rafmacalaba/data-use-mentions-extended huggingface dataset:
factors (RAP 2018). Users should remember that details of the local context like bus schedules and the frequency of landslides will cause departures from the model findings; we recommend careful analysis of such conditions when applying our findings on a small scale. Road‐blocking landslides in the monsoon and snowfall in the winter can have particularly dramatic effects on local transportation conditions.

# Model validation

## Consultations

We performed a thorough desk review of the model results to verify their general accuracy, consulting with peer organizations where necessary to ensure objective and comprehensive feedback. Our own knowledge of travel times in various locations was used to spot check the initial model results and make adjustments. After revisions, we consulted outside parties. The World Bank’s Far West Nutrition Program team provided us with detailed feedback on the accuracy of modeled travel times in the Far West using pre‐recorded point‐to‐point travel times from their trips to the region. Externally, we separately consulted with engineers and logistics specialists at the World Food Program’s Nepal Country Office and managers at the Rural Access Programme (RAP) to compare model travel times with actual travel times in areas where they work.

Based on these conversations, several adjustments to the draft models were made, for example reductions of walking path speeds and reductions of roadway status above 2,400 meters and in the Far West. Our consultations revealed that model travel times were usually 15‐20% too fast in most areas, especially over footpaths, and the model was consequently adjusted downwards. WFP logisticians also provided the principal inputs for monsoon season travel speed modifiers.

## Comparison to household survey data

Data from the “Nepal Household Risk Coping and Vulnerability Survey” (HRVS), a geo‐referenced nationwide survey, were used as a reference to estimate travel speeds and to validate model results. This survey randomly
purposes, a measure of financial dollarization<sup>6</sup> from Levy-Yeyati (2006) is also used, namely the ratio of deposit dollarization.

Our proxy for fiscal discipline is the _central government balance as percentage of GDP_ and the data is obtained from WDI and the IMF’s World Economic Outlook (WEO). Savings is measured as the ratio of gross domestic savings to GDP in local currency units taken from WDI whereas _private consumption_ is the ratio of household final consumption expenditures to GDP in local currency units from WDI. Finally, _export growth_ is annual percentage growth rate of exports of goods and services, _gross domestic investment_ is calculated as the ratio of gross capital formation to GDP in local currency units, and _inflation_ is the percentage change in consumer price index. All the variables mentioned above are constructed using data from WDI.

# **3. Econometric Methodology**

This section describes the econometric techniques we use to examine whether policymakers are able to sustain real exchange rate misalignments –and, more specifically, undervaluations, through policy actions. As a result, we empirically model the likelihood of sustaining a RER undervaluation as well as the magnitude of this undervaluation using limited dependent variable and censored variable techniques. In particular, we examine the impact of active economic policies on the likelihood (or incidence) of real exchange rate undervaluations using the _Probit_ analysis while the _Tobit analysis_ is used to assess the effects of economic policy on the size or magnitude of RER undervaluations.

## **3.1. The** **_Probit_ Model**

The _Probit_ model is a model of binary choice where the dependent variable takes the value of one whenever there is a sharp real undervaluation of the currency and zero otherwise. Suppose that _X_ is a binary variable that can only take two possible outcomes, zero ( _0_ ) and one ( _1_ ). We also have a vector _z_ of variables that is assumed to have an effect on the outcome _X_ . Hence, we assume that our probabilistic model
policy to vary the degree of persistence of government spending shocks.

When studying the key determinants a¤ecting the size of STMs, existing papers have focused on the importance of the persistence of the social transfer shock (Coenen et al., 2012; Romer and Romer, 2016; Alesina et al., 2017; Gechert et al., 2020; Pennings, 2020) and on the magnifying role of the more accommodative monetary policy on the STM (Coenen et al., 2012; Romer and Romer, 2016; Giambattista and Pennings, 2017; Mehrotra, 2018). Despite the above-mentioned progress, to the best of our knowledge, no study has focused on how the heterogeneity in the share of HtM agents and in the share of social transfers reaching HtM agents a¤ect the size of STMs. Existing quantitative papers estimating STMs for the United States (Giambattista and Pennings, 2017; Pennings, 2020) discipline the share of HtM agents to one-third based on Kaplan et al. (2014). Kaplan et al. (2014) relied on household surveys for the United States and eight other developed countries to measure the share of HtM agents based on liquid net worth, ...nding modest heterogeneity among developed countries.<sup>4</sup> Notably, no paper uses data to discipline the share of social transfers reaching HtM agents. It is generally assumed that social transfers solely reach HtM agents, which is referred to as perfect social transfers targeting. However, we document that this

> 3 The in‡uential paper by Oh and Reis (2012) was the ...rst to call attention to the need to have heterogeneous agents coupled with redistribution arguments to deliver a positive STM.

> 4 Kaplan et al. (2014) ...nd that the shares of HtM households (expressed in percentage terms) are as follows: Australia (19), Canada (30), France (21), Germany (32), Italy (24), Spain
Croatia (0.542) and the United States (average score, 0.62).<sup>18</sup> _Second_ , although the fat left tail and the thin right tail in the distribution of management score is evident in not only Russia, but also in Croatia, Mexico and Pakistan, the comparison with the US is starker. In the US, 18 percent of the establishments have a score higher than 0.75 and barely a quarter have a score lower than 0.50. _Third,_ among the components of management scores, an average surveyed Russian firm scores much lower on the structured management practices related to data driven performance monitoring relative to Croatia, Mexico, Pakistan and the United States. By comparison, adoption of structured management practices on human resources and target setting for an average surveyed is higher in Russia relative to that in Mexico.<sup>19</sup>

# **_Table 2: Russia not only far from frontier but also has large share of poorly managed firms_**

18 For comparator countries, the sample frame varies significantly. For example, MOPS in the US used the sample frame of the Annual Survey of Manufacturing, comprising of around 50,000 plants in each wave for the years 2010 and 2015. Nearly 74,000 responses for the two years were received, that is, an average response rate of 74 percent. Likewise, in Mexico the survey was conducted for nearly 25,000 firms in manufacturing and services, with a response rate of 96 percent. The survey in Russia is perhaps closest to that in Croatia where 727 firms were interviewed face to face across five broad regions, with response rate similar to that in Russia (about 17 percent). Although the survey in Croatia covered both manufacturing and services, in this paper we present comparisons with manufacturing only. It is also worth reminding that although much of the lagging regions in Russia is not surveyed, the survey also skips bigger cities and agglomerations such as Moscow,
Table 12 Occupation composition of town in-migrants (15 year old or above) by province: 2000

|__________<br>province|__________________<br>by province|_______<br>1|______<br>2|______<br>3|______<br>4|______<br>5|______<br>6|______<br>7|
|---|---|---|---|---|---|---|---|---|
|__________|__________________|_______<br>|______<br>(%)|______|______|______|______|_______|
|Zhejiang|within province|2.56|13.28|6.54|28.62|5.70|43.24|0.05|
||outside province|0.22|2.24|1.11|10.72|2.37|83.31|0.04|
|Henan|within province|4.05|21.30|11.49|28.36|12.02|22.60|0.17|
||outside province|2.29|12.06|8.79|28.62|10.03|37.89|0.33|
|Sichuan|within province|3.70|19.84|8.47|34.02|10.57|23.13|0.25|
||outside province|2.77|15.06|9.88|36.02|9.44|26.53|0.30|

________________________________________________________________________ Source: calculated from Tabulation on 2000 Population Census of the People’s Republic of China, Table L7-1b and L7-2b.

note: occupation title: 1, manager and administrative personals, 2. professionals and technician, 3.
The ideal index of financial reform would attempt to measure both the de-regulatory and the institution building aspects of the process. Unfortunately, short of using outcome measures such as the development of markets as a proxy - an approach leading **in** the present context to unacceptable endogeneity difficulties -- it seems impossible at present to find useful measures of institutional development. For these reasons we have chosen to build our index of reform from explicit policy changes which, though not wholly independent of wider economic conditions, should be less subject to endogeneity problems.

Our index thus summarizes exogenous changes in interest rate regulation, reserve requirements, directed credit, bank ownership (moves toward privatization), liberalization of securities markets, prudential regulation, and intemational financial liberalization. Based on an analysis of the historical evolution in each case we have identified the timing of major moves towards, and sometimes away from, a more liberalized system under each of these headings (Appendix 1 gives the details). This yields a matrix of zero-one variables for each country. Rather than attempting to use all of these variables in the econometrics -- that would use up too many of the available degrees of freedom -- we have constructed for each country the principal components of the matrix. We use the first principal component as our main liberalization index in the regressions of Section IV below. (As an alternative, we also experiment below with a weighted average of the more important principal components, using as weights the fraction of the total standard deviation explained by each component.) In all cases, a higher value of the index in a given year captures an overall more market oriented regime." The resulting index is shown in Figures 1-8, with data on financial depth (M2 or total credit to the private sector as a percentage of Gross National Disposable Income, GNDI), real interest rates, and the private saving rate (measured as a share of GNDI). We have used a definition of the private saving rate, both unadjusted and adjusted for
consuming a ‘western’ dietary pattern were at higher risk of GDM (RR 1.27; 95% CI 1.03, 1.56) in one meta-analysis of 13 cohort studies (Hassani Zadeh, Boffetta, and Hosseinzadeh 2020) with a similar effect size that was marginally non-significant in 2 other meta-analyses due to the lower confidence limit (Haghighatdoost et al. 2023; Quan et al. 2021).

Higher maternal adherence to a healthy diet (defined in various ways) was protective for GDM in 10 of 11 effect estimates (including between 6 and 26 studies), with the effect size ranging from 0.39 (95% CI 0.31, 0.48) for a high quality diet that adhered to national dietary guidelines to 0.86 (95% CI 0.76, 0.96) for a healthy dietary pattern rich in fruits, vegetables and whole grains (Haghighatdoost et al. 2023). Higher quality diet, as measured by higher adherence to Mediterranean, DASH, AHEI or plant-based diets, also was associated with lower risk of gestational diabetes (range of OR 0.51-0.66).

**_Hypertensive disorders of pregnancy_** - Higher maternal adherence to a healthy diet was associated with lower risk of gestational hypertension (OR 0.86) (Abdollahi et al. 2021) and pre-eclampsia (OR 0.78) (Kibret et al. 2019). Risk of pre-eclampsia was also lower among pregnant women consuming adequate vegetables and fruit, based on 4 and 5 studies in LMICs, respectively (Kinshella et al. 2021). Conversely, unhealthy dietary pattern adherence increased the odds of gestational hypertensive disorders (OR 1.23) (Abdollahi et al. 2021) and maternal diets rich in UPF also were associated with increased risk of pre-eclampsia (OR 1.28) (Paula et al. 2022).

## **Birth outcomes**

Seven (7) SRM summarized the evidence for the contribution of diet during pregnancy to
8 Before turning to that model, the Version 6 GTAP database needs to be augmented to include distortions to services sectors.<sup>5</sup> We follow Hertel and Keeney (2006) in assuming the removal of services distortions can be modeled as a technological change that reduces the cost to all sectors of imported intermediate inputs. The extent of those trade cost equivalent barriers in services are based on work by Francois, Meijl, and Tongeren (2005), who draw on the pattern of residuals from a gravity model of national imports estimated from the GTAP database. Their estimates are shown in Table 2.

# **The GTAP-AGR Model**

We employ a new variant of the widely used GTAP model (Hertel 1997) that is specifically oriented to analyzing agricultural markets, namely the GTAP-AGR model (Keeney and Hertel 2005). We use the standard GTAP model assumptions of perfect competition and constant returns to scale in production activities, a Constant Difference of Elasticities (CDE) demand system which permits differential price and income responsiveness across countries, and bilateral international trade flows handled through Armington elasticities by which products are differentiated by country of origin.<sup>6</sup> These Armington elasticities are region-specific, and are econometric estimates at the 57

> 5 We also insert a production subsidy for US cotton, following the WTO dispute settlement case which ruled that those subsidies belong in the Uruguay Round Agreement on Agriculture’s amber box, rather than in the green box as notified by the US. We conservatively estimate that subsidy to be 28 percent for 2001.

> 6 The Armington elasticities are very important determinants of the results, and those used in the GTAP model are quite conservative compared with those in, for example, the World Bank’s Linkage model (see the Appendix to Ch. 12 in Anderson and Martin 2006). For this reason, the key results in this paper can be considered lower-bound estimates.
# **I. Introduction**

For many policy makers in developing countries, improving the effectiveness of domestic revenue mobilization (DRM)—the generation of government revenues from domestic activities—has become a key policy objective to guarantee the financing of the basic functions of their state and provide essential public and infrastructure services (World Bank and IMF 2015; IMF 2018). Moreover, following the 2015 deadline for the attainment of the Millennium Development Goals, there has been a major shift in the importance assigned to DRM by the international development community, originating from the now widely accepted view that official development assistance is unlikely to be adequate to achieve the newly articulated and more ambitious Sustainable Development Goals (SDGs).<sup>1</sup> In this context, it is often argued that governments in developing countries should generate at least 15 percent of GDP in government revenues to finance their development goals in a sustainable and equitable manner (IMF 2018). However, despite the prominence of this goal on the international development agenda, little progress in raising revenue levels has been made. On average, revenue-to-GDP ratios over the past decade in low-income countries, and various countries in Sub-Saharan Africa and Latin America declined (World Bank 2021).<sup>2</sup> This paper provides a fresh perspective on the efforts performed to raise revenue levels in developing countries by comparing not only the absolute outcomes achieved (such as the achieved revenue-to-GDP ratios), as often done in the literature, but also taking into account differences in countries’ fundamental economic structures and constraints that are associated with different capacities to raise domestic revenues. Specifically, we address the following three questions: (i) ‘How efficient are low-income countries compared to countries belonging to higher income groups in mobilizing domestic revenue, given the national economic conditions and resources available to each country?’; (ii) ‘What factors account for the variation in relative efficiency in DRM across countries, i.e., what factors can explain why some
|
|**Weight-for-height**|1.26|1.05|1.15|2.22|2.73|2.51|2.79|2.80|2.80|
|**Preschool**|10.67|7.69|9.09|4.18|3.10|3.58|5.88|4.08|4.87|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Note:_ Tests for equality of means across treatment and control estimated in each round with *p < 0.1; **p < 0.05; ***p < 0.01.

16
||||
||(0.00118)||(0.00119)|(0.00138)|(0.000916)||(0.000915)|(0.00106)**|||||
|Share of hhlds with computer||-0.0968|-0.0657|-0.0732||-0.0381|-0.0212|-0.0107|||||
|||(0.0730)|(0.0718)|(0.0756)||(0.0381)|(0.0476)|(0.0393)|||||
|IDN exports per worker *||||-0.0065||||-0.0111|||||
|Share of hhlds with computer||||(0.00332)*||||(0.00321)***|||||
|Observations|2,933|2,933|2,933|2,933|2,933|2,933|2,933|2,933|||||
|R-squared|0.977|0.977|0.977|0.977|0.870|0.871|0.871|0.870|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita,
be youth (Republic of Sudan: National Population Council 2018). There are roughly equal numbers of males and females in the country’s urban and rural areas. However, there are a larger share of men above 50 years of age, whereas in the population aged 25–49, there are more women (Figure 2).

# **Characteristics of the labor market between 2009 and 2014**

Despite a significant increase in the number of jobs in Sudan, the labor force participation rate, at 53 percent of the working-age population, remains the lowest among the average rate of Sub-Saharan Africa, low-income and lower-middle-income countries.<sup>4</sup> The Sudanese economy added 2.5 million jobs between 2009 and 2014 while the working-age population increased by around 2.8 million and the active population by 2.6 million. As a result, there were measurable improvements for the working-age population between 2009 and 2014 with a more than 5 percentage point increase in the labor force participation, from 48 to 53 percent, and a decrease in the unemployment rate from 13 to 11 percent

> 4 Labor force participation rate is the proportion of the population aged 15–64 that is economically active: all people who supply labor for the production of goods and services during a specified period.

9
_Income and consumption index:_ includes the logarithm of monthly household income and consumption. Because these variables are continuous the index is constructed following the methodology outlined in Kling, Liebman and Katz (2007).

_Labor market index:_ includes a dichotomous variable for employment, and the logarithm of monthly wages and weekly hours worked.

Descriptive statistics for each of the outcomes and their components variables are described in Table 1.

# **III.B Data for the Taliban’s Geographical Presence**

To obtain information on the Taliban’s we online data geographical presence, scraped from the Foundation for Defense of Democracies’ Long War Journal on the districts controlled by Taliban, by U.S. Allies, and contested territories between the two groups.<sup>9</sup> The data includes a yearly evolution of the territorial control in Afghanistan. The classification is based on open-source information from NATO’s data in Afghanistan, press reports, information provided by government agencies, and the Taliban. To categorize the districts, they identify who provides government services, ensures security within the district, administers the district openly, and oversees local courts (see Appendix B.B for a detail description of the variable’s construction).<sup>10</sup> Figures 3 to 5 report province-yearly changes in the share of districts controlled by the Taliban, the U.S. allies, and the contested districts in dispute between the Taliban and the U.S. allies.<sup>11</sup> As seen in the figures, Taliban control was relatively stable from 2017 through 2019, after which drastic shifts took place across regions, in parallel with the U.S. withdrawal from Afghanistan.

> 9The Foundation for Defense of Democracies is Washington, DC-based nonpartisan research institute focusing on national security and foreign policy. It aims to strengthen US national security.

> 10The LJW data aligns with the territorial control analyses conducted by SIGAR and U.S. ForcesAfghanistan, which effectively tracks
--|---|---|---|
|Middle office reports to the board|0.227*|0.193*|0.345***|0.114|0.153|-0.006|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.10 Obligation to Cover Negative Equity and the Broader Governance Environment**

|Variables|Independence of<br>central bank|Corruption<br>Perceptions Index|Governance<br>Pillar Score|Government<br>Effectiveness|
|---|---|---|---|---|
|Obligation to cover negative equity|-0.194*|-0.479***|-0.357***|-0.483***|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.11  Obligation to Cover Negative Equity and Investment Policies**

|Variables|Allocation to<br>nontraditional<br>assets|Number of<br>eligible<br>currencies|Number of<br>eligible assets|Duration – Total<br>portfolio (months)|Duration –<br>Liquidity tranche<br>(months
, attendance records indicate that no children in C group received treatment. Additionally, school coordinators were instructed to make sure that only children in T group attended the courses. However, since we did not implement an additional compliance check, and considering that treated students attended approximately 60% of all club sessions, we use an intentto-treat approach to measure the ASP impacts, as we explain in the following section. We consider all children assigned to participate in the program as T group regardless of whether they were part of the ASP at the time of the endline survey.

# **3. Estimation Framework**

In this section, we describe the empirical strategy used to measure the ASP’s effects on student behavior, violence, and academic outcomes, and to assess the heterogeneity of the intervention by individual violence levels. Additionally, we study how the intervention affects nonenrolled children’s behaviors.

16