rafmacalaba/data-use-mentions-extended huggingface dataset:
The purpose of defining a structural revenue is to smooth out transitory fluctuations in actual revenues. As discussed in section III, structural prices are usually set at their (perceived) long-term levels. When production is not the focus of the analysis, to keep the model simple, we set structural production equal to actual production or a moving average. However, the LTGM-NR toolkit provides other specifications, such as using baseline as reference production.<sup>16</sup> **Fiscal rules.** The marginal propensity to invest, , captures the pro-cyclicality of fiscal policy. The user can choose any value of , although more common values θθ range between 0 and 1. This range nests three popular fiscal rules: θθ • captures a SSR (Structural Surplus Rule), as cyclical resource revenues are saved (when prices are high) and do not affect public investment.<sup>17</sup> θθ= 0

> 14 The LTGM-NR spreadsheet also allows the parameter to vary over time. 15 Capturing, for example, royalties from the concessions of exploration of natural resources, taxreceipts from private extractive enterprises and profits from state-owned companies. θθ 16 An equilibrium is defined as a collection of 15 endogenous trajectories

> 0 , R 0 D � where with each endogenous variable specified as a function of the exogenous paths ii ii jj ii jj gg RR RR tt tt tt tt tt tt tt tt tt tt tt tt tt tt tt �YY , QQ , RRh , KK , KK DDPP , YY , ii , ii , ii , MMRRPPKK} and initial conditions , MMRRPPKK �  �ii , zz , zz̆ jj∈{0,1, ... , NN} ttAAjj 0 tt ttNN0 ttωω0 ttρρ0 ttii0 ttii 0 pptt 0ttgg 0ttii 0ttii� that satisfy equations (1)—(15) for all _t_ . 17{gg̅ In the LTGM-NR-default the SSR and SSR-HR are very similar (given that spending is mostly , gg̅ , gg̅ , gg̅jj , gg
>and Standards<br>Board of India|Reserve Bank of India‟s<br>guidance on Financial<br>Inclusion|Innovative use of<br>technology to reach<br>customers|

Source: Pearce & Bajaj, 2006.

_Data availability_

**Improved data on financial inclusion and unbanked markets/customers is needed to underpin a sustainable expansion in access to finance** , as emphasized by the G20 Principle for Financial Inclusion, _Knowledge._ Household and enterprise-level survey questions can provide governments and regulators with the data they need for designing reforms and then monitoring the effectiveness of those reform measures. Financial service providers need market data on financial service demand and needs for new market segments that they have not had previous involvement in, in order to design appropriate financial products and delivery mechanisms.  In addition to financial regulator monitoring of progress on financial inclusion, government support may be needed in generating market data for financial service providers, at least initially to address this market failure.

_Use Government Payments as a Stimulus for Financial Inclusion_

**An area that the Government has direct control over is how it pays benefits (social transfers and pension payments) and salaries.** By shifting government payments to individuals (G2P, or „Government to Person‟) from payments in cash disbursed from post offices or bank tellers, to bank accounts and smart cards (or even mobile phones), significant advances in financial inclusion can be made. Governments make regular payments to at least 170 million poor people worldwide.  Yet in most countries, fewer than one-quarter of G2P payments to the

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.40|-79.75|-32.69|

Source: Author calculations based on ‘Our World in Data‘ (https://ourworldindata.org/).

The key finding of the GCRI based on production-related carbon stocks is that the rankings for both payers and recipients do not change much compared to the GCRI based on annual emissions. The United States and the Russian Federation are the top contributors based on stock values. China is the third largest contributor. Yet, the net contributions of China decrease drastically from $35 billion to $8 billion. The cumulative emissions since the 1990s have been low in China compared to the US and Russia. These numbers are computed using a production-based approach. With the consumption-based approach (reported in the Annex), however, China moves from paying into the fund to becoming a recipient.

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harmonized learning score is currently the only comparable measure of learning that covers a large number of countries.<sup>7</sup> Without ignoring the limitations surrounding the LAYS, this appears to be one of the most reliable indicators of quality education that is available and covers a large number of countries including developing countries.

# _Control variables (z)_

The education production function theory shows that inputs can be grouped into two main categories: financial and non‐financial resources. Financial resources come from three main sources: aid to the education sector ( _aid_ ), government expenditure ( _govspend_ ) and households’ spending ( _hhspend_ ).<sup>8</sup> Non‐ financial resources are related to supply‐side factors (availability of teachers for instance) and demand‐ side factors (population’s demand for education for instance). This implies that public expenditure may not be the only input for educational outcomes and the efficiency measurement may be adjusted accordingly. From the financial inputs side, _aid_ and _govspend_ are already captured by the input variable _x._ Countries’ income level dummies are included in _z_ to control for the population income, and to some extent, _hhspend_ . Regarding the non‐financial resources used by the education sector, the primary completion rate ( _pcr_ ) is included as a measure of the households’ demand for education and the capacity of the education system to deliver basic education, while the students‐to‐trained teachers ratio ( _sttr_ ) is a supply‐side indicator capturing the availability of trained teachers. Availability of qualified teachers has been recognized as one of the key ingredients for quality education (World Development Report, 2018). The unemployment rate ( _unemploy_ ) is introduced as an additional control variable capturing the economic environment and, to some extent, the demand for labor in the economy. The share of official development assistance ( _odashare_ ) is also included as a control variable for aid dependency.

> 7 See Patrinos & Angrist (2018) for a discussion
and is similar to Bodenstein & Guerrieri (2011) and Gars & Olovsson (2017). The key difference is that, in order to capture U.S. oil supply shocks and U.S. energy efficiency shocks, I model the United States as an economy that produces oil and either exports or imports it. Specifically, the model includes three countries: a net oil importer, a net oil exporter, and the United States. Intermediate-good producing firms in each oil-importing country choose capital, labor, and oil (as an energy source) to produce a distinct tradable intermediate good, where firms’ demand for oil is affected by energy efficiency shocks. The intermediate good is traded across countries to produce a final good, which is used for consumption and investment. Additionally, the model features oil supply shocks. The U.S. oil sector uses labor as an input to produce oil, which is affected by unexpected changes in productivity, while the net oil exporting country is endowed with a stochastic supply of oil that is affected by exogenous shocks. The U.S. energy efficiency and oil supply shocks are modeled as persistent but transitory processes and they are estimated using an indirect inference approach. The indirect inference method estimates these parameters by matching empirical impulse responses obtained from the FAVAR with impulse responses from an identical FAVAR estimated on data simulated from the theoretical model. The model abstracts from a number of features such as nominal frictions, the strategic behavior of oil producers, longrun growth, and different types of crude oil to focus on features that serve to demonstrate the main mechanisms underlying the empirical results.

My model-simulated impulse responses closely match the empirical impulse responses and provide a framework for interpreting the empirical findings. In particular, the results provide an interpretation for the mechanisms by which changes in U.S. oil production and consumption affect the global oil market and GDP across countries. A key parameter restriction that generates a larger 5
Bolter, 2020) and consequently it serves as a case study to see whether different legal constraints can lead to observed outcomes.

This paper assesses the short term consequences of the Venezuelan influx on the overall labor market and fiscal balance in the Brazilian state of Roraima, using a regression discontinuity framework, that exploits a sharp jump in population in Roraima in the first quarter of 2018, which witnessed an inflow of Venezuelans of about 19,497, which is 3.8% of Roraima’s population in the first quarter, and an inflow of about 117,347, which is 18% of its population in the year of 2018. Using the Continuous National Household Sample Survey for population and labor market data and the National Treasury Secretariat data for the fiscal variables, this paper finds that the population shock of about 6.7% in the working age population of Roraima was accompanied by a spur in social assistance expenditure of about 1.6 million Brazilian reais, which is a 50% increase from its value in the sixth bi-month of 2017. However, total government expenditure and fiscal balance did not increase significantly in relation to the increase in migration. On the labor market, the 6.7% increase in working age population led to an increase in unemployment among females living in the state by about 22%, and employment among females decreased by about 12%, suggesting that females are suffering disproportionately more from the displacement crisis as Roraima tries to absorb the excess supply in the short-run with no sizable effect among males. Employment among those with an education below college degree plummeted down by about 5%, while monthly income went up for the high skilled. It should be noted here that due to unavailability of nationality information, this paper focuses on the impact on the overall market including migrants, not just on the native community. Overall the results suggest that although there are some winners and some losers of the population shock, the more vulnerable groups of the labor force bear a disproportionate burden of the displacement crisis
success varies more within countries than across countries.

# **3. Data and methods: What is our empirical approach?**

We combine data on World Bank project outcomes from the Bank’s Independent Evaluation Group (IEG) with a new data set derived from project preparation documents and internal staff records to assess the contribution of project design and management, as well as other relevant factors, to development effectiveness. Specifically, we ask to what extent observed variations in project quality are associated with: (i) factors related to the selection and retention of key staff and managers of staff; and (ii) other project design and supervision factors that are within the World Bank’s control.

We draw on hitherto unexploited administrative data to assess the relation of other previously unobserved aspects of the project production function on outcome ratings. Specifically, we examine the contributions of the TTLs who either prepared or supervised the project, the Practice Manager<sup>7</sup> (PM) who oversaw project development, and the Country Director (CD) responsible for the country program within which the project was set. TTLs design and supervise Bank projects in collaboration with the client government; PMs make decisions on staffing and clear all aspects of project preparation and subsequent implementation; CDs shape the overall dialogue with the country and chair the meetings that finalize project design. For each individual staff in our data, we then use internal staff records to gather key staff characteristics. We also assess the influence of aspects of project design that the literature has identified as relevant, such as complexity and the clarity of project results frameworks.

> 7 This role has been titled differently over time at the World Bank, for this study we use the current title of Practice Manager and draw equivalency with previous titles Sector Manager and Division Chief.

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Policy Research Working Paper 9862

# **Abstract**

This study uses the Ethiopia Skills Profile Survey (2017) to examine the gender differences in livelihood opportunities and activities between refugees and host communities. The results show that refugees are significantly less likely to be in employment, and that household characteristics influence women’s economic opportunities. While having a female household head, access to agricultural land, and the number of female adults increased female participation in economic activities, conversely, higher numbers of children in the household significantly reduce women’s opportunities. Higher education attainment boosts both male and female refugees’ participation in wage employment. Among refugees, Somali refugees have relatively better access to employment opportunities compared to other refugee groups, especially refugees from South Sudan and Sudan.

This paper is a product of the Gender Global Theme. 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.org/prwp. The author may be contacted at ayeshwas@gmail.com.

_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
# Table 4: **Regression Discontinuity** – First Stage

This table reports evidence on the program-induced discontinuity in debt relief, based on bank data. The table additionally reports placebo tests using total eligible amount, eligible principal amount and eligible overdue interest. _Debt Relief_ refers to the net amount of debt relief granted. _Eligible Amount, Total_ (rupees) refers to the total exante overdue balance to which the program criteria were applied and is the sum of _Eligible Amount, Principal_ and _Eligible Amount, Interest_ . Coefficient estimates are obtained from regressions on treatment status and a local linear control function, using the sample of all surveyed households. Robust standard errors, in brackets, are clustered by bank and district. + p _<_ 0.10 ** p _<_ 0.05 *** p _<_ 0.01

||Coeffcient|_SE_|_N_|
|---|---|---|---|
|Implemented Debt Relief||||
|All Banks|37,156***|[1,858]|2,897|
|Commercial Banks|44,037***|[3,455]|1,475|
|Cooperative Banks|34,339***|[1,925]|1,422|
|Eligible Amount, Total||||
|All Banks|-2,939.7|[3,843]|2,442|
|Commercial Banks|-5,599.5|[5,603]|1,348|
|Cooperative Banks|4,186.5|[4,285]|1,094|
|Eligible Amount, Principal||||
|All Banks|-4,517.2|[3,225]|2,443|
|Commercial Banks|-4,850.4|[4,996]|1,349|
|Cooperative Banks|
is an implausible number of answers implying that a tails was obtained in the coin-toss. The AM procedure leverages this observation by asking a series of random response questions.<sup>3</sup> If a single respondent answers 'no' repeatedly, then the respondent must be reticent with a very high probability. In an application to Romanian businesses, AM showed that respondents identified as reticent typically had different response patterns to sensitive questions posed elsewhere in the

> 2 Azfar, Omar and Peter Murrell (2009): Identifying Reticent Respondents: Assessing the Quality of Survey Data on Corruption and Values, _Economic Development and Cultural Change_ , University of Chicago Press, Vol. 57(2), 387-411.

> 3 See section 2 for further details of the Nigerian implementation.

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a 7 digit classification. Not all information is published by the census. Data are withheld for national security reasons for certain products. When the number of establishments reporting is limited, information is withheld so as not to violate the privacy of individual firms by providing classified information to their competitors. The census includes all establishments employing one person or more at any time in the census year, but in a limited number of cases, single-establishment companies with fewer than 5 employees were not required to repo.rt to the census bureau. Generally speaking, the quantity and value of output of these latter establishments are estimated by the census bureau. Because we had to adjust the 1977 figures for the United States to a 1975 level we also used 1975 value figures at the SIC level from the Annual Survey of Manufactures 1975-76 (1979). Figures on quantity movements between 1975 and 1977 were taken from the 1982 US Industrial Outlook (1982). The adjustment procedures are explained in detail below.

In Brazil there is no analytic coding in the census. The detailed information on quantities and values by product in the volume Producao Fisica (vol. 2, part II, Rio, 1981b) is presented with a sequence of numbers from 1 to 13,678. Some of these numbered items refer to production in Brazil as a whole and others to production by state. The other main volume, Censo Industrial (vol. 2, part I, Rio, 1981a) gives an analytic breakdown of value, census value added, employment, and inputs for 24 major industry groups, of which 23 are part of the manufacturing sector. The numbering system in Censo Industrial is different from Producao Fisica, though the same sequence of branches is used in the two volumes. Table 3 of the Censo Industrial gives information for 1,299 "industries", but the finer breakdown
enumeration area, EA) was chosen within the village through simple random sampling.<sup>14</sup> In the selected EA, all households were listed. From these lists three households were randomly assigned to each of the eight modules. This was done through simple random sampling starting with module 1 and moving to module 8. The five alternative recall questionnaires were conducted in the span of the 14 days the survey team was in the EA to conduct the household diaries. Fortunately refusal and attrition after starting the survey are not an issue. Among the original households selected for the survey and assigned to a module, there were 13 replacements due to refusals. Three households that started a diary were dropped because they did not complete their final interview. This yields a final sample size of 4,029 households.<sup>15,16,17</sup> The basic characteristics of the sampled households generally match the nationally representative data from the Household Budget Survey (2006/07) (results not presented here but available from the authors upon request). Household interviews were conducted over a 12-month period, but because of relatively small samples within the period, we do not explore the survey assignment effects across seasons. Instead, the analysis reports the mean effect of questionnaire design across all seasons in Tanzania since each season is equally weighted in the data.

The randomized assignment of households to different questionnaire variants appears to have been successful when examining a set of core household characteristics, presented in Appendix Table 1, in terms of balance across characteristics relevant for consumption and consumption measurement. The table presents these mean characteristics by each module. At any point where there is a significantly different pairwise comparison, those pairs are indicated. Out of a possible 420 pairwise comparisons in Appendix Table 1, only 13 pairs are significantly different at the 5 percent level.

14 The equivalent of a village in urban areas is the _mtaa_ (Swahili for “street”). As there is no official subdivision of an _mtaa_ ,
Figure 18. Financial Equilibrium Contribution Rate <!-- Start of picture text --> 25%<br>20% 19.2%<br>15%<br>11.7%<br>10%<br>5%<br>0%<br>2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075 2078<br><!-- End of picture text --> Source: author elaborations based on administrative data, IESS However, and like most pension systems in the region, the IESS-managed pension insurance has fiscal resources as a financing source, which are equal to 40% of the total expenditure on pensions, as introduced in section two. By taking into account this complementary financing source, the total financial result would be negative over one decade later (approximately by 2045).

At present, Ecuador has just started a term of 40 years with the most favorable population age structure having the largest proportion of working-age population. This phenomenon, called demographic opportunity window, could favor a higher economic growth. Likewise, this demographic opportunity may provide an ideal setting to set the foundations in the long term for sustainable policies and foster economic and social opportunities. On the other hand, there is a certain urgency related to launching an institutional framework and adequate policies taking into account this gradual demographic change. That is due to two reasons: (i) institutional changes take time, and (ii) those people who will be older adults from 2060 on are the ones joining the labor force at present and, therefore, their current decisions are subject to the current game rules. Social and economic institutions, whether current and future, frame the decisions taken by people throughout their adult life, which define economic security in old age. In addition, a political reality normally imposes a longterm transition until full application of a new institutional framework. The more the reform postpones, the greater the need of it and
to produce a measure of management practices related to autonomy, denoted CS-autonomy. Similarly, the answers to questions on incentives and monitoring scores are combined to produce a CS-incentives/monitoring measure of management practices. The answers to questions on staffing and targeting topics are combined into a CS-other measure of management practices. The scores on each practice are converted into normalized z-scores by taking unweighted means of the underlying z-scores (so are continuous variables with mean zero and variance one by construction), where both are increasing in the notion of ‘better management’. For the CS-autonomy index, we assume greater autonomy corresponds to better management, and for the CS-incentives/monitoring measure we also assume the provision of incentives/monitoring corresponds to better management practices (as suggested to be so in private sector settings [Prendergast 1999]).<sup>8</sup> The CS-autonomy and CS-incentives/monitoring management scores are positively correlated ( _⇢_ = _._ 67) suggesting complementarity of these practices, but this did not have to be so. For example, substitution between practices could occur if bureaucrats have career concerns, and so performance incentives are not required once autonomy is provided. Alternatively, if bureaucrats

> 8These z-scores are constructed using data for all 45 organizations we can construct management practices for based on our civil servant survey. To address concerns over sample selection, we later show robustness of our main finding to redefining the z-scores based only on the 30 organizations for which project completion data exists.

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