rafmacalaba/data-use-mentions-extended huggingface dataset:
role (Hewa-Wellalage et al., 2022; Bertrand and Perrin, 2022).

Gender equality is crucial not only for its social value, but also for its significant impact on broader macroeconomic outcomes. For example, Morazzoni and Sy (2022) show that credit constraints that disproportionately penalize female-led firms lead to capital misallocation and aggregate production losses in the United States. Likewise, Ranasinghe (2024) finds that female-led firms face challenges in operating a business mainly due to higher credit market distortions which impose an implicit tax on capital. These distortions are especially higher in poorer countries, and negatively affect market shares and average firm size of female-led firms. It is not surprising that barriers to female entrepreneurship lead to large productivity losses, as shown in Chiplunkar and Goldberg (2021) for India.

We contribute to the literature by studying the misallocation of credit using cross-country data. Although country-level studies (e.g. Alesina et al., 2013; de Andrés et al., 2021; Morazzoni and Sy, 2022; Basiglio et al., 2023) allow for an understanding of financial frictions faced by female-led firms, they provide limited insights on the _entrenched_ differences in gender inequalities in certain groups of

> 1See for example, Coleman and Robb (2009); Muravyev et al. (2009); Bellucci et al. (2010); Stefani and Vacca (2013); Alesina et al. (2013); Ongena and Popov (2016); Aristei and Gallo (2016); Morazzoni and Sy (2022). However, some studies do not find evidence of gender-based differences in access to credit (Blanchflower et al., 2003; Bruhn, 2009; Bardasi et al., 2011; Aterido et al., 2013; Basiglio et al., 2023).

> 2Some studies also suggest that the rejection rates for credit applications vary by context, as evidenced in the United Kingdom where rejection rates for women-led firms were lower during the crisis perhaps due to their conservative financial behavior (Cowling et al
measurement power, we examine the relationship between these tools and representative development outcomes for each of the 17 SDGs, an overall SDG index, and several other common indexes. Finally, we offer some thoughts on other challenges, including potential mechanisms for their impacts on the economy, political economy factors, and future development of these tools. For illustration, we offer new analysis based on their latest data updates, including new data for the SPI in 2021 and 2022 that were not analyzed before.

# **Results**

We provide a brief overview of the various tools and highlight some key qualitative differences regarding their guiding principles, before offering a more detailed comparison over the dimensions, indicators, and countries covered by the tools. We subsequently examine the relationships of these tools with a number of key SDG outcomes and several other development indexes.

# **Overview of the tools and their guiding principles**

_Statistical Capacity Indicators and Index (SCI) and Statistical Performance Indicators and Index (SPI)_ Following the guiding principles that the source data should be publicly available and meet certain quality standards (e.g., as provided by the curators of the international databases), the SCI collected data from publicly available, international databases and NSO websites, with the major share coming from the former source. The SCI has been employed by different international and national agencies since its inception in 2004 to measure progress with development trends (United Nations, 2016), or areas of statistical improvement in member countries (OIC, 2012), or tracking the SDGs for child development (UNICEF, 2018). The SCI was widely employed in academic research covering different disciplines, ranging from economics, international development, political science to statistics. We offer in Appendix A, Table S1 a brief overview of some selected academic studies in the past decade that employ the SCI.

The SCI, however, has several key limitations, including an outdated framework, a focus on poorer countries only, and a lack of underlying conceptual and mathematical principles
the total number of observations in the regressions still

> 32 It should be noted that this assessment of the agent is not in any way linked to the agent’s performance in terms of number of transactions or volumes transacted. These assessments are carried out in the field by personnel that does not have access to the headquarters database on transactions.

> 33 Because the transactions performance category and its sub-components were not significantly associated with transaction numbers or volumes, we do not include it among the explanatory variables in the models presented in Table 7.

25
less likely to be detected within health services. A Demographic and Health Survey in Nicaragua found that over one-third of women who had experienced partner abuse had never told anyone about their situation, Although 57% of the women had suffered one or more injuries as a result of violence, only 13% had ever received medical attention for injuries. Even then, most women still did not disclose the cause of their injuries. Only 7% of women reported having ever sought help for violence at a health center or hospital (Rosales, Loaiza et al., 1999).

Most research indicates that if women are asked about violence in private, in a nonjudgmental and empathetic way, they will answer truthfully. In fact most women, regardless of whether they have been abused themselves, feel that physicians should ask their patients routinely about violence (Friedman, Samet et al., 1992; Velzeboer, Ellsberg et al., 2003) However, health providers are typically reluctant to ask women about experiences of violence, even when women show clear signs of abuse, for fear of offending women, or of opening up “a Pandora’s box” of issues that they will not know how to address (Sugg and Inui, 1992). Many providers worry that screening for abuse will add yet another burden to
to the control group. At the same time, we find evidence of significant heterogeneous treatment effects across dwelling categories. For those living in solid houses, bed net use increase by 9-to-11% compared to control, yet we find no significant effect on those living in non-solid dwellings. These effects are amplified in the last 3 months of the campaign, and for individuals with moderate levels of concerns about getting malaria. While incidence of high fever and malaria during the campaign was not affected, the campaign increased the likelihood of solid dwellers seeking medical assistance promptly (within 24 hours) in the event of a fever by 13%.

We use the cross-sectional sample to study impacts 1-to-3 months _after_ the campaign ended. The results indicate that prolonged exposure to the ad campaign, which led to cumulative behavior change, resulted in decreased malaria incidence. Self-reported household-level malaria cases decreased among solid dwellers by 44 % at the extensive margin, and by 53% at the intensive margin, compared to control, but not among those living in non-solid houses. The reduction in malaria incidence is larger among bed net owners and more so among households with at least 1 mosquito net per every 2 members, emphasizing the importance of technology adoption to prevent infection. Our survey-based findings are bolstered by the evidence of baseline balance in demographic and socio-economic characteristics across experimental arms and the consistency of impact estimates across different model specifications and estimators.

We complement this survey evidence with administrative health-facility data on recorded malaria cases from the Indian Health Management Information System (HMIS). Over the 9-month period following the launch of the campaign, monthly malaria incidence in urban areas decreased by 6.2 cases per million people, which is 30% of the overall monthly malaria incidence rate in the pretreatment period. By contrast, and consistent with our survey data, incidence did not significantly change in rural settlements, where non-solid dwellings are substantially more common. Moreover, while the campaign had no impact on the number of tests conducted to diagnose malaria, the urban positivity rates (positive cases
Portugal Spain<br><!-- End of picture text -->

**Figure 15.c: Hiring and firing practices rank      Figure 15.d: Redundancy costs (weeks of salary)**

Source a: Fraser Institute, Economic Freedom of the World Database, b‐c‐d: World Economic Forum, Global Competitiveness Index. The redundancy cost indicator measures the cost of advance notice requirements, severance payments and penalties due when terminating a redundant worker, expressed in weeks of salary.

_<u>Changing labor market regulation.</u>_ The evolution of structural indicators shows that the overall labor market regulation ranking, which had deteriorated for GPS, showed improvements in Portugal and Spain. Hiring and firing practices have improved substantially since 2010 for all. In this regard, the evolution of severance pay merits some attention. Surprisingly, even before the 2008 crisis, GI cut their severance pay requirements dramatically‐ it was not until 2012 that PS reduced theirs. The period 2012‐ 2014 shows continuing adjustment. All four countries made labor market adjustment easier by allowing firms to fire workers at low cost. Combined with large declines in wages in Greece, these changes, unsurprisingly, had long‐lived effects on the economy. Employment losses have been large. Recent research by the IMF addresses the impact of structural reforms in different economic contexts (IMF July 2016). They contend that the degree of slack in the economy should influence the choice of policy; job protection reforms (making it easier to sack people) can promote employment and output in good times, but further unemployment and output loss in bad times. In addition, together with wage declines, such reforms during fiscal contractions can be self‐defeating by depressing demand and output (and raising debt ratios).

36
the old NDC target or a randomly selected carbon tax. Second, it compares a carbon tax policy with mandated emission constraints for China. Most existing studies for China either analyze the emission constraints or carbon tax; they do not compare them. Third, it examines the impacts of carbon tax more deeply by considering nine alternatives for recycling carbon tax revenues; whereas the existing studies for China consider only a few schemes. Fourth, it analyzes the criteria used to reallocate carbon tax revenues for a given purpose, particularly when the carbon tax revenue is recycled to cut corporate income taxes. No existing studies for China have gone this much deeper while designing a carbon tax. Fifth, the study uses the latest data (the social accounting matrix or SAM for year 2017); the existing studies use older SAMs.

The paper is organized as follows. Section 2 briefly describes the basic structure of the model and data used for model calibration. The policy scenarios are designed in Section 3. This section also offers detailed explanations of how these scenarios are incorporated into the model. Main results from model simulations are discussed in Section 4, followed by key conclusions and policy implications in Section 5.

# **2. The Model and Data**

## **2.1 Basic structure of the model**<sup>**4**</sup>

A recursive dynamic CGE model is developed to simulate the economic effects of the policies considered in this study. The model consists of four economic agents: producers, households, government, and international trade. It captures the interactions within and between the agents. The production sectors are classified into 27 sectors, including six sectors for electricity generation. Table 1 defines these sectors.

**Table 1 Sector description in the CGE model**

|**Sector**|**Description**|
|---|---|
|Agriculture|Agriculture, forestry, animal husbandryand fishery|
|Coal mining|Miningand washingof coal|
# **1 Introduction**

The quality of internationally traded goods is strongly associated with income per capita: within narrow product categories, richer countries tend to import and export higher quality goods, which tend to carry a higher price (Schott, 2004; Hummels and Klenow, 2005; Hallak, 2006; Hallak and Schott, 2011; Feenstra and Romalis, 2014). While this earlier literature does not establish a causal relationship between product quality and economic performance, more recent evidence based on firm-level data reveals that access to high quality inputs is an important driver of firm upgrading in developing countries (Verhoogen, 2023). For example, Bas and StraussKhan (2015) find that tariff reductions on inputs led non-processing firms in China to increase the prices they paid for inputs and to increase the prices and quality of their outputs.<sup>1</sup> Focusing on Colombia, Fieler, Eslava, and Xu (2018) uncover an amplification effect in firm upgrading: tariff reductions on inputs lead firms to upgrade the quality of outputs, which increases their demand for other high-quality inputs; this gives incentives for local suppliers to upgrade, which in turn gives final-good producers further incentives to upgrade.

Among the set of inputs that matter for productivity growth in developing countries, there is growing evidence that digital goods are critical. Early evidence by Commander, Harrison, and Menezes-Filho (2011) reveals positive effects of information and communication technologies (ICT) in the performance of Brazilian and Indian manufacturing firms, while more recent evidence by Hjort and Poulsen (2019) shows that the arrival of submarine internet cables in Africa raised skilled employment, and led to increased firm entry, productivity, and exporting, thereby lifting average incomes. Using firm-level data for 82 developing countries, Cusolito, Lederman, and Pena (2021) find large positive effects of digital technology adoption on firm productivity.<sup>2</sup> Since digital goods are mainly produced in advanced or large emerging economies such as China, the quality and
and 28 percent of them suffered economic losses.<sup>3</sup> Based on judicial data, fraud is one of the most frequent crimes, accounting for 32 percent of cybercrime that involves 46,000 fraudsters.<sup>4</sup> The previous literature, mostly in criminology, has examined the behavioral patterns of fraudsters (Levi, 2008). Behind the commonly encountered fraud phone calls and emails are well-trained fraudsters in criminal organizations with sophisticated technologies.<sup>5</sup>

> 2 <u>https://www.tisi.org/4714</u>

> 3 - <u>http://www.gov.cn/xinwen/2019 09/17/content_5430594.htm</u>

> 4 <u>https://www.secrss.com/articles/15226</u>

> 5 Levi (2008) classifies the common fraud schemes into 6 categories and 34 items according to the characteristics of fraud victims (such as financial-services-related, non-financial-services-related, individuallevel, national-level, and so on). Based on the intermediation and formation of schemes, the Federal Bureau of Investigation (FBI) has categorized common fraud into 24 different items, including common schemes such as credit card fraud, investment fraud, Nigerian letter fraud (also called 419), Ponzi schemes, and

2
results show that this is not the case. In a given month, workers in the payroll account group are 12.8 percentage points as likely to make a non-withdrawal transaction than workers in the account only treatment group (p-value=0.000). Workers in the payroll account condition also accumulate balances that are, on average, BDT 334 (approximately 7% of the median monthly wage) higher than those of workers in the account only group (p-value=0.000).

Overall, these patterns indicate that our main treatment encouraged active account use, and suggests that active engagement helps inexperienced consumers to not only become sufficiently comfortable with the technology to perform an increasing number of transactions but also to accumulate formal savings in their accounts.

**Learning.** We next turn to the effect of payroll accounts on learning. We measure learning using the two outcome variables described in Section 3.3, above. Our first learning outcomes is an indicator for whether a transaction was performed _outside the workplace_ , where assistance with the account was not readily available. We measure this outcome in administrative data, which records the precise geographical location of each transaction and allows us to identify whether a transaction took place at banking and mobile money facilities outside the study location.

Figure 4 shows the share of respondents that made at least one transaction at a bank or mobile money facility outside the study site at endline. The figure shows that, unconditionally, participants in the payroll accounts group who interacted with the account more frequently were 58 percentage points more likely to make a transaction in a setting without assistance than participants in the bank or mobile account only group.

Table 4, panel A reports the corresponding regression results and reports results for different types of outside transactions. We use the same panel and specification as in Table 3 for this exercise, so that treatment effects can be interpreted as the average post-treatment likelihood of making an outside transaction for workers in the payroll and account only treatment conditions.

Table 4, columns (1
that conflict intensity could increase substantially the paper invites a recognition of the fragility risks Afghanistan faces going forward.

# **2. Previous research**

This paper is part of a growing literature on empirical studies of conflict. Departing from a longer tradition with roots in political science and strategic analysis, this literature builds on disaggregated data and statistical tools. Until not long ago, the lack of reliable data tended to dissuade this kind of approach. Conducting censuses and surveys is challenging in conflict affected areas, while administrative records tend to be incomplete in highly informal environments. But these obstacles are gradually being overcome thanks to the availability of non-traditional sources of data, from military statistics to satellite imagery to mobile phone traffic.

Several studies in this emerging literature explore the dynamics of conflict. Biddle, Friedman, and Shapiro (2012) and Hultman, Kathman, and Shannon (2013) use panel data to assess the relationship between troop deployment and violence-related casualties at the subnational or regional level. Data are from Iraq and across countries respectively. Both studies find that large deployments are effective at reducing violence, even in the most challenging environments. The impact of large deployments, however, depends on the number of troops and their equipment, as well as on having a clear mandate to use force to prevent conflict.

The key role played by international troops is confirmed by other studies. Also using data across countries, Hegre, Hultman, and Nygard (2010) conclude that the size of peacekeeping operations—as measured by troop strength or budget—is the main determinant of the ability to effectively curb a conflict. In the cases of Iraq and the Philippines, Berman et al. (2011), Crost et al. (2014a), Berman et al. (2013), and Sexton (2015) find that violence, as perceived by households, declines with the presence of international troops.

The impact of international troops on conflict intensity typically varies spatially. The abovementioned study by Hegre, Hultman, and Nygard (2010
# Figure 4: Food Expenditure Across Countries

## (a) Food Budget Share

## (b) Food Engel Curve Slope

<!-- Start of picture text --> Core Sample β Core Sample<br>Extended Sample β Extended Sample<br>CG<br>CD TD PG M Z<br>MZ CD RW<br>ST<br>BI N E TZ SNKM M AC G SZ RS TD ST PG SZ CRBR<br>BF R BJW BO PM E E MX NE BF BJ KMCM MA TN ZA UY CL<br>CM TN EC BI BO DOCOME MX<br>CO ECPE<br>DO ZA CRUYBR SN<br>CL<br>TZ<br>RS<br>5 6 7 8 9 10 5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD Log GDP per capita, Constant 2010 USD<br>(c) Formal Food Budget Share (d) Formal Food Engel Curve Slope<br>Core Sample PG<br>MA SZ<br>BI CDNE MZ BF TD RWTZ BJ ST SNKM CMBO CG TN DOECCORSPEME CRUYMXBR<br>ZA<br>CL<br>SZ<br>ME<br>PG TN RS ZA CRUYMX<br>MZ ST MA CO BR CL<br>BI CD NE BF TD RWTZ BJ SNKMCMBO CG DOEC PE β Core Sample<br>5 6 7 8 9 10 5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD Log GDP per capita, Constant 2010 USD<br>5<br>100<br>0<br>80<br>-5<br>60<br>-10<br>40 -15<br>Food Budget Share<br>Food Engel Curve Slope -20<br>20<br>-25<br>0<br>5<br>100<br>0<
<!-- Start of picture text --> HB poverty line Jf poverty rate Jj poverty gap fj Gini J} Lorenz curve [ household survey<br>0.000200% Veen"<br>‘0.000160%<br>0.000140%|<br>0.000120%<br>‘0.000100%<br>‘0.000080%<br>‘0.000060%<br>‘0.000040% /<br>aa ee<br>o.0000008% Or<br>800 1820 1840 1860 1880 1900 1920 1840 1960 1960 2000<br><!-- End of picture text -->
data base and the methodological approach taken. This precedes a synoptic overview of the 12 wisdoms revisited and the core findings obtained when submitting them to the data. Section three expands on each of them, including their implications for agricultural and rural development policies. Section four concludes.

# 2 Myths, Materials, and Methods

The LSMS‐ISA Initiative<sup>6</sup> supports national statistical offices in the collection of at least four rounds of nationally representative household panel survey data in eight African countries during 2008–20. The papers in this study mainly draw on the first rounds collected during 2009‐2012 in six of these countries (Ethiopia, Malawi, Niger, Nigeria, Tanzania and Uganda), which together cover more than 40 percent of the population in SSA and most of its agro‐ecological zones. While this does not make them representative for SSA as such, together they provide a broad picture of the emerging new reality, and also allow for

> 4 The Initiative is financed by a grant from the Bill and Melinda Gates Foundation, together with other contributors, and managed by the Development Economics Data Group of the World Bank Group. Several larger data initiatives are also underway to remedy the agricultural data situation, such as the Global Strategy to Improve Agricultural and Rural Statistics, and the ensuing regional Action Plans.

> 5 Other participating institutions included the <mark>Alliance for a Green Revolution in Africa, Cornell University, the Food and Agriculture Organization, the London School of Economics, the Maastricht School of Management, the University of Pretoria, the University of Rome Tor Vergata, the University of Trento, and Yale University. F</mark> or a ‐ ‐ detailed description of the project and its collaborators, see http://www.worldbank.org/en/programs/africa <u>myths and‐facts.</u>

> 6 For a detailed description and access to the data and their documentation, see <u>http://www