rafmacalaba/data-use-mentions-extended huggingface dataset:
- Ginn, Thomas D. (2022) “Prison or Sanctuary? An Evaluation of Camps for Syrian Refugees,” working paper.

- Heckman, James J. and James R. Walker (1990) “The Relationship Between Wages and Income and the Timing and Spacing of Births: Evidence from Swedish Longitudinal Data,” _Econometrica_ , 58 (6), 1411–1441.

- Hotz, V. Joseph and Robert A Miller (1988) “An Empirical Analysis of Life Cycle Fertility and Female Labor Supply,” _Econometrica_ , 56 (1), 91–118.

- Hotz, VJ, S McElroy, and S Sanders (1997) “The impacts of teenage childbearing on the mothers and the consequences of those impacts for government,” in _Kids having kids: Economic costs and social consequences of teen pregnancy_ , 55–143: Urban Institute Press.

- Hussam, Reshmaan, Erin M Kelley, Gregory Lane, and Fatima Zahra (2022) “The Psychosocial Value of Employment: Evidence from a Refugee Camp,” _American Economic Review_ , 112 (11), 3694–3724.

- Ibanez, Ana Maria, Andres Moya, Mar ́ıa Adelaida Ortega, Sandra V. Rozo, and Maria Jos ́e Urbina (2022) “Life Out of the Shadows: Impacts of Amnesties in the Lives of Migrants,” iza discussion papers, Institute of Labor Economics (IZA).

- Kaushal, Neeraj (2006) “Amnesty Programs and the Labor Market Outcomes of Undocumented Workers,” _The Journal of Human Resources_ , 41 (3), 631–647.

- Lalive, Rafael and Josef Zweim ̈uller (2009) “How Does Parental Leave Affect Fertility and Return to Work? Evidence from Two Natural Experiments*,” _The Quarterly Journal of Economics_ , 124 (3), 1363–1402.

- Lanari, Donatella, Luca Pieroni, and Luca Salmasi (2020) “Regularization of Immigrants and Fertility in Italy,” MPRA Paper 98241, University Library of Munich,
|2) FD is "First Difference" panel estimator<br>3) WG is the WG estimator<br>4) WG-Comp is the FD estimator (unweighted) with com<br>5) All specifications (except WG) use village-level data,<br>6) All coefficients are the coefficient on lagged inequality<br>7) FD-IV is the instrumented version of FD (with instrum<br>1) OLS is the pooled OLS panel estimator<br>_Notes:_|**Four-Period Differences**<br>_N_|**Three-period Differences**<br>_N_|**Two-period Differences**<br>_N_|_N_<br>490|**OLS**<br>**First Differences**<br>-0.012<br>(0.081)|
|---|---|---|---|---|---|
|parable specification to WG<br>weighted by the number of households<br>, with the standard village-level specification<br>ent sets described in the appendix)|0.402<br>(0.258)<br>162|0.290<br>(0.159)<br>244|0.260<br>(0.140)<br>326|408<br>326<br>408<br>326<br>490|**FD**<br>**FD-IV**<br>**WG**<br>**WG-Comp**<br>**OLS**<br>0.145<br>1.094<br>0.141<br>0.193<br>-0.003<br>(0.156)<br>(1.189)<br>(0.188)
are less likely to participate in the online job portals than males.<sup>14</sup> On the demand side, the employers who use the online job portal are smaller and younger and advertise higher salary jobs than the average employer in Pakistan, and many of them are in ICT.

# 3. Labor supply and demand

- 3.1 Labor market tightness and potential mismatches by basic qualifications

In this section, we use a Question and Answer format to navigate key research questions and findings. By exploiting the data feature that the information of both demand and supply sides is available, we measure and look into labor market tightness, which is the number of job vacancies per jobseeker. It shows the relative size of labor demand to labor supply. If the tightness is high, labor demand is strong relative to labor supply, and jobseekers may easily find jobs while employers may have difficulty in finding workers. That is, the market is a seller’s market. If the tightness is low, finding jobs is hard for jobseekers, but employers find workers easily. As illustrated in Figure 7, the tightness in Rozee.pk has been increasing, although it is below 0.2 in 2019Q1.<sup>15</sup>

**Figure 7. Tightness of the online labor market across 2012–2015**

_Source_ : Authors’ calculation using the data from Rozee.pk. _Note_ : Shown is the ratio of the number of new vacancies and the number of new jobseekers registered in a given quarter.

## **_Q. What level of education is the most demanded in Rozee.pk, and how easy is it for jobseekers to find jobs at their educational level?_**

## **_A. Undergraduate and postgraduate education is most demanded in Rozee.pk. However, due to a large number of jobseekers with undergraduate and graduate degrees, the job matching still_**

> 14 The females who do participate are more educated than the males in
private sector risk premia did not keep pace with or was properly anticipated with the rise in realized defaults that drove the compression in NIMs. It is important to highlight that the NIM is impacted by holdings of interest-bearing government securities and non-performing assets which do no accrue interest income – this might distort the picture.

# **Box 2: Data and main variables**

Data from World Bank FinStats 2019, World Development Indicators and International Financial Statistics is used to form a panel of about 75 EMDEs for the period 2003-2017.

- _Lending rates_ are defined as the volume-weighted average interest rate charged by banks on short- to medium-term loans with fixed interest rates and with own funds to individuals and corporations. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. For example, riskier segments such as unsecured consumer lending,

10
and the calorific value of coal as NRs 43.4 and 19.9 MJ per kg of coal, respectively, and the corresponding values for biomass pellets as NRs. 12.0 and 14.7 MJ per kg of pellet, respectively in 2022 based on information collected through a rapid survey by Timilsina and Malla (2023).

We estimated that the total cost of producing a brick can be reduced in the range of 6% to 9% across the provinces simply by substituting one-third of the coal with biomass pellets (Figure 7). The percentage reduction in brick production cost is much higher (11% to 18%) with a higher share of biomass pellets (50%). However, it may be unlikely that these levels of cost reduction can be achieved if the market for large-scale biomass fuels across the country does not exist. Clayfired bricks can only be produced in a carbon-neutral manner if it is possible to completely replace the current primary energy source such as coal, with carbon-neutral energy sources, such as renewable biomass fuels. In our survey, we did find one brick kiln (FC BTK) in western Nepal 19
As explained, CHS estimate purchased intangible investment directly whenever possible: that is, from the expenditure data of the industries purchasing the asset in question. When such direct data are not available, their strategy is to estimate purchased intangible investment using the revenue of the industries that produce the intangible investment in question. The availability of direct expenditure data is poorer in the case of Brazil, and as a result, we have to use the indirect approach – the revenue of the producing industry— more frequently. This is the most common difference between our sources and those in CHS. For example, we find no surveys in Brazil that estimate the amount spent by industries on computer software and databases, and have to resort to using the Net Operating Income (NOI) of computer software and database services industries from the Annual Survey on Services.

In cases where CHS use crude approximations to estimate something not captured in surveys or national accounts, we replicate their approximation using the closest Brazilian counterpart. Arguably, there are cases in which the CHS approximation could be considered unrealistic for Brazil. To give an example, assuming that as much as 20 percent of intermediate expenses in financial services industry go towards new product development could be too optimistic. But in the absence of any Brazil-specific or other developing country literature that could guide us towards a better approximation, we have chosen to use the number suggested to CHS by the US literature; this has the additional advantage of methodological comparability.

Most of the key surveys for our study are quite new to Brazil. For example PINTEC, our main source for R&D data, has been conducted in 2000, 2003, 2005 and 2008. This restricts us to estimating intangibles during 2000-2008, a much shorter time span than that examined in studies of intangible investment in developed countries, some of which go back to the 1970s. Future intangible asset studies on developing countries should expect a similar disadvantage in historical data availability.

Finally, while most existing studies of intangibles present only national aggregates, we present intangible
refugee settlement as well as all Ugandan parishes. Here we present results with the 150km cutoff as our main specification; SI Section S4 reports the results with the other cutoffs.

## **4.2 Main Independent Variable: Refugee Presence**

For our main independent variable of interest, we create a measure of refugee presence for each parish-year. We obtained shapefiles of refugee settlements over time, and collected population data for each settlement in our study years from UNHCR reports and with the help of UNHCR Uganda country staff. Figure S4 in SI Section S1.2.1 shows which settlements are open and their population levels across the study years.

> 12For details, see Section S1.1 in the Supplementary Information (SI), https://osf.io/sv92q/.

13
Policy Research Working Paper 9220

# **Abstract**

Public health systems that are capable of disease surveillance and action to prevent and manage outbreaks require trustworthy community-embedded public health workers who are empowered to undertake their tasks as professionals. Economic theory on incentives and norms of agents tasked with performing activities that society cares about yield direct implications for how to recruit and manage frontline health workers to promote trustworthiness and professionalism. This paper provides novel evidence from a survey of public health workers in Bihar, India’s poorest state, that supports the insights of economic theory and taken together yields ideas that can immediately be put to work in policy responses to the COVID-19 crisis. These ideas address problems of governance and trust that have bedeviled health policymakers. Managing the current and preventing future pandemics requires going beyond technical health policies to the political institutions that shape incentives and norms of health workers tasked with implementing those policies.

This paper is a product of the Development Research Group, Development Economics. 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 authors may be contacted at skhemani@worldbank.org.

_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
16 each falling around 4 percent. It is even higher in Latin America (7 percent) and South Asia other than India (10 percent). By contrast, the number of extreme poor in India (though not in the rest of South Asia) is estimated to rise, by 4 percent.<sup>5</sup> Under the more moderate definition of poverty—those living on no more than US$2 per day—the number of poor in developing countries would fall by nearly 90 million compared to an aggregate baseline level of just under 2.5 billion in 2004, or by 3.4 percent (notwithstanding the number in India below $2 a day still increasing, but by just 1.7 percent).

# **_(b) GTAP Model Results_**

Hertel and Keeney (2010) draw on the widely used global economy-wide model of the Global Trade Analysis Project (GTAP). Their study adopts the same price distortions as the other studies surveyed here, and runs the same scenarios, but generates its own world price changes from the GTAP model for the multilateral trade reform scenarios. Those prices changes alter border prices for the various countries in the GTAP model, a subset of which have attached to them detailed household survey data. This permits the authors to say something about poverty impacts across a range of diverse economies using an internally consistent framework that captures the distributive effects of all factor income changes.

This multi-country study focuses on 15 developing countries: five Asian (Bangladesh, Indonesia, Philippines, Thailand, and Vietnam), four African (Malawi, Mozambique, Uganda, and Zambia), and six Latin American countries (Brazil, Chile, Colombia, Mexico, Peru, and Venezuela). Overall, it concludes that removing current farm and trade policies globally would tend to reduce poverty, and primarily via agricultural reforms (Table 5). The unweighted average for all 15 developing countries is a headcount decline in extreme poverty

> 5 The rise in India
population size (or agglomeration). Finally, we investigate how other labor outcomes, such as labor force participation and unemployment/underemployment, change in tandem.

Our empirical approach to cross-sectional labor force surveys is an instrumental variable (IV) regression of the town population. To reduce an endogeneity bias, we instrument the current town

> 1Teal (2011) puts this well: “[i]n almost all the literature the empirical analysis has been confined to wage employment. In extending it to developing countries that is a rather serious limitation as most employment in such countries is... not wage but self-employment.”

> 2A study of Nepal by Fafchamps and Shilpi (2005) examines the relationship between the proximity to urban centers and various labor outcomes. They find that the transition to wage employment took place mainly in the public sector.

3
</sup> _}_ which can be ensembled into a single model _f_<sup>ˆ</sup> by aggregating the predictions of the individual base learners _{f_<sup>ˆ1</sup> _, ..., f_<sup>ˆ10</sup> _}_ .<sup>15</sup> The accuracy of the final model can be validated using standard validation techniques.

What this strategy achieves is essentially a form of simulation-based training. The intuition is that the final forecast model has to project a global food insecurity situation for 2020 and after, that is beyond anything seen in the 2015-2019 data but likely comparable to the dynamics in the 2007-2011 data when food prices spiked globally during a time when extreme poverty was more prevalent. Therefore, simulated data for this historical period adds crucial information about conditions outside of the 2015-2019 data perimeter. The synthetic data strategy should therefore not in the foremost place improve the average prediction for the 2015-2019 period (as a standard cross-validation exercise would be indicative of), but act to aid the extrapolation when covariates move out of 2015-2019 country intervals. If the non-parametric extrapolation is based solely on 2015-2019 dynamics, they may become highly unstable when many covariates go outside

> 14This is an adaptation of Multiple Imputation using Chained Equations, see for instance Azur et al. (2011) for details, in which a fully fledged optimized machine learning model is built and cross-validated (an algorithm built around the work of van Buuren and Groothuis-Oudshoorn (2011); van Buuren (2012)).

> 15There are several choices for aggregating base learners. In the current context, the downside of averaging linearly is that it makes the ensemble sensitive to outliers. The arithmetic mean has an upward tendency. Suppose the true value is 0, and 9 out of 10 base learners predict this correctly but one base learner predicts a large value, then this results in a relatively large prediction error of the ensemble. A popular choice to prevent this,
19 household incomes under conditions of a sharply increased income disparity and the advancement of the housing market is examined.

These issues are explored primarily by means of a comparative analysis of the findings of sociological surveys conducted in Moscow in December 1993 and in Janualy 1996, as well as in Vladimir and Gorodets (Nizhny Novgorod oblast) in October 1994 and in December 1996. A description of the surveys can be found in Annex 1.

# **2.1. Foundations and environment of the development of the housing market** in **Russia**

Housing reform in Russia did not abolish the old system of waiting lists whereby free housing was given (eventually) to people with very bad housing conditions. At the same time, Russians now have the opportunity to improve their housing conditions by _buying_ housing. Sales of new and secondary housing have begun. The secondary market began to develop at a fast pace as a result of large-scale housing privatization (as stated earlier in Sectionl, as of late 1996, 38% of the overall number of apartments subject to privatization were privatized, with the share of private housing in the housing stock rising to 55% from 33% before the outset of reform in 1990.12 The establishment of the housing market was accompanied by a number of problems. First, in the past few years commissions of new housing have slowed down considerably. Even in the decade preceding the period of reform (to say nothing of the 1960s and 1970s) the annual increase of the housing stock (in square meters of total space) was about 3%, while in 1996 -only **1%o.** Considering that a substantial part of new housing is still free, it is not surprising that its supply in the market is not large.'<sup>4</sup> Also typical are the high prices of new apartments (1.3 times higher than in the secondary market; see Table 2.1 below), which sometimes makes it difficult to
7.1 million students in 1990/1991 school year).

17 World Bank calculations based on a school-level database compiled on the basis of information provided by each of the oblasts and related jurisdictions for the 2009/2010 school year.

18 See for details the Ukraine Public Finance Review (phase II) in FY 2007-08. Currently education sector expenditures account for almost a quarter of consolidated budget (24.8 percent in 2010) and 7.1 percent of GDP. In 2010, expenditures from the consolidated budget for education reached UAH 77.9 billion - a ten-time increase over the 2000 level. This increase exceeded the growth rate of the nominal amount of expenditures of the consolidated budget for these years which multiplied 6.5 times. The bulk of the consolidated budget is earmarked for salaries and utilities which are mainly intended to maintain budgetary institutions and thus crowd out other expenditures. At the same time wages in education remain lower than in many other sectors of the economy including manufacturing, transport, communications, financial services, retailing, etc. In Bulgaria, for example, resources saved thanks to consolidation of schools allowed to increase wages in the education sector by 46 percent between 2006 and 2008. Efficiency gains allowed also allocation of more resources for capital investment within the sector (http://www.worldbank.bg/WBSITE/EXTERNAL/COUNTRIES/ECAEXT/BULGARIAEXTN/0,,contentMDK:226 <u>99182~menuPK:305444~pagePK:2865066~piPK:2865079~theSitePK:305439,00.html).</u> 11
|
||044|047|003|046|002|
|**Log of perca residential area**|.<br>(0.01)|.<br>(0.01)|.<br>(0.02)|.<br>(0.01)|.<br>(0.02)|
|**Roof is made of concrete/metal**|0.85|0.85|-0.01|0.84|-0.01|
|**sheets/tiles**|(0.02)|(0.03)|(0.04)|(0.03)|(0.03)|
||062|059|-003|060|-002|
|**Wall is made of burnt bricks/concrete**|.<br>(0.03)|.<br>(0.04)|.<br>(0.05)|.<br>(0.03)|.<br>(0.05)|
|**Floor is made of**|0.51|0.51|-0.00|0.51|-0.01|
|**concrete/cement/tiles/timber**|(0.03)|(0.03)|(0.05)|(0.03)|(0.05)|
|**Source of drinking water: piped**<br>**water/truck**|0.41<br>(0.04)|0.43<br>(0.04)|0.02<br>(0.05)|0.39<br>(0.04)|-0.01<br>(0.05)|
- Ura, K., Alkire, S., Zangmo, T, and Wangdi, K. (2015). _Preliminary Analysis of the Gross National Happiness Index 2015_ . Thimphu: Centre of Bhutan Studies.

Vasquez, E. (2016). ‘Social policy and programs focused on children in Peru (20102014): An evaluation based on the Multidimensional Poverty Index’, _Sociology and Anthropology_ , 4(10), pp. 887-909.

- Vijaya, R. M., Lahoti, R., and Swaminathan, H. (2014). ‘Moving from the household to the individual: Multidimensional poverty analysis.’ _World Development_ , 59: 7081.

Youseff, N. and Hetler, C. (1983). ‘Establishing the economic condition of womanheaded households in the Third World: A new approach,’ in Buvinic, Lycette, and McGreevey (eds.), pp. 216-243.

34
. As before, the extrapolation assumes distribution-neutral (negative) growth. Figure 2 below plots this dierence in forecasts  the GDP shock due to Covid  against GDP per capita for the 150 countries in our sample. We continue to assume that this additional poverty lasts only for a single year, so that the number of poverty-years is equal to this additional number of poor people.

Second, instead of using national poverty thresholds, we use the World Bank's income class poverty thresholds, as derived by Jollie and Prydz (2016), namely $1.90 per person per day in low-income countries (LICs); $3.20 a day in lower-middle-income countries (LMICs); $5.50 a day in upper-middle-income countries (UMICs); and $21.70 a day in high-income countries (HICs). These lines were obtained as the median national poverty lines for each income category in a data set constructed by those authors.

Figure 3 shows the break-even _α_ ˆ for all 150 countries in our global sample, plotted 12
**Figure 7: Participation in formal or non-formal training, by age**

<!-- Start of picture text --> 60<br>Participation in formal or non-formal training in the past 12 months<br>(% of adults), by age<br>50<br>40<br>EU 27<br>30<br>20<br>10<br>Serbia<br>Turkey<br>N Macedonia<br>Albania<br>0 Bosnia-Herzegovina<br>25-34 35-44 45-54 55-64<br><!-- End of picture text --> Source: Estimates based on Eurostat data. Refers to 2016 data.

## **Poor health is also reducing both participation and productivity among mature workers** . Good health

is a critical asset for productive work and a central component of human capital. Healthy older people are more likely to participate in the labor market, although again, in poorer countries, those in poor health may still be obliged to work to the extent they can. Health is not the focus of this paper and is not reviewed in detail here. It is worth noting, however, that health interventions may be included among low-hanging fruit for addressing productivity constraints in low- and middle-income settings. One area is nutritional deficiencies occurring as seniors need fewer calories but more nutrient-rich meals with age; other areas are broader lifestyle choices that influence later development of noncommunicable diseases (NCDs). The compression of morbidity and the extension of good health into later years permits longer returns to continuous investment in education.

# Demand-side constraints – Incentives and ability to hire and invest in mature workers

This section looks at employers’ incentives to retain or hire mature workers and adapt working situations to increase their productivity. Factors that constrain relevant opportunities include informal and formal institutions governing employment and wages, market failures that restrict the quantity and quality of training, and psychosocial and normative barriers to employers’ recognizing the potential of older workers.

*