rafmacalaba/data-use-mentions-extended huggingface dataset:
16 households per enumeration area in the survey sample. Besides the disappointing performance of the standard geospatial poverty estimation and the Meta wealth index estimates in this challenging context, this exercise also illustrates how much the inclusion of additional data from the partial registry improves the performance of the prediction. The partial registry effectively adds valuable information to the training data when using geospatial data for small area estimation. This enables the development of a much more accurate prediction model, using proxy welfare variables that are cheaper and easier to collect.

The importance of training data raises the question of whether estimating different models for different geographies, such as urban and rural areas, improves prediction accuracy. Estimating separate models may improve the accuracy of the estimates by better accounting for heterogeneity across regions. However, these models also utilize less training data, which reduces the richness of the prediction model in the typical case when the sample is used to select or tune models. Newhouse et al (2022) provide some evidence on this question, comparing monetary poverty estimates in Mexico derived from a national model, separate models for urban and rural areas, and separate models for each of six state groupings. Compared to a baseline national model, estimating models separately for urban and rural areas leads to a minor improvement in accuracy, raising the correlation with census-based estimates from 0.86 to 0.87 in sampled areas and from 0.70 to 0.71 in non-sampled areas. Estimating separate models for six groups of states led to a similar minor improvement. These findings for Mexico. however, do not necessarily generalize to other contexts, and additional evidence comparing the accuracy of models specified at different levels would be useful. Tree-based machine learning methods, such as those discussed below, provide more flexible alternatives that explicitly model interactions between predictors. Use of these models should therefore mitigate any benefit from estimating multiple models in disaggregated geographic regions or areas.

e. Out-of-sample predictions are significantly less accurate than in-sample predictions Informative sampling occurs when sampling probabilities for Primary Sampling Units vary in a
Policy Options for Broadband Infrastructure Strategies: A Simulation Model for Affordable Universal Broadband in Africa

## Edward J. Oughton<sup>12*†</sup>

1College of Science, George Mason University, Fairfax, VA, USA 2Environmental Change Institute, University of Oxford, Oxford, Oxfordshire, UK

*Corresponding author: Edward J. Oughton (e-mail: eoughton@gmu.edu)

†Research supported under the Effective Regulation for Digital Development program, supported by the Digital Development Partnership administered by the World Bank Address: College of Science, George Mason University, 4400 University Drive, Fairfax, VA

# JEL Classification

L5 Regulation and Industrial Policy; L96 Telecommunications

# Key words

Policy, broadband, universal service, 4G, infrastructure.

# Acknowledgments

The research was supported under the Effective Regulation for Digital Development program, supported by the Digital Development Partnership administered by the World Bank. The author would like to thank multiple colleagues for their review feedback, including Tania Begazo, Moussa Blimpo, Mark A. Dutz, Izak Atiyas, Toker Doganoglu, Vivien Foster, Justice Tei Mensah, and Timothy Kelly. This paper was presented at the World Bank Africa Series Seminar in March 2021. Additionally, a conference version of this paper was recognized with the Benton Early Career Scholar Award at TPRC48. Finally, the author would like to thank the governments of Senegal and Kenya for site location data.
1, episodes of USD appreciation vis-à-vis the Euro and other currencies seem to have negatively affected garment exports and have made trips to Cambodia more expensive for non-American tourists. The seeming decline in the competitiveness of the tradable sector may also partly explain the increasing share of credit flowing into construction and real estate that recent studies associate with higher financial risks (Ahmed et al., 2014; IMF, 2017).

**Figure 1** : US dollar appreciation correlates with a decline in Cambodian garment exports to the EU

<!-- Start of picture text --> 120% 10%<br>100%<br>5%<br>80%<br>60%<br>0%<br>40%<br>-5%<br>20%<br>0%<br>-10%<br>-20%<br>-40% -15%<br>Exports to EU, yoy EU/USD, quarterly change<br>Poly. (Exports to EU, yoy) Poly. (EU/USD, quarterly change)<br>Change in exports<br>Change in the exchange rate<br>1999-Q2 2000-Q2 2001-Q2 2002-Q2 2003-Q2 2004-Q2 2005-Q2 2006-Q2 2007-Q2 2008-Q2 2009-Q2 2010-Q2 2011-Q2 2012-Q2 2013-Q2 2014-Q2 2015-Q2 2016-Q2 2017-Q2<br><!-- End of picture text --> Source: European Central Bank, IMF Directions of Trade Database. Note: “Poly.” refers to a polynomial fit line to existing data points.

In order to gain a more complete understanding of the limitations posed by the lack of independent monetary and exchange rate policy under dollarization, this paper assesses the hypothesis that US interest rate increases, resulting in USD appreciation, negatively affect Cambodian exports to the EU. The remainder of this paper is organized as follows. Section 2 presents a brief literature review. Section 3 discusses the theoretical framework and estimation methodology. Section 4 describes the data and presents estimation results. Section 5
# **V. Cluster-level Productivity Analysis**

# **Data sources**

The Sri Lanka manufacturing sector data were provided by the National Statistics Office (NSO) in summary format by industry, firm size and provincial location.<sup>6</sup> We use data based on the 2004 Census of Industry, a comprehensive nationwide establishment census which collected data for fiscal year 2003. The data are aggregated by two-digit ISIC code, establishment size (5-9, 1019, 20-39, 40-99, 100+ workers) and nine provinces.<sup>7</sup> Data include counts of total employment, number of employees by gender as well as total output, total raw materials consumed (value added is defined as output minus raw materials), and a compensation bill. All currency accounts have been converted to 2005 USD at purchasing-power-parity.

We match these data to indicators constructed from the 2004 World Bank Enterprise Survey (WBES) dataset, which also covers data from fiscal year 2003. This survey collected detailed data on manufacturing establishment operations and constraints to business and investment for the first time in Sri Lanka. The dataset is comprised of 452 establishments stratified by province and industry.<sup>8</sup> We construct indicators of the business environment based on averages of objective indicators in given industrial ‘clusters’ (defined as firms in a given province, grouping of industries at the 2- digit level, and establishment size class). To ensure an adequate number of establishments in each cluster, we drop one dimension of the cell when there are fewer than five establishments in any given cluster until an adequate underlying number of establishments is reached (that is, we first substitute with location-size clusters, then industry-size to ensure that we have at least our cutoff of five or more establishments per cluster).<sup>9</sup>

> 6 Due to confidentiality restrictions, establishment-level data were not able to be procured through the NSO. 7 The Census of Industry surveyed all plants
its operations, especially in terms of ECD. Next, we describe the ONDH surveys that we have used in this study. Before presenting the results of the various analyses, we discuss the econometric methods used to estimate the impact of the INDH. The findings are then presented in detail. We conclude with a discussion of the implications of our findings for CDD policy and enhancing ECD.

# **_2 Background: National Human Development Initiative_**

The intention of the INDH is to combat poverty, decrease inequality, and improve levels of human development for Morocco under an approach based on good local governance (World Bank, 2012a). His Majesty the King of Morocco in a speech delivered on May 18, 2005, officially launched the INDH. The primary INDH program was launched in September 2005. The first phase of the program covered 2005–2010. The INDH was designed to be implemented across the country based on a platform comprising four programs. The element of the program we evaluate was designed to combat poverty in rural areas. It covers 403 rural communes where the poverty rate, as estimated in the 2004 poverty map, is greater than or equal to 30 percent. The second phase of the program (2011–2015) was officially launched on June 4, 2011 (World Bank, 2012a) and targeted additional areas.

# _2.1 Budget and Activities_

The INDH projects and activities were initially funded by DH 14 billion for Phase I (US$1.7 billion). Phase I included approximately 700 local participatory plans, 22,000 activities, and 5.2 million beneficiaries. The second phase will invest an additional DH 17.1 billion (US$2.1 billion) and additional phases are expected in the future. This makes the program a major countrywide initiative of substantial importance toward the development of Morocco (World Bank, 2012a).

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# **_5.2._** _The conditions for consistency_

Up to now, we have presented two broad approaches to analyse the performance of firms: partial productivity indicators and frontiers. We have argued that these frontiers provide a better, more complete, snapshot. However, there is no real consensus among researchers as to how to measure this frontier. Yet, the choice of method can influence regulatory decisions. The problem stems from the multiplicity of individual efficiency measures available. So a far question is whether efficiency studies are useful at all.

Trying to answer this question, Bauer et al. (1998) propose a set of consistency conditions with which the efficiency measures derived from the various sources must comply to be relevant to regulators. They must be consistent over time, and with the other performance measures used by the regulators. More specifically, the consistency measures are:

- The efficiency measures generated by the various frontiers methods must yield similar averages and standard deviations

- The various approaches must rank firms in a similar way;

- The various approaches must identify the same best and worse firms;

- The individual measures must be reasonably consistent over time and should not vary significantly from year to year;

- The various measures must be reasonably consistent with the results expected from the developments in the industry. In the particular case of regulated enterprises, for instance, the firms regulated through a price cap mechanisms are expected to be more efficient than those regulated through a rate of return regulation; and

- The efficiency measures must be reasonably consistent with other performance measures

- (accounts receivable and employees per 1000 conections, in this paper) used by the regulators.
criteria related to education, sector of occupation, or job sponsorship. To be eligible to apply for PEP, Venezuelan forced migrants only needed to: (i) have previously registered in RAMV; (ii) reside in Colombia by August 2018, when the PEP decree was issued; (iii) have a valid Venezuelan ID or other proof of Venezuelan citizenship; and (iv) have no criminal record or deportation order. PEP processing was free and migrants had to submit applications online. According to official records, 442,462 Venezuelan forced migrants registered in RAMV, and 64 percent of them (281,307 individuals) applied for PEP. The RAMV registry was implemented in 441 of the 1,122 municipalities in Colombia, including those with the highest number of Venezuelan migrants. The RAMV census was advertised on social media, in local newspapers, and through local organizations to support forced migrants.

# **III DATA**

We estimated PEP’s impacts using data from the first wave of the Venezuelan Refugees Panel Survey (VenRePS) that was administered to 2,232 households of forced migrants in Colombia. This section describes the sampling frame, data collection process, and outcomes measured by VenRePS. The methodological design was informed by a qualitative study conducted through 42 semi-structured phone interviews with forced migrants who had and had not registered in RAMV. The purpose of this study was to identify potential challenges to building a sample of RAMV and non-RAMV migrants, and to understand the factors that influenced one’s decision to register (or not) in the RAMV and PEP. Some lessons for the design of the sampling frame and data collection protocol are briefly mentioned below, while the overall results of the qualitative study can be found in Romero and Uribe (2021).

# **III. A Sampling frame**

It is challenging to design sampling frames for forced migrants because they are a vulnerable and hard-to-reach population. This is particularly true in Colombia, where there are no and Venezuelan are mobile and refugee camps
this paper and the difference is likely due to by the nature of the data, which includes household businesses whose larger revenue scales subject them to register and pay annual taxes by law, and are thus less prone to turnover.<sup>3</sup> Our unique dataset allows us to document that an additional 25 percent of household businesses suspend their activity for part of the year, with the average annual suspension duration exceeding 2.5 months. The informal economy in developing economies has been able to weather shocks and even expand employment during past economic downturns (Loayza and Rigolini, 2011). However, informal business, many of which operate in the services sector, were hit hard during the COVID-19 shock in 2020, and the suspension rate spiked to 40 percent of registered household businesses.

We analyze the effects of the initial outbreak of COVID-19 on household businesses in Vietnam utilizing two high-frequency data sources: the tax census data, which provides information on the extensive margin of household 1Household business refers to household-owned, or family run businesses, and it is a good approximation of informality in developing economies. (Loayza and Rigolini, 2006).

> 2Commune is a third-tier administrative unit in Vietnam, subordinated to district (second-tier) and province (first-tier). There are over 11,000 communes in the country.

> 3In Vietnam, a household business must register and are responsible to file and pay one annual lump-sum tax within a fiscal year if their annual revenue exceeds 100 million Vietnam Dong (VND), or approximately USD4,300. According to the 2017 Vietnam Enterprise Census (VEC), registered household businesses comprise about one-fifth of total informal businesses in Vietnam. Further discussion on household business characteristics is presented in Section 2.

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targets that the international community agreed to pursue at the 2000 UN Millennium Summit (MOPIC, 2003, 2010).

Nevertheless, Yemen’s government has stated that it views accelerated MDG progress and, if possible, full achievement of the MDG targets as a high priority. To this end, it has issued the abovementioned MDG Reports as well as one ―Needs Assessment‖ (MOPIC, 2005). Using a bottom-up approach, the latter study attempts to specify the requirements for full MDG achievements in terms of spending and service delivery. The analysis does not use an integrated approach but treats each sector that is directly linked to the MDGs (most importantly health, education, and water-sanitation) in isolation from each other and from the rest of the economy.

In terms of the required service expansion and other changes needed to achieve the MDGs covered in this paper, the analysis takes the information in the 2005 ―Needs Assessment‖ as its point of departure. However, it goes beyond that assessment by situating the pursuit of alternative MDG strategies in the context of Yemen’s economy, for each scenario simulating its impact on a wide range of interrelated economic indicators; in addition to key MDG indicators (related to poverty, health, primary education, and water and sanitation), the analysis covers the impacts on national accounts aggregates, macroeconomic balances (including the government budget and the balance of payments), the size of the government relative to the rest of the economy, as well as production and trade in different sectors. One key aspect of the analysis is that it considers the repercussions of relying on different sources (foreign or domestic) for the additional government financing that is required.

In terms of method, the simulation analysis is based on MAMS, an economy-wide model developed at the World Bank for MDG and development strategy analysis. Results from MAMS scenarios are passed on to a microsimulation model that relies on household survey data to assess effects on poverty and inequality.

More specifically, the initial set of simulations, which
same share as who are informal sector employees.

# **Figure 3. Distribution of urban employment by employment types, 26 African countries in the SSA region**

<!-- Start of picture text --> Avg. (Pop. weighting) 8.3 18.6 29.4 9.3 10.6 18.5 5.3<br>Avg. (Equal-country weighting) 10.8 22.2 34.9 5.7 10.8 11.7 3.8<br>Unpaid employees Informal Wage employee<br>Low-educated Self-employed High-educated Self-employed<br>Formal Wage employee (Public/State owned) Formal Wage employee (Private)<br>Employer<br><!-- End of picture text --> Source: Own elaboration based on GMD data.

> 38 ILO (2018) finds that 28 percent of informal workers in SSA are informal employees, 52 percent are s-e, 18 percent are contributing family workers. Among formal SSA workers, 75 percent are employees, 21 percent are self-employed and 3 percent are employers.

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# Interest Rates: A Two-Sector Banking System

The difference between the deposit ratas in the state and the private banks is evident in the data of Table 4. Given this d'fference, it is surprising that state banks were able to collect deposits. Some have argued that the deposits at state banks were insured by the government, but the failure of the foreign banks was unlikely and this argument is not convincing. A better explanation is that the government required public enterprises to keep deposits with the state banks. In July 1984, deposits of state enterprises, government agencies, and financial institutions were equal to about 46 percent of state bank time deposits and 71 percent of their demand deposits, (Balifio and Sundararajan, 1985.) Another explanation is that borrowers who benefited from the preferential loans (granted only by the state banks) were forced to keep balances with the state banks.

Table 4. Interest Rates in Indonesia

|Year|(1)<br>State<br>banks<br>6 month<br>deposit<br>end of<br>period|(2)<br>Private<br>banks<br>6month<br>deposit<br>end of<br>period|(3)<br>Singapore<br>MMR|(4)<br>State<br>Banks<br>Lending Rate|(5)<br>UsingCPI|(6)<br>If<br>X<br> GDP Deflator|(7)<br>Real Deposit<br>Rate<br>(1-5)|
|---|---|---|---|---|---|---|---|
|1972|15.0||3.57||6.42|13.59|8.58|
|1973|12.0||11.69||
and the new diet quality variables from individual-level data. This could then inform analysis for other countries in the same region of policy interest, which have individual quantitative dietary recall data but not associated with data on NCD risk factors.

The new data quality variables could help identify how diets vary across different population subgroups, and what the key issues are, such as insufficient diet diversity, insufficient consumption of foods protective against NCDs, or excess consumption of foods with risks for NCDs.

Knowledge of the economic costs of unhealthy food choices for different population subgroups could then inform policy priorities and be compared with estimates of the costs of different agrifood policies aimed at supporting better food choices. Examples of agrifood policies which were adopted with short- and mediumterm goals of increasing farmer income and meeting nutrient needs of undernourished populations are subsidies on sugarcane and palm oil. These subsidies aimed to provide inexpensive calories and fat. However, being cognizant of the long-run costs of these policies in the context of the nutrition transition, with growing risks of diabetes and risks associated with saturated fat, can help guide the evolution of agrifood policy. There are also implications for public health nutrition policy, in that information on costs can underscore the importance of policies such as nutrition information and communication, and nutrition and exercise interventions in school-age children and adolescents.

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expectations of upward mobility—particularly intergenerational mobility—and support for taxfinanced education reforms appears stronger during periods of relative economic stability.

# **8. Conclusions**

To move to steeper growth trajectories, middle-income countries of Europe and Central Asia must implement systemic structural reforms. The successful implementation of these often costly policies requires broad public support.

Earlier studies, focusing primarily on preferences for redistribution, have found that support for reforms tends to be higher among people who expect to benefit from such reforms and people who believe the outcomes of these reforms to be fair. Misperceptions about the impacts of reforms add frictions by reducing the public’s willingness to adjust to new realities and can even jeopardize achieving the reform objectives. Managing people’s expectations about their future welfare is crucial to consolidating political support.

This paper confirms the importance of self-interest and beliefs about fairness for support of taxfinanced education reforms. People who expect themselves or their children to be upwardly mobile are more likely to support tax-financed education reforms than people who expect their position to deteriorate. We conjecture that this higher support stems partly from the economic self-interest of upwardly mobile individuals to invest in improved education that will benefit their children. This relationship is partially mediated by normative beliefs about the fairness of economic opportunities in society and individuals’ willingness to take on risks. These results are robust to different formulations of mobility expectations and persist across three survey rounds spanning both stable and post-crisis economic environments.

Our results point to the critical role expectations of socioeconomic mobility and beliefs about fairness play in driving support for costly reforms. In addition, our findings highlight the challenges facing the countries of Europe and Central Asia, where beliefs that educational and professional advancement are not based on meritocracy remain widespread. Indeed, data from the 2016 round of the LiTS reveal that more than two-thirds of adults in the region report that having informal connections is important to secure a good government job, and almost 40 16