers, should moderate our treatment effects (e.g., distance between sections, connections via roads).

# **4. Results**

## **4.1 Medium-term Outcomes**

We present the main medium-run survey results in Table 1, which shows estimated effects on clinic utilization, health outcomes, and patient satisfaction.<sup>24</sup> Our tables follow a common format. Column 1 provides the control mean and standard deviation, which are zero and one exactly when looking at family-level meaneffects indices. Column 2 presents the average treatment effect (in standard-deviation units) when pooling the treatment arms in a comparison with control. Columns 3 and 4 separately estimate the average treatment effects for CM and NFA, respectively. Column 5 shows the difference between the average treatment effect in CM and NFA. Column 6 provides the F-test for the joint null hypothesis of no effect from either treatment. Finally, Column 7 gives the sample size used for each regression.

Table 1 shows uniformly positive treatment effects across families for CM. Specifically, the general utilization of clinics increases by 0.11 standard deviation units when we pool the treatments, and the separate effects are similar for CM and NFA.
# **1 Introduction**

In principle, place-based development programs can bring substantial advantages to firms (Duranton and Venables, 2018). The expectation is that firms in these zones would perform better because they would operate under better institutional arrangements, superior infrastructure, and benefit from other fiscal and non-fiscal incentives than their peers outside. Moreover, the urban economics literature emphasizes the returns from ”sharing, matching, and learning” brought about through agglomeration effects (Duranton and Puga, 2004) and improved matching between workers and firms through denser labor markets (Moretti, 2010).

In practical terms, policymakers find it attractive to adopt zone programs, under a variety of names, for understandable reasons (UNCTAD, 2019). Firstly, initiatives targeting subnational regions or discrete local areas are easier to implement than national-level enabling environment reforms, especially in countries with weak institutional capacity. Secondly, they are less costly to adopt than upgrading industrial infrastructure on an economy-wide scale, and if their financing is shared with private developers, the government budget burden is reduced. Thirdly, they can serve as an effective tool to attract international investors, who typically shop around globally for better investment incentives.

Despite the solid theoretical foundations supporting the advantages of spatial development programs, their real-world effectiveness is not assured. Global experiences with such programs have revealed potential pitfalls in their planning, development, and operation. These include costs stemming from inadequate strategic planning, a failure to adopt a demand-driven approach, neglect of critical market and government failures, a poor policy and regulatory environment, weak implementation capacity, and an inability to address environmental and social risks effectively (Zeng, 2021). As a result, the effectiveness of any given zone programs remains an empirical question.

The contribution of this paper is that it provides some answers to this question drawing from a comprehensive and unique firm-level survey conducted across South Asian countries. While there has been ample empirical work focused on the efficacy of zone programs in economically advanced countries (Neumark and Simpson (2015) provides
Petroleum Corporation (NNPC) based on public data on oil production, global oil prices, standard cost estimates and cost patterns from outdated accounts of the company. The then Managing Director of NNPC was so interested that he spent four hours discussing the financial model. In no other case was a person at this level willing to spend that much time discussing substantive issues without even a hint of a deal on the table.

While comprehensive accounts are problematic for corrupt practices, some form of monitoring is needed on whether bribers paid and whether bribes received their agreed share. When IMF staffers once probed the fiscal accounts of Nigeria they could not reconcile numbers. When pressing for explanations, they were - allegedly - in the end able to see an accounting of major expenditures maintained at the very center of government in an exercise book. While this is no way to keep professional accounts, it might be a rational method for keeping track of corrupt payments. Such informal accounting need not be as comprehensive as full accounts, but just needs to track critical payments. It need just be used for monitoring of corrupt agreements not for managing a company or government finances.

# **A Country Case: Nigeria**

What might be the overall outcome of a system that embodies the features discussed above for a whole country? Consider Nigeria. One can easily document a few striking patterns.

First, policy-making for large projects has been extremely stable – no external shock, no shift in relative prices used to dislodge or reshape projects, once they were agreed. Until recently, almost all large projects were already contained as proposals in a consultant report by Arthur D. Little in 1964. They progressed inexorably, albeit excruciatingly slowly at times. The poster child for the syndrome is the multi-billion dollar Ajaokuta steel plant: conceived in 1964, started in 1979 with outdated Soviet technology, completed in 2004, shut down again and re-opened in 2010. Official commission reports occasionally recommended that several projects be stopped, including Ajoakuta – with no effect<sup>8
at border crossings generates gains in terms of real income of around 2.5% in the region. Both cross-border coordination in the trade facilitation reform agenda and EU accession amplify these gains significantly. Similarly, EU accession boosts the benefits of road infrastructure improvements. In conclusion, the main policy message from the paper is that cross-border and cross-reform coordination is necessary to take advantage of regional integration opportunities.

# **2 Data**

The study focuses on the six Western Balkan economies, but it also includes data for the 27 European Union countries, the most important economic neighbor of the Western Balkans region. Since we are interested in assessing how impacts differ within countries and/or 2
1 The Role of Justice in Development: Empirical Evidence 14 a significant role on crime desistance. For instance, two studies find that increasing non-carceral punishments such as fines or probation (in the context of DUI and traffic offenses) has a deterrent effect on reoffending (Hansen, 2015; Gehrsitz, 2017). Another intervention such as replacing short prison sentences or pre-trial detention with electronic monitoring also has net benefits on reducing recidivism–possibly due to the avoidance of the criminogenic effects of prison–and may improve secondary school completion for young offenders (Di Tella and Schargrodsky, 2013; Monnery, Henneguelle, and Kensey, 2016; Østergaard Larsen, 2017). On the other hand, the evidence on increasing prison sentences for those on the margin of being incarcerated shows mixed effects on future offending and employment.

There are two studies highlighting the importance of the difference between time served in prison relative and the initial, expected sentence (Bushway and Owens, 2013; Monnery, 2016). Conditional on the actual time served, those whose recommended sentences are longer tend to be more likely to recidivate after released.

There is also substantial evidence that cognitive behavioral therapy is effective at reducing recidivism (Voorhis et al., 2004; Pearson et al., 2016; Barnes, Hyatt, and Sherman, 2017; Heller et al., 2017), and some evidence showing that drug courts (as an alternative to regular courts) can reduce recidivism (Prins and Officer, 2015). In addition, courts may increase access to jobs by providing rehabilitation certificates, shifting the risk of hiring someone from the employer to courts (Leasure and Stevens Andersen, 2016).

In a cross-country analysis using micro-data from 25 European countries, N. H. Mocan, Bielen, and Marneffe (2018) find that higher judicial quality, measured via the appointment procedures of judges and prosecutors, generates a two-fold effect: first, it diminishes individuals’ criminal and dishonest behaviors; and second, it reduces tolerance for unlawful behavior and increases regard
, but also as the expected response to the demand of both current and new users. The changes in the value of the elasticity occur because of the simulation of a large price drop for the item of interest, resulting in a larger magnitude in the quantity demanded and a decline in the absolute value of elasticity.

All current users of ICT services across the consumption distribution are expected to benefit from greater competition in the telecommunication sector, but the gains will disproportionately benefit households in the top consumption deciles because they adopt ICTs at higher rates and spend more on the services, both in absolute and relative terms. In contrast, the effects on poverty reduction among current consumers are expected to be modest because most households at the bottom of the consumption distribution and around the poverty line exhibit low ICT adoption and low ICT expenditures.

**Figure 2. The Market Share of the Telecommunication Monopoly and Consumer Prices**

<!-- Start of picture text --> 0 20 40 60 80 100<br>Market power (market size in %)<br>3<br>2.5<br>2<br>Price (competitive market = 1) 1.5<br>1<br><!-- End of picture text --> _Source:_ Elaboration based on data derived from the WELCOM Stata Tool.

The results indicate that, based on simulating a decline in Ethio Telecom’s market share in mobile services from 100 percent to 45 percent and a price elasticity of demand of −1.50, a shift to a more competitive market would induce a 0.22 percentage point reduction in poverty among current users 11
(2021) analyze the early socio-economic impacts of the pandemic in Ethiopia, Malawi, Nigeria and Uganda, where they estimate that 77% of the population lost income during the pandemic, while 60% of the adult population experienced either moderate or severe food insecurity.

Regarding the labor market impacts of the pandemic, the International Labour Organization (ILO) estimates that working hours in SSA declined by 7.3 percent in 2020.<sup>3</sup> The estimated working hour losses in Nigeria and Ethiopia, the two most populous countries in Africa, were 9.0 and 8.5 percent. For virtually all countries in Sub-Saharan Africa, however, the ILO estimates are not based on survey data, but are extrapolated from Google Community Mobility Reports and the Oxford Stringency Index (ILO, 2021). However, high frequency economic data are available for several African countries to aid this effort and to inform policies intended to mitigate the effects of the pandemic. For 12 SSA countries with high frequency phone survey data available, Kugler et al. (2021) and Khamis et al. (2021a) document that 24 percent of workers stopped working in the period April-July 2020, ranging from 8% in Madagascar to 62% in Kenya. Kugler et al. (2021) further show that job loss in SSA was higher among women, young workers, highly educated workers, and urban workers, with a particularly significant urban/rural divide.

In this paper, we use high frequency phone survey data from Ethiopia, Malawi, Nigeria, and Uganda to analyze the impacts of the COVID-19 crisis on work (including wage employment, self-employment, and farm work) and income, as well as heterogeneity by gender, family composition, education, age, preCOVID-19 industry of work, and between the rural and urban sectors. These four countries represent 34 percent of the Sub-Saharan Africa population and have pre-existing face-to-face panel surveys from 2010 to 2019, which were used as a sample frame for the High-Frequency Phone Surveys (HFPS
A. Sanghi and D. Johnson <!-- Start of picture text --> 80<br>200<br>60 Business Services<br>150 Construction<br>Business Services Design, Development & Testing<br>Design, Development & Testing Electricity<br>40 Education & Training Extraction<br>Headquarters 100 Headquarters<br>Manufacturing ICT & Internet Infrastructure<br>Sales, Marketing & Support Logistics, Distribution & Transportation<br>20 50 ManufacturingSales, Marketing & Support<br>0 0<br>Industry Activity Industry Activity<br>Average Capital Investment (USD Millions) Average Capital Investment (USD Millions)<br><!-- End of picture text --> (a) Average Chinese FDI by industry activity (b) Average FDI by industry activity Figure 25: Chinese investment per project highest in headquarters creation and manufacturing (2003-2015) Source: fDi Intelligence from The Financial Times Ltd 2015

# **3.2.2 Corruption is the biggest obstacle for Chinese firms**

Chinese companies identified corruption as the most significant obstacle to doing business in Kenya: 53 percent of respondents said corruption was a “very significant obstacle” and 15 percent mentioned corruption as a “significant obstacle” (SACE 2014). Crime, theft, and personal safety was the second most significant obstacle: 37 percent said it was “very significant” and 25 percent responded “significant” (SACE 2014). 32.3 percent of foreign companies in the World Bank Enterprise Survey (2013) said corruption was a major constraint. According to a Kenya National Bureau of Statistics (KNBS) survey, 76.4 percent of respondents said corruption hurt their business. The negative perception of corruption was consistent across all sectors: manufacturing, transportation, financial services, and wholesale and retail trade (KNBS 2013).

The BPI also finds that the Kenya Revenue Authority (KRA) is more likely to visit Chinese companies. The KRA visited Chinese companies 3.
**Account use.** Figure 3 shows patterns of account use over time for the payroll account and account only groups for the first six months post treatment.<sup>36</sup> The figure is based on administrative data, so that there is no scope for interviewer demand effects. The figure reveals that employees assigned to the payroll account treatment use their account more actively than employees assigned to the account only treatment group. Figure 3, panels (a) to (c) show that they make more withdrawals, more deposits, and more send-money transactions.<sup>37</sup> The observation that employees in the payroll account group use their account more frequently overall (Figure 3, panel (d)) is in part mechanical, given that workers in this group receive their wages into the account and make withdrawals to cover daily expenses. In Figure 3, panel (e) we therefore exclude withdrawals and show that employees in the payroll account treatment condition use their account much more actively, even when we consider only deposit, payment, and send-money transactions.<sup>38</sup>

Table 3, reports treatment effects on account use. The dataset we use for this exercise is a panel that records the universe of transactions for all participants who received an account as part of our study. Because administrative data is not available for the control group, and we are most interested in a comparison of the payroll account and account only groups, our analysis uses the fact that treatment date was staggered over time and randomized at the individual level. We define a time-varying treatment indicator for each account in the data that is equal to zero prior to account opening and equal to one thereafter. This allows us to estimate treatment effects for the payroll account and account groups. Table 4, panel A uses dummy variables for all outcomes, so that the treatment effect estimates can be interpreted as the share of workers in each treatment condition that made a given type of transaction.
labor market losses. Overall, the share of the working age population who reported being employed in April-June 2020 was, on average, 31 percent lower than prepandemic levels. However, as we note in the previous section, the absolute magnitudes of these initial job losses, inclusively in the earlier studies, should be interpreted with caution, as the magnitude of any recall bias associated with the framing of the prepandemic employment question in the HFPS is unknown.<sup>15</sup> Given that many countries did not have a survey in all three periods, it is possible that dynamics of certain indicators across the three time periods may be a function of the composition of the sample within each time window. To address this concern, we also consider employment estimates for a sample restricted to countries with data points in all three periods, such that the sample composition remains constant across the three time periods. We attempt this whenever possible and include results for a “country panel” in Annex 6, however, data coverage varies significantly depending on the harmonized indicator. Overall, employment trends for a panel of 31 countries are similar to the full sample (Table A6.1).

Updated estimates reaffirm that the initial impacts, as well as the pace of the subsequent gains, varied considerably across country groups. MICs experienced the largest employment losses in the initial stage of the pandemic, followed by an increase in employment levels in the second half of 2020 and through 2021. Among a small sample of HICs, the initial magnitude of job losses was much smaller, but has been followed by a much slower pace of recovery. In LICs, losses of employment were subdued, compared to those reported in MICs. There could be several contributing factors behind lower employment losses in LICs. First, the extent of restrictions to mobility and economic activity, as measured by the OxCGRT stringency index, was somewhat lower on average in LICs. In addition, LICs have a much higher share of agricultural employment and a higher share of population
of the schools participating in the two experiments. The school census data and standardized exam scores were obtained through the National Council for Education Development (CONAFE). The survey data are publicly available on the World Bank Microdata Library.<sup>6</sup> At the school-level, we merge the school census data with parent surveys, which were completed by the head of the parent association. At the student-level, we merge the national standardized exam with

> 5The doubling of the grant was made possible through various donors including: Cinépolis, Deutsche Bank, Fundación Lazos, Fundación Televisa, Gillette Hall, JP Morgan Foundation, Panamerican Development Foundation and Western Union Foundation.

> 6Mejia and Filus (2018) have used the survey data from the double grant experiment to explore which variables predict improvement in student test scores.

7