rafmacalaba/data-use-mentions-extended huggingface dataset:
done assuming that Social Security contributions are fully paid by employees, personal income tax by income recipient and VAT/excise taxes by the consumer. No assumptions about tax evasion are adopted. In addition, administrative and capital expenditures are included in the redistribution of in-kind transfers (i.e., public education and health) (see Table 2). Expenditures in education and health are scaled-down to make administrative data more commensurate to survey data (more on this below).

# **<mark>Assumptions</mark>**

The analysis of the distributional impact of taxes and transfers considers the different income concepts and how taxes and transfers affect these concepts.<sup>13</sup> There are five primary income concepts used to reflect income before or after transfers and taxes: Market Income, Net Market Income, Disposable Income, Consumable Income, and Final Income. A succinct definition of each of these concepts and the main taxes and transfers included in this study is summarized in Table 3.

Market Income is constructed as the summation of grossed-up wage income and other net market incomes. The HSES includes information on net wages, therefore, these wages are grossed-up by adding back in an estimated SSC employee contribution as well as personal taxes paid on wages, non-farm income, and rental incomes. A flat income tax of 10% is applied to all these sources of income and a 10 percent Social Security Contribution (capped at MNT 192,000 per month).

13 This section owes to the methods developed by (Lustig & Higgins, 2013) and (Lustig N. , 2017). MONGOLIA Distributional impact of taxes and transfers / May 2018 7
, see Galiani & Weinschelbaum (2012) and Ulyssea (2018). For an explanation of the distinction between informal employment and informal sector, see Chapter 25 in “Informal aspects of the economy” in _System of National Accounts 2008_ (United Nations Department of Economic and Social Affairs, 2010) and Chapter 8 “Employment in the Informal Economy” in Key Indicators of the Labour Market 2015 (ILO, Geneva, 2016).

17
- Provide certainty in the market by ensuring a transparent and predictable insolvency law, while at the same time maintaining the flexibility required to allow viable companies to reorganize;

- Maximize the value of assets and preserve the insolvency estate to allow equitable distribution to creditors. For instance, recovery rate varies among the economies from 4.4 cents on the dollar claimants in Philippines to 92.5 cents in Japan (World Bank, 2010);

- Strike a balance between liquidation and reorganization;

- Ensure equitable treatment of similarly situated creditors, recognize existing creditors’ rights, and establish clear rules for ranking priority claims;

- Provide for timely, efficient, and impartial resolution of insolvency. For instance, Ireland provides the fastest bankruptcy procedure – less than 4 months – whereas in many developing countries the process takes many years: Mauritania – 8 years, India – 7 years, and Maldives – more than 6 years (World Bank, 2010).

Importantly, it should be noted that these principles must be considered in the context of the unique political structure, legal culture, and economic and social framework of each country. Considering the political, economic, social, and judicial differences between countries, a “one size fits all” approach is not wise and workable in this area of law (Gromek, Broc and Parry, 2006)

# **_2.2 The Importance of Legal and Judicial Efficiency_**

The evolution of the bankruptcy framework depends on the balance of political powers in a particular country, causing the bankruptcy regimes to differ significantly even among developed countries (Westbrook, 2002). A study of 37 high-income and developing countries finds that 7
estimated the number of poor people by calculating the number of individuals below the 2019 Guinean poverty line of 5,006,362 Guinean francs or $1,385 international dollars (purchasing power parity (2019); $) per year.<sup>[12]</sup> We considered total consumption expenditure as the wealth of individuals, and total consumption expenditure minus OOP health expenditure as the impact of health expenditure on the wealth of individuals. We conducted the analyses for the entire population and by quintile. We calculated quintiles before and after health expenditures. For already poor individuals (individuals in quintiles 1 and 2), the change in poverty was equal to their total consumption expenditures.<sup>[9]</sup> For CHE and IHE, we considered all health expenditures as <mark>OOP</mark> health <mark>payments b</mark> ecause of the small proportion (3%) of individuals with some form of prepayment mechanism.

# **Identification of CHE and IHE drivers and estimation of the potential FRP of subsidies**

We defined the drivers of CHE and IHE as the expenditure categories resulting in the highest prevalences of CHE and IHE cases (previously calculated).

We estimated the FRP associated with THE and expenditure categories as the equivalent of the prevalence of CHE and IHE cases they induce. We estimated FRP for the entire population and by quintile. For IHE, quintiles were calculated before and after health expenditures.

Statistical analyses were performed using STATA software (StataCorp LLC, College Station, TX, version 16.1). We report the number of individuals at the national level rather than the number of respondents to the LSMS. Expenditure data were converted to 2019 international dollars using the rate 1 international dollar = 3,614 Guinean francs.<sup>[12]</sup>

# **Results**

# **Characteristics of the study population**

The LSMS included 41,449 individuals. We excluded 15 (
sup> Each cluster has to include 100 1-year-old children and thus it might be spread over several districts, towns and villages and might include both rural and urban areas. For this reason, within each cluster one or more communities can be identified. In total the YL includes people living in 82 communities.

The YL survey contains information about the socio-economic status and the composition of the household, the mother’s characteristics and social capital, the child health and nutritional status among the others. Most sections of the questionnaire, included the social capital section, have been administered to primary caregivers. The primary caregiver is defined as “the person in the household who can best answer questions about the [sampled] child. This will usually be the mother except where the child has been adopted/fostered or is being bought up by other family members”.<sup>11</sup> For the purposes of this study, the sample is restricted to primary caregivers who are biological mothers. These represent more than 95 percent of total cases.

## **4.1  Measures of alnutrition**

Anthropometric measures are often used to determine the extent of malnourishment among

> 9 For India, the sample is limited to the state of Andhra Pradesh.

> 10 These includes 3 clusters in the department of Lima, and 17 in Amazonas, Ancash, Apurimac, Arequipa, Ayacucho, Cajamarca, Huánuco, Junín, La Libertad, Piura, Puno, San Martín and Tumbes.

> 11 Young Lives, _Fieldworker Instruction Handbook_ , pag. 17

7
The enterprise sample in Moldova was built from the database of all former and still state-owned enterprises complied by the Ministry of Economy (our counterpart) in March 1997. 1he database comprised about 800 companies located throughout Moldova, including in the T ransnistria region. We used the methodology described for the Georgian sample to select a smnaller group of enterprises which was representative of the overall distribution of economic acEivity across manufacturing sectors. Letters with the questionnaire were sent to 320 companies (excluding companies in Transnistria). Those were followed by phone calls and some on-site visits. in total, about half of the companies (149) answered the questionnaire. Most companies wvere located in Kishinev (113), with some companies from other regional centers: Balti (14), Soroca (12), Drochia (4), Nisporeni (4), and Dobrugea (2). As in the Georgian sample, the largest iiuinber of companies (38) came from the food and beverage sector, followed by textiles and apparel (25), and machinery (24). The rest of the sample consisted of companies in the construclion imaterials (18), metals (15), chemicals (12), pharmaceuticals (9), and wood and furniture (8) sectors.

The data from the collected surveys were then entered in spreadsheets by our counterpart anencies and sent to the World Bank for further analysis. In both sample, a number of managers had given implausible (for example, profit rate of 120% of sales) or incomplete answers. Given l!. already small size of the samples, we chose to carry on further phone interviews with those ianiagers and attempt to clarify their reports rather than exclude the companies from the surveys. This approach may have introduced some bias in the survey, to the extent that the data on those firrmis were subject to more scrutiny and corrections. Since there were no other reliable sources of inrduLstrial data in either Georgia or Moldova to make comparisons possible at the time of our sLurveys,
person**<br>**nation of both**|
|Population|0.347***|0.251***|0.598|-0.402|
|Income|0.104***|0.185***|0.289|0.289|

Source: Elasticities of income and population reported in earlier tables Table 9 shows these elasticities and confirms that as cities grow in population, in incomes, or in both, they add floor space by building both upward and outward. These estimates have profound implications for a growing city’s supply of floor area, and also for its tendency to sprawl outward—especially in the absence 11 We do not directly estimate the elasticities using our measures of total floor area as they only cover cities for which we have height data. Instead we use the estimates of elasticities for built-up calculated with the GHSL sample, which includes almost all cities.

sample, which includes almost all cities. 12 These properties come from the log properties. – cst ppPP(T The elasticities for total floor area with respect to income or population are then derived. TT) = ppPP(BBsseepptt−sspp ∗ HHaaeeaahtt/3) = ppPP(BBsseepptt−sspp) + ppPP(HHaaeeaahtt) ⇒ddppPP(TεT TT) = ddppPP(BBsseepptt−sspp) + ddppPP(HHaaeeaahtt)= εb + εhe T b he TT,eeiicc eebbtt−bbuu,eeiicc eehtt,eeiicc 13 The elasticities for total floor area per person with respect to income or population are then derived. The elasticities for total floor area per person with respect to income or population are then derived. TT,uuppuu eebbtt−bbuu,uuppuu eehtt,uuppuu εε = εε + εε

> T b he 13  The elasticities for total floor area per person with respect to income or population are then derived.    The elasticities for total floor area per person with respect to income or population are then derived. T b he T b he
the formal and informal sectors affect the earnings paid in each sector and make it unclear what the magnitude or sign of the differential should be even in an unsegmented market. Informal earnings should exceed formal sector wages by the expected value of unreceived benefits, and fall below them by the taxation that is often evaded. Earnings both in contract and self-employment may reflect a premium for risk, lifestyle, and in the latter case, the implicit costs of capital invested, and the value of unpaid work by family members that the MES suggests comprise 34% of micro-firm employees. Informal salaried workers are among the youngest (table 1) and the MES reports that roughly 30% are related to their employer. Their reported earnings may therefore incorporate training costs (see Hemmer and Mannel, 1989 and Roberts, 1989) or unobserved payments in kind.

# _i. Estimates of earnings differentials_

Using the vast but undetailed NTJES, the first columns of tables 2a and 2b show the percentage change in hourly real earnings generated by movements of individuals among sectors. This holds worker characteristics constant and leaves the variations in the characteristics ofthe work itself as the residual explanatory factor. The next columns account for these characteristics more than has been possible previously and present changes in real wages net of taxes,' and real wages net of taxes per hour among the four sectors.<sup>9</sup> For all three- transitions into self-employment, the s Payments were calculated based on the Mexican tax tables. It was assumed that all informal workers avoided paying taxes and all formal workers paid.

9 The non-normality of the distributions makes the sample mean an inadequate measure of the central tendency of the data and its significance. Two alternate measures are presented in each column: the mean as determined by a robust estimation technique, and the median. The median is largely unaffected by the nonnormality of the distribution and is robust to outliers. The robust mean attempts to recover the information in the tails while compensating to some extent for outliers and non-normality. All
strategy. Section 4 presents the main empirical results. Section 5 discusses heterogeneous effects and the mechanisms through which savings accounts may have affected savings and other outcomes. Section 6 concludes.

# **2.** **<u>Experimental design and survey data</u>**

The experiment was a collaborative effort of Opportunity International Bank of Malawi (OIBM), Alliance One, Limbe Leaf, the University of Michigan and the World Bank. Opportunity International is a private microfinance institution operating in 24 countries that offers savings and credit products. Alliance One and Limbe Leaf are two large private agribusiness companies that offer extension services and high-quality inputs to smallholder farmers 5
Table 2). Compared to baseline scenarios (i.e., no climate change), the high climate change scenarios increase the average poverty headcount by almost 64% in East Asia & Pacific, by more than 100% in South Asia, and by a staggering amount of more than 300% in Latin America & Caribbean. In Latin America & Caribbean, extreme

> 1We include only regions with substantial coverage of poor countries in this region-level analysis. The available and workable survey data for some regions, such as Europe and Central Asia, are limited to only countries within the high income and upper middle income categories, where the international extreme poverty line is not the most relevant measure for poverty.

5
We explore the economic magnitude of effects of increasing SCI, trade openness, and primary school enrollment by one standard deviation on the log of real GDP per capita. For panel estimations, we use the within-country standard deviation of the variables. For all the estimations, the magnitude of the effect of increasing SCI by 1 standard deviation (ranging from 0.002 to 0.009) is larger than the magnitude for primary school enrollment (0.0001 to 0.001). Comparing SCI with trade openness, the magnitude of the effect of increasing SCI by 1 standard deviation is slightly larger than the magnitude of trade openness in the system GMM estimations (0.0023 vs. 0.0016) but more than twice as large using the IV estimations (0.009 vs. 0.004). The caveat with comparisons of the effects of data transparency, trade openness, and education is that they may entail different costs. Unfortunately, there is little data available on the amount invested by countries on data transparency (World Bank, 2021). However, there is some indication that data transparency is less costly than for example improving education systems.<sup>12</sup> The developing Middle East and North Africa (MENA) region is the only region in the sample that experienced a decrease in the SCI score between 2005 and 2018, thus offering another assessment of the economic magnitude of the impact of SCI on the level of GDP per capita. Within this period the average SCI score for the region fell by approximately 4.4 percent, while trade decreased by 2.7 percent (using 2018 as the base). Between 2005 and 2017, primary school enrollment rates increased by 1.5 percent.<sup>13</sup> The base OLS estimates with the cross-section data show a loss of -1.8 percent in GDP per capita between 2005 and 2018 due to the fall in SCI. This is larger than the -0.4 percent decrease in GDP
than land it rents in or residential or swidden land.<sup>28</sup> We make a distinction between “landless” and “non-cultivating” households; the latter include those who rent out all their land.<sup>29</sup> The unemployment rate among the landless is low. Based on adults who report that they did not work because they could not find work in the last 12 months, only 2.6% of landless rural households in 2004 had an unemployed adult, and this was almost identical in 1993 (2.5%).<sup>30</sup> The rate was lower in the MD (2.1% in 2004; 1.6% in 1993). However, time-use data might well reveal higher rates on underemployment, depending on the season.

Table 1 provides summary statistics. For rural Vietnam, the landlessness rate increased by two-thirds over the period, to slightly more than 12% in 2004. (These are population weighted; the proportion of households who were landless rose from 8.4% to 13.6%.) The rise in landlessness naturally put upward pressure on the inequality of land holdings.<sup>31</sup> In all years, the poverty rate is higher for those with land than for the landless. Similarly, mean consumption is higher for the landless. There was a sharp contraction in the incidence of poverty, which occurred at roughly the same rate for those with land as the landless. Over 1993-2004, the proportionate gain in consumption was higher for those with land; the ratio of the mean consumption for the landless relative to those with land fell between 1993 and 2004 from 1.33 to 1.24, though within the period it fell (to 1.21 in 1998) then rose (back to 1.33 in 2002).

> 28 We tested two alternative definitions, one that excludes auction land and one that additionally excludes land contracted from state
1 _− δ_ ) _qt_ <u>(</u> _<u>ω</u>_ + ∆) _Mt_ <u>(</u> _<u>ω</u>_ + ∆).

The introduction of new sources of demand for labor requires that we take a stand on the empirically consistent definition of aggregate labor demand that we should use to define average size in the model. Following the practice of our various firm-level databases, which define total employment of the firm as the aggregate across all of its workers, ranging from production to managerial and research positions, we measure firm size as the aggregate of employment across production ( _l_ ( _ω_ ) + _fc_ ) and innovation ( _χ_ ( _qt, ω_ )) purposes. Formally, then, average firm size in the model is given by 32
The survey covered six self-regulatory and trade association organizations such as ICMA, JSDA, and KSDA (now KOFIA). Of these, only two had separate reporting lines for the two functions, and none had separate entities or premises.

Of the 25 exchanges, 17 reported they had formal procedures for managing conflicts of interest, and the government regulator reviews the procedures of 15 of the 17.

NYSE Regulation’s independent status was outlined previously. Similarly, the Tokyo Stock Exchange (TSE) established a TSE Regulation subsidiary. ASX established ASX Markets Supervision in 2006 to oversee supervision of issuers, participants, futures, and prudential risk and enforcement. The ASXMS board has three directors from the ASX board and two independent directors. As noted earlier, Brazil’s Bovespa recently created a new independent entity, now BM&FBovespa Market Supervision, to perform SRO functions.

Other exchanges have addressed conflicts by formally separating management of regulation functions from business operations within a single corporate structure. This group includes HKEx, which has strong firewalls between the business and the Stock Exchange’s listings department and the Nasdaq OMX Nordic Exchanges in Scandinavia.

Several independent SROs have organizationally separated their regulatory and trade association operations. Arguably, the best practice is to formally separate the roles in different organizations. But like exchanges, some associations perform dual roles with separate governance and organizational structures for regulation functions. The AFEI survey cited above showed that six industry associations have both self-regulatory and trade association functions, including the JSDA, KOFIA and ICMA.

In Japan, the JSDA remains a dual-purpose institution, but in 2004, it adopted a new organizational structure to separate the governance of its SRO operations. An independent SelfRegulation Board is entrusted with decision making on SRO matters. Its 11-member board consists of the JSDA chairman, the vice chairman in charge of regulation, five representatives of member firms, and four public members. Separate departments conduct industry association and SRO operations. Finally, an independent board that includes public representatives oversees JSDA’s self-regulatory operations.

In Canada
Figure 12: Vehicle use per country <!-- Start of picture text --> 30000<br>USA<br>ECU<br>20000<br>AZE IRL<br>CRI ISR DNK<br>LVA<br>FRADEUNZLBELAUTSWE<br>CMR THA TURHRV EST GBRNLDAUS NORCHE<br>FIN<br>10000 SVN<br>POLHUN<br>ROU<br>LTU<br>MLT<br>TUN PER MEX ESP<br>MMR<br>MDA<br>0 NIC BLR KAZ<br>6 8 10 12<br>Log of gdp per capita<br>Average number of annual km per passenger car<br><!-- End of picture text --> Source: IRF data for passenger car ownership and car-km per country. The average number of annual km per passenger car is calculated by dividing the total number of car-km per the number of passenger cars. The graph shows averages over the period 2015-2017 to limit the extent of reporting mistakes.

# **4 Income as a main determinant of transport consumption**

As documented in the previous section, there are large differences across countries and within countries across households with different incomes. Calculating elasticities of transport demand based on cross-country averages would therefore miss the large differences in transport demand and use of carbon-intensive goods that exist within countries. This section uses household survey data to estimate transport elasticities. Harmonized survey data across countries with sufficient transport-related questions are rare. We therefore focus on 18 countries for which we use the rich collection of data from LSMS surveys. Households decide whether to consume transport goods and services, and by how much, to increase their mobility and their economic and social opportunities. We look at the role of income as a main determinant for each of the margin identified in the previous section.

## **4.1 Household-level estimation**

In order to quantify the determinants of transport expenditures at the micro-level, we estimate the relationship between the