rafmacalaba/data-use-mentions-extended huggingface dataset:
3.410 TFN|1.480|0.471|<br>3.143|
||9 TotalN|1.464|0.344|4.250 TFN|1.491|0.321|<br>4.645|
||10 TotalN|0.811|0.500|1.622 TFN|0.680|0.510|<br>1.333|
||11 TotalN|1.153|0.410|2.813 TFN|0.767|0.478|<br>1.606|
||12 TotalN|0.792|0.366|2.162 TFN|0.876|0.341|<br>2.567|
|Average||0.924|0.395|2.543|0.866|0.399|<br>2.353|
|Note:|TotalN|=(TF +TI)/6.|Mean = 2.29|TFN= TF|/3. Mean = 2.46|||

The key results from the illustrative simulations in Table 4 are:

- (i) The coefficient (slope) estimates from using only the Formal measures of quality are about 5-10% lower than from using Formal and Informal measures;

- (ii) The estimated standard errors from using only the Formal measures of quality are about 1-3% higher than from using Formal and Informal measures.

- (iii) Estimated t-values are more often lower than higher from using only the Formal measures of quality but, on average, the difference was a reduction in estimated t-values of around 7%.

The illustrative simulation results from this data set would not justify abandoning the idea of carrying out empirical work with only the formal variables in place. This is encouraging in itself and supportive of the work that we report in our companion paper, which uses the DPS data on regulatory governance –
to using cross-sectional data for analysing the effects of family planning programs.<sup>1</sup> To address program placement challenges in such contexts, one approach is to use variables that influence program placement, but that are unrelated to individual fertility as done for Tanzania (Angeles et al. 1998).<sup>2</sup> A woman in Tanzania exposed to family planning throughout her fertile lifespan is found to have 4.13 children compared with 4.71 children in the absence of family planning programs.<sup>3</sup> Lingering concerns remain, however, that some of the variables used to identify placement (such as child mortality levels and the presence of other family planning services) may also be correlated with unobservable variables that influence both placement and fertility decisions.

To address non-random program placement when evaluating the effect of family planning programs in Ethiopia, this study exploits detailed information on area characteristics and the geographical allocation decisions of the family planning programs. The identification strategy is novel in that it draws on the insight that areas compete for limited resources and that ordinal rankings (as opposed to cardinal scores of the ranking criteria) are often used to discern between competing demand.<sup>4</sup> To fix ideas, assume that there are only three areas, A, B and C, that compete for resources from the government. Using the extent of urbanization as an example, we expect that the degree of urbanization of area A will affect fertility in area A, but that the degree of urbanization of areas B and C will have little or no effect on fertility in area A.<sup>5</sup> Because the three areas compete for resources the _relative_ degree of urbanization may, however, affect the program placement decision.<sup>6</sup> This opens up the opportunity to use rankings as identifying variables (as opposed to levels). Imagine that urbanization is highest in area A, followed by B and
facilities, and of each facility type (e.g., front-line clinics versus hospitals).”_ Study size 10 Explain how the study size was arrived at _See the relevant text: “In total, the sample includes information from 89,826 vignettes from 13,754 providers and absenteeism observations from 22,747 providers across 8,061 facilities in 10 countries. In each country, the sample size was selected in order to provide representative data for the country and for sub-groups (e.g., rural versus urban areas).”_

- Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why

_See the relevant text: “We present averages and 95% confidence intervals by first averaging all observations across facilities (or health workers) in each country (using country-specific facility weights to calculate a representative country average), and then averaging over the country means to calculate an overall average for the whole sample. (This is a suitable approach if we treat the country as the core unit of observation; i.e., we want to know how countries compare and how they perform as a group.)”_ 39
of the<br>global climate|ECMWF Reanalysis<br>v5 (ERA5)|0.25°, three-<br>hourly, 1979–<br>2018|ECMWF|
|Precipitation|Atmospheric<br>precipitation<br>estimates from<br>rain gauge and<br>satellite<br>observations|CHIRPS 2.0|0.05°, dekadal<br>(10-day), 2000–<br>2020|CHIRPS|
|Soil moisture|Soil Water Index<br>(SWI) at a depth of<br>40 cm|SWI10-40|0.1°, dekadal,<br>2007–present|Copernicus Global<br>Land Service|
|Vegetation index|Normalized<br>Difference<br>Vegetation Index|MOD13C2 Version 6|0.05°, monthly,<br>2000–2020|MODIS|

_Source:_ Blanchard and Sousa 2021.

_Note:_ CHIRPS = Climate Hazards Group InfraRed Precipitation with Station; ECMWF = <mark>European Centre for Medium-Range Weather Forecasts;</mark> MODIS = Terra Moderate Resolution Imaging Spectroradiometer.

The approach is based on random sampling of stochastic perturbations of historical data to generate stochastic realizations of precipitation values over Malawi. The stochastic catalogs of NVDI and SWI are then derived from the stochastic precipitation catalog.

More specifically, the 10,000-year stochastic precipitation catalog has a spatial resolution of 0.05° and dekadal temporal resolution. It was developed using random sampling of stochastic perturbations of atmospheric moisture data (ERA5 [( <mark>European Centre for Medium-Range Weather Forecasts)</mark> Reanalysis v5]) between 1979 and 2018, at a native resolution of 0.25° and three-hour intervals (as an input to the process). While CHIRPS data could not be used in place of ERA5 at the modeling stage, they were used to augment a climatological adjustment to reflect “real” rainfall readings and climatology compared to ERA5.

41
for Australia, whereas Trends in International Migrant Stock reports data for 1970. Overall, however, the fact that the totals are remarkably close in every decade adds credence to the estimates here.

# _IV. THE EVOLUTION OF GLOBAL BILATERAL MIGRATION_

The greatest strengths of the global migration matrices are their bilateral coverage, the number of decades covered, and the disaggregation by gender. These data are too rich for a full analysis of all movements between all pairs of countries. Instead, this section summarizes the major trends in the evolution of bilateral migrant stocks, based primarily on World Bank regions.<sup>25</sup>

# _Global Trends_

The migration matrix for the 1960 census round reflects a realigning world in the postcolonial era. Over the 1960-2000 period, the composition of world migration 25 Appendix 1 details the World Bank regions: South Asia, East Asia and Pacific, Sub-Saharan Africa, Latin America and the Caribbean, Europe and Central Asia, and Middle East and North Africa. High-income Middle East and North Africa refers to the predominantly oil producing countries in the Persian Gulf (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) and to Israel.

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_Economic profit._ The calculation we use differs in two ways. First, we replace the deposit rate with the country’s prime lending interest rate (taken from the World Bank’s _World_ _Development Indicators_ ). For comparison, we also use the US prime interest rate in some calculations.<sup>8</sup> We thus replace the MIX subsidy adjustment with: Subsidy adjustment = total borrowing * (prime lending rate) - interest expense on total borrowings.

Second, we add an adjustment for implicit subsidies to equity: Equity adjustment = Total donated equity amount * (prime lending rate) This gives us a formula for economic profit: Financial revenue / [Financial expense + Operating expense + Net loan loss + Net inflation adjustment + Subsidy adjustment + Equity Adjustment].

The work here updates our previous work with smaller, earlier samples of MIX Market data. Cull et al. (2009) use a sample of MIX Market data with 346 microfinance institutions in 67 countries covering nearly 18 million active borrowers, drawn from 2002-4. Cull, DemirgüçKunt, and Morduch (2007) analyze 124 MFIs in 49 countries.

In the present sample we analyze the most recent data on MFIs between 2005 and 2009.

The entire database includes 3,845 institution-years, reflecting 291 million borrower-years. We focus on a cross-section with the most recent data for each institution. Most of the most recent

> 8 Where the interest rate is not available in the _World Development Indicators_ , we use data from country publications. For example, we take India's rates from the Indian government statistics website (Chapter 24 "Banks, Table 24 Money rates in India"). Available at:

> http://mospi.nic.in/Mospi_New/site/India_Statistics.aspx?status=1&menu_id=14 ".

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- ii. Avoid using central stores for purchase of materials, and tailor a specialized procurement system to the needs of this project.

- iii. Create bundled contracts with competitively set rates for works that will be repeated with many variations across many different slums. This will permit financially strong and competent large contractors to bid for the works and complete the works quickly to good quality standards.

- iv. Allow contract costing at market rather than PWD rates, which do not always capture price escalations and therefore discourage strong contractors from bidding. Creating a system only attractive to smaller, weaker contractors slows down progress of works due to poor contractor performance and financial problems. Our estimated costs reflect market rates, and they are still quite affordable.

- v. Permit outside contracting of specialized skills required only for the limited period of the project. This can reduce reliance on AMC engineers whose workload will not permit rapid execution and approval of a large number of small projects.

- b. Start work quickly in priority neighborhoods (criteria to be set) to demonstrate ability to achieve results for communities willing to work with the AMC. This work should start before completing comprehensive and detailed surveys of needs in the city. Such exercises can work in parallel. Based on this initial survey and the many other initiatives undertaken to pilot slum upgrading under RAY, AMC should be able to estimate the costs accurately enough to produce project documentation and obtain funding for a city-wide program.

- c. Adapt financial requirements for participating households so that they can meet their financial obligations over time, thus ensuring accountability of contractors and participating NGOs.

- i. Rely less on micro finance for household contributions. Requiring beneficiaries to qualify for a micro loan for their contribution to the project (Here estimated at Rs. 5000 per household based on consultations with community groups) slows down project implementation by 3-6 months. If households are allowed to pay in their contribution based on the progress of works, as would any household hiring their own contractor, the need for
, we include unbalanced variables at baseline as additional controls.

We report manipulation checks in Tables C.2 and C.3 based on survey responses. Over 85 percent of CM communities report a meeting held by one of our implementing partners to discuss working with the clinic to improve health service delivery; the average CM community reports 2.5 such meetings. Around 44 percent of control communities report meetings as well. This does not reflect non-compliance by our implementing partners; rather, community meetings unrelated to our interventions are not uncommon across 17
these skills. We further corroborate our findings using data from a similar sample from Cambodia, which shares key features with the data from Pakistan.

Our results show that more schooling is associated with higher cognitive skills and, to a smaller extent, greater SEM skills. Labor earnings are correlated with years of schooling, and cognitive and SEM skills conditional on years of schooling, with the size of the associations varying with migration from the

> 2 See Foster and Rosenzweig (1996) on the relationship between returns to schooling and technological change in an agrarian economy; Duflo, Dupas, and Kremer (2021) on the experimentally estimated returns to secondary education in Ghana; McKenzie, Stillman, and Gibson (2010) on the returns to international migration between Tonga and New Zealand; Bryan, Chowdhury, and Mobarak (2014) on the experimental returns to seasonal migration in Bangladesh and Beegle, Weerdt, and Dercon (2011) on consumption growth among migrant and non-migrant households in the Kagera region of Tanzania.

2
probably also endogenous, but we do not take explicit measures with them because their returns lie beyond our research questions. Time and individual fixed effects, however, tackle issues of omitted variables for all independent variables, decreasing bias of all estimated coefficients.

Given that firms construct their knowledge stock by investing in innovation activities, these decisions are very likely endogenous to firms’ productivity, and exogeneity of regressors cannot be assumed. Unobservable omitted variables such as know-how the firms’ workers possess, or the quality of managerial capabilities could affect both firms’ decisions regarding innovation and firms’ productivity. In addition, while larger knowledge stocks may increase firm productivity, more productive firms are more likely to be exposed to and aware of innovation opportunities. Hence, these firms may be more prone to investing in innovation and increasing their knowledge stock than less productive firms, causing reverse causality issues.

We tackle time-invariant omitted variables—both observable and unobservable—by exploiting the panel structure of our data with the inclusion of firm and time fixed effects, as has been done in other papers using ENDEI survey databases (see, for example, Brambilla and Tortarolo (2018)). We also explore an instrumental variable (IV) approach to estimate the returns of R&D over productivity. Claiming exogeneity of variables coming from our own ENDEI database is questionable and the restrictions to merge other potential instruments from external sources at the firm level due to ENDEI’s confidentiality issues limits our analysis. However, it is important to try to minimize potential biases due to the endogeneity. Next, we describe the IV strategy and present the best IV candidates. As we shall see, the instruments proposed are statistically strong and exogenous, but the results of the IV estimation differ sharply from those of our panel data estimation, which raises questions about the accuracy of the point estimates.

Our first IV candidate is a dummy variable on firm’s _knowledge_ about the existence of public support programs for innovation activities offered by the Ministry of Science and Technology (Ministerio de
s responsibility to finance schooling. At the higher education and post-compulsory levels, the onus is not on government to fully provide and finance it, but certainly to put in place the conditions for increasing access to quality opportunities. This is an area where financial innovations make sense. There are options for governments, such as using the evidence from labor market returns to education to implement financial innovations – and use future earnings to finance higher education (Bentaouet Kattan, Macdonald, Patrinos 2018). High returns suggest that it makes sense to expand higher education – but in an equitable and sustainable manner.

In terms of increasing cognitive skills, the task is difficult, and developing countries lag far behind. Yet, we know how to do it. Education systems that do well prepare children early on, reform continuously, and use information for improvement. There is a need to focus on basic skills, early development, and to measure and improve early reading. High-performing East Asian countries offer some useful lessons (World Bank 2018). These include: (1) aligning institutions to ensure basic conditions for learning by ensuring that the basic conditions for learning are in place in all schools; (2) concentrating effective, equity-minded public spending on basic education, including the channeling of public resources to schools and districts that are falling behind; (3) ensure children are ready to learn in school; (4) selecting and supporting teachers throughout their careers to allow them to focus on the classroom, starting with raising the selectiveness of who becomes a teacher; and (5) assessing students to diagnose issues and inform instruction.

6
we define our three key indicators which reflect, respectively: local-area community-level remoteness; the geographical scale of population dispersion across a country; and, country-wide, dispersion-adjusted population density, or inversely sparsity. The second section presents empirical estimates for each of the PICs and compares the results of our new measures to other spatial geography-related indicators. The third section contextualizes the results for the PICs in a broader global context, capitalizing on data available for countries included in the World Bank’s sub-national geographic database. A final section describes some extensions to our benchmark work and concludes.

# 1. Indicator definitions

Population settlement patterns vary considerably across countries, and sometimes in nuanced ways which are difficult to capture in a simple, empirical indicator. This section presents three related indicators which reflect variation in population settlement patterns: (i) an indicator of community remoteness within a country context (a local-area-level indicator), (ii) an indicator of the geographical scale of population dispersion (a country-level indicator), and (iii) an indicator of dispersion-adjusted population density, or inversely, sparsity (a country-level indicator).

These indicators are designed with practical evaluation in mind. They can be calculated using commonly available geo-coded population census data, disaggregated at a local-area level; e.g. by enumeration area, or jurisdictional district areas. In theory, all three of our indicators can be rooted in a conceptualization of remoteness at the individual level and evaluating the indicators at the individual-level would yield the most accurate picture of the depth and severity of remoteness of settlements across the country. However, census data are rarely made available geo-coded at the individual level (i.e. indicating where each individual in the country lives) for obvious reasons. As such, we focus the definitions and discussion below on indicator definitions at the more applicable, community level (which effectively assumes that all individuals in a local area are located at the centroid of that area). For readability, we use the term 3
behavioral measures. The STEP data sets are a rich globally comparable information base that include direct and indirect measures (or proxies) for different types of skills as well as information on individual education, training and labor market trajectories and family background characteristics.

The results of this study have important implications for policy and programs focused on improving the education, training and skills profiles of workers to better suit the needs of the labor market. Our results show that skills yield significant payoffs in the labor market, even after controlling for education and other relevant individual and family background factors. This is particularly the case for our two measures of cognitive skills, reading proficiency and complexity of computers use on the job, while noncognitive skills show some significant, albeit small effects on earnings. Even after controlling for schooling, we find the association between earnings and reading proficiency (our first measure of cognitive skills) to be large and stable while the complexity of computer use on the job (our second measure of cognitive skills), has the largest association with earnings across the countries in our sample.

Beyond these overall findings, our results suggest that there is significant heterogeneity across countries in how skills are valued in the labor market. There is also some indication of possible subgroup differences by gender, employment status, and occupational group. These differences across groups highlight the need for additional analysis for a more nuanced understanding of the role of skills in labor market success and context-specific programming that takes into account the characteristics of the education and training systems and labor market needs of each country.

The paper is structured as follows. The next section (Section II) briefly describes the literature on the effect of skills on wages. Section III provides a description of the data and the analytic sample used in the empirical analysis. Section IV provides a discussion of the methodology. The results are reported in Section V, and Section VI provides conclusions.

# **II. Literature Review**

There is a vast econometric literature that examines returns to education and training
of light—the “digital number” (DN)— emanating from within these boundaries (the intensive margin).<sup>14</sup> There is also a measurement reason for distinguishing between lit area and light output within an area. Some DMSP satellites distribute measured light less intensely over a wider area and others focus it into a smaller area.<sup>15</sup> For example, Liu et al (2012) note that

> 12 The census lists 53 million-plus cities but two are within the Delhi agglomeration (Faridabad and Ghaziabad) and four others (Malappurum, Srinigar, Kollam and Kannur) were too dim to be detected at the 50% threshold used for the other cities. At the 20% luminosity threshhold we use to distinguish towns from rural areas, these four cities contribute just 0.4% of total lit area (and 1.3% of big city lit area, if measured at that threshold). 13 The area expansion rate of the 23 cities that were million plus in 1991 was not significantly different from the others that crossed the million threshhold between 1991 and 2011, indicating that there is no evident selection or survivorship bias by defining the sample of big cities by their meeting a criteria set at the end of the period. 14 Technically, we measure areas by starting at the center of each big city, where lights should be brightest, and as the algorithm moves outwards and comes across pixels less illuminated than the brightness threshold it searches in a different direction. If the algorithm finds no contiguous pixels with light above the threshold except those closer to the city center that it has already scanned over, it sets a boundary.

> 15 To briefly digress, DMSP was to detect clouds, for daily weather forecasts to help Air Force pilots, rather than to detect ground level activity to help economists. Photons from the observed area enter a scanner and create a pulse of electrons that map to a DN ranging from 0-63; this range was because on-board