rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Appendix 1: Registration Process in Belo Horizonte**

A firm wishing to formally register must undertake the following steps:

**Step 1** : Access the Chamber of Commerce (JUCEMG) website (www.jucemg.mg.gov.br) and fill out a Consulta de Viabilidade (feasibility consultation). The main purpose of this step is to find out if the desired business name is available and if they can open their firm at the desired address. This step will also show the licenses needed and the costs involved, based on the main activity of the business.

**Step 2** : Once a positive response is received, access the Receita Federal do Brasil - RFB (Federal tax service) website (https://www14.receita.fazenda.gov.br/cadsincnac/inicioAction.do) - CadSinc (Synchronized Database) and fill out the Coleta WEB. CadSinc brings together the cadastral (land ownership) procedures of agencies and entities involved in the process of formalization and legalization of companies.

**Step 3** : After receiving the response from step 2, print the DBE (Document Basic Input).

**Step 4** : Go to the website of JUCEMG (www.jucemg.mg.gov.br) and click on Portal de Serviços (Serviços WEB/Integrador). Click on the link Integrador and then the link Gerar Novo FCN/REMP. This step will integrate the Consulta de Viabilidade data with the CadSinc data and generate the documents that will be the base of the Contrato Social (state registration for limited liability companies (LLC)) or REMP (state registration for individuals).

**Step 5** :  Click on DAE and print it. The DAE is the document with the amount the entrepreneur should pay to continue the formalization process. All the steps up to now are online and free of charge. If the firm is a LLC the entrepreneur will pay R$ 165.53
# <u>Closing the data gap when surveys cannot be collected</u>

Measuring monetary poverty by survey-to-survey imputation<sup>12</sup> Many countries operate in an environment where household consumption data are partially missing. This includes situations when: i) consumption data are available at one point in time but are not available in the second point in time, or ii) situations when consumption data exist in all points in time but are not comparable. “Imputation” is the key method to provide poverty estimates in the absence of consumption data.

## <u>Method</u>

Measuring monetary poverty is possible only when information about consumption or income is available. When consumption data are not comparable across two survey rounds or do not exist in one of the periods, (survey-to-survey) imputation methods can be used. The necessary condition is availability of at least one HBS that includes consumption data. Key to the survey-to-survey imputation is the estimation of a model that explains consumption or income as a function of different social-economic and geographic characteristics for the same year consumption or income data are available. This model is then used to predict consumption in the second year of the survey, using the same explanatory variables. As a result, each household in the second year will get “imputed” consumption data, making it possible to construct a poverty rate. Detailed technical reviews of the survey-to-survey imputation methodology can be found in Dang et al. (2014) and Dang et al. (2019).

Several countries applied this methodology in the MENA region. Dang et al. (2004) used Jordan’s Household Income and Expenditure (HIES) surveys from 2008 to 2010 to impute consumption to the Labor Force Survey during the same period. They validated the results using actual poverty rates from the HIES. The results were encouraging, with imputation-based poverty estimates not showing statistically significant differences from true poverty rates.

Douidich et al. (2013) used the method in Morocco, and Cuesta and Lara
(percentage local government involvement in road service delivery); b) decentralization of construction (percentage share of local government in construction activities); c) decentralization of maintenance (percentage share of local government in maintenance); and d) decentralization of administration, which is a proxy for deconcentration<sup>1 7</sup> .

An important econometric issue arises in estimating the equations (11) and (12). The terms **(.t** and **s)** represent random effects deriving from the unmeasured levels of effort expended by each country at a given period of time. I Using panel data, the omitted variable problem arises when we have country specific and time-invariant portions of our error term ( **Iijt** = Pijt + Vijt )- A possibly important country omitted variable could include differences in various institutional and governmental structures that are difficult, if not, impossible to quantify. This specification treats (v,jj) as a country specific omitted variable (fixed effect) that may be correlated with the included regressors. Country dummies are included to eliminate country specific effects and OLS is applied to the data.<sup>20</sup> To the extent that the country specific omitted variables are correlated with included regressors the paramneter estimates obtained by OLS method without country dummies may be biased and inconsistent.

# **3. Empirical Analysis**

## **_Data_**

The empirical analysis employs three data sets: (a) cross-country panel including observations in 35 countries over 10 years; (b) time series data for the case of Korea from 1968 to 1992; and (c) a panel data set across eight states and their respective districts from 1980 to 1992 in Germany. With these three data sets we investigate the impact of decentralizing road provision at three levels of analysis: jurisdictions are countries and we are looking at the share of local government financing of road works--the cross-country 17 Rondenelli (1983) provides a
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 22 Numerous African countries capture more private funds than Côte d’Ivoire, 2002-07 average of PPI disbursements***

<!-- Start of picture text --> power ICT transport water<br>% of GDP<br><!-- End of picture text -->

*calculated as PPI commitments smoothed out over 3 years

**Figure 23 PPI commitments to Côte d’Ivoire over 1990-2008 period**

<!-- Start of picture text --> power ICT transport<br>US$ millions<br><!-- End of picture text --> Source: World Bank and PPIAF, PPI Project Database. (http://ppi.worldbank.org), in current $ millions Côte d’Ivoire can also address the funding gap by adopting lower cost technologies or choosing less ambitious targets for infrastructure investment. Côte d’Ivoire could make substantial savings from appropriate technological choices in a number of areas, including power, roads, and WSS. First, further development of regional power trading under the WAPP would reduce Côte d’Ivoire’s power needs. This would reduce the annual infrastructure funding gap by as much as $0.1 billion a year. Second, the 42
138}|
|**Transactional sex over past year**|.039<br>.043|{.941}|
|**_C. Human Capital and Economic A_**|**_ctivities_**||
|**Skills: Literacy [0,1]**|.209<br>.232|{.818}|
||[.262]<br>[.254]||
|**Pregnancy Knowledge [0-100]**|.648<br>.661|{.477}|
||[.278]<br>[.272]||
|**HIV Knowledge [0-100]**|.573<br>.607|{.911}|
||[.199]<br>[.198]||
|**Enrolled only**|.289<br>.223|{.363}|
|**Engaged in income generation only**|.334<br>.325|{.606}|
|**Engaged in both**|.166<br>.240|{.152}|
|**Engaged in neither**|.210<br>.211|{.764}|

**Notes:** Column 3 reports p-values from a test of equality of means carried out by OLS regression of each characteristic. Regressions include strata (district) dummies and standard errors are clustered at the village level. Intimate partner violence is defined as the threat or use of physical violence from the respondent's partner. For the Empowerment Index, respondents were asked whether men, women or both should be responsible for the following activities: earn money for the family; have a higher level of education; do washing, cleaning and cooking; fetch water if there is no water pump or tap; feeding and bathing children; help the children in their studies at
279|0.075|0.157|
|B. Allowing diferences across gender||||
|Female _×_ Treat _×_ Post2007|0.043|-0.122<sup>_∗∗∗_</sup>|-0.072|
||(0.005)|(0.036)|(0.104)|
|Mean Control|91%|92%|47%|
|R-squared|0.029|0.076|0.169|
|Observations|1,019|3,011|1,437|
|Sample: Early & Fully Exposed: Age in 2007|1-15|10-20|12-24|
|C. Allowing diferences in exposure||||
|Age at 2007 _×_ Treat|-0.002|0.009<sup>_∗∗_</sup>|0.018<sup>_∗∗_</sup>|
||(0.004)|(0.004)|(0.009)|
|Treat|-0.001|-0.133<sup>_∗_</sup>|-0.294<sup>_∗_</sup>|
||(0.038)|(0.070)|(0.171)|
|R-squared|0.393|0.098|0.166|
|Observations|735|1,395|969|

_Source_ : Iranian Households Income and Expenditures Surveys (HIES). Note: The table presents estimates of the average and gender differences effects of the sanctions on enrollment rates by crucial ages (estimated coefficients of _T reat × P ost_ 2007 ( _γ_ in equation (1)) in panel A, and estimated coefficients of _F emale × T reat × P ost_ 2007 ( _γ_ 1 in equation (2)) in panel B): enrollment in the
<sup>TTyyRRiiSSmmqq ggM</sup> ∗ΔSSttqqmmb (MMiiq iiMMmm T UU) vviimmgg SSmmRRmmSSiittyy (MM T UU) Typical value of a container is computed as an average mean across commodities, hence representative of lower end values.

With the moving capacity for vessels during the period of interest being 18MTEU, the typical value of goods implied by the observed connection is about **40,000 USD per TEU** . This value is indeed at the lower end of the value per TEU statistics (Annex 4). Therefore, capacity constraints and willingness to pay may explain the very high-rate bursts observed in recent periods of disruptions.

# **7. Conclusion**

The pandemic-induced crisis and other disruptions from 2020 onward significantly impacted shipping capacity and freight rates. While many regions experienced disruptions in their supply chains, the proposed metric and stress index effectively capture relative maritime supply chain disruptions despite their simplicity. Beyond measuring global trends, localized stress signals highlight chokepoints warranting a level of policy interventions. This framework could be extended to other scales (e.g., feeder loops or domestic shipping) and supply chains, where visibility data exists. Systematically producing the Stress Index, including disaggregated data at the regional or port level, could automatically inform productivity and capacity bottlenecks, flagging needs for infrastructure or operational improvements. Port productivity benchmarks like the World Bank’s CPPI, or dwell time made available in the World Bank’s Logistics Performance Index (2023) could be complemented with recurrent localized stress series, with ‘bursts’ flagging port investment 17
Policy Research Working Paper 9766

# **Abstract**

This research explores the relationship between laws that discriminate on the basis of gender and the probability that a female-owned business begins operating in the informal sector. This is achieved by tracing the origins of formal businesses surveyed in the World Bank Enterprise Surveys and merging this with information on the level of legal equality between genders as measured by the Women, Business and the Law database. In addition, the research explores whether starting a business informally has any differential effect on subsequent firm performance depending on the gender of the owner(s). The results show that gender discriminatory laws increase the likelihood that firms with female owners will begin operations in the informal sector; as expected, this does not hold for enterprises that are solely owned by men. Furthermore, the research provides evidence that firms that began operations informally have poorer performance years later—a relationship that exists both for firms with female owners and for firms fully owned by men. The results show notable variation by region.

This paper is a product of the Global Indicators Group, Development Economics and the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mhyland@worldbank.org and aislam@worldbank.org.

_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 views of the International Bank for Reconstruction
|**Impact assessment**|Where data processing is likely to result in high risk to individual rights, the controller<br>must conduct an impact assessment prior to processing, including the foreseen<br>measures to address the risks|
|---|---|
|**Designation of data**<br>**protection officer**|Controllers and processors must designate a data protection officer under certain<br>circumstances, including if the bulk of the processing activities require regular and<br>systematic monitoring of data subjects on a large scale, or if it consists of processing of<br>sensitive data on a large scale|

A key aspect of data protection is who monitors and enforces the implementation of the regulation. The establishment of a capable and effective implementing agency is central to ensuring adequate implementation of the regulation and to providing individuals with a policing entity to which resort in case of violations. When it comes to the enforcement of cybersecurity measures, a single central regulator tends to be the most adequate solution, providing oversight and complaints management (UNCTAD 2016). They can inform <mark>data subjects about their rights, supervise and assist data controllers with regard to compliance, and ensure the enforcement of data regulations. Data protection requirements also include registration with the authority and notification of data processing activities (UNCTAD 2016). Depending on the ease, cost, and recurrence of these requirements, they may impose a burden for businesses trying to enter a foreign market (UNCTAD 2016). The OECD revised its privacy guidelines in 2013, highlighting the importance of establishing privacy enforcement authorities (OECD 2013).</mark> Most countries that have adopted a comprehensive framework on data protection have buttressed it with a data protection agency (DPA). Certain countries, such as France, have had a DPA since the 1970s, when large state-owned datasets were the main concern of the public. While most DPAs are independent agencies, some countries have embodied them
so that parallel trends hold only after conditioning on a vector of pre-treatment covariates. Finally, estimates use the doubly robust estimator based on stabilized inverse probability weighting and ordinary least squares proposed by Sant’Anna and Zhao (2020).

# **5 Results**

Table 3 shows the main results of estimating the impact of entering export markets on the probability of adopting, which are based on the average treatment effect on the treated from

> 7Although data from the Ministry of Trade includes information on the first and last years a given firm exported, the method proposed by Callaway and Sant’Anna (2021) assumes that treated units remain treated during all subsequent periods.

> 8Under the no-anticipation and parallel trends assumptions, group-time average treatment effects are identified in periods when _t ≥ g_ (i.e., post-treatment periods for each group). In practice, we also estimate pseudo group-time pre-trend coefficients (when _t < g_ ), which we can use to test the parallel trends assumption.

11
# **Impacts of International Migration and Remittances on Child Outcomes and Labor Supply in Indonesia: How Does Gender Matter?**

Trang Nguyen<sup>*</sup> Ririn Purnamasari<sup>**</sup> .

> <u>*</u> East Asia and Pacific Region, The World Bank.  Email: TNguyen16@worldbank.org

> **East Asia and Pacific Region, The World Bank.  Email: rpurnamasari@worldbank.org

We thank Ahmad Ahsan, Richard Adams Jr., Daniel Mont, and participants at the June 2010 conference on ―Cross-Border Labor Mobility‖ in Singapore for their very helpful comments and feedback. We are grateful to Survey Meter in Indonesia for constructing the IFLS panel data and for providing data support. Kalpana Mehra and Aaron Szott provided great research assistance. The opinions expressed here are those of the authors and do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent.
on electricity based on the self-reported “electricity charges” in the PECS.

- (ii) Estimated the quantity of electricity consumed by dividing total expenditure on electricity by the tariff (which varies by consumption slab).

- (iii) Calculated the direct effect as the subsidy amount per kWh (between 0.178 and -0.0371) times the (estimated) electricity quantity (kWh) used domestically consumed per the PECS.<sup>57</sup>

- The analysis focuses on direct effects and assumes no indirect effects since the domestic electricity subsidy is only provided to households and not to electricity producers/distributors.

# **4.5.2. Fuel subsidies**

# **Simulation of gasoline and diesel fuel subsidies is based on self-reported PECS households’ expenditure**

**data and subsidy calculated from administrative data (0.0686 and 0.1962 NIS per liter for gasoline and diesel, respectively).**<sup>58</sup> The simulation calculates the direct effects of fuel subsidies for residents in the West Bank and Gaza, and indirect effects for residents in the West Bank only. The direct effects of subsidies on gasoline and diesel were calculated by multiplying the subsidy per liter of fuel by the

> 55 Estimation of distribution margin (inclusive of all surcharges and costs) of 20 percent in the electricity sector is provided by the World Bank Energy Team based on regional average distribution margins.

> 56 Cost of supply after 20 percent margin calculated as NIS 0.53/ KwH * 1.2.

> 57 Note that due to the data limitation, the subsidy estimation is based on assumption of “volumetric consumption” instead of Increasing block tariff (IBT). Furthermore, the estimation only includes “Tariff Differential subsidy” and not” cross subsidy” within the tariff structure.

> 58 The per liter subsidy for gasoline and diesel was calculated by taking the difference between the retail price and GDoP price for the year of analysis.

32
where C0A = I0 −∑pn0 tEEte−rt + ∑cn0 tAe−rt and where ctA are the variable costs associated with abatement.

In the next section, we focus our attention on the non-joint costs function given by equation (6). However, we also consider as an alternative the joint cost function given by equation (4). As it turns out, there are few practical consequences from choosing version of the applied model over the other.

# **2.2 The Empirical Emissions Abatement Cost Function**

Assuming fixed input prices, the basic expressions for the log-log and log-quadratic functional forms of the abatement cost for project _i_ can be given by: where C is the net present value of total abatement costs, A is total emissions abatement, and q is a vector of control variables (e.g., project duration, project types, and location).<sup>8</sup> Equation 7 is nested in 8 and the two are indistinguishable when γ is indistinguishable from zero. Given the parameter estimates, the marginal cost of abatement can be computed for different types of CDM projects corresponding to The vector of input prices, w, associated with the cost functions is not always observed; however, for a given time period and a given location, the prices of the inputs are likely the same. Thus, dummy variables for different project types, location, and time periods can be used as a proxy for the missing input price vector. Said more formally, let xm be the vector of inputs associated with a specific mitigation methodology, for example generating solar power. The associated vector of prices can vary by time (t) and

> 8 In the current setting, we suppress the time subscript assuming that the equilibrium level of abatement would be the same in each year. This restriction is consistent with the CDM pipeline dataset in which expected emissions abatement and investments are annualized based on the PDDs. We expect to relax this assumption in future work when data on
<br>20.9<br>20 14.5<br>11.4<br>9.5<br>7.4 7.1 7.4<br>2.2<br>0<br>Ratio = 1 (reference)<br>Bandung, Indonesia Karachi, Pakistan Kathmandou, Nepal Manila, Philippines Mumbai, India Phnom Penh, Cambodia Chonburi, Thailand Malé, Maldives Seoul, South Korea Bangkok, Thailand Port Vila, Vanuatu<br><!-- End of picture text --> _Source: (UN-Habitat, 2003). Based on consumer surveys by Asian Development Bank._

> 9 Detailed tables on water prices in Dar es Salaam and in Asian cities may be found in the Appendix section (Table 6 ; Table 7).

15
# **Table of Contents**

|**1.**In|troduction ..........................................................<br>1|
|---|---|
|2.|The data: Livings Standard Measurement Study Surveys in fifteen countries .....................**.**.......<br>3|
|3.|Who has infrastructure services? .........................................................<br>.<br>6|
|3.1.|Global infrastructure coverage ..........................................................<br>6|
|3.2.|<br>Coverage in urban and rural areas ..........................................................<br>8|
|3.3.|Coverage by country .......
.06|
||(0.023)|(0.026)|[0.800]|
|Observations|1,105|1,110||
|Percent of incorrect answers|-0.057***|-0.050***|0.17|
||(0.014)|(0.015)|[0.680]|
|Observations|1,105|1,110||
|Percent of omitted answers|-0.053**|-0.079***|1.62|
||(0.020)|(0.020)|[0.203]|
|Observations|1,105|1,110||

_Note:_ ** significant at 5 percent; *** significant at 1 percent. Robust standard errors in parentheses clustered at school level. The _p_ - values are in brackets in the third column. Controls: (a) students’ characteristics (gender, age, nationality, parent’s education, if the student missed, at most, one class per month, if the student has internet in his/her home), (b) teacher characteristics (gender, age, years of experience, if she/he has postgraduate certificates), and (c) school characteristics (school size, seventh grade size, seventh repetition rate, FEPBA score in Language, school district, or location). All the regressions exclude the classrooms where the Human Body unit was not taught.

_Source_ : Authors analysis based on experimental data generated for this study.

**Table 7. Results on Science learning according to teacher experience**

|_Independent variable_|(1)<br> (2)|(3)|
|---|---|---|

3