rafmacalaba/data-use-mentions-extended huggingface dataset:
18 features from whether the website is functioning, fast to load, available in multiple languages, and optimized for mobile viewing; to whether it includes marketing features such as customer testimonials, appealing aesthetics, a clear statement of what differentiates their product or service, and includes a clear call to action towards contacting the company or making a purchase. The final measure is based on the company’s business Facebook page, and scores the company on 15 features including whether they actively post, provide clear descriptions of what the company does, include customer reviews, highlight new products or services, offer promotions, and include attractive images.

In Appendix B, we use these digital presence data to test whether the firms that did not answer the endline survey differ systematically in these follow-up outcomes from the firms that did. The attriting firms have similar google, website, and Facebook scores, and, coupled with the balance on baseline observables for the responding sample, provide us with additional confidence that attrition is not likely to be causing systematic bias.

# **3.2 Estimating Treatment Effects**

To estimate the impact of the program on the outcomes of interest, we use the following regression for firm _i_ in randomization stratum _s_ : This regression includes dummy variables for each of the randomization strata, implicitly controlling for the combination of baseline diagnostic score, country, and whether they have a digital product. Robust (Eicker-White) standard errors are used. The coefficient of interest _β_ corresponds to the intention-to-treat effect (ITT), which is the effect of being offered the full intensive training and one-on-one consulting program, compared to just being offered the short webinars in the control group.

# **3.3 Impacts on Digital Presence, Business Practices and Pitching to Judges**

Table 2 tests whether firms implemented changes in their digital presence and business practices that had been recommended in the training, and whether they were able to pitch more effectively to judges. Columns 1 through 3 examine the impact on digital presence. We see positive
Indicator for Mexico is calculated as the sum of three, equally weighted components: (1) absorptive capacity, (2) adaptive capacity, and (3) transformative capacity. Absorptive capacity measures the ability to prepare for, mitigate or recover from the impacts of negative events using predetermined coping responses (e.g. early warning systems). Adaptive capacity measures the ability to adjust to better respond to future shocks (e.g. adjusted planting behavior). Transformative capacity measures the ability to fundamentally change when existing conditions become untenable (e.g. livelihood transformation). The report on climate resilience indicators can be viewed <u>online.</u><sup>17</sup>

**IDB – Disaster Indicators.** The Inter‐American Development Bank has developed several indicators, including the (1) Disaster Deficit Index (DDI), (2) Local Disaster Index (LDI), (3) Prevalent Vulnerability Index (PVI), (4) Risk Management Index (RMI), and the (5) Index of Governance and Public Policy for DRM (iGOPP). The (1) DDI measures the economic loss a country could suffer when a catastrophic event takes place, and implications in terms of resources needed to address the situation. It is calculated by dividing the losses from a _maximum considered event_ by the _economic resilience_ of a government (that is, the availability of internal and external funds). The (2) LDI measures the propensity of a country to experience small‐scale disasters and their cumulative impact on local development. It is calculated as the sum of three local sub indicators on the number of deaths, people affected, and economic losses, with associated weights. The (3) PVI measures vulnerability conditions by measuring exposure in prone areas, socioeconomic fragility and lack of social resilience. It is calculated as the average of three types of composite indicators – on exposure, fragility, and resilience, with associated weights. The (4
caused rapid rises in food prices (e.g., currency crises, etc.), in particular, have played a key role in pushing the country to a near famine in 2018 and again beginning in 2020 (e.g., OCHA 2019; Favari et al. 2021; OCHA 2021).

# **Section 3. Data**

Since the conflict escalated in March 2015, traditional data collection in the country has been significantly limited. In parts of the country, permission from authorities is needed for any data collection, survey teams are unable to perform longer and more detailed surveys that are needed for traditional welfare metrics and national accounts information, and there are widespread reports of interference in sampling and household responses to try to influence the outcome of data collection (e.g., OCHA 2021; Tandon and Vishwanath 2021; Favari et al. 2022). These hurdles, in addition to an already difficult security situation, make it impossible to rely on traditional data collection.

Given the substantial constraints and the need for high frequency data, we use the most complete household survey that captures displacement status- the mobile Vulnerability and Assessment Mapping Survey (mVAM) conducted by the WFP. The monthly survey began in August 2015, just months after the escalation of violence and the initial surge in forced displacement. Importantly, the data provide an opportunity to analyze household decisions during an active conflict.

The mVAM is a monthly survey of approximately 2400 households used to assess the food security situation in the Republic of Yemen. The survey is stratified by governorate/capital city, where potential respondents continue to be contacted until a sufficient number of completed responses have been met in each governorate based on its share of the total population (aside from the island of Soccatra).<sup>7</sup> The survey is a rolling panel, where the call center initially surveys 2400 households reached via random digit dialing (RDD). In each subsequent month, 80 percent of the sample is composed
# Source: Authors’ elaboration

The main idea of the algorithm is to produce for each country the full ownership tree (i.e. direct and indirect subsidiaries) of: (i) “Public Authorities”; (ii) entities identified as SOEs in the National Legal Form; (iii) entities identified as SOEs from the Supplemental database; and (iv) entities identified as SOEs in the GUOs. In each step, the algorithm ensures that firms are classified as either corporatized or noncorporatized based on the national legal form, and that branches and inactive entities are removed. The detailed algorithm runs as follows:

1. Use the clean Links file<sup>33</sup> to identify the set of entities that are labeled as “Public Authority” under the variable “Entity Type” and their respective BvD ID (company ID). ORBIS classifies governments and government institutions, such as ministries or municipalities, under this category.

2. Use the information on BvD IDs, shareholders, and direct percentage to construct the ownership tree for each entity labeled as “Public Authority”.<sup>34</sup> These ownership trees are constructed using the iterative and recursive process described below:

- For each public authority, identify its direct subsidiaries (entities and their BvD IDs that report the public authority’s BvD ID as a direct shareholder).

- Using the direct percentage, keep all direct subsidiaries for which the public authority’s shareholding direct percentage is greater than or equal to 10%. If the shareholding percentage is unknown, and the subsidiaries are not branches or inactive firms, keep the direct subsidiary so as not eliminate potential SOEs.

- Next, for these direct subsidiaries linked by a known shareholding percentage, identify the set of their subsidiaries. To do this, identify the entities (and their BvD IDs) that report a direct subsidiary’s BvD ID as a shareholder, and did not already appear in the first (previous) level of subsidiaries.

- Using
health and income risks related to the COVID19 pandemic.

# 6.4. Migrants’ Responsiveness to Labor Demand

Prior studies show that the effects of local labor-demand conditions on immigrants’ labor market outcomes are stronger than those for natives (Azlor et al., 2020). Importantly, immigrants are more responsive to changes in local labor-demand conditions, choosing locations with more available jobs and filling labor shortages more rapidly (Cadena and Kovak, 2016). The responsiveness of migrants to labor demand occurs not only geographically, but also occupationally, where migrants are found to be significantly more mobile than natives in changing jobs (Green, 1999). Occupations with labor shortages or openings that attract migrants are typically also characterized by poorer and riskier working conditions (Orrenius and Zavodny, 2009), more manual work (Peri and Sparber, 2009), and lower skill requirements (Dustmann and Frattini, 2013). All of these factors make those jobs less attractive to natives who may prefer to stay unemployed, depending on the welfare system (Constant, 2014). These more precarious occupations could, in turn, be more exposed to the negative shocks that surfaced during the pandemic.

To assess the role of labor demand by occupation, we follow a recent study by the European Commission (2015); we proxy for labor demand by using the share of new hires in total employment in each ISCO-08 occupation.<sup>17</sup> The EU-LFS provides information on whether a

> 17 We acknowledge that this measure captures not only changes in demand in an occupation (because increases in demand are associated with larger share of new hires), but also other characteristics such as differences in turnover

20
# **c. Technical assistance cost estimation**<sup>**17**</sup>

The key bottleneck of survey implementation in data-scarce environments is not solely lack of funding but also lack of technical capacity. Indeed, while many countries receive technical assistance from international development partners in conducting household surveys, detailed data on technical assistance expenditures are seldom available. Table 2 provides a breakdown of technical assistance expenditures across major expenditure categories (i.e. staff time, consultant expenditures, and travel and miscellaneous costs) for selected countries and their baseline (national multi-topic household surveys that were supported by the LSMS-ISA program and that are among the surveys in Table 1).

The figures are based on disaggregated expenditure records obtained from the LSMS-ISA trust fund in support of the types of technical assistance activities detailed here. For each survey, the activities spanned a 24-month period, cutting across two to three fiscal years depending on the survey period of implementation. The cross-country average for technical assistance cost per survey is USD 613,956, based on expenditures of USD 525,904 for Ethiopia ESS 2011/12, Malawi IHS3 2010/11 and Nigeria GHS-Panel 2010/11.

For defining the budget envelope for technical assistance over the period of 2016-2030, our analysis assumes an average cost of USD 540,000 (in 2014 prices) per survey to provide direct technical assistance for survey design and implementation and for the dissemination of the resulting unit-record data in an anonymized fashion (preferably within 6 months of completion of fieldwork). This is closer to the lower bound in Table 2 and is anchored primarily in the LSMS experience with providing hands-on technical assistance in 8 data-deprived settings in Africa as part of the LSMS-ISA initiative since 2008.

Assuming 12 months of fieldwork, the budget would be allocated across a 24-month period that includes a 6-month design phase, a 12-month implementation phase and a 6-month data processing, editing, analysis and dissemination phase. Table 3 provides a template for a budget of approximately USD 540,000 to finance
# **3. Vulnerability Analysis and Mapping (VAM)**

VAM is a tool to identify the degree of food insecurity and vulnerability at the municipal level in Bolivia. It assigns a value from 1 to 5 to each municipality according to level of food insecurity and vulnerability, where 1= very low, 2 = low, 3 = medium, 4 = high, 5 = very high.<sup>5</sup> Developed by the Universidad Privada de Bolivia and Social and Economic Policy Analysis Unit ( _Unidad de Analisis de Políticas Sociales y Ecónomicas_ —UDAPE) in the Ministry of Development Planning, the VAM uses a World Food Program methodology (WFP, 2010)<sup>6</sup> and it is widely accepted in Bolivia as a formal measurement of food insecurity. VAM measures are available for 2003, 2006 and 2007 for all 327 municipalities across the nine departments that constitute Bolivia.

The VAM is constructed with community-level data using principal component analysis. Among the components used for all three iterations are: urbanization rate, rural population density and its square, proportion of institutionally attended births, schooling years, log of per capita consumption, under-five malnutrition rate, altitude, rainfall and a flood propensity categorical variable (four values). Components used only in the 2003 VAM and not used in subsequent updates include: dependency rate, life expectancy, agricultural potential (a categorical four-point scale variable capturing soil capacity), forestry potential, road density, draught frequency, frost days per year, low weight at birth and per capita household food expenditures.

Although changes in VAM scores between 2003 and 2007 were not uniform, the average vulnerability status by department decreased over time, suggesting that, on average, municipalities within each department have reduced their vulnerability to food insecurity (Figure 1). The exception is Cochabamba, the only department where average vulnerability to food insecurity increased over time. Tarija and the three _llanos_ departments of Santa Cruz, Beni and Pando registered, on average, between moderate and
# **Section 1: Sources and Coverage of Remittance Statistics in Developing Countries**

In general terms, a remittance transfer can be divided in three phases, as illustrated in Figure 1. The first phase occurs in the country where the migrant works and the transaction originates. A remittance transaction is originated when a migrant goes to a bank, money transfer company, credit union, or any other originating point (such as grocery store, post office, gas station, etc.) to transfer funds to relatives or friends in his home country. The second phase takes place when funds are transferred from the originating country to another foreign country. Finally, the third phase occurs when funds arrive in the destination country and are paid to the beneficiary at a commercial bank, money transfer company or any other distribution point (such as ATMs, grocery store, post office, etc).

<!-- Start of picture text --> Figure 1. The Remittance Industry -Key Phases<br>Originating Points in Transfer Payment Points in<br>Country A Country B<br>Money Transfer Commercial<br>Companies Banks<br>Commercial Money Transfer<br>Banks Companies<br>Transfer<br>Migrants Credit Credit<br>Unions Unions Beneficiaries<br>Post offices, Post offices,<br>grocery stores, retail stores,<br>etc. etc.<br>1 2 3<br><!-- End of picture text --> It is important to note that in most cases, banks, credit unions, post offices, exchange bureaus, and micro-finance institutions in developing countries act as “paying agents” of financial institutions and money transfer companies located in the countries where the transactions originate. Only a few banks or financial institutions in developing countries have branches or subsidiaries overseas that serve both senders and recipients of remittances.

The first issue this survey attempted to explore was to what extent the balance of payment statistics of developing countries reflect the
. Structural models (Blau, 1996; Gustman and Steinmeier, 2004) suggest that labor supply decisions of older couples reflect preferences for shared retirement. Second, work decisions may be affected by the health status of a spouse. One may plausibly observe an added worker effect, in which a spouse’s health shock leads to increased labor supply so as to insure income against the earnings-loss associated with the health shock (e.g., Coile, 2004a), or alternatively find that spouse care requirements will

> not yet have appropriate panel data for the three countries in this study, we are not able to control for dynamic relationships between health and wealth.

> 27CHARLS asks 19, IFLS asks 18 and KLoSa asks 17. Each of the surveys asks questions related to functional abilities such as: ability to walk for a kilometer, ability to go up and down stairs, difficulties lifting 10 or 20 lbs, draw water from a well, ability to perform household chores, bath oneself or take medication.

17
suggest that a conditional gender wage gap emerges only after some time, after maternity and the ensuing childcare activities which apparently involve women more than men.

Such findings are echoed in a recent literature on developing countries. Pastore (2010a) finds that the gender wage gap, which is not statistically significant among young teenagers in Mongolia, becomes statistically relevant and sizeable among the adults and the prime-aged group. The paper uses a data set similar to the one used in this paper and is hence particularly interesting for comparative purposes, although cultural, economic and political differences between Mongolia and Azerbaijan are quite important, despite both having been in the Soviet (and more recently, Russian) sphere of influence.

Pastore, Sattar and Tiongson (2013) use the same data for Kosovo, a country with documented experiences of gender discrimination and gender-based violence. Nonetheless, the authors find that

> 1 Kunze (2003; and 2005) is the only author who finds a gender wage gap from labor market entry on, but this is probably due to the specific sample of manual workers with education in vocational training. These workers are essentially employed in male-dominated professions, where men have a competitive advantage which apparently pays off in terms of earnings.

3
position is the so-called Tobin tax (i.e., preventing socially excessive borrowing in good times), the use of capital controls in good times will inevitably make it more likely to see capital controls in bad times as well. Binding capital controls in bad times will naturally prevent the fiscal authority from keeping tax rates or government spending constant over the business cycle and lead to procyclical fiscal policy.

A more subtle, but equally disruptive, friction in a model with uncertainty is the presence of incomplete markets, which allows for only risk-free (i.e., non-statecontingent) borrowing. In other words, the absence of complete markets (in the Arrow-Debreu sense) that would allow the fiscal authority to borrow conditional on every state of the world —and thus replicate the first-best fiscal policy in a neo-classical model —renders fiscal policy procyclical. In fact, Fernandez et al. (2021) show, in the context of a DSGE model how, under certain conditions, incomplete markets is all that is needed for the optimal (in a second-best sense) fiscal policy to become procyclical. Further, procyclicality continues to be the best policy if sovereign risk is added to the picture (see Cuadra et al., 2010, Bianchi et al., 2019, and Dzhambova, 2021).<sup>21</sup> To capture the effects of capital controls and other financial frictions which, effectively, curtail borrowing in bad times, we will resort to the well-known Chinn-Ito (2006) financial openness index. This index, constructed based on information from the IMF, is normalized between 0 and 1, with higher values indicating more financially-opened economies. As of mid-2022, the Chinn-Ito index covers the period 1970-2019 for 182 countries.

# 3.2 Political economy explanations

The second major group of potential explanations for the procyclicality of EMDE relies on political economy considerations. As discussed in the Introduction, all these political economy factors would make it more diffi cult to manage the abundance triggered by positive shocks to commodity prices.
small. Health employment is also a decentralized activity and offloaded to Local Govemments. State owned enterprise employment is also from the above mentioned study and relate to 1991. Military employment data include conscripts (31,000), but do not include personnel in paramilitary organizations, i.e. Carabineros (31,000).

Data on GDP at market prices is a 1993 estimate and is taken from World Tables 1995. Consolidated Central Government wages and salaries are for 1993 and are taken from IMF Government Finance Statistics Yearbook, 1995. Data on wages in manufacturing are from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1993.

# Colombia

Unemployment rate is drawn from CIA Factbook 1995 and refers to 1994 Data on paid employment in nonagricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1992. Data on Central, Non central govemment and education employment are from Departamento Administrativo del Servicio Publico, Contralria General de la Republica, Departimento nacional de Estadistica and relate to 1996.

Education employment corresponds to 'teaching personnel" category only and does not include personnel employed in " Fondos Educativos Regionales" (10,496) and in the "Planta decentralizada' (10,627). Health Sector employment is taken from Municipal Health Services Project Staff Appraisal Report dated May 14,1993. The data only relates to Public Sector data (p. 4 para 1.9, indicates that the ratios in Public Health service are 1.3 for nurses, 12 for nurse assistants, 3.8 for promoters and 2.5 for doctors). This equates to 4,508 nurses, 42,818 nurses' assistants, 13,554 promoters and 8,921 doctors. The Ministry of Health, along with the National Planning Department, is responsible for guiding health sector policy, for providing administration support for lower administrative levels of the system and for evaluating and monitoring performance. Colombia is decentralizing management and fiscal responsibility for public sector programs to the country's 33
# **Chapter 5**

# **Unit level models: Linear indicators**

Battese, Harter and Fuller (1988) proposed a linear regression model with random area effects for data at the unit level, known as the nested error model, to estimate small area means. The model is defined as where **x** _di_ is a 1 _× p_ vector of auxiliary variables for unit _i_ from area _d_ . The first component of **x** _di_ is typically set to one to allow for a common intercept, and the remaining elements are the values of auxiliary variables that may vary by units or only by areas, **_β_** is the _p×_ 1 vector or regression coefficients, common for all the areas, _ud_ represents the random effect of area _d_ and _edi_ is the unit-level regression error. Area effects _ud_ and errors _edi_ are all independent, and _kdi_ are known heteroscedasticity constants, _i_ = 1 _, . . . , Nd_ , _d_ = 1 _, . . . , D_ .

Battese, Harter and Fuller (1988) utilized the nested error model mentioned above to estimate county means of crop areas under corn and under soybeans. They obtained unit-level data on corn and soybeans production from farm-interview data and auxiliary information from LANDSAT 26
due to the widespread availability of location based data and the publication of some studies that raised concerns on the biases of CDR, this type of data was relegated to a second place.

In most of the global South, smartphone penetration and access to reliable internet are still low, with penetration values for smartphone below 30% in most African countries [11]. This factor is a critical constraint to use of Location Based Data in urban analytics in most African cities. On the other side, because of high penetration of phones both in rural and urban areas (estimated at 77% of population in Sub-Saharan Africa [12]), CDR data are still the most reliable method to gain understanding of urban and human mobility in cities in the global South [13-14]. In this 2