rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 1: The Rise of the Service Sector**

<!-- Start of picture text --> Value added by sector, 1980-2009 Economic Growth Decomposition: Sri Lanka and Selected Comparators<br>1.000.90 10090 24 7 19 6 8 23 10 15 11 8 112 5 2 18 6 12 5 0 0<br>33 14 32 31<br>0.80 80 38 29 28 47 32<br>70 51 40 46 49<br>21 32 44<br>0.70 43<br>60 30<br>50 24<br>0.60<br>50<br>88<br>0.50 40 77<br>68 69<br>0.40 30 55 63 56 62 63<br>0.30 20 46 43 43 42 49 48 51 50 44 44<br>30<br>10<br>0.20<br>0<br>0.10<br>0.00<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 Developing countries China India Sri Lanka Indonesia USA Korea, Rep. Thailand Malaysia Singapore<br>Source: World Development Indicators, World Bank. 2011. Agriculture Industry Services Source: World Development Indicators, World Bank. 2011. Service Industry Agriculture<br>% of GDP<br>1980-85 2000-09 1980-85 2000-09 1980-85 2000-09 1980-85 2000-09 1980-85 2000-09 1980-85 2000-09 1980-85 2000-09 1980-85 2000-09 1980-85 2000-09 1980-85 2000-09<br><!-- End of picture text -->
, our data consist of 1,488 households in each round. In 2008, this yields 8,986 individuals. In 2011, the sample is 9,002 individuals and in 2013, it is 8,608 individuals.

# _3.2 Indicators for Economic Outcomes and ECD_

The outcomes examined in this paper relate to both economic development and ECD. Three different outcomes underlie our analyses of economic development—income, consumption, and assets. For reasons discussed below, all of our outcomes are expressed in per capita terms. Originally in nominal Moroccan dirham, all outcomes are updated into real, 2013 terms using the consumer price index (CPI) (World Bank, 2015). Economic outcomes are analyzed both in terms of annual real 2013 per capita levels and in terms of the log values of the annual real 2013 per capita levels, where coefficients can be interpreted in terms of percentage changes.<sup>10</sup> The value of income was based on questions<sup>11</sup> about household income in the past 12 months, where respondents (each member of the household who received any income, in cash or in kind) could list all household sources of income<sup>12</sup> and their amount in daily, weekly, biweekly,

> 9 If possible, the originally sampled household was replaced by the new household that had moved into the same dwelling.

> 10 Interpreting coefficients in semi-logarithmic equations as percentage changes is an approximation. Small changes in continuous variables come closest to reflecting percentage changes. Larger changes and using dummy rather than continuous variables causes greater divergence (Halvorsen & Palmquist, 1980).

> 11 Income source, its regularity, periodicity, and its amount.

> 12 Sources of income included (1) sale of family farm products; (2) wages from agricultural activity; (3) salaries from non-agricultural activity; (4) agricultural self-employment; (5) non-agricultural self-employment; (6) being an agricultural employer; (
, such as the American Robin ( _Turdus migratorius_ ), to a maximum of 20,000 randomly sampled records. This filtering approach ensured that computational resources were effectively used, without sacrificing the integrity of the data.

We adhered to GBIF’s occurrence reporting protocols, which ensure consistency in the way species records are documented and shared across the global community. This adherence to protocol is essential for maintaining the reliability and comparability of biodiversity data.

## **Species Occurrence Mapping**

To generate species occurrence maps, we employed machine-based techniques for pattern recognition and cluster analysis. For most species (93.6% of the dataset), we applied a spatial boundary construction method known as the alphahull algorithm (Pateiro-López and RodríguezCasal, 2010). This method, well-established in the literature (Guo et al., 2022; Kass et al., 2022), allowed for the precise delineation of species' occurrence regions across terrestrial, freshwater, coastal, and marine environments. For the remaining species, we utilized a k-means clustering algorithm combined with convex hulls to estimate their geographic distributions.

To validate the accuracy of our generated maps, we compared them with expert-verified maps from well-regarded datasets of mammals (Marsh et al., 2022), ants (Kass et al., 2022), and vascular plants (Borgelt et al., 2022). These comparisons demonstrated strong correlations between our maps and those generated by subject-matter experts. Figure 2 provides two examples of species occurrence regions: one for an endemic species, _Scherya bahiensis_ , which is 7
which requires additional information unavailable at this stage, such as the quality of rice seeds, irrigation and soil.

> 8 Labor costs: The number of workers multiplied by their reported salary.  For those non-wage workers, the average salary for men and women is assumed for the estimation. An alternate specification used labor inputs measured in man days per hectare and showed similar results.

> 9 The rice price influences the quantity of urea used by providing farmers with the means and incentives to buy fertilizer. Although the dried grain price at which farmer’s sell their output rice (the information provided in the survey) may differ from the price  farmers faced when buying  inputs (not provided by the survey),  both prices will be strongly correlated.  Therefore, this model uses this post-harvest  information from the survey  as a  proxy for  the rice  price.

**12**
Policy Research Working Paper 10482

# **Abstract**

Quality assurance systems have been implemented or are under development in a number of low- and middle-income countries in an effort to observe the quality of education and deploy targeted measures to improve quality. This paper shares lessons learned on the potential ability of quality assurance systems to observe quality and inform action, drawing on data from a pre-primary quality assurance system in the Arab Republic of Egypt. A nationally representative study of kindergarten classrooms was conducted, using a detailed diagnostic research tool administered by independent enumerators from a data collection firm. A subsample of these kindergarten classrooms was randomly assigned to also be observed through a short quality assurance system tool, half of them by independent enumerators, and the other half by the existing cadre of government kindergarten supervisors. The quality assurance system tool was developed for scale and financial sustainability; thus, it could be administered in roughly one-third of the time of the diagnostic tool, at one-third of the cost. Overall, the results illustrate that at the national level, the quality assurance system tool can identify important areas for improvement, and thus inform broad policy actions. Further, the results are consistent whether an independent data collection firm or a government kindergarten supervisor acted as enumerator, suggesting that quality assurance system data collection efforts can be embedded within ministries of education and implemented in a regular and sustainable manner. At the school and teacher level, however, there were several areas where the quality assurance system data were inconsistent with the diagnostic data. This underscores how quality assurance systems are best used as a formative system, a starting point for quality enhancement, and not as a summative system that directly targets, punishes, or rewards specific schools.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank
skills, and health a country’s population accumulates over their life-cycles. In addition, we explore how skill-mobility (proxied by a country’s net migration-to-population ratio) impacts on country GVC participation. The following subsections discuss each of these three skill indicators and how they are constructed.

# **_a. Skilled and unskilled labor content in exports_**

Skilled labor data are very difficult to obtain especially for African countries. Even data on the education level of workers, as a proxy for skill-level, are not available for most SSA countries. We therefore generate skilled and unskilled labor content in exports (LACEX) using data from GTAP’s Input-Output (IO) tables and a methodology developed by Cali et al. (2016). Based on the International Labor Organization’s (ILO) classification, skilled labor comprises professional workers who consist of managers and administrators (including far managers), professionals, and para-professionals. Unskilled workers (production workers) category consists of tradespersons, clerks, salespersons, personal service workers, plant and machine operators and drivers, laborers and related workers, and farm workers (Liu et al. 1998; Vo and Tyers 1996; Dimaranan and Narayanan, 2009 and 2014). Since there is no complete global data set with disaggregate employment by worker type, the GTAP team initially used labor force surveys and national censuses of 15 different economies where data are available to develop a statistical model to explain labor payment shares in the sample regions. In a second stage, they use the results to predict labor payment shares in 37 aggregated sectors for unobserved regions. The authors subsequently mapped individual country data to GTAP concordance to address inconsistencies in occupational classifications used by different governments. The GTAP approach in splitting total labor payment into payment to skilled and unskilled labor has been used in several GTAP IO database version (GTAP 1-7) which are used in this analysis. Liu et al. (1998) provide a detailed description of the methodology used in GTAP to disaggregate labor payments by skills level.

The
some low-income countries, the institutional rates are not reflective at all of actual retail exchange rates. In such countries, some effort is needed to construct reasonable exchange rate data from country-specific sources. In this paper, daily rates from the ECB finance were used and aggregated to monthly data using averages. We then obtain monthly parallel market rates available from FAO through the FMPA, correct the monthly aggregated official rates using a linear regression to correct for the typical spread that retail consumers face, and in the result replace observations with the FMPA data where available. The result
PPML. All estimates are obtained with three-year interval data and three-way xed eects, including origin-time, destination-time, directional pair xed eects, and all other control variables from columns (3) and (4) of Table 2, respectively. The estimates of all xed eects and controls are omitted for brevity. The standard errors in all specications are clustered by country pair. See text for further details.

Since, due to perfect or near-perfect collinearity, we could not obtain informative estimates of the eects of all types of investment provisions with an indicator-variable approach, 30
the IHPS was made possible by the technical and financial assistance from the World Bank LSMS Plus (LSMS+) initiative, which aims to improve in IDA countries the availability and quality of individualdisaggregated survey data on asset ownership, work and employment and entrepreneurship. For more information on the LSMS+, please visit: http://surveys.worldbank.org/lsms/programs/lsms-plus.

5
20th percentile)|42.5|66.5|0.4|0.1|100|
|Score: 895 (30th percentile)|32.1|55.4|0.8|0.2|100|
|Score: 909 (40th percentile)|24.7|45.5|1.8|0.5|100|

# **Model 3**

|_Eligible share_||||||
|---|---|---|---|---|---|
|Existing Samurdhi beneficiaries|40.2|37.1|3.8|2.2|18.8|
|PMT cutoff score*||||||
|Score: 878 (20th percentile)|46.8|36.9|0.3|0.2|11.1|
|Score: 895 (30th percentile)|74.5|63.9|0.9|0.4|23.2|
|Score: 909 (40th percentile)|89.2|82.0|3.5|1.8|36.4|
|_Distribution of beneficiaries_||||||
|Existing Samurdhi beneficiaries|21.4|39.5|4.1|1.2|100|
|PMT cutoff score*||||||
|Score: 878 (20th percentile)|42.1|66.4|0.5|0.2|100|
|Score: 895 (30th percentile)|32.1|55.0|0.8|0.2|100|
|Score: 909 (40th percentile)|24.5|45.0|1.9|0.5|100|

See notes for Table 2.

25
The Colombian government created in 2018 the _Permiso Especial de Permanencia_ (PEP) to facilitate their integration. Prior to the PEP, the government rolled out a voluntary registration process, the _Registro Administrativo de Migrantes Venezolanos_ (RAMV), with the sole purpose of identifying the location and needs of irregular migrants. In July 2018, the government unexpectedly announced that all migrants registered in RAMV, a little more than 440,000, were eligible for applying to the PEP program through a free and online tool. Of those, 64% registered into PEP. PEP granted legal migratory status for two years, access to education and health, eligibility to social programs, job permits, and an identification card to access private services. For a detailed description of the program see Ib ́a ̃nez et al. (2022).

The design and rollout of PEP lend themselves to estimating the causal effects on the resilience of forced migrants to negative shocks. The unexpected and broad nature of the program (available to all RAMV migrants and with no eligibility criteria), allows for ruling out anticipatory effects and strategic behaviors that have affected the evaluations of other similar programs.

# **III Data**

We use two waves of the Venezuelan Refugees Panel Survey (VenRePS), administered in the second semesters of 2020 and 2021. Figure A.1 in the appendix depicts the timeline for the PEP roll-out, the VenRePS waves, and the lockdowns put in place by the Colombian government. The baseline sample covered 2,232 forced migrant households who arrived in Colombia between January 1, 2017 and December 2018, including 1,135 who registered in RAMV and were thus eligible for PEP (from here on RAMV migrants), and 1,097 irregular forced migrants (non-RAMV migrants from here on).

The sample of RAMV migrants was randomly selected from the RAMV census. The sample of non-RAMV migrants was randomly selected by combining databases shared by associations of Venezuelan and referrals from who were sur- refugees migrants being veyed as part of the
measure (PCA 5), 57.52% is explained by the extensive/operational measures of informality (no license, no registration, no VAT registration number). The remaining 42.48% is explained by the two intensive proxies of informality (contributions to employees’ social security and bank account).

# **3.2.2 Types of Informal Firms: Necessity, Parasite, and De Soto**

Informal firms can either be born out of necessity with a survival motive; are parasitic such that they choose to remain informal to evade costs; or are high-potential but per the De Soto view remain informal due to burdensome regulations. The type of informality is correlated with the initial productivity of firms when they enter the market and is contingent on the human capital of the entrepreneurs (Ulyssea, 2018). We use the pre-pandemic productivity measure to analyze the distributions across formal and informal firms. To this end, we take the following steps: _First_ , we regress productivity on firm attributes such as age and other initial characteristics, including size, sector, whether the firm is female-led, and country fixed effects: Where _γs_ are sector fixed effects; _φc_ are country fixed effects; and _νj_ are size effects, where _j_ =small, medium, and large.

_Second_ , using the residual of this regression, that is, unexplained labor productivity, we calculate the 25<sup>_th_</sup> and the 75<sup>_th_</sup> percentiles of the distribution of this conditional productivity by country and sector for fully formal firms - on both extensive and intensive margins - as measured by PCA(5).

In the last step, we map the distribution of informal firms to one of the three groups: below 25<sup>_th_</sup> percentile; between 25<sup>_th_</sup> and 75<sup>_th_</sup> percentile, and; above 75<sup>_th_</sup> percentile. We
variable for the endogenous dummy variable in the outcome equation, it adds an estimate of the omitted variable in the equation. It turns out that PSM can act as an effective adjuvant to all these methods.

With respect to the choice among various evaluation methods, this comparison suggests situations that are best suited to each method. Hence, no single method can be considered ideal in all circumstances, and one should consider a flexible application of available methods. In the end, the plausibility of an evaluation method hinges critically on the correctness of the socioeconomic model underlying program design and implementation. It also depends on the quality and quantity of data available. The specification of the underlying socioeconomic model must be grounded on a sound understanding of political and socioeconomic determinants of participation, and all relevant factors that influence outcome besides participation.

# **_3.  Numerical Implementation_**

This section illustrates how some of the algorithms and estimators described above might be implemented in EViews. We first describe the data used. Next, we explain the estimation of the propensity score. We also discuss the computer code and output for nearest-neighbor and kernel matching respectively. Finally, in the true spirit of impact evaluation, we present matched estimates along results from alternative methods.

# **_Data_**

Our numerical implementation is based on two data sets from Dehejia and Wahba (1999)<sup>12</sup> . These authors explain that the available data sets are constructed from the data underlying Lalonde (1986). The data for the treated is contained in the file NSWRE74_TREATED.TXT. This is the male sub-sample of a 185 observations from the National Supported Work (NSW) Demonstration. This was a temporary employment program seeking to help disadvantaged workers<sup>13</sup> acquire basic job skills through work

> 12 The actual data sets were downloaded from http://www.columbia.edu/%7Erd247/nswdata.html 13 The program targeted women receiving aid for dependent children (AFDC), ex-drug addicts,
participant’s employment rates. However, these effects tend to be contingent on program design and differ across participant groups (e.g., Levy Yeyati et al. 2019; Autor et al. 2017; Card et al. 2018; Groh et al. 2012). Research provides limited support for the notion that ALMP enhance earnings (Betcherman et al. 2005; Levinsohn and Leibbrandt 2014). Among the rare macro-level studies, Caicedo et al. (2022) indicate an overall positive impact of ALMP on output and welfare, but also highlight their significant distortionary effects.

Drawing on new global data from the World Bank that covers 191 countries in 2019 and 2020, this paper aims to provide an analysis of the patterns of UB and ALMP across different regions and income groups and investigate their association with labor market outcomes.<sup>2</sup> It also utilizes the comprehensive labor market and social assistance policy inventory of the World Bank to present the patterns of UB and ALMP reforms implemented in 223 economies throughout the Covid-19 pandemic. Econometric analysis of the linkages between UB, ALMP and labor market outcomes is conducted using new data from 2019 and the ordinary least squares (OLS) regression model that incorporates interaction terms between income groups and UB and ALMP and includes a wide range of control variables.

> 1 Throughout this paper, the term “unemployment benefits (UB)” is used to refer to “unemployment insurance benefits” and it does not include unemployment assistance.

> 2 Data set is available at https://www.worldbank.org/en/research/employing-workers/data.

2
Rules Dataset, 2015 and Budina et al. (2012)), compared to only 10 in the 1990s.

Despite this increased popularity of fiscal rules, relatively little work has been undertaken on their functioning. We try to fill this gap by examining the effectiveness of different types of fiscal rules in promoting counter-cyclical fiscal policies. Moreover, we focus on the role of institutional quality and try to find whether there is a more optimal solution to promote counter-cyclical fiscal policy (i.e. which deals with the underlying political source of the problem). This analysis is motivated by Frankel et al. (2013), Bergman and Hutchison (2015) and Bergman et al. (2016) which argue that good and strong institutions have beneficial effects on public finance. In addition, we revisit the effect of exchange rate regimes on the cyclicality of fiscal policy and more interestingly, we try to find whether the effect of fiscal rules depends on the exchange rate regime in place.<sup>2</sup> In other words, we focus on the effect of rules conditional on the exchange rate regime in place (in line with a Mundell-Fleming approach). To the best of our knowledge, this issue has not yet been addressed in the literature.

The contribution of this paper to the literature is threefold. First, it contributes to the emerging literature on the impact of institutions and fiscal rules on the cyclicality of fiscal policy since it empirically addresses the issue of their complementarity or substitutability (Bergman and Hutchison (2015), Wyplosz (2005) and Wyplosz (2012)) in reducing pro-cyclicality and promoting fiscal discipline. Second, to the best of our knowledge, this is the first paper which examines the disciplining effect of fiscal rules, conditional on the exchange rate regimes. Third, it tackles the heterogeneity of fiscal rules since it examines the disciplining effect of different types of rules (expenditure rules, budget balance rules, debt rules and revenue rules).

We use a panel of 153 advanced, emerging
Policy Research Working Paper 6325

# **Abstract**

Alternative scenarios are considered for reducing by one billion the number of people living below $1.25 a day. The low-case, “pessimistic,” path to that goal would see the developing world outside China returning to its slower pace of growth and poverty reduction of the 1980s and 1990s, though with China maintaining its progress. This path would take another 50 years or more to lift one billion people out of poverty. The more optimistic path would maintain the (impressive) progress against poverty since 2000, which would instead reach the target by around 2025–30. This scenario is consistent with both linear projections of the time series data and nonlinear simulations of inequality-neutral growth for the developing world as a whole.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mr1185@georgetown.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team