rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3 Experimental Design**

This section describes the study’s design, outlined in figure 2, including an overview of the recruitment and eligibility criteria, onboarding process, and intervention details.

<!-- Start of picture text --> Screening<br>on consent, smartphone, Internet, business ownership<br>Baseline Survey<br>Randomization<br>stratified on baseline Facebook marketing<br>Onboarding to WhatsApp Groups<br>collecting business pictures & information<br>Control Group Treatment Group<br>Logo Created & Shared Logo Created & Shared<br>Marketing Support for up to 6 Months Marketing Support for 3 Months<br>Asynchronous Marketing Training Asynchronous Marketing Training<br>Follow-Up Survey<br>6 months after baseline<br><!-- End of picture text --> Figure 2: Experimental Design

## **3.1 Recruitment and Onboarding**

The sampleframe consists of current and previous clients of Microfund For Women, one of the largest microfinance institutions (MFIs) in Jordan. The MFI provides eligible low-income individuals in Jordan with uncollateralized group and individual loans. As suggested by its name, the MFI particularly caters to women, which make up the majority of its clients, but does not specifically require borrowers to be women or to have a business.

To recruit participants, I randomly sampled potential study participants from the MFI’s beneficiaries database, which included active and recent borrowers across Jordan. Surveyors called and informed participants that we are conducting a study on the impact of new technologies on 9
.5 percent, and that the subsidy is not large enough (13.3 percent). Together, these data suggest that learning about managerial ability is not a central issue among these firms; the lack of hiring appears mainly to reflect a lack of demand for labor. However, we note that a sizeable number of respondents not hiring workers stated that they had not yet found the right employee (22.3 percent) or that they lacked the capital required to make the employee productive (27.1 percent). Hence, these data do indicate some potential role for search frictions and capital constraints.

- _Importance of access to capital_ : Among those not hiring workers, responses from the October 2009 survey indicate that only 40 percent would need to make a capital investment

24
**Table 4. Differences in non-Response Rates**

||Sequence|Coaches|Coaches|
|---|---|---|---|
||vs.|vs.|vs|
||Control|Control|Sequence|
||(1)|(2)|(3)|
|Missing (or omitting)student test|0.008|0.025|0.017|

_Source:_ Author's analysis based on experimental data generated for this study.

_Note:_ Each entry indicates the mean difference between the two experimental groups in the column for the students who missed or did not complete the student test. * indicates that the difference of means test is significant at 10 percent; ** significant at 5 percent; *** significant at 1 percent.

# **Table 5. Mean and Standard deviation of learning outcomes**

|Variable|C<br>|ontrol G<br>|roup<br>|Se<br>|quence G<br>|roup<br>|C<br>|oaches G<br>|roup<br>|
|---|---|---|---|---|---|---|---|---|---|
||N|Mean|Sd|N|Mean|Sd|N|Mean|Sd|
|Science score|790|0|1|771|0.506|1.119|801|0.681|1.156|
|Science score (Basic skills)|790|0|1|771|0.185|0.998|801|0.292|0.966|
|Science score (Medium skills)|790|0|1|771|0.554|1.088|801|0.583
turn, sell their products to the wholesalers-cum-retailers, retailers and large farmers. Wholesalers sell to retailers as well as farmers. Retailers, in turn, sell their product to farmers, but farmers frequently buy directly from the

> 4 The 1999 DGHS data, produced by the Health Information Unit of DGHS, indicates that over 30,000 cases of poisoning were reported in 1997, however, they do not classify information by the source of poisoning.

> 5 The comparable figure for the US is 71/100,000.

6
Figure 4: Poverty rates across urban versus rural areas <!-- Start of picture text --> (A) $2.15 poverty line<br>Official definition DOU DB<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>(B) $3.65 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Poverty rate (%)<br>Poverty rate (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Each boxplot shows the distributions of poverty rates over different geographic areas in 16 SSA countries. WorldPop 250m is used for the DOU and DB methods. The dashed lines represent the average national poverty rate in the sample.

Figure 5: Comparison of urban poverty rates between official and DOU/DB urban definitions <!-- Start of picture text --> (A) DOU vs. official definition (B) DB vs. official definition (C) DB vs. DOU<br>60 60 MWI 60 MWI<br>MWI<br>50 50 50<br>NER NER<br>40 40 40<br>UGA UGA UGA<br>TZA TZA TZA<br>30 NER 30 30<br>TCD TCD<br>ETH ETH<br>20 ETH 20 LSO 20 LSO<br>BFATCD LSOAGO BFA AGO BFAAGO<br>10 GABGINSENMRTCIVGNBGHA 10 GABGINSENMCIVRTGNBGHA 10 GABMRTGINSENGHACIVGNB<br>10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60<br>
Pichilingue and Sargento as “nonexclusive” cooperatives for the purposes of testing our hypotheses below.

Finally, it is important to understand whether any given fisher or cooperative can influence the market price of fish through their catch decisions. There are a large number of fishers in the area. As of 2010, based on data compiled by the National Commission of Aquaculture and Fishing (CONAPESCA), the number of fishers in La Paz alone was estimated at 974 people (748 cooperative members plus 226 unregistered fishers) (Leslie et al. 2015). According to data collected from La Paz fish markets by researchers from CBMC and the Scripps Institution of Oceanography, the three cooperatives we studied (Punta Abreojos, Pichilingue, and Sargento) were estimated to each provide approximately 12%, 5%, and 8%, respectively, of the fisheries product sold in the La Paz market (Sanchez, Nieto, Osorio, Erisman, Moreno-Baez, and Aburto-Oropeza 2015). Abreojos is more focused on the export market, however. These numbers come from an effort to enumerate the major players in La Paz markets and may be an over-estimate as smaller sellers are not easy to find. The size of the market shares suggest that these cooperatives will have 9
, skills and experience to be able to manage programmes in new ways”. The scope of problems ADG was tasked with fixing was large and diverse to the point of being unmanageable; indeed, to some degree we consider it a reflection of their inexperience that they took on a performance‐based contract with these payment terms.<sup>19</sup> The firm also struggled to support effective monitoring of outcomes, with Bank staff expressing serious concerns about ADG’s ability to collect credible data and carry out quality data analysis. In the absence of the firm carrying out a household survey (as had been recommended in the ToRs), it was very difficult for Bank staff to monitor their progress. Our analysis of ADG’s reports also indicates that their reporting skills were weak – language was often convoluted and opaque, and there was little reflection on problems fixed, adaptations made, or assumptions revised.<sup>20</sup> The reports tend to follow a more traditional structure, focusing on inputs delivered. A failure to methodically capture collectively identified problems and their causes appears to have led to limited reassessment of assumptions and reduced the quality of ongoing learning. Adaptation was certainly encouraged and incidents of it during implementation are evident, but it only got going latterly, shortly before the TA itself ended altogether.

Some Bank staff and federal level actors questioned whether ADG had sufficient technical understanding and expertise to carry out their assistance role. According to the federal level actors (with whom ADG had an unfortunately antagonistic relationship), ADG implemented technical solutions in the states that were ineffective and not based on a good understanding of what interventions would have the best results. Partly, this critique reflects a more traditional best‐practice

> 19 It is worth noting that the other bidding firms had substantially higher cost proposals, suggesting they did not judge the performance payment aspect worth the risk.

> 20 To some extent this is because the Bank staff also struggled with how to monitor and evaluate this work and found
Studies regarding intergenerational transfers in China are few, possibly because available data are in short supply and not well suited to study this set of questions. In recent years, with China’s rapid development and aging, more studies have been done (Cai et al., 2006; Chou, 2010; Lee and Xiao, 1998; Giles et al, 2010; Goh, 2007; Secondi, 1997). For example, Secondi (1997) used data from a large 1988 household survey to test the hypotheses of altruism and exchange and to study the size and direction of transfers in rural China. He found that most of the money flows appeared to be transfers from adult children to elderly parents and remittances from migrants. Cai et al. (2006) addressed how households with elderly members coped when enterprise-based or local public pension systems failed to provide sufficient income. They found evidence that the transfer flow was from children to parents and that private transfers responded to low household income of retired workers when income fell below the poverty line. Giles et al. (2010) find that there is more risk associated with transfers into households with migrant adult children. All of these studies have the same weaknesses that transfers are defined as a household aggregate for which the donors are unspecified, rendering these studies unable to differentiate between inter-generational and intra-generational transfers.

# **2. Data**

We draw on the recently released 2008 pilot of the China Health and Retirement Longitudinal Study (CHARLS), a survey conducted from July to September in 2008 by the National School of Development at Peking University (Zhao et al., 2009). As one of 7
this paper is the analysis of employment transitions for different population groups during the pandemic to examine not only the scope of employment changes, but also changes in the quality of employment across different population groups.

Despite the uneven impact of the pandemic across population groups, the estimated shortrun impacts of COVID-19 on aggregate within-country income inequality so far appear to be mixed (World Bank 2022b).<sup>5</sup> However, research on the inequality impacts of previous pandemics suggests that they could peak 4-5 years after the onset of the pandemic (Furceri et al. 2022). The impacts of the COVID-19 pandemic on inequality could worsen over time

> 5While traditionally vulnerable groups appear to have experienced greater losses as seen in group-wise comparisons, the final impact on within-country inequality depends on where these groups are located in the overall income distribution. In an analysis combining prepandemic household survey data with information from high-frequency phone surveys and macroeconomic growth projections, Mahler et al. (2022) estimate the change in the Gini index after COVID-19. These projections complement Ginis published by National Statistical Offices and estimates from the literature and household surveys conducted in 2020. The result shows a mix of positive and negative changes in within-country inequality, with the magnitude being small in most cases.

5
Reduction Fund (PRF), a community-driven development program aimed at reducing poverty through infrastructure improvements in education, drinking water, irrigation, health, and transport. An evaluation carried out in 2015 showed that the PRF had been successful in improving infrastructure in rural areas but that post-completion, road quality deteriorated quickly. Under pressure from harsh rainy seasons and vehicular traffic, the access roads typically fell into extreme disrepair within a couple years and often became unusable. Aiming to provide a part-time supplementary income-earning opportunity to at-need households and to extend the lives of the access roads, a pilot Road Maintenance Group (RMG) program was established, in which female villagers from poor households would be trained and formed into work units to provide basic road maintenance services.

Two eligibility criteria were used to recruit interested RMG members from each village:

- I. Candidates must belong to a poor household, based on PRF poverty ranking, assigned in 2016 and updated by village heads on the day of the selection.<sup>4</sup> Only one woman from each such household could participate in the program.

> 4 Dervisevic et. al. (2020) describe this process and confirm targeting is effective.

5
factors, led to tight power supply conditions and high spot prices in Philippines in 2010 and 2013, as further discussed later. However, inflexible gas‐supply has been at least partially alleviated recently due to the 2010 IPP administration contract of the 1.2 GW Ilijan plant, and the commissioning of two new power plants in 2016 (The Lantau Group, 2011, 2013).

Hydrological conditions also affect the power sector due to the significant share of run‐of‐river and dam hydro power plants, which account for 16% of dependable generation capacity in 2017. Hydrological conditions are especially important during El Niño meteorological phenomena which result in unusually hot and dry weather in Philippines. Such an event coincided with maintenance shutdown of the Malampaya gas field during 2010, leading to high spot prices and tight supply conditions, as further discussed later.

# 3 Market design

The Philippine power market is composed of bilateral contracts, and an energy‐only bid‐based power pool (the Wholesale Electricity Spot Market, or WESM). The market operator (PEMC) develops a least‐cost generation schedule and determines the market‐clearing spot price considering all power injections and withdrawals from the grid, based on generation offers. However, the spot price is used to settle traded quantities net of bilateral contracts (that is, quantities not covered by contracts). In turn, contracts are settled privately by the parties.

> 5 Department of Energy, “2016 Coal Statistics”. Available online: https://www.doe.gov.ph/energy-resources/2016- <u>coal-statistics</u>

> 6 Department of Energy, “Private Sector Initiated Projects” as of December 2017. Available online: <u>https://www.doe.gov.ph/private-sector-initiated-power-projects</u>

> 7 Natural gas-fired power plants reached an average capacity factor of 70% (with respect to registered capacity) during 2016 (Market Assessment Group, 2017).

6
Klapper and Panos (2011) find that financial literacy is negatively correlated with private pension planning in Russia. In Chile, Landerretche and Martinez (2011 find that pension plan literacy increases savings, as well as the probability of pension fund type switching. Skog (2006) finds that older, healthier, more educated, married male workers, as well as union members, higher income, and workers at larger companies are more informed about the Chilean pension system.

# **_Financial literacy is associated with more sophisticated investment behavior_**

A variety of evidence indicates that financial literacy is correlated with investment behavior. Volpe et al. (2002) find, in a survey of online investors, that older male investors with graduate degrees have higher investment literacy. Calvet et al. (2009), analyzing Swedish households, find that financial sophistication increases with financial wealth and household size, and is positively correlated with holding risky assets. Bucher-Koenen and Ziegelmeyer (2011) find that while German investors with lower levels of financial literacy were less likely to have invested in the stock market, and thus less likely to have lost money in the financial crisis, they were also more likely to realize losses on the assets they did sell.

Several studies have moreover found causal effects of financial literacy on investment behavior. Van Rooij and Lusardi (2007), in an analysis of Dutch survey data, find a causal effect of financial literacy on stock market participation. Abreu and Mendes (2010), in a survey of Portuguese investors, find that higher levels of education and financial literacy have a positive impact on portfolio diversification, while Guiso and Jappelli (2009) find similar results for Italian investors.

# **_Financial literacy affects debt and mortgage outcomes for individuals_**

The financial crisis has been blamed in part on low levels of financial literacy in the U.S., particularly among households who defaulted on their mortgages. Gerardi et al. (2010) indicates that there may be some truth to this, as the study finds
Overviews, Cash flow, BvD ID changes, Banks’ financials, Insurances’ financials, Industry's financials, and Key financials.

> 23 ORBIS is not a single database but rather a collection of separate modules and partitions of data that required further processing to be able to construct the BOS database. For instance, the ownership links are provided for each year in a separate module from the annual financial indicators and from the entity type (atemporal file).

> 24 The information includes the ownership participation of any company A in a company B, on the date of the link.

> 25 This is the total number of connections identified in the form entity A has participation (x%) in company B previous to the cleaning developed by (Cusolito, 2020), which helped to remove historical ownership links that were no longer valid.

12
**Parameters.** The labor share is set to match the average share of labor compensation in non-resource GDP over 2000-2019. This requires adjusting the PWT labor share, which applies to the whole economy, usually less labor intensive than the non-resource sector. Specifically: t ⁄ 0 PPWWPP where t is the share of labor compensation in GDP in period tt tt tt _t_ , taken from Penn ββ = ββ x RRDDPP RRDDPP PPWWPP 0 World Table 10 (PWT10), and is non-resource GDP in period _t_ . The baseline ββ calibration sets equal to the average value of tt over recent years. RRDDPP To calibrate the resource rents in resource industry tt , , we use information on ββ ββ natural resource rents shares, provided by the Global Trade Analysis Project ii ii γγ (GTAP), and averaged over 2004, 2007, 2011, and 2014.<sup>27</sup> The tax rate in the resource sector, , is calibrated using data on government natural resource revenues and resource GDP from the IMF’s World Commodity RR ττ Exporters Dataset (IMF-WCE). Specifically, is set to match the longest available historical average of resource revenues as a share of resource GDP since 2000. RR ττ

The fiscal rule in the Default submodel requires the calibration of the marginal propensity to invest, (see equation (13)). For BBR-HR and SSR, is set to one and zero, respectively. For BBR, is the average ratio of public investment to total θθ θθ expenditure over 2000-2019, taken from the IMF-WEO and the Investment and θθ Capital Stock Dataset provided by the IMF Fiscal Affairs Department (IMF-FAD). The External-Balance model requires five additional parameters: the private investment crowd-in parameter the average tax rate on the non-resource economy 0, the debt-elastic interest spread , the world real interest rate , and how the λλ, budget balance responds to debt . measures the response of private investment WW ττ ψψ ff to the fiscal balance (equation (19)). Its default value is set to
If the exogenous total number of entrants by region is available, then:

# **c. Data requirements**

The proposed approach may be applied to data on household consumption, typically data from household expenditure or consumption surveys. Three key variables must be drawn from the surveys: (1) a per capita welfare measure to estimate poverty (often calculated as the total consumption or income of a household, divided by the number of household members or adult equivalent members), (2) per capita consumption or expenditure on a good or service of interest, and (3) a relevant poverty line estimated according to household consumption or income.

In addition to microdata from household surveys, a minimum set of parameters defining baseline and reform scenarios of the underlying market structure or a given industry are required, including the number of new firms competing in a market and price elasticities of demand. The model for the extensive margin module that estimates new consumers also requires demographic and spatial variables derived from the household expenditure or consumption survey. The dependent variable for the probabilistic model is a binary dummy indicating whether the household or individual (denoted by h) has any positive or null consumption in the good or service of interest. The (proportional) price change h and the (proportional) welfare change, h, must be included, along with a set of household and individual characteristics and policy variables linked to the use or consumption of the ddpp ddI IIPPIIuu good or service of interest (age, sex, geographical location, literacy, educational attainment, employment status, household assets, and services).<sup>16</sup>

# **3. The mobile telecommunication industry in Ethiopia**

As of 2019, Ethiopia was one of the three countries in the world in which the mobile phone market was a monopoly. In 2018, among countries in Africa, Ethiopia ranked 4th lowest in mobile phone penetration (37.2) per 100 people and 12th lowest in fixed broadband subscriptions (0.05)
Cross-country evidence shows that more concentrated banking systems are less likely to suffer systemic fragility; at the same time, competition also contributes to greater stability (Beck, Demirguc-Kunt and Levine, 2006; Schaeck, Cihak and Wolfe, 2006). When analyzing the channels through which concentration might be positively associated with banking system stability, Beck et al. find tentative evidence that more concentrated banking systems allow better possibilities for banks to diversify risk. On the other hand, they do not find any evidence that it is easier for bank supervisors to monitor more concentrated banking systems or that stability results from market power and the consequent increase in franchise values of banks in more concentrated banking systems. In summary, higher concentration levels do not necessarily imply less competition, but might affect bank stability through other channels. Considering bank-level data, researchers have arrived at different conclusions. On the one hand, Boyd, de Nicoló and Jalal (2006) find banks are closer to insolvency, i.e. more likely to fail, in countries with more concentrated banking systems. On the other hand, Schaeck, Cihak and Wolfe (2006) and Schaeck and Cihak (2007) show that banks have higher capital ratios in more competitive environments.

Summarizing, there is no clear indication that competition is detrimental per se for bank stability or that a more concentrated banking system necessarily implies less competition. Similarly, cross-country studies have shown little effect of market structure on net interest margins, but rather highlighted the importance of a contestable and open banking market without restrictive regulatory policies (Demirguc-Kunt, Laeven and Levine, 2004). Evidence for Uganda has shown that macroeconomic policies and deficiencies in the contractual framework are the most important factors explaining high spreads and margins (Beck and Hesse, 2009). While small scale can also explain high spreads, market structure indicators are not significantly associated with bank efficiency. Overall, while larger banks might result in greater overall efficiency, the resulting higher concentration is not necessarily associated with increased or reduced efficiency. The effect of market structure