rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Highlights**

- The opportunity for economic integration offered to half a million Venezuelan forced migrants in Colombia, in the form of a regularization program, had negligible impacts on native electoral outcomes.

- Results from a survey experiment determined that the null political impacts of the program is not explained by lack of information about the program among the Colombian population.

- The null political impacts of the program are possibly attributed to voter’s indifference to the program due to multiple factors including (1) the absence of negative economic or social effects of the program, (2) the low media and political salience of the program, (3) the cultural proximity between Colombia and Venezuela, and (4)

- The results suggest that in developing countries, native political behaviors can remain unaffected by policies facilitating the economic integration of forced migrants already present in the country.

2
# **IV. Methodology**

The macroeconomic impacts of the financial crisis are transmitted to households through the following: (a) financial markets via reduced access to credit, eroding savings and asset values; (b) labor markets via reduced employment, wages and remittances; (c) product markets via lower growth and production, and relative prices changes; and (d) government and non-governmental services such as public and private education, health and social protection services.<sup>6</sup> For Latvia, the dominant short-run crisis impact was expected to come from labor markets through reduced wages and employment. To measure the distributional impact of the financial crisis, aggregate shocks to employment and GDP must be linked to individual households, which can be done by combining aggregate information with household survey data.

Our base simulation assumed that workers were employed in the formal sector, or the informal (grey) sector. The expectation was that the formal sector would cut _both_ the size of the workforce and wage rates, but the informal sector would cut _only_ wage rates.<sup>7</sup> Our simulations used projected employment reductions, but wage reductions were computed such that the total sector GDP growth rates were as predicted above. An implicit assumption was that wage growth rates correspond exactly to GDP growth rates.

Abundant literature exists on the characteristics of workers most likely to be laid off when a sector contracts. In this note, we considered four layoff models. First, we postulated the determinants of employment, based on a range of observable worker-specific characteristics such as gender, education level, age (a proxy for experience), location of residence, and so forth. Then, for each worker we computed a propensity score  the likelihood that an individual with those observable characteristics would be employed. Next, the model assumed that the first workers to be laid off would be those with the least probability of being employed. The second and third models of layoffs were based on worker age; older
based on lost-years. In the case of Sweden, ignoring the age distribution of deaths would inate the importance of mortality by a factor of 4.5.<sup>19</sup> <!-- Start of picture text --> 0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-99<br>2,000<br>1,500<br>1,000<br>500<br>0<br>Covid-19 Deaths<br><!-- End of picture text --> Figure 1: Distribution of Covid-19 deaths per age in Sweden as of early June.

To estimate each country's number of poverty-years, we rst take the income distribution for each country from PovcalNet for 2018, which is the latest year for which data are available. Next, we scale these distributions to 2020 by assuming that all household incomes grow in accordance with growth rates in real GDP per capita, meaning that the growth is distribution-neutral. We do so under two dierent growth scenarios: (1) using GDP growth estimates for 2019 and 2020 from around June 2020, which incorporate the expected impacts of the pandemic and associated policy responses, and (2) using GDP growth estimates for 2019 and 2020 from around January 2020, before Covid-19 took o. Central Bank growth estimates are used for Sweden, Belgium and the United Kingdom while growth estimates from the January and June 2020 edition of the World Bank's Global Economic Prospects (GEP) are used for Pakistan, Peru and Philippines (The World Bank, 2020).

Finally, following Lakner et al. (2020) but using each country's most recent national poverty line, the impact of Covid-19 on poverty is computed by comparing the number of poor under the two growth scenarios.<sup>20</sup> Denoting the number of poor people in year

> 19If the same number of deaths were to be distributed at random in the population, they would generate 4.
Holden, Paul and Vassili Propenko, “Financial Development and Poverty Alleviation: Issues and Policy Implications for Developing and Transition Countries”, IMF Working Paper, Washington, D.C., 2001.

- Jacob, Katy, Malcolm Bush, and Dan Immergluck. “Rhetoric and Reality: An Analysis of Mainstream Credit Unions’ Record of Serving Low-Income People” A report issued by the Woodstock Institute, February 2002.

- Kennickel, Arthur B., Martha Starr-McCluer and Annika E. Suden, “Family Finances in the U.S.: Recent Evidence from the Survey of Consumer Finances”, _Federal Reserve Bulletin_ January 1997 pp. 1-20

- King, Robert G. and Ross Levine, “Finance and Growth: Schumpeter Might be Right”, Quarterly Journal of Economics, 108, pp 717 -737, August, 1993.

- Martell, Javier, Paul Panichelli, Rich Strauch, and Sally Taylor-Shoff. “The Effectiveness of Scoring on Low-to-Moderate Income and High-Minority Area Populations,” A report issued by Fair, Isaac, and Company (San Rafael, CA) 1999.

- Orozco, Manuel. “Attracting Remittances: Markets, Money, and Reduced Costs,” A report commissioned by the Multilateral Investment Fund of the Inter-American Development Bank, January 2002.

- Richter, Lisa and Christopher Tan. “Retail Financial Services Initiative: A Report on Innovative Products and Services for Low-income and Unbanked Customers,” A report issued by the National Community Investment Fund, April 2002.

- Robinson, M.S _., The Microfinance Revolution, Sustainable Finance for the Poor,_ ISBN: 0-82134524-9, World Bank, Washington D.C., 2001.

- Rutherford, Stuart, “ _The Poor and Their Money_ ”, University of Manchester, Institute for Development Policy and Managemet, Manchester, 1999

- Schreiner, Mark, Margaret Clancy, and Michael Sherraden.  “Saving Performance in the American Dream Demonstration: A National Demonstration of Individual Development Accounts,” A report issued by the Center for Social Development at
more conservative Heckman estimates from a six-country study of 10,144 individuals show that child sponsorship is responsible for increases in monthly income of about $13–17 over an unconditional baseline of $75, or an increase of 17.3–22.9%. This effect of child sponsorship on future labor income is due principally to sponsored children entering the labor market as adults who would not have done so otherwise, particularly for women. We find that men realize an added $6.74 of monthly income from higher wages conditional upon employment but that estimations on the impact on wages of women cannot reject a null hypothesis of zero.<sup>13</sup> Given that the cost of sponsorship to sponsors was $28 per month during the time in the 1980s and 1990s when the individuals in our study were sponsored and that the average length 15
Figure 1 shows the evolution of total net assets (TNAs) in all equity and bond funds by region. Panel A plots total assets for equity funds between January 1996 and December 2000 and between June 2001 and November 2010.<sup>18</sup> Panel B displays total assets for bond funds between July 2002 and November 2010. The figure shows not only the large increase in total assets over time, but also the sharp declines around crises, particularly around the Asian and Russian crises and the global financial crisis. A similar pattern is observed for bond funds. The figure also shows that, as a group, bond funds are much smaller than equity funds (100 versus 599 billion U.S. dollars in November 2010), even though the mean fund is of a similar size.

It is interesting to observe not only the variation in TNAs but also that of country weights, for which we focus on the period around the global financial crisis. Figures 2 and 3 show the weights for equity and bond funds, respectively, with global funds at the top and global emerging funds at the bottom. The figures illustrate the evolution of weights for some of the main regions of investment within emerging and developed countries. In particular, they show the weights in: (i) emerging economies (emerging Asia, emerging Europe, and Latin America), developed Europe, and North America for global funds and (ii) emerging Asia, emerging Europe, and Latin America for global emerging funds. The figures also mark some of the main events around the global crisis: the nationalization of Northern Rock, the collapses of Bear Stearns and Lehman Brothers, and the near collapse of AIG.

Figures 2 and 3 show several noteworthy features of the data. First, weights fluctuate substantially over time. Second, there are significant reallocations across regions, especially at times of stress. For example, the figures for equity funds show that, even though the epicenter of the crisis was in the U.S.
return, whereas other firms may have sales that fluctuate around the threshold for filing. Another difference between the ‘perfectly compliant’ firms and other formal firms is that they may have a lower risk tolerance and/or greater exposure to the tax authority, which means they are less willing to avoid paying taxes. These characteristics could also be associated with other aspects of business operations (e.g. investment in R&D).

The second step in aligning the two data sources involves solely focusing on firms for which there are five or more employees and dis-aggregating the analysis by firm size. Specifically, we look at the trends over time across both data sources for ‘perfectly compliant’ firms with 14
**<u>Table 1: Descriptive Statistics of Key Variables</u>**

|<br>Variable|<br>Mean|
|---|---|
|_Whole sample (N =_|_378,691)_|
|Years of schooling|7.5|
|_Educational level:_||
|None|11%|
|Primary|39%|
|Secondary|34%|
|Higher|16%|
|_Secondary school type:_||
|General|20%|
|Vocational|80%|
|Education reform 1997 affected|29%|
|Children aged 15 or less in household|1.0|
|**_Workers (N = 9_**|**_7,050)_**|
|Annual earnings|7,342 $US|
|Years of schooling|10.1|
|Years of labor market experience|21.0|
|Private sector employee|76%|
|Public sector employee|24%|

Source: 2017 Turkish Household Labor Force Survey (HLFS)

# **Earnings function estimates**

Table 2 presents our earnings function estimates of the private overall rate of return to one extra year of schooling by gender, economic sector and secondary school curriculum.

**<u>Table 2: Private rates of return to one year of schooling, Mincerian estimates (%)</u>**

|<br>Reference group|<br>Ordinary least squares|<br>Selectivity corrected|
|---|---|---|
|All workers|8.8||
|Males|8.3||
|Females|10.3|13.4|
|_Economic sector:_|||
|Private|6.5||
|Public|7.9||
|_Secondary school graduates:_|||
|General curriculum|2.4||
|
The two different ways an input can affect the know-can or can-do relationships have different policy implications. If an input simply shifts the function upwards, it can be implemented as a stand-alone intervention for all health workers meaning it does not need to be targeted to certain types of health workers or implemented alongside complimentary policies. Our empirical results show that a policy that increases **positive worker behavior** will improve performance uniformly for workers at all levels of capacity. Conversely, if an input increases the slope of a function by rotating the function, then it can be most effectively implemented as either a targeted intervention or alongside a complimentary intervention. We show that a policy that increases **feeling valued by the facility** will only increase performance for workers above a certain capacity threshold and will decrease it for workers below the threshold. Therefore, policy makers should either increase **feeling valued by the facility** for workers above this threshold only, or increase **feeling valued by the facility** and capacity through two interventions simultaneously.

In section 2 we introduce the Three-Gap model and outline three specifications that demonstrate how the role each input plays in improving performance can be empirically examined. In section 3 we use data from hospitals in Liberia to illustrate how this model can paint a fuller picture and provide policy makers with better information about how to improve the quality of care. Section 4 concludes.

# **2 The Three-Gap Model of Quality of Care**

The Three-Gap model starts with three measures of performance — performance, capacity and knowledge — and three gaps: the gap between what a health worker should be doing and what they have the knowledge to do (the know gap); the gap between what they have 5
Discussion Paper, (14-037).

Schulte, I., & Heindl, P. (2017). Price and income elasticities of residential energy demand in Germany. Energy Policy, 102, 512-528.

Siksnelyte-Butkiene, I. (2021). A systematic literature review of indices for energy poverty assessment: a household perspective. Sustainability, 13(19), 10900.

Ssennono, V. F., Ntayi, J. M., Buyinza, F., Wasswa, F., Aarakit, S. M., & Mukiza, C. N. (2021). Energy poverty in Uganda: evidence from a multidimensional approach. Energy Economics, 101, 105445.

<mark>Sokołowski, J., Lewandowski, P., Kiełczewska, A., & Bouzarovski, S. (2020). A multidimensional index to measure energy poverty: the Polish case. Energy Sources, Part B: Economics, Planning, and Policy, 15(2), 92-112.</mark> Sun, M., Han, C., Nie, Q., Xu, J., Zhang, F., & Zhao, Q. (2022). Understanding building energy efficiency with administrative and emerging urban big data by deep learning in Glasgow. Energy and Buildings, 273, 112331. https://doi.org/10.1016/j.enbuild.2022.112331 Thema, J., & Vondung, F. (2020). Expenditure-based indicators of energy poverty—An analysis of income and expenditure elasticities. Energies, 14(1), 8.

Thomson, H. Snell, C, 2013, "Quantifying the prevalence of fuel poverty across the European Union, Energy Policy, Volume 52, Pages 563-572, ISSN 0301-4215, <u>https://doi.org/10.1016/j.enpol.2012.10.009</u> Thomson H, Bouzarovski S, Snell C. 2017. Rethinking the
<mark>In practice, this would require knowing the type of information available in both the data sets. The</mark> questionnaire of the household survey, which forms the basis for the national PMT, is readily available, but what indicators can be constructed using the humanitarian database in not public information. Although, there is no need for sharing the micro-data, concerns related to data sharing and running into data privacy infringement are a possibility.

How many MoLSA beneficiaries could come from the humanitarian database will depend on circumstances. The displaced households, by all descriptions, are likely to be severely vulnerable. Thus, depending on the availability of budget, political will, and the technical and administrative capacity, referral may, even, rely on non-PMT approaches such as group/geographical targeting or combination of the two. Moreover, perceived ISIS affiliation, fear of persecution and other grievances may mean not all eligible households will be willing to be referred. The possible transitional pathways, thus, will differ depending on these circumstances.

Following the completion of the MCNA survey in 2018, humanitarian agencies updated the vulnerability assessment criteria for CLCI program with separate models for each region, namely: Kurdistan (comprising three governorates in Kurdistan), North (comprising five Northern governorates), and rest of the country (Center and South) corresponding to the World Bank’s regional definition for PMT formula. To be consistent with the new targeting and to assess eligibility into the government’s CT program (SSN) of both the previous and the potential future CLCI beneficiaries we formulate two sets of regional pseudo-PMTs:

1. Three regional models based on common set of indicators between the SWIFT and the 2016 VM model that was devised on the Vulnerability Assessment data (hereafter VM);

2. Three regional models based on common set of indicators from the SWIFT and the updated VM model (SEVAT) that was devised on the 2018 MCNA survey (hereafter SEVAT).

Means and standard deviations of the variables common to the national model and the
with a higher (lower) probability of

> 31 For the migrant sub-sample that is drawn from the VMS, information on economic conditions (such as income levels or the type of work) prior to the migration decision is not available beyond whether or not he or she was employed.

> 32 The per capita land area owned by the household members consists of “farming, forestry land and water surface area for aquaculture, residence land and garden, pond next to housing land” (the VHLSS 2004).

> 33 There are two reasons why the _Enterprise Survey_ data are more suitable to compute the number of foreign jobs than the data from the VHLSS 2004. First, the VHLSS is likely to under-represent the temporary migrants working in the foreign sector since the sampling of the latter data is based on the households’ permanent registration status. Second, the number of workers who reported working in the foreign sector (278 observations) in the VHLSS 2004 is too small to reflect the varying employment opportunities at the province and urban/rural level.

> 34 The logit model can be viewed as a special case of the multinomial logit model when J=2.
5 The WSF3Dv1 analysis framework introduced by Esch et al. (2022) consists of three automated processing modules: a first workflow defines the building fraction (BF) within each 90 m grid cell based on TDX-DEM, TDX-AMP, and S2. The second module analyzes height differences occurring along building edges in the TDX-DEM elevation data to derive the average building height (BH) at 90 m gridding. A last processing workflow combines the previously derived BF and BH information to calculate the building volume (BV).<sup>1</sup> To determine the building fraction BF, the temporal maximum of the Normalized Difference Vegetation Index (NDVI ~~m~~ ax) is calculated for all WSF2019 pixels from a time series of _∼_ 2.27 million S2 granules with _<_ 60% cloud cover (level 2A, bottom of the atmosphere reflectance) acquired in 2018-2019 (Marconcini et al., 2020). Using NDVI ~~m~~ ax and building footprint data from OpenStreetMap (OSM, 2017), an ensemble of support vector regression models is trained separately for all K ̈oppen-Geiger climate zones to estimate the BF for each WSF2019 settlement pixel.<sup>2</sup> The resulting information on BF (%) can be employed to define the total building area BA (m<sup>2</sup> ) of each cell by applying BF as a multiplication factor to the cell’s 12*12 m ground area.

The generation of the building height (BH) product is based on analyzing local height differences at vertical edges (VE) in the 12 m resolution TDX-DEM within the settlement area of WSF2019. In the TDX-DEM, VEs appear as distinct punctual or linear height variations along the outlines of quasi vertical structures such as buildings, walls, trees, or hedgerows. A given pixel in TDX-DEM is considered as a VE candidate if the ratio between the height of the target pixel (above sea level) and the median height (ditto
firms (Hjort and Poulsen 2019). New research also shows that the rollout of mobile broadband Internet has increased household consumption and reduced moderate and extreme poverty in Nigeria (Bahia et al. 2020).

This paper builds on the nascent literature on the welfare effects of mobile Internet in developing countries, and, in contrast to previous studies, it sheds light on the welfare impacts of both fixed and mobile broadband Internet coverage. Senegal has experienced a more rapid expansion in digital technologies over the past decade relative its peers, such as Côte d’Ivoire, Kenya, and Rwanda. A growing share of Senegalese have gained access to cellphone services and mobile and fixed broadband Internet. A recent report suggests that 98.7 percent of users access the Internet through mobile phones, emphasizing the importance of mobile broadband Internet in the country (ARTP 2019).

The study exploits the two most recent household budget surveys undertaken in Senegal: the 2011 Deuxième Enquête de Suivi de la Pauvreté au Sénégal (Second Poverty Monitoring Survey, ESPS-II) and the 2017–18 Enquête Légère Expérimentale sur la Pauvreté (2017–18 Light Experimental Poverty Assessment Survey, ELEPS). It integrates the data of these surveys with data on the expansion of mobile broadband coverage and fiber-optic network infrastructure to examine how the coverage of and proximity to digital infrastructure may influence household consumption and poverty status. It matches the locations of households—based on GPS coordinates of census enumeration areas (EAs, the primary sampling units)—with maps of terrestrial backbone networks and 2G and 3G coverage maps.<sup>3</sup> As discussed more extensively by Hjort and Poulsen (2019), the proximity of a given location

> 1 2G technologies enable voice, SMS, and limited Internet access, while third-generation (3G) technologies enable more rapid Internet browsing and data downloading.

> 2 The number of active mobile broadband subscriptions per 100 inhabitants in Africa in 2017 was 34.0, while the corresponding rate for fixed broadband subscriptions was 0.4 (
. The extent to which expenditures are misreported in these ways is presumed to depend on the item in question and the characteristics of the household.

In India, there has been debate in recent years as to the impact on measured poverty and inequality of having altered the reference period over which (recall-based) consumption data has been collected in two key rounds of the National Sample Survey (see Deaton and Kozel, 2005). Lanjouw (2005) argues that, in Brazil, the application of an inappropriately short reference period over which purchases of foods are recorded in the 2002 _Pesquisa de Orçamientos Familiares_ consumption survey diary might account for the surprisingly high frequency of households who report zero consumption of certain key food items.

In Ghana, Scott and Amenuvegbe (1991) ran a well-documented experiment for 144 households on the impact of recall duration on reported expenditure (not consumption value) of the 13 most