rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure 7 Impact of taxes on progressivity**

A. Scatterplot of tax revenue on the share of direct taxes in total taxes B. Scatterplot of direct tax share on prosperity gap <!-- Start of picture text --> Percent of total tax revenue Factor<br>80 7<br>70 6<br>5<br>60<br>4<br>50<br>3<br>40<br>2<br>30 1<br>20 0<br>0 10 20 30 40 50 20 40 60 80<br>Tax to GDP Direct tax (percent of total tax revenue)<br><!-- End of picture text -->

- C. Predicted prosperity gap and progressivity

<!-- Start of picture text --> 4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>10 20 30 40 50 60 70 80 90<br>Direct tax (percent of total tax)<br><!-- End of picture text --> _Sources:_ International Monetary Fund; Authors’ calculations _Note:_ A.B. Binscatter with 50 bins where observations along the vertical and horizontal axis are partitioned and then binned into one single point.

# **6.3 Government spending volatility and uncertainty**

economy or contribute to higher growth volatility. Government spending may be more volatile in countries with lower tax revenue. These economies may also have less opportunity to borrow undermining their ability to stabilize the business cycle and implement effective countercyclical policy.

The literature on economic and policy volatility and growth presents a compelling case for the negative impact of volatility on economic growth. Ramey and Ramey (1995) demonstrated through data from 92 countries that higher
# **The Causal Mechanism of Financial Education:**

# **Evidence from Mediation Analysis**

Fenella Carpena and Bilal Zia<sup>1</sup> _Keywords:_ Financial Education, Financial Literacy, Financial Knowledge, Causal Mediation Analysis, Mechanism of Impact, Impact Evaluation _JEL Codes:_ C93, D14, G21, O12 1 Oslo Business School, Oslo Metropolitan University (fenella.carpena@oslomet.no) and the World Bank (bzia@worldbank.org), respectively. We are very grateful to Saath Microfinance for their constant support. We thank Stuti Tripathi, Bhakti Shah, and the Center for Microfinance at the Institute for Financial Management and Research for excellent field work and research assistance. This study is part of a broader research project in India which the authors undertook in collaboration with Shawn Cole and Jeremy Shapiro, to whom we are grateful. We are also grateful to Simon Galle, Xavier Giné, Arie Kapteyn, Annamaria Lusardi, and David McKenzie for many helpful comments and suggestions. Earlier versions of this paper benefitted from coverage in the World Bank’s Development Impact and All About Finance blogs. Research funding from the World Bank Research Department is gratefully acknowledged. The study is registered in the AEA RCT Registry, ID # AEARCTR-0000173.
insurance offers very limited protection.

**Corresponding author and contact details:** Magnus Lindelow, World Bank, 1818 H Street NW, Washington, D.C. 20433, USA.  Tel. (202) 458-0125. Email: <u>mlindelow@worldbank.org</u>

**Keywords:** Health insurance; risk; China.

World Bank Policy Research Working Paper 3740, October 2005 The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org.

**Acknowledgements:** Our thanks to the Carolina Population Center at the University of North Carolina at Chapel Hill, the National Institute of Nutrition and Food Safety, and the Chinese Center for Disease Control and Prevention for making the China Health and Nutrition Survey available. Agbessi Amouzou helped preparing the data. We are grateful to Ren Mu for providing us with disaggregated income data.
studies typically face. The data set consists of about 12,000 firms in 27 developing countries.<sup>1</sup> (iii) This study is also able to account for firm- and country-specific time invariant omitted variables using data available for a panel of firms for a subset of countries in the sample. Panel data are available for 664 firms across a subset of 12 Eastern European economies.

An important issue raised in the literature is the choice of control variables in crime estimations (Moody and Marvell, 2010). We are also concerned about several omitted variables, including institutions or the presence of democratic governments that may bias our variable of interest – the size of the police force. We address this using a sensitivity analysis that essentially checks the robustness of our empirical specification. We find that our results are unaffected by the inclusion of several additional institutional and other control variables. Our results are also robust to various other sensitivity checks.

Section 2 describes the data, section 3 provides the estimation and results, and sections 4, 5, 6 and 7 provide instrumental variable estimations, panel data estimations, robustness checks, and conclusions, respectively.

# **2 Data and Main Variables**

The data for firm-level variables are collected by the World Bank’s Enterprise Surveys. The Enterprise Surveys (ES) use standard survey instruments to collect firm-level data on a country's business environment from business owners and top managers. The surveys cover a broad range of topics including access to finance, corruption, infrastructure, crime, competition, labor,

> 1 Note that although Enterprise Surveys covers a wide range of economies, it is the combination of police data, and the selection of country surveys that follow a consistent sampling methodology that restricts the same to 27 countries. Details are provided in the data section.

7
users, often beyond a given community, requires that, in addition to clearly defined property rights, reliable information on such rights is broadly available at low cost. To allow this, public registries normally make complete, current, and authoritative information on property rights assignment available to interested users. If imperfections in markets for credit and insurance are not too severe and non-agricultural opportunities are available, this can facilitate transfers to bring land to its most productive use and spur development of a financial system.

The conceptual basis for credit-effects of land titling is that immobility and indestructibility make land ideal collateral but that it is very costly for banks to determine whether plots offered as collateral can be accepted.<sup>1</sup> A reliable and comprehensive central registry dramatically reduces the cost of such enquiry, making it possible to exchange land in impersonal markets and use it to secure loans. If lack of reliable information on land ownership precludes access to credit for large segments of land owners but land

> 1 In general, this would require physical inspection and inquiry with neighbors to ensure that there are no other ownership claims and some form of a registry to ensure that the land has not already been pledged as a security for other transactions.

5
Common principles for processing of personal data include the need to have a legitimate reason for any processing activity, usually obtained through consent of the data subject. Alternatives to consent address contractual or legal obligations the firm may be subject to as well as the interests of the data subject or the public. The majority of the regulations analyzed feature elaborate frameworks in this regard. An array of legal bases for data collection and processing gives processors more freedom under certain circumstances. Additionally, all the regulations reviewed required that personal data be stored in a manner that allows for the data subject’s exercise of the right to access the information and to request its correction or deletion if

**<mark>Table 9. Adoption of domestic regulation on data governance</mark>**

||**None**|**Partial**|**Full***|**Comments**|
|---|---|---|---|---|
|**Albania**|||●||
|**Armenia**|||●||
|**Bangladesh**|●||||
|**Burkina Faso**|||●||
|**Canada**|||●||
|**Colombia**|||●||
|**France**|||●||
|**Honduras**||●||The Constitution grants right to<br>access and deletion|
|**Indonesia**|||●||
|**Kazakhstan**|||●||
|**Kenya**||●||Access to Information Act grants<br>right to access and deletion|
|**Korea, Rep.**|||●||
|**Kyrgyzstan**|||●||
|**Malaysia*
**Figure 11. Uganda’s railways, like those of the rest of East Africa, are lightly used, 2005**

<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>Source: AICD railways database 2009.<br>Million Traffic Units per route km<br>Sudan, SR Tanzania, TRC Tanzania, TAZARA East Africa Kenya, KRC Uganda, URC Central Africa West Africa Southern Africa North Africa<br><!-- End of picture text --> _Uganda’s railways recorded relatively high traffic by East African standards, though low in absolute terms._ (figure 11). Uganda’s railway has been recording 0.8 million traffic units annually, above the East African regional average of 0.6 million, and well ahead of rail networks in Sudan and Tanzania. Nevertheless, traffic flows on East African railways are low relative to other regions of Africa, which are themselves lightly used by global standards..

_Poor operational performance has made Uganda’s railways uncompetitive relative to roads._ Uganda’s railways have recorded low labor productivity, locomotive productivity, and wagon productivity (table 5). The recent concession of the railways of both Kenya and Uganda did _Source:_ AICD railways database 2009. not lead to immediate improvements in the performance of the system, with efficiency impeded by obsolete equipment, lack of adequate maintenance, and lack of spare parts.

_Competition from the road sector has further reduced the usage of Uganda’s railways_ . The lack of a strong marketing effort in the face of competition from the road sector has contributed to declining rail freight volumes. This was compounded by the liberalization of coffee transportation in 1990, removing the government mandate that the industry use rail transportation (World Bank 2005).

**Table 5. Performance of Ugandan railways, 2005**

||Labor<br>productivity|
relating to both parents born in Ghana, teacher experience (and occasionally teacher education level) and larger enrolment in 8<sup>th</sup> grade. In mathematics, the most important contributors are generally the same, along with a score increasing effect of the coefficients associated with more homework for students below the median.

# **5. Conclusion**

This paper brings to light certain factors underlying the substantial over-time performance improvement in mathematics and science in Ghana. Performance improved more for better performing students in mathematics, while the opposite was the case for science where students at the bottom of the score distribution experienced a spectacular increase in science scores. More specifically, inequality in scores decreased over time, mostly in science.<sup>7</sup> Most of the increase in scores is accounted by changes in coefficients (and a smaller part by improvements in characteristics). Within the coefficients component, measured or unmeasured characteristics not accounted for (and therefore captured by changes in the constant) dominate this effect. However, for students below the median the effect associated with school and student characteristics is substantial.

One could speculate that among the possible unaccounted characteristics, a school being private vs. public could be of importance in accounting for the over-time increase in scores. This is because a substantial proportion of grade 8 students attended private schools in 2007, private schools are generally perceived to produce better performing students, and most importantly, over the short period between the two TIMSS surveys there was a large increase in the number of private schools in Ghana (by 36% only between 2005/6 and 2007/8). Evidence is also given suggesting that the structure of the gap related to large vs. small communities changes drastically; characteristics improved substantially for pupils in schools in large communities relative to smaller communities.

> 7 On the other hand, standard inequality measures suggest a light decline in inequality in scores in mathematics as well.

31
Africa Region, Washington, DC.

- ———. 2010a. ―Sudan Poverty Assessment. Part 1: Poverty Profile of the Northern States.‖ Draft version, Poverty Reduction and Economic Management Unit, Africa Region, Washington, DC.

- ———. 2010b. ―Sudan Poverty Assessment. Part 1: Poverty Profile of the Southern States.‖ Draft version, Poverty Reduction and Economic Management Unit, Africa Region, World Bank, Washington, DC.

- ———. 2010c. ―Sudan: Issues in Urban Development: Phase 1—Overview of the Urban Landscape.‖ Urban and Water Unit / Sustainable Development Department, Africa Region, World Bank, Washington, DC.

- ———. 2011. ―Energy Sector Strategy Note for South Sudan.‖ Draft note, World Bank, Washington, DC.

- Yoshino, Yuataka. 2010. ―Presentation of Key Findings from the Trade Facilitation Assessment Field Work: Case of Southern Sudan.‖ Draft presentation, Juba, Sudan.

53
small for the dependent and control variables. There are instead noticeable differences for the two Gini, GDP per capita and income between sample ‘1’ and sample ‘2’. This is due to the fact that some countries and years are lost with the smaller sample and this has an impact on the mean of key variables, particularly those variables estimated at the country/year level. For the discussion that follows, it is important to keep in mind that sample ‘2’ is a sub-sample of sample ‘1’ and represents a sub-set of countries and years. Table A2 in the annex provides details on the samples considered in this paper.

We start by estimating the full model as described in equation [1] and including robust and regional clusters and country and year fixed effects. A robust estimator allows to relax the assumption that regressors and error term are identically distributed whereas the regional cluster option let us relax the assumption that individual observations within regions are independent. Country and year dummies control for country heterogeneity and time dependence. This is what we could call a standard approach when working with a pooled sample of world citizens. It is also the approach followed by Alesina et al. (2004) that we said we use as a benchmark for our tests. The exercise is repeated for the Gini WVS and Gini WIDER and for the two samples considered.<sup>15</sup> Results are shown in Table 1. The coefficients for both Gini are negative and significant in both samples indicating that higher income inequality is associated with lower life satisfaction. The fact that this result is consistent across different Gini and different samples shows that results are robust. Sign and significance concord whether we use the Gini WVS (constructed with mid-class values from the ten-steps income variable contained in the World and European values surveys) or the Gini WIDER (imported from the UNU-WIDER database). They also concord if we use the Gini WIDER constructed with different income measures or the Gini WIDER constructed only with disposable income and they
-|---|
|Total Average Monthly Profits|17,351|
|Generalized self-efficacy index|26,033|
|Error management Score|28,663|
|Entrepreneurial identity index|25,359|
|Career Fit Confidence: More Committed to staying in sector|25,527|
|Career Fit Confidence: Good relationships in sector|26,431|

The number of employees includes all workers who regularly work in the enterprise, including full-time, part-time and family members. The average monthly profits are self-reported and asked directly to the respondent i.e. not calculated from revenues minus expenses. Generalized self-efficacy index refers to one’s own belief in their own competences (based on Schwarzer, R., & Jerusalem, M. 1995). Error management score captures behavior and attitudes individuals display when confronted with errors and mistakes (based

> 12 The International Finance Corporation (IFC) defines a woman-owned enterprise as either: (a) 51.0 percent ownership/stake held by a woman/ women; or (b) 20.0 percent ownership/stake held by a woman/women AND 1 woman as CEO/COO (President/ VicePresident) as well as 30.0 percent of the board of directors being women where a board exists. The definition of a femaleowned/male-owned firm used in this paper is comparable to the one used by the IFC.

> 13 For those cases in which firms have multiple owners, they could be mostly men, mostly women or have the same number of women as men. Taking this into account, we identify two main definitions for determining a male-owned and female-owned firm. In the first definition, when the respondent reported they were an owner we assign the firm as male-owned if the respondent reported that most of the owners were men and we assign the firm as female-owned if the respondent reported most of the owners were women, we follow the same assignment for when the respondent’s role is the manager. In the second definition
ISR TUR RUS NOR IDN AUS CHN JPN BRA COL IND ARG SAU USA<br><!-- End of picture text --> Source: Own computations. Data: OECD TiE. The country sample covers 38 OECD countries plus 13 non-OECD countries. Note: Employment in exports can be decomposed into its (1) direct contribution in the exporting sector, (2) indirect contribution in sectors supplying to the export sector, and (3) contribution of reimported inputs.

Linkages to supplying sectors have been traditionally high for manufacturing exports, exceeding 62 percent of jobs in exports on average in 2018 (Figure 15). These can include agricultural inputs used in the food and beverages export sectors (such as fruits and vegetables), agricultural inputs used in apparel and shoe exports (such as cotton or hides and skins), but importantly also business services used in all of manufacturing exports (ranging from research and development, to design, to transport and logistics, to marketing services) – which all create jobs. In other words, the high export-employment elasticities found in manufacturing (section 2) underestimated their potential to create jobs in other sectors. Similarly, mining exports are known to create jobs in other sectors, in particular supporting services industries.<sup>19</sup> Interestingly, the share of indirect jobs linked to mining exports has expanded from 46 to 64 percent between 1995 and 2018, or that of direct jobs declined from 54 to 36 percent. This may be linked to

> 18 While the computation of jobs sustained by domestic demand would require international input-output data, this is a reasonable estimate.

> 19 Estimates of job multipliers in non-mining sectors for Sweden’s mining sector range from 0.2 to 0.5 percent, i.e., one mining job supports between 0.2 to 0.5 jobs in other sectors. It is highest in services including retail trade, transport, and hotels and restaurants (Moritz et al. 2017).

17
disruption in supply chains in many industries and the implementation of social distancing policies that reduced mobility and promoted remote work.<sup>5</sup> <mark>In this section we obtain estimates of employment elasticities for the agriculture, industry, and services sectors using different model specifications. Using these elasticities, we project the level of employment for 2020 and compare them against actual employment data. We cover 15 countries in four regions. The sample includes Albania, Bulgaria, North Macedonia, Romania, and Türkiye from the Europe and Central Asia region; Brazil, Colombia, Costa Rica, Ecuador, Mexico, and Peru from Latin America and the Caribbean; Indonesia, the Philippines, and Vietnam from East Asia and the Pacific, and South Africa from Sub-Saharan Africa. These countries were selected based on the availability of post-crisis employment data from Labor Force Surveys or other official</mark> sources.

<mark>To calculate employment elasticities, we use data on sectoral employment and sectoral GDP levels for the period 2010 to 2018</mark><sup>6</sup> <mark>from ILO and World Development Indicators, respectively. To project the 2020 employment level, we use the 2019 employment level from ILO and the 2020 estimated GDP change from the World Bank’s Macro Poverty Outlooks. Finally, we compare our employment projections with actual data on 2020 employment levels from National Statistical</mark> Offices.

# **2.1 Employment elasticity models and results**

<mark>We compare estimated elasticities from six different estimation methods. These models are estimated for each country separately. First, we calculate sectoral</mark> _<mark>average elasticities</mark>_ <mark>. The average sectoral elasticities are defined as the average among annual elasticities obtained as the ratio between the percentage change of employment and the percentage change of GDP between consecutive years. Second, we calculate sectoral</mark> _<mark>average elasticities without outliers</
unavailable for other countries, occupation-level scores for the United States can be multiplied with the share of each occupation by industry, region, gender or income group elsewhere.<sup>2</sup> It is also possible to construct a composite index of F2F interactions and HBW, but this entails transforming one of the two measures because the former is continuous and the latter binary.<sup>3</sup> The four tasks that underlie the F2F interactions index with consumers are drawn from the list of “work activities” in the O*NET database. There are potential alternatives, such as “contact with others (face-to-face, by telephone, or otherwise)”, “face-to-face discussions” and “physical proximity”, which derive from the list of “work contexts”. Several authors, including Blinder (2009), caution against their use because “work contexts” are very general, mix many elements, and provide “mechanical” indices. As a result, they are more prone to endogeneity and can fail to capture the relationship between F2F interactions and the quality of remote work.

“Work activities” used in this paper capture the fundamental reasons as to why F2F interactions may be important, e.g. in order to influence others. Hence, in these cases, we know why and for what F2F interactions are necessary, giving a good measure of the implications of remote delivery on the quality of work. Conversely, “work contexts” focus on outcomes rather than fundamental inputs. The risk of endogeneity here is that as firms adjust to the crisis, the outcomes are more likely to change than the inputs. For example, a worker may typically have lots of F2F discussions in normal times, but a firm may invest in ICT due to COVID-19, thereby reducing that outcome. However, the fundamental task (input) remains unchanged by the crisis, such as the need for salespeople to be able to sell and influence others.

Beyond this general caveat, there are specific