rafmacalaba/data-use-mentions-extended huggingface dataset:
15 Europe (CEEC-8) and Romania that, together with Bulgaria, are on the EU accession path as well as ten CIS economies (no data are available for Tajikistan and Turkmenistan). The diagnostic tool is the World Bank’s _Cost of Doing Business_ comparative survey (World Bank 2005a). The compiled data shed lights on tangible components shaping the legal cost of conducting business in Bulgaria in comparative perspective. We look into seven areas covered by the survey: starting business, labor market flexibility, registering property, contract enforcement, bankruptcy, and protection of investors.

As a first step, we assess the overall formal regulatory ease of doing business relative to transition countries. Data tabulated in Table 7 contains information on overall ranking in terms of ease of doing business overall and in each area. It was derived as follows: Each area has between one (protection of investors—disclosure measure) and five different indicators (access to credit and labor market flexibility). Except for contract enforcement (four indicators), three indicators describe the state of regulatory practices in other areas. Since rigidity of employment indicator is an average of three other indicators, we take into account the average indicator together with firing cost. We normalize each indicator taking best practice among CIS/CEEC states as 100. We calculate the average for each of seven areas covered by ‘doing business’ survey. The average across areas gives an indication of the overall ease/difficulty of doing business in a country as juxtaposed against the best conceivable regulatory regime, that is consisting of most businessfriendly arrangements in the region. The higher values ranging between maximum of 100 (best) and 0 (worst) indicate a better relative performance.

**Table 7** : _Ranking of CIS/CEEC states in terms of overall ‘formal’ ease of doing business in 2004_

||Starting<br>business|Flexibility of<br>employment|Registering<br>property|Enforcing<br>contracts|Closing<br>business|Protecting<
where y<sup>c</sup> has been replaced by a function of the realized yields on other plots that share similar characteristics to the plot in question but did not incur any losses.<sup>32</sup> Multiplying this by the planted area ha<sup>c</sup> thus gives the attainable production. Losses then constitute a deviation of the realized harvest Y<sup>r</sup> from what would have been expected based on past years or the performance of yields on plots in unaffected areas, respectively.

The remainder of this section looks at the data requirements of both approaches as well as their likely accuracy.

# <u>What do we see in the data?</u>

We start by regarding the suitability of using past yield data to proxy for attainable yields from a data availability perspective. In order to ascertain a robust trend or average in yields, we require high frequency (e.g. annual) panel data reaching back several years. Micro-level panels of this frequency are exceedingly rare and, in our case, the LSMS-ISA panel data only cover recurring survey rounds every two to three years. While such high frequency data is currently unavailable at a sufficiently disaggregated level<sup>33</sup> it may become available in the future.<sup>34</sup> Another challenge in this regard relates to split-off households and those that move. The LSMSISA survey in both Ethiopia and Malawi tracks households that move to a new place whereas the latter also tracks households that split from the originally sampled household. For both groups of households, comparable past information on yields would be missing.<sup>35</sup> Irrespective of this, yields in the context of the LSMS-ISA data are measured at the plot level. Ideally, the panel data used would thus also be identified at the plot-crop level in order to hold differences in soil quality and other plot characteristics constant. However, plot boundaries and crop
The fact that women face a range of disadvantages in this setting is starkly quantified in our baseline data: respondents are on average 18 years old, yet 60% are in relationships, 28% are married (with a third of marriages being polygamous). While their age at marriage is close to 16, the average age at marriage of their husbands is almost double, at 31. Despite their young ages, nearly half have children. For those in relationships, 46% report being subject to intimate partner violence. These traits of early marriage, childbearing and exposure to violence all have obvious long term consequences on women’s ability to acquire human capital and lead financially independent lives [e.g. Jayachandran 2015].

Our analysis is based on a 2 _×_ 2 factorial design, where one dimension is the quasi-random assignment of the severity of Ebola-related disruption to villages, and the other dimension is the random assignment of villages to ELA treatment or control. We use retrospective data collected from village leaders at the tail end of the crisis to construct an index of village level disruption, proxying the loss in economic opportunity and health service provision that each village experienced. We make precise the four identifying assumptions for the factorial design and provide supportive evidence for each. On the assumption of the quasi-random assignment of disruption, we document how our disruption index is largely uncorrelated to contemporaneous village characteristics, and uncorrelated to past measures of health related behaviors, knowledge and state capacity. This is in line with existing work using spatiotemporal models to simulate the spread of Ebola and measure the local intensity of the shock. This body of work also suggests the geographic incidence of the outbreak is largely uncorrelated to economic, social or political characteristics of locations [Backer and Wallinga 2016, Maffioli 2017, Fluckinger _et al._ 2018].

Our monitoring data confirms there was an extensive roll out of the ELA program despite the circumstances: (i) 70% of clubs opened on time (by September 2014); (ii)
initiation of breastfeeding (% children < 2<br>Diarrhea. prevalence (% children. < 3 years) (DHS) bu,4 Exclusive breastfeeding (Sol4tenee-Onisa) 46<br>(NNS)<br>Complementary feeding Health system performance<br>Minimum dietary diversity (% children 6-23 mos) 22 Received all 8 basic vaccinations (% of children 80<br>(NNS) 9 12-23 mos) (DHS) 7<br>Minimum acceptable diet (% children 6-23 mos) 12<br>(NNS) 61<br>Skilled birth assistance (% of livebirths) (DHS)<br>Water & Sanitation Poverty<br>Ps 95 Poverty rate (% of pop, US $3.20 line) (FIES)<br>Improved drinking water (% of pop) (DHS) 7<br>78<br>14<br>Improved, non-shared sanitation (% of pop) (DHS) 67 Poverty rate (% of pop, US $1.90 line) (FIES) NS<br>2000 20102017 2000 20102017<br><!-- End of picture text -->

Source: The source of<sup>eachindicatorisindicatedinparentheses.DHS=Demographic&HealthSurvey,NNS=Philippines</sup> National Nutrition Survey, Food & Nutrition Institute, Department of Science & Technology (FNRI-DOST), WHO = World Health Organization, WB = World Bank. Notes: Risk factors labeled as showing a clear improvement/ clear worsening are those with average rates of change greater than 0.5% per year or less than -0.5% per year, depending on the indicator.
14 their way into the research world. I will focus here on a single question recurring in policy circles: is there a dominating bias in favor of working on the alleviation of rural poverty rather than urban poverty?<sup>14</sup> Is the story the same across sectors (i.e. water vs. energy vs. telecoms vs. transport)? Are the needs of tomorrow’s poor well anticipated? Are tomorrow’s poor likely to be mostly urban?

There is plenty of evidence showing that poor people urbanize faster than the population as a whole, which implies that anticipating their needs will require a stronger focus on urban issues.<sup>15</sup> Yet in many regions, the sheer numbers argue otherwise. In Africa, for instance, more than two-thirds of the population remains rural, and it will likely be a while until the urban population becomes the majority. More generally, experiences across countries suggest that a majority of poor people will still live in rural areas long after most people in the developing world live in urban areas (Ravallion 2002). So, if there were a rural bias, would it not be rational?

This is not a new debate. Almost 30 years ago, Lipton (1977) and Mellor (1976) were concerned with the opposite question: was the urban bias of the international community rational? Ultimately, the choice of the priority is an empirical question (Reardon 2001; Sahn and others 2003). In some regions, such as Africa, rural infrastructure needs are likely to continue to drive the agenda (Mwabu and Thornbecke 2004). In others, such as many parts of Latin America and Asia, urban needs should take priority. Since the priority is an empirical matter, sector-specific poverty data on access for various income groups are needed—but are not available for most developing countries. There are reasonably good databases on the number of poor (although the measurement of poverty continues to be a source of conflict
and immigration status). All panels use the same sample but have different subject outcome variables. The R-squared for each set of regressions is reported within each panel, while the common sample characteristics and controls included are reported at the bottom of the table. Column (1) shows the raw relationship between the PISA-based school management index and student performance, only controlling for country fixed effects. The raw relationship ranges from just over 4 to almost 5 points on the PISA scale.<sup>16</sup> Recall that 41 points on the PISA scale for math is equivalent to about one year of learning, and thus the raw correlation is equivalent to about one month of learning for math (similar for the other subjects). Column (2) includes school

> 14Prova Brasil was also administered in 2013.

> 15See Jerrim et al. [2017] for a thorough review of how to best use PISA scores and survey weights.

> 16PISA is standardized across years and countries such that the mean is 500 and the standard deviation is 100.

10
including the “ _cajas de compensación_ ” associated with certain employee organizations) . The ‘unbanked’, on the other hand, have no deposits or savings in any such products mentioned above.

6. **Less than 40% of Bogotá’s adult population use formal sector financial services.** This percentage is higher than in Mexico, but lower than the proportion prevailing in Brazil (Figure 3.1).

**Figure 3. 1  Use of Financial Services In Bogotá, Mexico City, urban Brazil and OECD countries**

<!-- Start of picture text --> 100 50,000<br>91<br>90 45,000<br>80 37,610 40,000<br>70 35,000<br>60 30,000<br>50 43 25,000<br>39<br>40 20,000<br>30 24 15,000<br>20 10,000<br>6,230<br>10 1,810 2,710 5,000<br>- -<br>Mexico D.F Bogotá Brazil USA<br>Banked population GNI per capita<br>GNI per capita in 2003 (US$)<br>Percentage of banked population<br><!-- End of picture text --> _Source: Mexico, Bogotá and Brazil data, own studies,US Federal Reserve Bank, 2002_

**Figure 3. 2  Use of Banking Services as a Percentage of Population**

<!-- Start of picture text --> Funds/ Stocks/ Bonds 0.7<br>CDT 2.5<br>Mortgage 2.9<br>Savings Account w ith special aw ards 3.0<br>Personal Credit 3.8<br>Use of telephone for banking services 3.8<br>Current Account 7.2<br>Credit Card 8.0<br>Debit
figure, we split banks into two

> 31 We further check for differences in the non-linear trends of bank lending volumes to local governments and private firms across states before and after the reform. To examine bank lending volumes to local governments, we run a series of regressions using the specification summarized in Equation 3:

> where _HighDebtStates,16Q1_ is indicator variable that equals one for states that in the quarter prior to the FD Law had public indebtedness above the median and zero otherwise. _Monthm_ is an indicator variable that equals one in month _m_ and zero otherwise, while the other variables are defined as before _._ To analyze bank lending volumes to private-sector firms, we run a series of regressions akin to Equation 3, where observations are at the firm-bank-month level _._ The coefficients of interest _βm_ are plotted in Figure A1. We corroborate that prior to the Law, the dynamics of bank lending to local governments (Panel A) and private-sector firms (Panel B) were similar across states, and only begun diverging after the implementation of the Law.

> 32 In Table A3B, we compare a series of statistics prior to the FD Law between banks with an exposure to local governments in April 2016 above vs below the median. The results show that banks with different exposures to local governments were statistically similar along several characteristics and help us rule out that banks of varying exposures to local government debt are different in other dimensions, such as their capital and liquidity ratios, or their

18
model. Therefore, to find the coefficient that is multiplied by the damage index ( ), the following calculation is performed: _[exp (-0.0177)-1] *100._ 21 Records in nightlight image data are the DN values, ranging from 0 (no light) to 63 (maximum light). 22 The calculation was as follows:  The calculation was as follows: ff _[[[exp (-0.0103)-1] *100] *(0.2)]*[1/12.1]_ .

> 22 The calculation was as follows:  The calculation was as follows: _[[[exp (-0.0103)-1] *100] *(0.2)]*[1/12.1]_ .

16
# **1. Introduction**

According to the International Labor Organization (ILO), child labor has declined worldwide in the past 15 years. But the numbers are still alarming. In 2012, 168 million children ages 5 to 17 years were in child labor. More than half of them, 85 million, were in hazardous work.

Labor laws and regulation are the first-order policy instruments advocated by international organizations such as the ILO and used by many national governments worldwide to fight child labor. Indeed, ILO Convention 138 on the Minimum Age for Admission to Employment of 1973 and ILO Convention 182 on the Prohibition and Immediate Action of the Worst Forms of Child Labor of 1999 have been ratified by 168 and 180 countries, respectively, at the time of writing. Convention 138 recommends that national laws should set the minimum employment age above 15. Convention 182 defines an individual under age 18 as a child. It lists the types of occupations that should be restricted until age 18 and defines the worst forms of child labor that should be prohibited in all circumstances.

There is a consensus that very early exposure to work is harmful to a child’s human development. In fact, most countries have put in place laws prohibiting work for children under a certain age. However, there is not much agreement on the minimum age at which a child should be allowed to work.

For instance, of the 62 mostly developing countries with publicly available data on child labor from the second and third rounds of UNICEF ́s Multiple Indicator Cluster Surveys (MICS) for the 2000s, two countries set the minimum employment age at 12, 23 at age 14, 21 at age 15, 11 at age 16, and five countries at age 18 (Edmonds and Shrestha, 2012).

In the United States, the _Fair Labor Standard Act_ (FLSA) uses age 14 as a general rule for the minimum employment age, but that does not prevent significant variation in regulations across states. According to the Department of Labor, 18
With this defined, the expected value of the location effect conditional on the residuals of the households within the location can be obtained: as well as the variance: EB prediction is expected to perform well in the presence of large _η_ , if many of the locations are covered by the survey, and if the distributions of the error terms approximate a normal distribution (Van der Weide, 2014).

# **4.2 Monte Carlo Simulations**

ELL (2003) use various specifications in their paper. The options and methods differ depending on how the variance for the decomposed error terms is obtained. The simulation may be done via parametric drawing of the parameters, and via bootstrap. The PovMap manual (Zhao, 2006) only details the parametric approach. Nevertheless, the newest version of the software has incorporated a bootstrap approach to obtaining the parameters. The steps for the simulation are:

- ˆ ˆ

- 1. Obtain GLS coefficients by drawing from _β_<sup> ̃</sup> _GLS ∼ N βGLS,_ Var _βGLS_ . For reasons that will � � ��

- become apparent in the following steps, this approach is only possible when the user chooses ELL variance estimation.

- (a) A different approach relies on obtaining bootstrapped samples of the survey data used in the first stage, this is repeated for every single simulation. The latter method yields a set of _β_<sup> ̃</sup> for every single simulation.

2. Under the ELL variance estimation approach the cluster component of the error term, _η_ ̃ _c_ is obtained by drawing from _η_ ̃ _c ∼ N_ �0 _,_ ˆ _ση_<sup>2</sup> � where we:

- (a) Draw _σ_ ˆ _η_<sup>2</sup><sup>_∼Gamma_</sup> � _σ_ ˆ _η_<sup>2</sup><sup>_,_Var</sup> � _σ_ ˆ _η_<sup>
<!-- Start of picture text --> 2.50<br>r=x ——Articulate truck<br>© ,<br>g 2.00 —— Heavy truck<br>go —— Passenger car<br>8= 1.50<br>=}<br>%wv 1.00<br>8<br>5<br>0.50<br>3 mn<br>ad<br>8 0.00<br>a 2 4 6 8 10 12 14 16 18 20<br>International Roughness Index (m/km)<br><!-- End of picture text --> <!-- Start of picture text --> 3.50<br>= 2.99<br>>S 3.00<br>=3 2.50<br>% 2.00 1.84<br>8s<br>1.33<br>£150 442 .<br>—_<br>2 1.00<br>3<br>8 0.50<br>in<br>0.00<br>Paris Madrid Paris Duisburg<br>to Madrid _ to Paris to Duisburg to Paris<br><!-- End of picture text --> Binx =
2015 2017 2019 2021<br><!-- End of picture text -->

# _Firm informality_

23. **Firms in Indonesia are dominated by Micro Small and Medium Enterprises (MSMEs).** The 2016 Economic Census, the last one available, classifies 99 percent of firms in the country as MSMEs. A large share of these MSMEs are micro firms, almost 60 percent, with fewer than 5 workers. This is in contrast to peer countries like Malaysia and Vietnam, which also have a large share of firms categorized as MSMEs. In those two countries, the informal sector is significantly smaller at around 9 percent (2016) and 27 percent (2019), respectively.

<!-- Start of picture text --> Figure 4. Percent of firms registered when they Figure 5. Percent of firms competing against<br>started operations informal firms<br>Percent of firms Avg. over 2009-19 Percent of firms Avg. over 2009-19<br>EMDEs 80<br>EMDEs<br>EAP<br>100 60 EAP<br>80<br>60 40<br>40<br>20<br>20<br>0<br>0<br>Source: World Bank Enterprise Survey, World Bank Staff Calculation.<br>India Brazil Turkey India Brazil<br>Turkey<br>Thailand<br>Indonesia Thailand<br>Philippines Indonesia<br>Philippines<br><!-- End of picture text --> 24. **Indonesia’s firms are predominantly informal and seem to compete with the formal sector firms.** Only 45 percent of Indonesia’s firms were legally registered when they started operations, a significantly lower share than EMDEs, EAP region, and peer-country averages 10
<!-- Start of picture text --> a. Regional livestock losses by b. Regional livestock losses c. Current regional<br>type in 2040 (%)} by typein 2100 (96) livestock distribution<br>(base year: 2015)<br>SSPs ssPt<br>bad = Kiventach =A yentoct<br>*% “ Gobat ass<br>» Pa)<br>— Ar -<br>a<br>sess e ssp2S5PS 18 ssP2 = & st<br>=<br>ay ~— me ~<br>—ony *<br>— wm ~<br>Omer TM<br>sspa sSP3 SSP3<br><!-- End of picture text -->
external finance in the empirical tests below to better gauge the firm’s financial condition provides a useful robustness check on our main results.

# **III. Empirical tests**

We rely on the World Bank 120 city survey of Chinese manufacturing firms conducted in 2005. It covers 12,400 firms located in 120 cities of all Chinese provinces except Tibet. In each province, the provincial capital, which is often the most populous city, is automatically covered, and additional cities are selected based on the economic size of a province. One hundred firms were sampled in each city, except for the four mega cities (Shanghai, Tianjin, Beijing, and Chongqing) where 200 firms were selected. Within the top 10 industries in terms of value added in each city, firms were randomly selected, including large, medium-sized, and small firms. Our sample is thus quite representative of China as a whole, and of firms of various sizes. This is a key advantage when compared to other studies of financial constraints in China since external validity about the existence of financial constraints depends on the representativeness of the sample. Given the geographic imbalance in economic development and the government preferences towards large firms in China, results based on data from selected provinces and firms of certain size can be misleading.

The variables used in our analysis come from a questionnaire consisting of two parts: the first asks for qualitative information about the firm in the survey year and is filled out by firms' senior managers; the second covers financial and quantitative information, much of which goes back three years, about the firms' production and operation, and is directly obtained from the firms' accounting books through the assistance of the firms' chief accountants.

16
of individual loans, extracted from the PCRs, and that represent the universe from which our samples of 20,000 loans are drawn; panel b shows the frequency distribution of the dollar value of the 20,000 randomly selected portfolios; and finally, panel c shows the distribution of credit losses of the 20,000 randomly selected portfolios as a fraction of the face value of the respective portfolios.

A visual inspection of the charts shows clear differences among the three samples. The concentration of loan-dimension toward the smallest size category is more evident in Argentina (50 percent of all the loans extracted from the credit register are smaller than US$100,000) and in Mexico (80 percent of all the loans extracted from the credit register are smaller than US$100,000).Due to the different sample construction method adopted in Brazil, loans smaller than US$100,000 appear to be less numerous (37%), as they only represent the portion of small loans that are part of global exposures larger than US$300,000. The different definition of the universe is reflected in the distribution of the sampled portfolios and of their exposure to credit losses. The charts for Mexico and Argentina show considerably more skewed distributions for both portfolios’ value and credit losses than the comparable distribution computed for Brazil. As a supplement to the visual information provided by the charts, we have summarized in Table 1 a set of descriptive statistics of the distribution of the 20,000 randomly sampled portfolios.

# **3. Empirical Results**

The main results of the samples just described are reported in Table 2, where we have indicated for each country the value of expected losses (EL) and the value of unexpected losses (UL) associated to each percentile level of the (right tail of the) 8
<!-- Start of picture text --> H<br>θ = 0<br>1<br>0 < θ < 1<br>θ = 1<br>Dominated by A<br>Larger EU<br>B than A<br>A<br>H(A)<br>Smaller EU<br>Dominates A<br>than A<br>LE<br>0<br>LE(A)<br><!-- End of picture text -->

**Figure 3:** A and B are robustly ranked even though _H_ ( _A_ ) _< H_ ( _B_ ) and _LE_ ( _A_ ) _< LE_ ( _B_ ).

**Table 2:** An example of robust comparison: Pakistan and Bangladesh in 2019.

||Headcount|Life|Poverty|Poverty Free|
|---|---|---|---|---|
||ratio|Expectancy|Expectancy|Life Expectancy|
||||(_LE ∗H_)|_LE ∗_(1_−H_) =_PALE_1|
|Pakistan|4.3%|64.8|2.8|62.1|
|Bangladesh|6.7%|73.6|4.9|68.8|

differs from the ranking provided by _H_ . Proposition 5 describes the conditions under which _PALEθ_ comparisons are robust.

**Proposition 5.** _(Robust comparisons with PALEθ)_

_(i) For any two societies A and B, PALEθ_ ( _A_ ) _≤ PALEθ_ ( _B_ ) _for all θ ≤_ 1 _if and only if_ _(ii) There exist societies A and B for which PALEθ_ ( _A_ ) _≤ PALEθ_ ( _B_ ) _for all θ ≤_ 1 _even though H_ ( _A_ ) _< H_ ( _B_ ) _. These societies are such that H_ (