rafmacalaba/data-use-mentions-extended huggingface dataset:
Brazil.<sup>44</sup> We compiled a rich administrative data set from Ecuador that merges employer-employee matched data with education census, firm registry data, and firm-level customs data for the 2006-2017 period (details are provided in Appendix E). Similar data have recently been used by Ad ̃ao et al. (2022). We replicate the analysis of the impact of firms’ GFC foreign shocks and of the mechanisms driving long-run effects using these data.

The worker responses to firms’ foreign shocks due to the GFC in Ecuador are qualitatively similar to those in Brazil: persistent reductions in worker employment occurring only for the

> 44Ecuador’s labor market is more rigid than that of Brazil in areas related to firing costs according to recent Doing Business indicators. When dismissing a redundant worker, a firm in Ecuador must notify or consult a third party whereas that is not necessary in Brazil and it also must pay substantially larger severance payments. For example for workers employed for 10 years (or more) at the firm such payments consist in more than 50 weeks of salary in Ecuador but only 17 weeks of salary in Brazil. Measures of job turnover (creation and destruction) based on the employer-employee data sets show a substantially larger de facto job turnover for Brazil than Ecuador prior to the GFC. As of 2007, job creation and destruction rates in Brazil were 16% and 3%, while they were 4% and 2.6%, respectively, in Ecuador.

24
accession protocol to the WTO, representing the period during which the WTO commitments were phased in. These results reflect the impact of the tariffs as fully phased in by 2020, as if they were imposed on Russia’s trade patterns in 2012. For the ECU, a baseline of 2008-12 is used, since the changes to tariffs in Kazakhstan and Belarus are phased in over 2010-2012. 2008 is chosen as the initial year instead of 2010 both because of availability of annual tariff data in Kazakhstan and Belarus and because 2008 represents a more normal set of conditions than 2010, which is immediately following the Great Recession of late 2008-09.<sup>7</sup>

## **2.2 Main Results**

# **_a)_ Assessment of the change in Russian imports from CIS and GSP countries and WTO members after Russia implements its WTO commitments and the effect on the ECU CET.**

As a result of tariff cuts related to Russia’s WTO Accession Protocol commitments, to be reached by 2020, the total value of Russia’s imports compared to 2012 is projected to go up from $314.8 billion to $318.3 billion (a 1.1 percent increase), across all products and trading partners. This is a relatively small impact. Nevertheless, imports of nearly 60 percent of Russia's product lines are expected to rise. Welfare gains arise from a projected increase in consumer surplus of $371.39 million (0.02 percent of Russia’s GDP in 2012).<sup>8</sup> This estimate of welfare gains appears relatively small. It is important to bear in mind that the estimate reflects only the static efficiency gains from removing the tariffs. They do not count dynamic gains, e.g. from technological spillovers associated with imported intermediate goods, nor do they take into account the effects of trade facilitation or other provisions of the ECU. Nor do they include interindustry effects. For example, cheaper imports
Policy Research Working Paper 8689

# **Abstract**

Public financial management enables government to implement policy. Financial management information systems are a central element of PFM in that they facilitate government financial transactions and subject them to rigorous budgetary controls. Therefore, the adequate use of FMIS systems supports the implementation of fiscal rules and provides the basis for holding the executive accountable for implementing the budget. Yet, the integrity of expenditure transactions and effectiveness of budget controls is rarely assessed. This paper explores the meaning of adequate use of FMIS systems and points to data analytics techniques that can shed light on how the use FMIS systems affects expenditure data integrity and effectiveness of budget controls. This methodology is applied to Pakistan and Cambodia, which reveals two very different problems. In Pakistan the analysis of the transactions profile suggests that a large share of the budget is not subjected to FMIS internal controls. In Cambodia the majority of the budget is channeled through the system, but the method exposes very large advances to commercial bank accounts that are subsequently drawn upon but outside the system. Both of these raise concerns about the effectiveness of the FMIS system. Deploying data analytics techniques can help assess whether FMIS systems serve form or function. Judging a system by its use is informative about governments’ revealed preferences in expenditure management. This paper puts the spotlight on the foundation of expenditure data and advocates for a transparent, methodical and evidenced based approach to FMIS deployment and expenditure management reform.

This paper is a product of the Governance 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.org/research. The authors may be contacted at mpiatti@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
in the vicinity, the lower the number of students that treatment schools can attract. Likewise, the older the treatment school, the lower the growth in student enrollment. These results also hold for the secondary school entrance exam scores measured at Check 3.

Next, we use a survey of 30 head teachers conducted a year after control schools enter the program. The survey elicited responses both on the composition of applicant pools and the degree of selectivity. The data show firstly that more than two-thirds of applications are publicly funded students. Second, the data show that both in program and non-program schools, the admission rate is very high at 93%. Finally, even though these responses are the most vulnerable to measurement bias, the distribution of quality between admitted students and applicants is very similar, suggesting that schools are not very selective at all.

Finally we use data from interviews with 11 head teachers and focus groups with 20 parents conducted by one of the authors in December 2014. In response to the question on whether they are selective in admission, 10 of the 11 schools indicated that they admit just about everyone who applies and qualifies for the PPP.

The preponderance of evidence suggests that the observed selection is driven by households and not schools. Since this program is effectively an expansion of school choice for budget constrained households, desirable attributes such as proximity to the school potentially underpin the results we obtain

> 16 We also tested heterogeneity by being founded by a private individual (as opposed to the local public goods that community founded schools may be). The results were very noisy and highly sensitive to the inclusion of controls, presumably due to the fact that the majority of the schools were founded by a private individual. As such, we report only heterogeneity by competition and age.

> 17 We run the models with and without controls; results are stable to the inclusion of baseline covariants.

14
Policy Research Working Paper 9856

# **Abstract**

This paper examines the labor market and jobs in urban Kinshasa, by drawing on a recently collected household survey and other data sets. It particularly focuses on labor supply and employment patterns, job characteristics, and their spatial nexus. The analysis first shows that female and young workers are more likely to experience unemployment and underemployment than other workers in Kinshasa.

Second, the availability of good quality jobs is still limited in Kinshasa. Third, in addition to the scarcity of good jobs, which are concentrated in the city core, poor accessibility due to the limited connective infrastructure and transport system further reduces job opportunities for people living in the outskirts.

This paper is a product of the Poverty and Equity 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.org/prwp. The authors may be contacted at ybatana@worldbank.org or snakamura2@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
provides detailed information about this sample across countries and years. After this, we drop firms for which we cannot identify the sector in which they operate. This give us a sample of 131,347 observations.

If we further restrict this sample to manufacturing industries, which is the focus of our analysis, we end up with a sample of 74,723 observations corresponding to 59,820 firms. Of these firms, 79.4 percent appear only once in the database; 17.0 percent appear twice; 3.0 percent appear three times; 0.5 percent appear four times; and 0.1 percent appear five times. Table 7 in section A of the Appendix displays detailed information about this sample across countries and years. A common feature of many firm-level databases from developing countries is the presence of missing values for variables needed to measure firm performance (e.g., labor, sales, capital, and materials, and investment). For example, in our sample, labor is the variable with the least proportion of missing values (2.3 percent), followed by sales (14.2 percent), materials (31.8 percent), capital (32.8 percent), and investment (58.2 percent).

To maximize sample size, correct selection in misreporting, and gain efficiency, we impute data for sales, labor, capital, materials, and investment using the largest WBES database available, which contains 131,347 observations, and a pseudo-Gibbs sampler (Lee and Carlin 2010; Van Buuren, Boshuizen, and Knook 1999).<sup>4</sup> The explanatory variables used for imputation include email adoption, website adoption, export status, managerial experience proxied by a dummy variable that identifies firms with managers with above-median years of experience. It also controls for country, industry, and survey year. We do not impute data for email adoption, website adoption, export status, and managerial experience as we are interested in understanding
~ in 1989 for Germany, coinciding with the fall of the Berlin Wall that led to the unification of East and West Germany in 1990.<sup>14</sup> Figure 2B shows that South Africa had a break for Et in 1991, the year trade ln[Dt ~~]~~ sanctions began to end when the country repealed its Apartheid legislation, leading to a new democratic government in 1995. In the case of Germany, however, the tests also suggest 1989 for ln[PPttde] but 1994 for ln[MDtt] and 1985 for ln[PPttmd ~~]~~ . In the case of South Africa, the breakpoint tests also indicate 1987 for ln[PPttde], 1990 for ln[MDtt], and 1989 for both intercept and trend of ln[PPttmd ]. After testing alternatives and considering the history, we choose 1990 for Germany and 1991 (versus 1995) for South Africa for an intercept break. As a final check, we confirm the existence of a cointegration equation with the Johansen test (using both the Trace and Max-eigenvalues rank tests).

Conflicts, regime changes, and crises could also affect data quality. In this regard, we follow WDI's data vetting process about the first year to use while allowing for data splicing from the U.N. national accounts for one or two data series of a country. We only include countries with at least 20 years of time series data.

> 14 Unlike former Soviet republics and the allied communist states, Germany has data prior to the unification in 1990 and that goes back to 1970.

12
# **2 Context, data and methodology**

## **2.1 Data source**

Our analysis draws from data collected between April and June 2021, from 4,459 individuals spread over 40 communities in three Tanzanian cities: Dodoma, Dar es Salaam, and Iringa. The dataset constitutes the baseline of a randomized control trial of an SES training intervention, conducted in collaboration with the international development organization BRAC.<sup>3</sup> The analysis in this paper focuses specifically on the baseline information collected on individual respondents’ demographics, own and parental education history, SES, cognitive ability, social desirability, and gender attitudes.

## **2.2 Measuring socio-emotional skills**

A measurement team including some of the authors of this paper developed a framework — of 14 SES (Appendix Figure A1) informed by existing frameworks, consultations with psychologists, focus group discussions, literature on which SES matter for labor outcomes, and theory on gender gaps in levels and returns to SES (Delavallade et al., 2020). The list of skills includes four “awareness” SES (emotional awareness, self-awareness, listening, and empathy) and 10 “management” SES (emotional regulation, self-control, perseverance, personal initiative, problem-solving and decision-making, expressiveness, interpersonal relatedness, influence, negotiation, and collaboration). An alternative way to categorize the skills is that “intra-personal” skills are composed of self-awareness and self-management while “inter-personal” skills are composed of social awareness and relationship management. Throughout our analysis, we present results for these two alternative classifications of SES. Examples of the measures used for each skill can be found in Appendix A6

> 3The RCT study, forthcoming, tests the impact of trainings focused on different sets of SES. It is designed to examine which SES are most teachable to vulnerable male and female youth, and which matter most for economic empowerment. The results are expected to inform updates to one of BRAC’s flagship programs, the Empowerment and
card; (ii) whether it got work on the scheme during the last 365 days, for which responses were coded under three options: got work, sought but did not get work, and did not seek work in MGNREGS; and (iii) if the household got work, the number of days of work, and the mode of payment. In addition, the daily status block in Schedule 10 collected information on activities for all household members during the week preceding the survey, including number of days worked and wages received, if the respondent worked on MGNREGS public works (PW).

Note that in this paper we limit our definition of participation and rationing to whether households got work or did not get work. Unmet demand can also take the form of fewer days of work than desired. Many households who participated were no doubt rationed in that they would

> 8 Data is from the official Government of India website for MGNREGS (http:\\nrega.nic.in).

> 9 This is described in a number of the papers in Khera (2011).

> 10 In Dutta et al. (2012) we provide supportive evidence on this point for Bihar, based on our surveys in 2009/10.

5
was primarily due to concerns about side effects from the COVID-19 vaccine. Respondents stated that health experts were the most likely to be able to increase their willingness to get vaccinated.

# <u>5.1.1 Awareness of COVID-19 (based solely on the phone survey)</u>

Awareness of the existence of COVID-19 in general in PNG is very high, however there is a much more limited understanding of the number of cases and COVID-19 related deaths. A prior representative phone survey in PNG showed more than 95 percent of respondents were aware of the existence of COVID-19 (World Bank 2020). In the phone survey we focus on in this paper, around 9 out of 10 respondents were aware of specific actions the government has taken to address COVID-19 in regard to the health and/or education sectors. However, when asked to estimate COVID-19 cases numbers and deaths across PNG or within their province, almost 80 percent of respondents stated they were unsure. The remaining respondents that provided an estimate were remarkably accurate. On average, respondents provided very similar estimates of the perceived number of COVID-19 cases in their province as what was actually the case in terms of reported figures (see Figure 4).

**Figure 4 – Reported and perceived number of COVID-19 cases by province**

<!-- Start of picture text --> 7000<br>6000<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>Actual Perceived<br><!-- End of picture text --> Note: This chart is based on answers provided by the 20 percent of respondents that did not select unsure to question 22 in Appendix B. AROB - Autonomous Region of Bougainville, NCD – National Capital District.

# <u>5.1.2 Awareness of COVID-19 vaccines (based on both the phone survey and online experiment)</u>

A significant minority of respondents in both surveys had not heard
# **Appendix 2: Public-use database with estimates of adult mortality**

In the course of carrying out this study, we created two databases of adult mortality estimates based on the original DHS datasets, both of which are publicly available for analysts who wish to carry out their own analysis of the data. The databases are available at http://go.worldbank.org/RIIWM53VS0.

The first database ( **survey_mortality.dta** ) reports adult mortality estimates derived from each survey that was carried out.

The second database ( **country_mortality.dta)** reports the same adult mortality estimates but averages the estimates for the same country and period from different rounds of the DHS. The weights used to average across survey rounds are the inverse of the variance of each estimate, so that more precise estimates are given more weight. This second database only includes the “adjusted” mortality estimates (the ones we use in our analysis) but also includes the national level social and economic indicators that we use in the analysis. Note that this database includes countries for which we do not have adult mortality estimates, but for which we have under-5 mortality.

The naming conventions for the adult mortality-related are as follows. Variables are named:

# **GGG** _ **MC** _ **AAAA**

**GGG** refers to the population subgroup.  The values it can take, and the corresponding definitions are in the following table:

|All|All|
|---|---|
|Fem|Female|
|Mal|Male|
|Rur|Rural|
|Urb|Urban|
|Rurm|Rural/Male|
|Urbm|Urban/Male|
|Rurf|Rural/Female|
|Urbf|Urban/Female|
|Noed|No education|
|Pri|Some or completedprimaryonly|
|Sec|At least some secondaryeducation|
|Noedm|No education/Male|
|Prim|Some or completedprimaryonly/Male
15% in 2035) because most of energy efficiency in the residential buildings in Georgia is expected to occur in the baseline due to existing policies and programs. The potential reductions of GHG emissions in the commercial sector are relatively smaller as compared to that in the residential sector in absolute amount (i.e., million tons). However, in terms of percentage reduction, the commercial sector potential is much higher than that of residential buildings in Georgia thereby implying more space to improve energy efficiency in the commercial and public buildings. Note however that the aggregate emission reduction are, however sensitive to several parameters assumed, such as rate of penetration or adoption of efficient devices and process in the buildings. We have carried out later sensitivity analysis on these parameters.

# **Table 1: Aggregated GHG mitigation potential of energy efficiency measures considered in this study**

||**2020**|**2025**|**2030**|**2035**|
|---|---|---|---|---|
|**(a) Armenia**<br>**Residential Sec**<br>|<br>**tor**||||
|Total GHG emissions from heating, lighting, refrigeration,<br>cooling andTV use (milliontons)|2.96|3.05|3.15|3.23|
|GHG mitigation from the options considered in this study<br>(milliontons)|0.56|0.84|1.12|1.31|
|% reduction of GHG emissions due to the options<br>consideredinthis study|18.9%|27.6%|35.7%|40.5%|
|**Commercial/public**<br>|**Sector**||||
|Total GHG emissions from cooling and street lighting<br>
It is unclear to what extent the high rates of informality and turnover among youths are voluntary or the result of deeper structural factors (Perry et al., 2007). In this paper, we test whether a temporary wage incentive can encourage Mexican secondary school graduates to obtain and stay in formal employment. We first present stylized facts that motivate our intervention. Entry-level hourly wages are 9%-17% lower for young labor market entrants in the formal than in the informal sector. This pattern is in line with evidence from other LMICs showing that youths with primary and secondary education earn higher hourly wages working informally (Shehu and Nilsson, 2014). However, while informal wages stay relatively constant, formal wages in our study sample grow by about 25% (35%) over the first six (twelve) months, driven by substantial training and on-the-job skill gains. Low entry-level wages in formal jobs matter given that they are lower than the reservation wages for about one-third of school leavers. To address the initial (wage) disincentive of formal work, we design an employment incentive that pays workers the equivalent of about 20% of the average formal entry level wage for up to six months for holding a formal job. These temporary incentives may lead to long-run employment gains as they help youths to accept and retain formal jobs despite lower starting salaries.

We conduct an experiment in partnership with the Mexican government to test the effectiveness of this formal employment incentive in a sample of almost 2,000 graduating students across 13 upper secondary schools in the San Luis Potosi metropolitan region. This area has a large industrial base (including automotive production), and firms report struggling to fill vacancies, frequently ensuing from high worker turnover. About 80% of students in our sample attend schools with a vocational focus while the remaining 20% are enrolled in general schools that traditionally prepare them for tertiary education. We randomly assign half of the graduating students in each school to be eligible for the wage incentive and test its effect on employment outcomes