rafmacalaba/data-use-mentions-extended huggingface dataset:
specific smaller IO tables in detail in Section 3.1. The next step is to calculate a set of indicators that summarize the GVC engagement of each country. We describe the four indicators that we focus on in Section 3.2.

One of the indicators that we calculate is the percent of domestic value added that is contained in the exports of each sub-Saharan African country. This indicator is one of the few instances where detailed information from sub-Saharan Africa appears in World Bank (2017), a highly influential volume containing recent research on the role of GVCs in development. Table 7.4, which is taken from IMF (2015), shows the complement, the percent of foreign value added in exports that is also calculated using the Eora data. While extremely interesting, it only shows the ratio for the aggregate economy. Given the dominance of the agriculture sector in most countries and the importance of domestic value added in agriculture everywhere, the aggregate pattern shown is unlikely to be representative for the comparable fraction in the manufacturing sector, which is our primary interest. We discuss both the aggregate fraction and the fraction for manufacturing in Section 3.5.

The other GVC indicators that measure the importance of foreign input sourcing and exporting of intermediate inputs or final products are also calculated separately for all sectors (in each country). They record interactions with eight different regions in the world, two of which are defined relative to the country of interest, and they are available for three years that are each ten years apart (1995, 2005, 2015). Together these are more indicators than can be easily described in this report. We therefore make all four indicators that are each defined at the level of (i) the African country of interest, (ii) eight domestic sectors, (iii) eight partner regions (including itself), and (iv) three years, available in a data file.

The overarching message from the analysis is that the large increase in international trade and the disintegration of production
# **III. KOREA’S ETI INTRODUCTION**

## **1. Key Drivers of ETI**

## **Strong ICT and E-government Development**

The introduction of ETI in Korea is intricately intertwined with advances in Korea’s ICT and e-government. After the government started to build the National Electronic Backbone Network<sup>27</sup> in early 1980s and the High Speed Information and Communication Network<sup>28</sup> in mid 1990s, Korea emerged as one of the world’s most advanced countries in ICT and e-government. According to the UN e-government survey, Korea’s e-government development index ranked 13<sup>th</sup> in 2003, 6<sup>th</sup> in 2008 and 1<sup>st</sup> in 2010, 2012 and 2014 (UN, 2014, 2012, 2010, 2008, & 2003). Korea’s ICT Development Index has ranked 1<sup>st</sup> since 2008 (International Telecommunication Union, 2013, 2012, & 2011). Household Internet access reached 98.1 percent in 2013.<sup>29</sup> It was the combination of strong ICT development, service enhancement through e-government, and the general population’s familiarity with ICT that ultimately provided the support needed for further e-government service and set the stage for the introduction of ETIs in Korea.

## **Korea’s Success with Electronic Tax Administration**

For tax administration, the NTS established in 1997 the Tax Integration System to collect, store, and analyze tax information from taxpayers and electronically connect the regional tax offices in support of centralized and integrated tax administration. After the Ministry of Finance and Economy<sup>30</sup> proposed amendments to the Framework Act on National Taxes to pave the way for electronic tax filing in 1999, the NTS set up Home Tax Service, an integrated web portal, in 2002 to provide e
</sup> can provide policymakers with useful information on relative cost effectiveness.

Beyond methodological considerations, data access and quality problems were also cited as significant barriers. Evaluators frequently cite difficulty in extracting reliable expense information—even from administrative data—to reflect the financial cost considerations most relevant to policymakers. Though hardly limited to the World Bank, low levels of baseline financial data collection at the World Bank impedes the ability to conduct CBA at either the start or the end of projects (IEG 2010). Economic costs are even more challenging. Data on opportunity costs are rare, notwithstanding their centrality to economic analysis. Even more challenging than these considerations of basic cost data are the important issues of apportioning Cost (and often benefit) components when an evaluated project is part of a larger multi-arm effort with multiple outcomes and benefits.

These data and methodological challenges reflect an apparent lack of up-front planning to do VFM types of analysis. While the measurement of benefits is now approached in impact evaluations with careful planning well before implementation begins, it is unusual for Cost

> 10 The second paper in the VFM Component 2 series is a case study that pioneered a tool to address many of these concerns while generating locally relevant, user-informed valuations of the environmental effects of World Bank projects across the globe.

> 11 http://www.povertyactionlab.org/policy-lessons/education/student-participation.

> 12 http://www.wsipp.wa.gov/BenefitCost.

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outstanding guarantee volume of the European Association of Guarantee Institutions’ (AECM) members as of end-2020. We then link CGSs’ guarantee portfolio data to a set of sectorally and spatially disaggregated transition and physical risk vulnerability indicators, which allows us to identify the portfolio shares at risk per indicator. We also identify sectors that are most relevant for CGSs to focus on by creating a transition risk score which combines exposure with GHG intensity. Finally, we conduct a stylized stress test to assess potential financial losses that CGSs could incur under adverse climate-related scenarios. To our knowledge, we are the first to investigate the vulnerability of CGSs to climate-related financial risks.

**Results show that European CGSs’ vulnerability to climate-related financial risks is non negligible.** We estimate that about one-third of CGSs’ guarantee portfolios is towards sectors that have elevated exposure to a disorderly energy transition. This is partly mitigated by the finding that most of the exposures are towards sectors that are in the 30<sup>th</sup> -60<sup>th</sup> percentile of GHG intensity. Combining guarantee exposures with GHG emission data, we also find a relatively high degree of portfolio concentration, with 72 percent of the transition risk for CGSs driven by five sectors. European CGSs are also exposed to a broad range of climate-related physical risks, especially wildfire (56 percent aggregate CGSs’ portfolios in our sample), coastal flood (41 percent), and river flood (6 percent). Within CGSs’ portfolios, we estimate that 24-31 percent of guarantees are towards sectors that have elevated exposure to climate change and weather

3
most Malagasy children were passing the majority of the questions in the gross motor module, and so that module was cut from the version used in Madagascar for this study, which therefore assesses children for fine motor skills, language learning, and social skills.

Note that the MDAT is only used for one child per household within the age range that the MDAT is designed for. If there was more than one eligible child in the household, one of the eligible children was chosen at random. As such, the sample size for the MDAT and its component indices is smaller than the total number of households surveyed since not all households have children in the appropriate age range, resulting in a sample of 2,757 for the child development outcomes.

# **4.3: Key Variables**

Key outcome variables are defined as below. As discussed earlier in the theory of change, we broadly classify our outcome variables into Behaviors, Proximate Outcomes, and Long-Term Outcomes. The list below reflects this classification.

- **Behaviors:** These are variables measuring various parenting or nutrition ‘actions’ that caregivers may take. Positive Parenting Behavior: This variable captures how many of three positive parenting behaviors (following up on the education of the child, checking up on the child’s health, and playing with children) the parent reports engaging in over the preceding twelve months. Thus, it ranges in value between 0 and 3.

Interaction With Children: This variable captures how many of each of 6 parenting behaviors(read a book, told stories, sang songs, went for a walk, played with, taught to count or draw) the parent reports engaging in with their two youngest children over the preceding 3 months. Each of these behaviors is coded as a 0-1 binary, so that this variable ranges in value between 0 and 6.

Preparing Diverse Meals: A binary variable taking the value 1 if the household reports having usually prepared ‘diverse meals’ including fruit over the preceding 12 months,
# **Figure 9 Examples of Countries With Inferior fit in the Distribution Tails**

<!-- Start of picture text --> A) Argentina 2019 B) Panama 2019<br>4 12<br>2 10<br>8<br>0<br>-2 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 6<br>4<br>-4<br>2<br>-6<br>0<br>-8 -2 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91<br>-10 -4<br>-12 -6<br>Lower bound Upper bound Upper bound Lower bound<br>Actual growth Old Model Actual growth Old Model<br>New - Rescaling New - No Rescaling New - Rescaling New - No Rescaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

_Scale and flexibility factors_ Table 3 also shows cases where the percentage of percentiles that fall inside the confidence interval is very small or even zero. The GICs for labor and total income are presented for Chile 2017 and Uruguay 2019 in Figure 10. In the case of Chile 2017, the GICs show a scale factor indicating that the estimated income growth falls short when compared to the actual growth at each distribution percentile. For Uruguay 2019, the GICs show that there are some parts of the income distribution where more flexibility is necessary, and they do not necessarily correspond to the tails of the distribution (percentiles 13-20 and 71-88). In both cases, the GICs for only labor income indicate that the _new model without rescaling_ estimates an income growth rate closer to the actual growth than the other models. This way, results suggest that the differences in levels are mainly due to the non-labor income, where more flexibility might contribute to better estimating the changes in the total income distribution
<!-- Start of picture text --> Figure 1: The Growth of Trade Agreements and<br>Labor Provisions<br>0.16<br>0.14<br>0.12<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>Labor Provisions Agreements<br><!-- End of picture text -->

**Notes:** Figure shows the coverage of total country pairs that are engaged in a trade agreement in the Handbook of Deep Trade Agreements data that also have data from the CEPII gravity model dataset as shown in Figure 2.

pg. 22
poverty line (in 2019, R$ 178, USD 42.7), and the amount depended on the number of children or pregnant women in the family. In 2019, the program paid R$ 41 (USD 9.8) per month per child or pregnant woman, with a maximum of five variable benefits per family. The variable benefit was linked to education and health conditions (see Table 1 for an overview of the rules).

Table 1: Bolsa Familia rules as of 2019

|Extreme poor families (monthly p.c. in-<br>come below 89 R$/21.4 USD)|Poor families (monthly p.c. income below<br>178 R$/42.7 USD)|
|---|---|
|• Basic beneft (89 R$/21.4 USD per<br>family)<br>• Variable beneft (41 R$/9.8 USD<br>per child or pregnant women, max.<br>5 variable benefts per family)|• Variable beneft (41 R$/9.8 USD<br>per child or pregnant women, max.<br>5 variable benefts per family)|
|• Top-up beneft after all other ben-<br>efts (top-up to reach 89 R$/21.4<br>USD p.c. income)||

**Notes:** The table describes the rules of Bolsa Familia as of 2019.

BF was not – and still is not – a constitutional right but a budgetary program, meaning that not everyone who was eligible for the program during our study period necessarily received it; the program had a waiting list. Each municipality registered vulnerable families and was allocated a budget based on poverty estimates from the Brazilian Institute of Statistics (Instituto Brasileiro de Geografia e Estat ́ıstica – IBGE). The process of including new beneficiaries from the waiting list as spots
# **_Table 1. Modeling the Poverty and Distributional Impacts of Macroeconomic Shocks and Policies_**

|**Models/Approaches**|**Macroeconomi**<br>**Model**|**c**<br>**Linkage Variables**|**Specification of**<br>**Distribution**<br>|
|---|---|---|---|
||||**(Micro-Model)**|
|**A. Distributional Focus (No E**<br>**Lorenz Curve Approach**|**xplicit Macro-M**|**odel)**<br>||
|_1. POVCAL_|• Implicit|• Mean consumption/income of<br>the distribution|• Parameterized<br>Lorenz function|
|_2. SimSIP Poverty_|• Implicit|• Mean consumption/income of<br>the distribution|• Parameterized<br>Lorenz function|
|**Unit Record Approach**||||
|_1. PovStat_|• Implicit|• Per capita household<br>consumption<br>• Sector of employment of the<br>household head<br>• Growth rate of per capita output<br>in the sector of employment|• Unit record data|
|_2. The Envelope Model_<br>_(Microsimulation-1)_|• Implicit|• Changes in supply prices for<br>household production activities<br>• Changes in demand prices<br>• Changes in incomes|• Unit record data|
|_3. Household Income and_<br>_Occupational Choice Models_<br>_(Microsimulation-2)_|• Implicit|• Changes in commodity prices<br>• Changes in incomes (wages and<br>self-employment income)<br>• Changes in employment by type<br>f tin|• Unit record data|
.007)||(0.008)|
|Government debt (% GDP)||-0.002||-0.003|
|||(0.003)||(0.003)|
|Private debt (% GDP)||-0.002||-0.003|
|||(0.002)||(0.002)|
|Oxford fiscal support (% of GDP)|||-0.003|0.806|
||||(0.603)|(0.696)|
|Oxford government response stringency index|||0.246<br>(0.528)|0.169<br>(0.540)|
|Constant|-6.626***|-7.877***|-6.759***|-8.104***|
||(1.059)|(1.219)|(1.144)|(1.352)|
|Observations (countries)|113|105|102|94|
|Pseudo R<sup>2</sup>|0.446|0.474|0.435|0.465|

Note: The sample consists of EMDEs countries only (i.e., upper-middle, lower-middle and low-income countries). The dependent variable is the number of financial sector policy measures taken (FPRAI). Robust standard errors. *, **, and *** represent statistical significance at 10%, 5%, and 1% two-tailed level, respectively.

In ancillary regressions, we explore the role of additional banking sector characteristics described in Section 3.2. As noted, the sample size decreases substantially, so the analysis should be interpreted with care. The results, reported in Appendix C, Table C2, show that economic development and population remain positively and
presents a challenge due to the limited set of information collected in SAKERNAS. Yet this is an important task as exit and exclusion can lead to very different policy responses.

A number of studies have also alluded to the heterogeneous nature of informality in Indonesia. In other words, elements of exit and exclusion may apply to firms and workers to varying degrees. Empirically, very little is known regarding the nature of this heterogeneity. This may be partly due to difficulties in operationalizing this concept. While singular measures such as income or output provide a useful starting point, future studies will have to think more deeply about how this concept can be captured more effectively and holistically. Once heterogeneity has been established, the next empirical task would be to differentiate between firms and workers for whom informality is a voluntary choice versus those for whom it is a last resort. This would require us to go beyond comparisons based on workers’ employment status (i.e., self-employed informal workers vs. salaried informal workers) – an area that has already received considerable attention in the literature. Instead, more work will be needed to understand to what degree exit and exclusion apply to different socio-demographic groups (e.g., gender, age, skills or educational attainment); by industry or sector; by geographic location (e.g., urban vs. rural); or even by the level of economic development or poverty within a region.

More broadly, previous studies have relied heavily on binary categorizations of informality wherein firms and workers are either considered formal or informal. However, some scholars have suggested that informality is better represented as a continuum. This theme was also touched upon by Rothenburg and co-authors (2016), when they observed incomplete formalization among micro and small enterprises. Future work can thus examine whether this characterization of informality is accurate and identify where firms fall short in the process of formalization. In doing so, policies can be designed to address the specific barriers that prevent firms from completing the registration process.

Indeed
here because they are for economic infrastructure (high A projects) and, increasingly, for social infrastructure (high H projects).

A recent empirical study of 9,121 PPPs in 107 developing countries from 1997 to 2017 shows how the size of these investment projects is associated with contract features, the institutional and enabling environment in a country, and MDB involvement in the transaction/partnership (Fleta-Asín and Munoz, 2023). Specifically, regarding contract features, investment volumes are higher when the private partner takes on more risk and greater responsibilities, and when the contract is awarded through a competitive method. Regarding the financial instruments themselves, guarantees and loans are associated with larger PPP sizes. In terms of the enabling environment, open, business-friendly economies where property rights are well-established (and enforced) attract larger PPP investment volumes. A better institutional environment, as reflected in control of corruption, political stability, regulatory quality, and wellestablished rule of law, is also associated with larger PPP investment sizes. Finally, MDB involvement is associated with larger PPP investments.

While significant associations from such a comprehensive database of PPPs are instructive, they cannot be used to predict outcomes from transplanting preferred contractual features and financial instruments to less suitable institutional/enabling environments because those associations are driven by non-random selection into better environments. While, to our knowledge, no sound counter-factual analysis of the drivers of PPP investment volumes has yet been undertaken, Fleta Asín and Munoz (2023) find that MDB support is more strongly associated with PPP investment size in weaker economic and institutional environments. Given the large number of interactions between MDB involvement and other explanatory variables in the models in that paper (see, e.g., Fleta Asín and Munoz, 2023, Tables 6-8), it is difficult for us 21
coverage for China is comparable to that of Orbis; Worldscope has information for 2,950 firms. However, for India, the coverage is more limited with only 2,592 firms included in the database, most of which with information available only from 2005 onward. A comparison of the sample of Indian firms in Orbis and Worldscope suggests that the Worldscope sample is biased toward larger firms. Hence, we need to take this fact into account when interpreting the results on capital expenditures for India.<sup>18</sup>

# **3. Capital market development and firm financing**

Since the 1990s, China and India have undertaken significant efforts to expand the scope and depth of their financial systems, including the development of their capital markets. Both countries have also relaxed the rules and regulations for companies to raise capital in equity and bond markets and for domestic and foreign investors to invest in capital markets. Given this background, we next examine the evolution of commonly used aggregate indicators of financial sector development. We then analyze the extent to which the development of the financial system has implied a more widespread use of capital markets as a source of financing for corporations.

18 Although survivorship bias could arise in different ways, we believe that it is not affecting our conclusions. This bias could appear if, for example, risky firms that raise capital de-list or die and are not covered in our sample. Then, we would tend to overestimate the effects of the capital raising activity because we would not observe their poor performance. However, it could be the case that more established, blue-chip firms are the ones that raise capital through equity and bonds. Not observing firms that de-list or die (which to do not tend to raise capital in this instance) would lead to an overestimation of the performance of the control group, and we would thus tend to underestimate the difference between the treated and the control group. Ex-ante, it is difficult to know whether and how survivorship bias might affect our results.
To quantitatively decompose the impact of the withholding rate increase into the default eect, the salience of enforcement eect, and a possible reduction in under-payment, we are currently awaiting a new payment dataset that contains payments by all taxpayers, including the large rms paying through a special online platform.

# 7 Conclusion

Although withholding schemes for rms are widespread in low income countries, and in low compliance sectors in high income countries, they have been largely ignored by the public nance literature. This paper proposes an explanation for the attractiveness of withholding schemes. In a simple Allingham-Sandmo model with third-party reporting, withholding can create a compliance default if reclaiming the tax withheld is costly, and can enhance the salience of tax enforcement.

Exploiting a unique ten-year panel of tax declarations and third-party information and withholding reports for the universe of rms in Costa Rica, we estimate compliance gaps on the extensive, intensive and payment margin. The estimates suggest that compliance is satisfactory on third-party reported margins, and expanding the coverage of information reporting leads to substantial increases in reported taxable income. An event study nds that rms increase their reported taxable income by 20-50% when being information-reported for the rst time by a trading partner. Withholding further enhances compliance. A doubling in the sales tax withholding rate increases tax payments by 33% among rms subject to withholding and by 8% in aggregate. This eect is driven by incomplete reclaim of the tax withheld, and reduced misreporting. We nd no evidence that withholding reduces output, which suggests that the liquidity costs associated with the regime are neglegible. As withholding has the largest impact on small rms, analyzing the distributional consequences of withholding is an important avenue for future research.

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