rafmacalaba/data-use-mentions-extended huggingface dataset:
Other studies of the RHVP have been undertaken since the program was launched in 2009. To provide as complete a picture as possible in this evaluation report, the authors draw data and findings from these other sources.

In 2011 Mazzilli conducted a cost-modeling exercise for the HealthyBaby voucher service, adopting a Ugandan SP’s perspective of public or external resources. She drew on voucher sales and claims data, as well as cost data, which included reimbursements to voucher SPs for actual services offered to clients, along with program operation costs (Mazzilli 2011). These costs were obtained from the program headquarters in Mbarara and drawn from the program database, a review of financial reports, and interviews with program staff. The government facility costs were drawn from national sources (Mazzilli 2011).

Brody and colleagues conducted semistructured interviews on workload and job satisfaction in a total of 35 facilities, including eight that accepted the HealthyBaby vouchers, seven that accepted the HealthyLife vouchers, another seven that accepted both vouchers, and 13 nonvoucher facilities (Brody, Irige, and Bellows 2015). In each of the facilities, SPs were selected for interviews based on the following criteria: (1) proprietors and administrators, (2) clinical officers, midwives, and laboratory technicians, and (3) nurses, nursing assistants, and laboratory assistants. A total of 76 SPs were interviewed, with 56 from the intervention facilities and 20 from comparison facilities (Brody, Irige, and Bellows 2015). Job satisfaction was measured using an adapted version of the ten-item Minnesota Job Satisfaction Scale.

# **<<A>>** Analysis

The evaluation of the population-level impact of the voucher program was initially designed to approximate the counterfactual—that is, what the outcome of the program would have been in the absence of OBA voucher subsidies for comparison with the outcome of the actual interventions. However, since the design was not implemented as planned, the analytical approach adopts a post hoc definition of the counterfactual. Two definitions are used: (1)
process in detail.<sup>5</sup> Progress reports cover the _entire_ range of bureaucratic activity. While some of these projects are public-facing, others are purely internal functions or intermediate outputs. We were able to use quarterly and annual progress reports from bureaucracies to identify 3628 projects underway during 2015. The projects undertaken by each organization in a given year are determined through an annual planning and budgeting process jointly determined between: (i) the core executive, mainly the Ministry of Finance and the sector minister representing government priorities; (ii) the organization’s management, based in large part on consultatively developed medium-term plans; and (iii) ongoing donor programs. This schedule of projects is formalized in the organization’s annual budget (approved by Parliament) and annual workplan. The quarterly and annual reports that we use to code project completion thus detail the projects that the organization’s workplan committed the organization to working on during the time period of study.

Progress reports detail targets and achievements for individual projects the organization is charged to deliver. The Appendix describes how we hand-coded and harmonized the information to measure projects and project completion across the Ghanaian civil service. Three key points are of note in relation to this process. First, each quarterly progress report was codified into project line items using a team of trained research assistants and a team of civil servant officers seconded from the Management Services Department (MSD), an organization under OHCS tasked with analyzing and improving management in the civil service. MSD officers are trained in management and productivity analysis and frequently review organizational reports of this nature, making them ideally suited to judging project characteristics and completion.

Second, coders were tasked to record project completion on a 1-5 scoring grid, where a score of one corresponds to, “No action was taken towards achieving the target”, three corresponds to, “Some substantive progress was made towards achieving the target. The output is partially complete and/or important intermediate steps have been completed”, and a score
account. While this somewhat more involved approach yields rather imprecise estimates, the estimation suggests that trade costs also raise the price at which RC goods are being sold in Kinshasa.<sup>19</sup>

**Significant price differentials are also found when we consider goods imported from overseas.** Results taking account of all our sample goods, including overseas imports, are shown in columns (3) to (8) of Table 7. Several findings emerge:

- Brazzaville prices systematically appear to be higher than Kinshasa prices, across the range of regression specifications (Table 7, columns 3 to 8).

- Overseas imports are 38 to 67 percent more expensive than their domestically produced equivalents. (Table 7, columns 4 to 8). This difference is not a perfect measure of trade costs, but it is suggestive of continuing high overseas import costs, confirming estimates reported elsewhere (World Bank, 1991 and 2010c).

- There is no systematic difference in prices of RC-produced goods and their DRC-produced equivalents, on a given market. Coefficients on the binary variable _RC_good_ are never statistically significant (Table 7, columns 5 to 8).

- Average prices are equalized within cities: no individual market offers systematically higher or lower prices than any of the other sample markets. Hence, market segmentation is diagnosed only between the two capitals, not within them.<sup>20</sup>

**Overseas imports appear on average to be sold more cheaply in Brazzaville than in Kinshasa.** This is implied by negative coefficient on the interaction term given by the product of the binary variables _Brazzaville_ and _Overseas_good_ (Table 7, columns 7 and 8). On the face of it, this finding runs counter to the dominance of the Matadi-Kinshasa-Brazzaville route over the Pointe NoireBrazzaville-Kinshasa route suggested by the official data (see Section 2.2). One explanation could be that this result is too imprecise to be afforded much weight. Indeed, in one specification, the relevant coefficient is not statistically
. in The Hague, see also table A11.6 and A11.7): asphalt: 1 cubic metre = 1.045 ton

- A14 - Bricks

- Brazilian quantities are converted from square metres to single units using the following conversion factors provided by Vereniging De Nederlandse Baksteenindustrie, De Steeg, Nethevlands (see also table A14.7): ceramic bricks: 1 square metre = 65 single units

- Mexican quantities are converted from tons to single units using the peso unit value for "Other Bricks" of which the quantity was given in single units (see also table A14.6 and A14.7).

- A15 - Iron and Steel

- US figures on quantity and value of shipments are derived from table 6a-2 in the Industry Series, Blast Furnaces, Works, d Rolling and Finishing Mills (MC77-I-33A, Change Sheet, October 1980). This table was originally derived from the Current Industrial Reports, MA-33B, Steel Mill Products _cof_ the 1977 Census of Manufactures.

- - The commodity items "sponge iron" and "pig iron" in the Brazilian industry 178 "Iron and Steel Making and Manufacture of Iron and Steel Products" are excluded from the sample because it wa3 classified elsewhere (in other industries) in Brazil and the USA.

- A17 - Motor Vehicles and Equipment - The figures for 134 items in the Mexican industry 3819 "Manufacture of Other Parts and Acessories for Motor Vehicles" are presented in a consolidated form.

- See separate note on the unit value adjustment for passenger cars.
Key Natural Hazard Statistics for 1985-2018 <!-- Start of picture text --> Number of People Affected<br>100M<br>1M<br>a<br>a<br>fo)<br>& 10k | |<br>1985 1990 1995 2000 2005 2010 2015<br>@ Flood<br><!-- End of picture text -->

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, Tan, J.P., Barnum, H.N., Dixon, J.A., 2001. Economic Analysis of Investment Operations: Analytical Tools and Practical Applications. The World Bank, Washington, DC. doi:10. 1596/0-8213-4850-7.

- Bikas Udhyami, 2018. A Study Into Development Data in Nepal. Report study. Bikas Udhyami. Lalitpur, Nepal. Available from https://www.nepalindata.com/media/resources/items/19/ bA ~~S~~ tudy Into Development ~~D~~ ata ~~i~~ n ~~N~~ epal.pdf.

- Binswanger, H.P., Khandker, S.R., Rosenzweig, M.R., 1993. How infrastructure and financial institutions affect agricultural output and investment in India. Journal of Development Economics 41, 337–366. doi:10. 1016/0304-3878(93)90062-R.

- Bird, K., Hulme, D., Shepherd, A., Moore, K., 2002. Chronic Poverty and Remote Rural Areas. SSRN Scholarly Paper ID 1754490. Social Science Research Network. Rochester, NY. Available from https://papers.ssrn.com/abstract=1754490.

- Booth, D., Hanmer, L., Lovell, E., 2000. Poverty and Transport. Report. Overseas Development Institute. Available from https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/3554.pdf.

- Branke, J., Scheckenbach, B., Stein, M., Deb, K., Schmeck, H., 2009. Portfolio optimization with an envelopebased multi-objective evolutionary algorithm. European Journal of Operational Research 199, 684–693. doi:10.1016/j.ejor.2008.01.054.

- Bryceson, D.F., Bradbury, A., Bradbury, T
the geographical relationships in disease spread.

First, Model 1 estimates a linear regression using all 22 covariates and possible confounders of interest that are summarized in table 3. These include population density, gender, age groups, marital status and household composition, the share of migrants, as well as several population health indicators. Particularly the health indicators are important because PM2 _._ 5 is known to affect population health. This may result in important pre-conditions that lead to more severe COVID-19 disease. Pre-conditions captured by the data include the share of population with a long-term illness (including asthma), the share of people that smoke and admit to guidelines on alcohol use, the share of people diagnosed with obesity or overweight, as well as variables on populations exposed to traffic noise.

> 13. The treatment of spatial autocorrelation and spatial residual correlation took a firm position in quantitative geography after the contributions by Cliff and Ord (1969, 1972). Spatial econometrics as a subfield of econometrics was rapidly developed as a means to analyze sub-country data in regional econometric models (Anselin, 2010). Good introductory books exist, apart from the one referenced in the main text (LeSage and Pace, 2009) is one other. The (Q)MLE is worked out, for example, in (Lee, 2004). The field is still actively developed, with recent advances focusing on time series dynamics and non-linearity (Beenstock and Felsenstein, 2019; Andr ́ee, 2020).

> 14. The first case was detected on February 27 in Loon op Zand in Brabant, but that same night a case was also confirmed in Amsterdam. Within 4 days, 10 cases had been confirmed in 6 cities across 4 provinces with multiple sources of infection, it took till March 23 for lockdown policies to be announced, giving ample time for spread from multiple points, see https://nos.nl/artikel/2325309-beatrixziekenhuisgorinchem-gesloten-om-coronavirus-tien-patienten-in-nl.html

> 15. While the spatial autoregressive models only include the rate of infective subjects in neighboring
# **B Details on the measures technology**

Appendix B describes remaining sector-specific business functions and related technologies. This section also explains the construction of technology measures in detail, which includes EXT index, aggregation of technology measures, normalization, and regional-level analysis. The FAT survey collects granular level of technology information at the business function level. In the main text, we explained how we constructed MOST, EXT, and GAP indices. In this section, we describe the technology structure and provide specific formula to construct the EXT index depending on four different types of technology structures. We also explain how we aggregate function-level technology measures at the firm-level or higher level (e.g., region-level).

## **B.1 Sector-specific business functions**

Modules B and C of the FAT survey collect information on the technologies used in key general and sector-specific business functions. In Section 2.1, we displayed two figures (Figure 1 and Figure 2) that portray the general business functions with related technologies for each business function, and the sector specific business functions in food processing sector with related technologies.

Here, we represent the business functions and associated technologies for the remaining sectors: crops (Figure B.1), livestock (Figure B.2), wearing apparel (Figure B.3), automotive (Figure B.4), pharmaceutical (Figure B.5), wholesale and retail (Figure B.6), transportation (Figure B.7), financial services (Figure B.8), health services (Figure B.9), and other manufacturing(Figure B.10). To identify business functions and technologies, we applied the same procedures. We began with reviewing the journal articles and technical reports to understand the consensus on definition of business functions and specific technologies. Based on our research, we had several internal review process with sector specialists at the World Bank Group to specify business functions and technologies for each sector. Additionally, we engaged
the last week did you or another adult in the household skip a meal due to lack of money or resources?”; and (2) “was there a time in the last week when you or another adult in the household went an entire day without food due to lack of money or other resources?”. The responses are binary, 1 for “Yes” and 0 for “No”.

We also consider measures related to social distancing and school attendance. For social distancing, the survey question is “Have you adopted social distancing/self-isolation?” We use two measures of educational disruption. When analyzing wave 1, we construct a measure of school dropout based on “Have children in the HH attended school (primary/secondary) before school closures?” and “Have children in the HH engaged in ANY learning or educational services since school closures?". Because the first of these two questions is generally not asked after the first wave, we only consider the latter of those questions when analyzing all waves of data.

We take the mean of each well-being measures at the regional (state/province) level by month to create a panel of regions.<sup>4</sup> Appendix Table 1 shows the number of regional observations by month in 2020. All variables have data beginning in April 2020. However, the panel data are highly unbalanced with most observations concentrated in June, July and August, implying that the HFPS surveys covered most countries in the three months.

The summary statistics of the regional-level HFPS well-being measures for wave 1 and all waves in 2020 are reported in panels A and B of Appendix Table 1.

# 2.2 Explanatory Variables

The main explanatory variables are a set of real-time indicators including night-time lights, air pollution, and EVI (a vegetation index) measured from satellites, Google mobility data, and the popularity of Google search of certain keywords. These are listed in Table 2. Summary statistics of real-time indicators for wave 1 and all waves
difficult as these countries (except Albania) do not have national input-output tables.

# **3.2 Data: Description and Sources**

To perform the empirical analysis, we employ a newly constructed dataset, The International Trade and Production Database for Estimation (ITPD-E), which is developed and maintained by the U.S. International Trade Commission (ITC), c.f., Borchert et al. (2021 _a_ ). The original ITPD-E consists of inter- and intra-national trade flows for 243 countries and 170 industries for the years between 2000 and 2016.<sup>14</sup> The inclusion of domestic trade flows in the ITPD-E is a crucial feature for the implementation of our methods. Another very important feature of the ITPD-E with respect to our purposes is that it covers all EU countries and all CEFTA economies (except for Kosovo) as well. This is an advantage over other databases, e.g., WIOD and the GTAP datasets, which are widely used for general equilibrium counterfactual analysis. However, neither WIOD nor GTAP covers the CEFTA countries. Albania is an exception.

Given the methodological purpose of our paper, and for computational and expositional purposes too, we make several choices regarding the dimensions of the ITPD subsample that we employ for our analysis. First, we focus on 2013, which is the latest year that allows most comprehensive coverage across most countries and across most sectors. In the robustness analysis, which we report in the Supplementary Appendix, we reproduce our estimates with panel data over the period 2000-2016, and the panel estimates confirm the robustness of our main findings. On the country dimension, we limit the analysis to cover one hundred and four (104) economies, which include the fifty (50) largest exporters in each ITPD-E industry as well as all EU economies and CEFTA countries.<sup>15</sup> 14Of the 170 sectors in ITPD, 26 are in Agriculture, 7 are in Mining and Energy, 120 are in Manufacturing
2

# 1. Introduction

-

Over the last three decades the world has witnessed a marked divergence in saving rates, particularly dramatic within the developing world: saving rates have risen steadily in East Asia, stagnated in Latin America, and fallen in Sub-Saharan Africa. These regional saving disparities have been closely matched by diverging growth experiences: across world regions, higher saving rates tend to be correlated with higher income growth.

This large variation in saving performance across countries and over time raises a number of questions. Why do saving rates differ so much across countries and time periods? How much do public policies contribute to these saving disparities, in comparison to other structural and non-policy saving determinants?

From the policy perspective, there are serious questions about the size -- and sometimes even about the sign -- of the effects of policy variables on saving rates. How effective is fiscal policy in raising national saving? Does financial liberalization -- by raising interest rates, encouraging consumer and housing lending, and raising financial depth -- inhibit or encourage private saving? Does foreign lending crowd out national saving? Or perhaps growth-enhancing policies -- such as macro stabilization and structural reform -- would be more effective in raising saving through higher income and growth than any direct saving incentive? In this paper we address the above questions empirically, by exploiting what we believe is the largest cross-country time-series macroeconomic data set on saving and related variables assembled to date. The data set is unique because of various features. First, it encompasses industrial and developing countries and covers nearly 30 years of data. Second, it provides alternative saving measures (for the nation, the central government, the public sector, and the private sector separately; unadjusted and adjusted for inflation-related capital gains and losses). Third, it has been subject to extensive quality checks, which among other things allow us to identify problematic observations and set them aside if necessary.

The objective of the paper is to use this large data set to establish the stylized facts concerning the effects on
similarity of magnitude of all accuracy statistics across quarters indicates that the sample has been divided adequately across quarters – the division yields poverty and welfare statistics of similar levels of accuracy across quarters.

**Table 12 Annual division level accuracy statistics for urban and rural areas**

|||**Rural**<br>**RSE of**|||**Urban**<br>**RSE of**||
|---|---|---|---|---|---|---|
|**Division**|**RSE of**<br>**mean**<br>**nominal**<br>**per capita**<br>**expenditure**|<br>**mean**<br>**nominal**<br>**total**<br>**household**<br>**expenditure**|**SE of**<br>**mean**<br>**poverty**<br>**headcount**<br>**ratio**|**RSE of**<br>**mean**<br>**nominal**<br>**per capita**<br>**expenditure**|<br>**mean**<br>**nominal**<br>**total**<br>**household**<br>**expenditure**|**SE of**<br>**mean**<br>**poverty**<br>**headcount**<br>**ratio**|
|Barisal|1.8%|1.9%|1.4%|3.5
goal setting and counseling. In Carpena et al. (2017), we focus on the Average Treatment Effect (ATE) and find that financial education alone did not bring about large changes in financial behavior. However, financial education with goal setting encouraged relatively _simple_ follow-up activities (e.g., writing a budget), while financial education with counseling led individuals to undertake more _complex_ financial actions (e.g., opening a savings account).

In contrast to Carpena et al. (2017), this paper moves beyond the ATE and explores the underlying channels for the effects of financial education, goal setting, and counseling. We employ causal mediation analysis (e.g., Baron and Kenny, 1986; Imai et al., 2011; VanderWeele, 2015; Acharya, Blackwell, and Sen, 2016), an approach that allows us to quantify the extent to which the treatments influence outcomes through a specific mediating variable. To this end, our empirical method consists of two components. The first component concerns measurement of financial literacy, which represents our proposed mediator. Whereas the “Big Three” questions by Lusardi and Mitchell (2009) assess financial literacy using _specific_ topics such as diversification, we take a comprehensive approach that considers _general_ dimensions of financial knowledge. Thus, we conceptualize financial literacy into three broad categories: financial numeracy, financial awareness, and financial attitudes.

Through a series of questions in the endline survey, we designed each of these three dimensions to capture a different aspect of financial literacy. Financial _numeracy_ deals with calculating interest rates, summing expenses, and other similar computations. These skills may facilitate better fiscal management and more effective comparisons of financial products. Next, financial _awareness_ emphasizes fundamental financial concepts (e.g., household budgeting) as well as basic information about financial products (e.g., deposit insurance, loan fees). This type of knowledge may enable wider adoption of financial products and promote simple financial habits. Finally, financial _attitudes_ encompass