rafmacalaba/data-use-mentions-extended huggingface dataset:
- - <u>http://siteresources.worldbank.org/INTPROSPECTS/Resources/334934 1184090646382/Lofgren Diaz-Bonilla-2010-MAMS-Descr+Math-June10.pdf</u>

Lofgren, Hans, Martin Cicowiez, and Carolina Diaz-Bonilla (2012) “MAMS – A Computable General Equilibrium Model for Developing Country Strategy Analysis”, pp. 159–276 in Dixon, Peter B. and Dale W. Jorgenson, D.W. (Eds.), Handbook of Computable General Equilibrium Modeling. North Holland, Elsevier B.V.

Luecke, Matthias, Alex Oprunenco, and Valeriu Prohnitchi (2011) _Options for a Deep and Comprehensive Free Trade Agreement between Moldova and the EU_ . German Economic Team Moldova, Policy Paper Series, PP/02/2011, Berlin/Chisinau, May.

National Bank of Moldova (2012) Balance of payments data. Available at: http://www.bnm.md.

Oprunenco, Alex (2011) _The Impact of the Russian Wine Embargo: Estimation of the Economic Impact, Using a Computable General Equilibrium Model for Analyzing the Impact of the Economic Policies Relevant for Moldova’s European Integration_ , Expert Grup, Chisinau, February.

Prohnitchi, Valeríu, Alex Oprunenco (2010) _Moldova 2009: State of the Country Report_ , Expert Grup, Chisinau.

Prohnitchi, Valeríu (2011) _Moldova and EU Liberalizing Trade: How much for the Agro-industrial Sector to Suffer? Using a Computable General Equilibrium Model for Analyzing the Impact of the Economic Policies Relevant for Moldova’s European Integration_ . Expert Grup. Discussion Draft. Chisinau. February.

Qehaja, Driton (2012) _Fiscal Policy Responses to External Crises: The Case of Moldova 1998-2010_ , IMF Working Paper WP/12/82, March.

Statistics Moldova (2011) _External Trade of the Republic of Moldova 2001-2010_ , Statistical Yearbook.

Statistics Moldova(2011) National accounts data. Available at: <u>http://www.statistica.md/index.php?l=en</u> Walewski
following the methodology in Kling, Leibman, and Katz (2007). These results are reported in Columns 9 and 10, and show that the aggregate impact on business investments is large, positive, and statistically significant.

A notable finding in this analysis of business outcomes and practices is the difference in effects of the training on individuals with below and above median financial literacy at baseline. We find that both entrepreneurs with below and above median financial literacy changed some of their business practices, such as separating personal accounts from business, and making investments in their business; however, only entrepreneurs with above median financial literacy at baseline reported increases in sales and profits as a result of the training. These findings suggest that baseline knowledge and information conveyed in the training act as complements in increasing the productivity and sales of a business.

# _V.VI. Evaluation Results – Treatment Effects on Loan Behavior_

Adding to our analysis on business outcomes, this section investigates whether the business training program changed loan behavior. In order to do so, we analyze very detailed, high frequency administrative data from Partner. Since our sample may borrow from other sources than Partner, we supplement the administrative data with a question on the firms’ overall loan portfolio from the follow‐up survey.

26
(0.123)|
|Mean in control|0.907|2.199|0.547|1.236|
|Adjusted R-squared|0.078|0.194|0.259|0.318|
|Observations|13464|13464|13464|13464|
|_Panel C:_||_Outside-vil_|_lage Transfe_|_rs_|
|Treatment|-0.012|-0.017|-0.001|0.003|
||(0.014)|(0.019)|(0.006)|(0.008)|
|Treatment_×_Endline|-0.031|-0.034|-0.004|-0.007|
||(0.022)|(0.031)|(0.010)|(0.014)|
|Mean in control|0.107|0.141|0.037|0.047|
|Adjusted R-squared|0.066|0.074|0.037|0.036|
|Observations|13464|13464|13464|13464|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (2) estimated with OLS. The sample includes observations from ultra-poor households surveyed at the midline and endline surveys. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in column 1 is a dummy variable (DV) equal to 1 if the respondent’s household ever receives any food transfers from other households The dependent variable in Panel A, column 1 is a dummy variable (DV)
in average years of schooling, taken from the Barro and Lee (2015) dataset.

Variables are taken as averages during the period 1990-2018, primarily due to widespread sectoral data availability only starting in the 1990s. This is a shorter horizon than over which the VARs are estimated. However, results are robust to using longer averages including the 1980s for non-sectoral data and shorter averages since 2000. The overall explanatory power of the covariates is modest but material for the employment impact in year 1, with an adjusted-R<sup>2</sup> of 0.18 in the best-fitting specification, particularly given the wide range of factors that will influence this outcome across economies. There are many statistically significant determinants of the employment effect found despite the simple nature of the proxies for structural and technological change (Table 2).

**_Rising industrial employment_** . An increasing share of employment in the industrial sector since 1990 is strongly correlated with the negative employment effects of technology shocks at all horizons.<sup>6</sup> A 10 percentage point rise in the industrial employment share over this period is associated with a 0.1-0.2 percentage point larger negative impact on employment on average from a technology shock. The effect is statistically significant at the 1% or 5% level in each of the horizons. The average _level_ of industrial employment during this period is also separately a statistically significant determinant of the employment impact at the 5 and 10-year horizons but it loses significance when included in the same estimation as the _change_ in employment share. As will be discussed in the following section, all advanced economies in the sample lost employment share in the manufacturing sector since 1990. However, a smaller loss of employment share in the industrial sector, relative to a large loss, remains a significant determinant of the size of the negative employment impact. Changes in the share of employment in agriculture and services are not statistically-significant determinants of the employment impact.

An interpretation of this finding is that economies that have made productivity
<br>Paraguay Mexico 40 Argentina<br>El Salvador<br>20 Honduras El Salvador Costa Rica Honduras<br>30<br>Guyana<br>20 40 60 80 100 20 40 60 80 100<br>Share of public spending (%) Share of public spending (%)<br>a. Enrollment b. Graduation<br>Enrollment rate (%) Graduation rate (%)<br><!-- End of picture text --> _Source_ : UNESCO for 2017 enrollment rates (panel a); own calculations based on SEDLAC (household surveys) for 2017 graduation rates (panel b) and on UNESCO for 2017 share of public spending in higher education. _Notes_ : Panel a shows gross enrollment rate, defined as the ratio between higher education enrollment and the number of individuals ages 18-24. Panel b shows graduation rate, defined as the ratio of number of individuals ages 25-29 who have graduated from higher education, and number of individuals ages 25-29 who have ever started higher education. Share of public spending is defined as government spending in higher education relative to total higher education spending.

To capture this evidence and investigate the potential effects of free college, in this paper we develop a dynamic model of college enrollment, performance, and graduation. In the model, a college student faces risks that may prevent her from completing a class or remaining in college,

> 1Among the countries surveyed by OECD (2018), college is free at public institutions in Argentina, Brazil, Cuba, Czech Republic, Denmark, Ecuador, Estonia, Egypt, Finland, Germany, Greece, Iceland, Mexico, Norway, Panama, Poland, Slovenia, Sweden, Turkey, and Uruguay.

2
direct medical costs. Bardach and colleagues (2023) produced similar estimates for children, adolescents and adults in Argentina alone.

Walters and colleagues (2019) estimated the global human and economic costs of not breastfeeding according to recommendations, including 974,956 cases of childhood obesity each year, 595,379 childhood deaths per year (659 months) from diarrhea and pneumonia, and 98,243 women’s deaths from cancer (breast and ovarian) and T2DM. The total annual global losses associated with inadequate breastfeeding are estimated to be between US$257 billion and US$341 billion (0.37%-0.70% of global gross national income), including US$1.1 billion in health care costs, US$53.7 billion in economic losses due to premature mortality, and US$285.4 billion in cognitive losses.

# Search 2: Evidence on the link between diet quality and health/nutrition outcomes **Search results and characteristics of selected studies**

The study selection process for the second literature search objective is shown in **Figure 3** . The total number of records screened, after removing duplicates was 2,335 and 222 full texts were screened. We identified a total of 82 systematic reviews with meta-analyses (SRM) that included data from LMICs and estimated the burden of nutrition and/or health outcomes associated with specific dietary risk factors. The following sections provide an overview of the evidence available for the diet’s contribution to nutrition and health outcomes, organized by life-stage group.

## Pregnant and lactating women

A total of 19 SRM articles were found that included women of reproductive age in LMICs and assessed the association of diet with maternal and/or neonatal nutrition and health outcomes. **See Table 4.**

### **Maternal nutrition outcomes**

Five (5) SRM assessed risk factors for maternal nutrition outcomes, including anemia (Seid et al. 2023; Geta, Gebremedhin, and Omigbodun 2022; J
2

# 1. Background

Low levels of engagement among women in MNA’s labor force vary from country to country yet female unemployment is consistently high across all nations. Female labor force participation (FLFP) is as high as 58% in Qatar, 57.5% in Kuwait, 55% in Djibouti, and as low as 14% in Jordan, 12% in the Syrian Arab Republic and 6% in the Republic of Yemen.<sup>1</sup> In the Kurdistan Region of Iraq (KRI), according to the labor force and demographic surveys conducted in 2012, 2013, 2014, 2015 and 2017, FLFP was 12.2%, 12.7%, 13.1%, 14.8% and 13.8% respectively.<sup>2</sup> Female unemployment in nearly all countries in MNA is at least twice as much as that of their male peers. For example, the unemployment rate for women in Iraq is 3 times higher than men, at 31% compared to 10% in 2017.<sup>3</sup> Social norms and cultural beliefs dictate behavior in ways that affect important development outcomes for women, including their participation in public life, access to economic opportunities, control of finances, educational attainment, and ability to choose their health care (Bernhardt et al, 2018, Field et al, 2016, Outhread et al, 2013, Banerjee et al, 2018). Although the MNA region began to intensify their efforts to get more women to join the workforce in 2009, studies have hypothesized that social and cultural barriers to female employment participation persist. Gender and Development and the ensuing Opening Doors: Gender Equality and Development in MNA reports were instrumental in identifying such informal institutions as key binding constraints on the achievement of gender equality in MNA (World Bank, 2012, 2013).

Responsibilities towards family are also central in women’s decision-making processes when it comes to the world of work. For
Nilson, da Silva et al. 2020; Nilson, Metlzer, et al. 2020; Vega-Solano et al. 2023). An additional eight studies were found that estimated costs (usually mortality and DALYs) associated with specific dietary risk factors such as diets high in red meat (Liu et al. 2022) or processed meat (Rocha et al. 2023), high in sugar-sweetened beverage intake (Bardach et al. 2023; Li et al. 2021), low in fiber (Zhuo et al. 2022), or low in dairy (Javanbakht et al. 2018). One problem with these studies of individual dietary risks, is that it is not possible to add them up to obtain overall estimates of the costs of unhealthy diets, since the risk factors may be correlated and/or interact.

A global Cost of Not Breastfeeding tool is also available to support country quantification of the human and economic losses, including loss of life, lost productivity and increased costs to health systems. The total annual global losses associated with inadequate breastfeeding are estimated to be between US$ 257-341 billion, or 0.37-0.70% of global gross national income (Walters et al. 2019).

There are a sizeable number of studies which estimate the cost of undernutrition, as measured by stunting and/or wasting among children under five years of age. A couple of recent examples, comprehensive in geographic scope, include ECLAC and WFP (2017) and the Cost of Hunger in Africa series, supported by African Union and led by at least 21 individual countries performing national studies (https://www.wfp.org/publications/cost-hunger-africa-series). The main costs included are costs of morbidity, mortality, and cognitive losses associated with undernutrition, measured as the costs of foregone future productivity. To these, the costs of treating severe acute malnutrition or community management of acute malnutrition can be added. These estimates typically focus on children (0-23 months, or 0-59 months) where the impact of diet
that used individual-level data from the COVID-19 Behavior Tracker, found that peers help in driving (and undermining) vaccine acceptance. They argue that people who receive warnings against the vaccine from family and friends are less willing to take it, and more likely to share negative information about vaccines with others.

# **2.2 Hypotheses**

Several hypotheses emerge from the existing literature about how intra-household dynamics are likely to relate to attitudes towards COVID-19 vaccines.

The first hypothesis that emerges from the existing literature that can be explored with descriptive data is as follows:

**– Hypothesis 1 People’s willingness to get vaccinated for COVID-19 will be closely correlated with whether other household members are willing to get vaccinated for COVID-19.**

This hypothesis is a natural extension of the widely accepted phenomenon whereby people’s decisions are influenced by the behavior of others around them, particularly in the same household. There is good reason to believe this is likely to apply to COVID-19 vaccines. Especially as information about the availability and reported benefits (or lack thereof) from COVID-19 vaccines is likely to be shared within households and, in some instances, access to vaccines may be provided to household members collectively (e.g., through door-to-door vaccination campaigns).

The second hypothesis that emerges from the very limited prior experimental work on boosting people’s willingness to get a COVID-19 vaccine is as follows:

**Hypothesis 2 - Respondents who receive information about the benefits of being vaccinated will be more (less) likely to state they are willing (unwilling) to get**

9
|Mumbai|Hindi|Hindi and English|
|---|---|---|
|Pimpri-Chinchwad|Hindi and English|Hindi|
|Bhubaneswar|Hindi and Oriya|Hindi|
|Delhi|Hindi|Hindi and English|

# Interviews with government officials

Two interviews in each of eight cities were planned: one with a revenue officer to gain insights on aspects property tax issues, and the other with an officer from the stamp duty and registration office to gain similar insights on those topics; both interviews were expected to provide general insights about gender and property ownership locally. Additionally, gender-disaggregated data on property ownership, including data from different time points over recent years to observe any gender trends, were to be sought from all interviewees for the relevant geographies.

The eight cities selected reflect a range of discounts on stamp duty and property taxes. Table A5.8 shows the two additional cities, beyond those listed for FGDs in Table 6 (see section 4.2), where interviews with government officials were sought. Legislation for these cities includes a stamp duty discount for females. The eight cities include those where both gender-based stamp duty and property tax discounts exist, where neither exists and where the legal framework suggests gender-based discounts exist but do not seem to be implemented in practice.

Table A5.8: Cities where interviews with government officials were requested

|**State**|**City**|**Stamp Duty discount for females**|**Property tax discount for females**|
|---|---|---|---|
|Punjab|Ludhiana|Yes|No|
|Uttarakhand|Dehradun|Yes|No|

## Profile of Participants

### _Revenue officers_

Two types of revenue officers were considered for this interview, listed here in order of preference:

1. Officers with 8 to 10 years of experience as head/superintendent of the revenue/property tax department.

2. The additional
unit (e.g., Kebele)—ensures that two nearby units from the same town or city are not separated into different analyses. For example, the approach ensures that a brightly lit pixel in a city’s center and a dimly lit pixel in the same city’s periphery are included in the same group. To test the sensitivity of these groupings, we also divide Woredas into two groups: those that had zero baseline nighttime lights, and those that had some positive baseline lights.

# **3.4 Descriptive Statistics**

Table 1 shows descriptive statistics of outcome variables at the Kebele level, and Figure 2 shows maps of dependent variables and road upgrades. Nighttime lights and urban land are heavily concentrated in Ethiopia; most of the country is neither lit nor settled. However, during the study period, Ethiopia experienced significant growth in both urban area and the intensity of lit pixels. The number of Kebeles with some positive nighttime lights increased 103% from 1996 to 2013 and further increased by 414% from 2013 to 2016.<sup>7</sup> In addition, the number of Kebeles with some urban land grew by 96% (from 542 to 1,062 out of 15,714). In contrast to nighttime lights and urban area, much of Ethiopia is classified as cropland, and

> 7Much of the increase from 2013 to 2016 is likely driven by 2013 using DMSP-OLS data and 2016 data using DMSP-OLS-like data, which is derived from VIIRS and better captures low-level light.

11
have been granted. Re-contracting of loans in favor of longer maturities or lower interest rates has also been common. The hope is that because the health crisis is temporary, so too will be the financial distress of firms and households. However,

> Reinhart (1999), Bordo, Eichengreen, Kliengbiel, and Martinez-Peria (2001), Reinhart and Rogoff (2008, 2009), Schularick and Taylor (2012), Laeven and Valencia (2013), Bordo and Meissner (2016).

> 5 See World Bank (2020b) for a cross-country database on these policies.

5
policies and FDI have also been identi...ed as important factors for moving up GVCs, based on ...rm-level evidence for China and Bangladesh.<sup>21</sup> Hence, we expect tari¤s and FDI (as well as deep PTAs) to be signi...cant in determining GVC participation. But these determinants may be subject to endogeneity concerns that we address in detail in Section 3.1.

# 2.2.5 Institutional Quality

As discussed in Antràs (2016), what distinguishes GVC trade from traditional trade are the intense ...rm-to-...rm interactions characterized by contracting and specialized products and investment. Weak contract enforcement is thus a signi...cant deterrent not only for traditional trade, but also for GVC trade.<sup>22</sup> Because the performance of a GVC depends on the strength of its weakest link, production delays driven by weak contract enforcement might be particularly harmful in GVCs. In addition, the presence of relationship-speci...c investments (e.g., the customization of products) and the exchange of large ‡ows of intangibles (such as technology, intellectual property and credit) reinforces the potential role of institutional quality as a signi...cant determinant of relational GVC participation. Some emerging evidence ...nds a correlation between a stronger rule of law and stronger GVC integration.<sup>23</sup> We expect institutional quality to be important in determining GVC participation.

# 2.2.6 Connectivity

Transport costs remain, according to surveys of developing country suppliers, the main obstacle to entering, establishing, or upgrading in GVCs.<sup>24</sup> The geographic centrality of a country can attract downstream production stages in GVCs. But geographic centrality is more related to centrality in the transport network than to distance. Thus, logistics and communication infrastructure, port and customs e¢ ciency, and information technology networks could
desirable outcome in most LICs. Of course, it is difficult to determine what the optimal level of revenue should be in each country, and there is no guarantee that more revenues will automatically lead to productive public expenditures and better development outcomes. Nevertheless, given that the governments in many LICs are often unable to meet even the most basic development needs, it seems very unlikely that increasing government revenues (including by raising tax-to-GDP ratios) would not be desirable in most LICs.

At the same time, it is important to note that countries with lower income levels also tend to feature less favorable economic conditions for generating government revenues. Figure 5 shows the average value of the DEA input index for each income group in 2016-2019 together with the disaggregated values for each of the four included input factors. Unsurprisingly, countries with lower income levels tend to feature smaller values of the DEA input index, indicating less favorable conditions for revenue mobilization. Simple averages of the DEA input index for income groups are: LIC (0.09), LMIC (0.15), UMIC (0.21), and HIC (0.40). Individual economies with the highest values of the DEA input index include Singapore (1.00), Luxembourg (0.90), and Hong Kong SAR, China (0.83). Countries featuring the lowest values of the DEA input index include Niger (0.02), Uganda (0.03), and Tanzania (0.04).

**Figure 3.** Revenues Over Time, by Income Group and Region

<!-- Start of picture text --> Panel A: Income groups Panel B: Regions<br>40<br>40<br>30<br>30<br>20<br>20<br>LIC<br>10 LMIC 10 EAP ECA<br>UMIC LAC MENA<br>HIC SA SSA<br>1980 1990 2000 2010 2020 1980 1990 2000 2010