rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Appendix A: Methodological details**

## **A.1 Tabulations by National Statistical Offices**

For Australia, we use data on income growth by quintile from Table 2 of Australian Bureau of Statistics (2021) and apply these growth rates to the 2019 welfare vector for Australia. The data from the Australian Bureau of Statistics reflects gross equivalized income while the 2019 welfare vector we use reflects per capita disposable income, creating an inconsistency between our 2019 and 2020 welfare vectors. The Australian Bureau of Statistics (2021) only makes growth rates available comparing the second half of 2020 with the second half of 2019. We do not have data from the first half of 2020.

For Canada, we rely on growth rates of disposable income by quintile for 2020 (Statistics Canada 2022). Though the growth rates use equivalence scales, we apply them to our 2019 welfare vector that is per capita based. The growth rates are nominal, so we deflate them all with the CPI.

In China we rely on growth rates in per capita disposable income of rural/urban households by quintile (Table 6-3 and 6-12 in National Bureau of Statistics of China 2022). We face two challenges when using this information: (1) the quintiles are created at the household level in contrast to our 2019 welfare vector for China which is at the individual level, and (2) we use consumption data for China, for which no quintile tabulation is published. We ignore the first issue and match the growth rates implied by each quintile to the 2019 distribution for China. Since the National Bureau of Statistics of China (2021) publishes _mean_ growth rates of consumption by urban/rural areas, we subsequently scale the urban/rural vectors to match the growth rates in consumption in 2020. We thus assume that the differences in growth rates along the distribution are the same whether income or consumption is used.

For Japan, we use quintile-level data on the growth in disposable income per capita from Table 22-1 of the
, World Trade Organization, World Bank World Development Indicators, www.PolicyUncertainty.com, Baker et al. (2016), and authors’ calculations. See Table 1 for details on sources and definitions. Notes: Heteroscedasticity-robust standard errors corrected for clustering at country level are reported in parentheses. * p<0.10; ** p<0.05; *** p<0.01. Panel covers 18 countries and period 1995-2018. Growth indicators are computed as log-differences. A lag effect is allowed in specifications 1b and 2b. Specifically, the EPU aggregate for year _t_ is based on monthly indexes from January through December of year _t_ , in specifications 1a and 2a, and from November of year _t-1_ through October of year _t_ , in specifications 1b and 2b. Results for goods import volume specifications are robust to using data on goods import volume from World Trade Organization.

> 4 Data on services import volumes are not reported separately by any international organization.  Therefore, we do not run a separate regression for services import volumes.

9
1%|3%|-|8%|
||Per Employee|52.04|492|39.40|205.43|197.21|-|151.56|

a Intensity for firms doing R&D b For Pakistan only 23 firms with information available Source: Author’s elaboration from Enterprise Survey (2014) Investments in equipment appear as the most important investment in knowledge capital (except for Pakistan). Leaders do more training than laggards, with nearly half of Indian firms providing training to their workers and spending an amount per employee that doubles that of the laggard group. However, all countries in the region have spent less money on training per worker than on R&D. Few firms in the SAR acquired new licenses.

The pattern of R&D is very heterogeneous when considering firm characteristics, both within and across country groups (Table 3). In India, small, non-exporters, national and very young firms are more R&D intensive, while in Bangladesh, the pattern is slightly different, with large, exporters, foreign and old firms as more R&D intensive.<sup>5</sup> More importantly, In Pakistan there is a very large concentration of R&D activity in an extremely low number of firms.

> 4 Details upon request.

> 5 These findings were statistically significant.

9
bureaucracy, the availability of information on the local economic situation, quality of life and quality of urban development and safety (IACE, 2019, 2018, 2017, 2016).

- _Proximity to the port (POR)_ : This is a binary variable that indicates the existence or not of a maritime freight terminal within 60 km of the governorate. The proximity to port is a critical determinant for certain FDI decisions particularly for foreign investors with significant share of local production turned to export (INS, 2019).

Finally, we include in the model a dummy variable to capture the behavior of foreign investors in response to the 2011 revolution. This variable is given the value 0 for the year preceding 2011 and 1 for the post revolution years.

# **_4.2. Methodology_**

Using regional annual data from 24 governorates over the period spanning from 2008 through 2018, we start by constructing and estimating a model measuring the impact of a set of determining factors for the attractiveness of FDI . Similar to past research that have provided strong empirical evidence for certain persistence of the FDI across regions and an endogeneity in the relationship between investment inflows and certain macroeconomic determinants, we chose to run a dynamic panel model that accounts for the lagged dependent variable in the explanatory variables (Campos and Kinoshita;2003, Walsh and Yu;2010). This approach, Kinoshita and Campos (2002) and Batana (2011) argue, will allow to incorporate the past FDI inflows as a proxy for the agglomeration effect. In this paper, we follow the same approach by using a GMM model. As pointed out by several authors in dynamic panel models where endogeneity of certain variables is suspected, the results of OLS and fixed effects estimations will be biased upwards and downwards, respectively. To correct for this bias (finite sample bias), the specifications of our model below follow the method suggested by Arellano and Bover (1995) and Blundell and Bond (1998) to estimate the model using the system GMM approach. As
<mark>On the brighter side, with support from the World Banak and other partners, the country is working to create a</mark> comprehensive, integrated, and effective SSN and with sufficient fiscal space the dedicated social protection could include the welfare and recovery of IDPs and returnees.

Plans to explore how to integrate the MPCA targeting models used by the humanitarian agencies (CLCIPMT) and the SSN targeting model used by the Government of Iraq (MOLSA-PMT) started in 2018 through a Social Protection workshop convened by FCDO (formally DFID, with the Ministry of Labor and Social Affairs ( MoLSA), the World Bank (WB), the Cash and Livelihoods Consortium for Iraq (CLCI), the CWG and key UN agencies in attendance. The workshop aimed to develop a roadmap document for the transition from MPCA to state-led SSN. Technically, it involves ways to identify beneficiaries of the MPCA caseloads eligible for assistance under the SSN. One of the initial outputs from the eligibility analysis exercise is the PPMT developed by the World Bank. PPMT is a probabilistic model that predicts the likelihood that a household eligible for MPCA would also be eligible for SSN. The PPMT results could assist the government in prioritization, either by ranking the most vulnerable households receiving humanitarian cash transfers and/or by identifying the conflict-affected areas where a larger number of IDPs receiving humanitarian cash transfers would likely be eligible for SSN assistance.

The procedure and result of applying the PPMT on two caseloads of the humanitarian data sets - the CLCI Legacy caseload and the new MPCA caseload are available in the associated desk review paper (Phadera, et al., 2022). Results from applying the PPMT models to these data sets suggest potential for both significant referral numbers and a sequenced referral strategy. To allow for budget flexibility, the desk analysis focused on the level of expansion (e.g. targeting the poorest 15, 20, etc. percent nationally) and the degree of confidence of a successful referral. However, given the small number of overlapping proxies
strategy of the previous rounds of the panel, a sub-sample of 334 localities of the 506 original localities was selected.<sup>7</sup> As Panel A in Table 1 shows, this first filter meant that richer eligible households in less marginalized localities would become the sample target. Each of the selected localities was visited and the surveyors looked for the 18,564 households included in the 1997 sample that inhabited that locality. The targeting strategy employed at the beginning of the program to select beneficiaries defined 12,519 of these households as eligible. We will focus our analysis in this set of households, since they provide the variation that would be relevant for the analysis.<sup>8</sup> Priority was given to households that had young members at the start of the data collection (1997-2000). In total, 7,824 eligible households were located in the 2017-18 data collection. Hereon, we

> 6At the beginning of the program, priority was given to rural localities. In Mexico, rural localities are defined as those with fewer than 2,500 inhabitants.

> 7Localities that were more accessible and as a result richer within the 506 original full sample were selected for the ENCEL 2017-18. A set of localities that were added in the 2003 round are not included in the sampling framework for the 2017-18 round.

> 8Non-eligible households could have enrolled later to the program. However, comparing non-eligible households from treatment and control communities would involve selecting households that initially did not receive the program and were not affected by the random allocation of treatment.

8
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Water supply and sanitation

## **Achievements**

The share of the population in Mali that continues to practice open defecation or use surface water is among the lowest in the continent. In 2006, about 4 percent of the population relied on surface water, which is eight times lower than the average for low-income countries in Sub-Saharan Africa. Similarly, around 20 percent of the population practices open defecation, which is roughly half the level in other low-income countries (table 7).

**Table 7. Benchmarking water and sanitation indicators**

|||Low-income<br>countries|Mal|i|Middle-income<br>countries|
|---|---|---|---|---|---|
||Unit|,<br>mid-2000s|Early 2000s|Late 2000s|,<br>mid-2000s|
|Access to piped water|% pop|10.5|8.9|10.8|52.1|
|Access to standposts|% pop|16.2|20.2|15.5|18.9|
|Access to wells/boreholes|% pop|38.3|65.1|69.5|6.0|
|of which, protected wells/boreholes|% pop||14.2|29.8||
|of which, unprotected wells/boreholes|% pop||50.9|39.7||
|Access to surface water|% pop|37.4|5.8|4.2|13.0|
|Access to septic tanks|% pop|4.9|6.0|2.4|40.8|
|Access to improved latrines|% pop|9.9|40.8|47.6|1.4|
|Access to traditional latrines|% pop|50.
HNLSS, by contrast, suggested a poverty rate for 2009/10 of 56.4 percent (more than 10 percentage points higher) corresponding to 89.4 million people.

_Figure 1 Backcasted trend in poverty rates and number of poor at the US$1.90 poverty line, pass-through of 1_

- a) Poverty rate, % (US$1.90) b) Number of poor, millions (US$1.90)

<!-- Start of picture text --> 60.0 95.0<br>56.4<br>89.4<br>90.0<br>55.0<br>85.0<br>50.0<br>78.5<br>80.0<br>46.3<br>45.0<br>42.841.8 39.1 75.0 71.4<br>40.0<br>70.0<br>35.0<br>65.0<br>30.0 60.0<br>Backcast, pass-through=1 PovcalNet Backcast, pass-through=1 PovcalNet<br>Poverty Rate (percetnt)<br>Number of poor(millions)<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br><!-- End of picture text --> Note: the figure shows the backcasted poverty rates series at the US$1.90 poverty line (panel a). Using household consumption data from the 2018/19 NLSS and sectoral GDP growth rates from the World Bank-MFM-Tool, we backcast household consumption over the previous decade by applying the same growth rate to household consumption and mapping the sectoral information to the household’s head sector of employment. Number of poor (panel b) is estimated using WDI population data.

Using the same methodology and data we can also estimate
firms comply with 91 percent of compliance questions, 92 percent of nondisclosure questions, but 88 percent of disclosure questions.

**Table 6: Number of firms and mean compliance rates over time**

|**Year**|**Firms**|**All**|**Disclosure**|**Non‐disclosure**|
|---|---|---|---|---|
|2010|65|0.888|0.876|0.890|
|2011|116|0.911|0.902|0.913|
|2012|136|0.908|0.902|0.909|
|2013|139|0.917|0.895|0.921|
|2014|177|0.901|0.843|0.911|
|2015|219|0.887|0.834|0.895|
|2016|253|0.911|0.871|0.918|
|2017|286|0.922|0.881|0.929|
|2018|106|0.942|0.876|0.947|
|All years|457|0.910|0.875|0.916|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

Table 7 explores the non‐compliance rate before and after program announcement, for all compliance questions, all compliance questions that are related to public disclosure, and all compliance questions within each compliance cluster. For now, we limit the analysis of the public disclosure program to focus on the date of program announcement.<sup>8</sup> The non‐compliance rate for all questions and non‐disclosure questions are different: 10% versus 13% before the program announcement, and 8% and 12% after the program announcement.

It
poverty line to determine the poverty status of each household in each simulation, which is aggregated across simulations to estimate poverty in each area. Estimates of mean squared error are obtained through a parametric bootstrapping approach, which is described in detail in Gonzalez-Manteiga, et al (2008) and Molina and Marhuenda (2015). We leverage the open nature of the code to make one modification to the EMDI package related to the use of weights, which allows for household size to be used as a weight when aggregating the simulated household welfare outcomes into area-level poverty estimates.<sup>16</sup> As a robustness check, we also utilized the second and third versions of an alternative software package, the Stata SAE package (Nguyen et al, 2018).<sup>17</sup> The main differences between the packages are summarized in Table 4, and discussed in Annex A. These differences in design and methodology across different packages, particularly the choice of a non-parametric versus parametric bootstrap method, affects the resulting estimates of poverty rates and their mean squared errors. Comparing simulated small area estimates against actual census data provide evidence on how these differences affect estimated poverty rates and mean squared error in two use-case scenarios.

_Table 4: Differences between Stata SAE and R SAE package_

||Stata ELL||Modified R EMDI|
|---|---|---|---|
||Version 2|Version 3||
|Heteroscedasticity<br>correction|Optional|Optional|Not available|
|Type of bootstrap|Traditional Non-Parametric<br>clustered bootstrap with<br>varyingsample composition.|Parametric bootstrap<br>with constant sample<br>composition.|Parametric bootstrap with<br>constant sample<br>composition.|
|Model fitting method|Henderson method 3 with<br>non-parametric bootstrap|Henderson method 3|Maximum likelihood<br>estimation|
|Empirical Best<br>estimation|Optional|Required|Required|
|Sample
# **1. Introduction**

Most entrepreneurs, particularly female-led firms, are unable to grow their businesses beyond smallscale operations. Firm growth is constrained by many factors, including access to finance, skills, technology, and networks. Evidence points to significantly greater challenges in accessing finance among female-led businesses compared to their male counterparts.<sup>1</sup> The growth of women-led firms is further constrained by contextual factors such as regulatory and legal restrictions, social perceptions about women, and gender-based violence (Ubfal, 2023). As policy makers seek to improve the business environment for private investments, a deeper understanding of the specific constraints faced by women entrepreneurs can help maximize the efficacy of these efforts. This paper contributes to an understanding of financial constraints faced by women-managed firms, especially in contexts of stringent social norms, by using firm-level data from 61 countries.

Gender disparities in financial access can be attributed to demand-side factors, including social and cultural norms that affect the ability of female entrepreneurs to apply for credit (Cole and Mehran, 2018) or access external funds (Aspray and Cohoon, 2007; Asiedu et al., 2013; Guzman and Kacperczyk, 2019). On the supply side, explicit discrimination, such as imposing higher interest rates (Muravyev et al., 2009; Alesina et al., 2013), or additional implicit requirements on collateral (Cowling et al., 2020) or guarantors (Brock and De Haas, 2023), increases the probability of their credit application rejection (Aristei and Gallo, 2016).<sup>2</sup> These factors can vary significantly by country context where pronounced gender bias, in addition to systemic issues in the development of financial institutions, can play a crucial role (Hewa-Wellalage et al., 2022; Bertrand and Perrin, 2022).

Gender equality is crucial not only for its social value, but also for its significant impact on broader macroeconomic outcomes. For example, Morazzoni and Sy (2022) show that credit
without stray light correction)

- 3) Version 4 DMSP-OLS Nighttime Lights composites. The lights from cities, towns, and other sites with persistent lighting, including gas flares. Ephemeral events, such as fires have been discarded. Calibrated across sensors and years using Elvidge 2014 coefficients

- 4) Average precipitation per year, created using UDel Precipitation data set (v4.01)

- 5) Average air temperature per year, created using UDel Air Temperature dataset (v4.01)

- 6) Global slope (in degrees) derived from Shuttle Radar Topography Mission (SRTM) data set (v4.1) at 500m resolution

- 7) Global elevation (in meters) from Shuttle Radar Topography Mission (SRTM) data set (v4.1) at 500m resolution

- 8) Binary indicating locations with deposits of known on-shore oil and gas deposits

- 9) Standard MODIS land cover type data product (MCD12Q1) in the IGBP Land Cover Type Classification

- 10) Yearly value for Normalized Difference Vegetation Index (NDVI). Created using the NASA Long Term Data Record (v4) AVHRR data

> 3 Data available at <u>http://geo.aiddata.org/query/#!/</u>

20
-1.57%|0.131|-3.72%|
|National<br>0|0|42.92||0.157||
|National<br>50%|1|42.69|-0.53%|0.156|-0.59%|
|National<br>50%|0.5|42.86|-0.15%|0.157|-0.31%|

Source: Computed by authors from EICV data 22
- Lerner, Josh, Morten Sorensen, Per Stromberg (2009) What Drives Private Equity Activity and Success Globally. manuscript, Stockholm School of Economics.

- Levine, Ross (2005) Finance and Growth: Theory and Evidence. In Philippe Aghion and Steven Durlauf (eds.) Handbook of Economic Growth 865–934.

- Lucas, Robert E (1990) Why doesn’t capital flow from rich to poor countries. _American Economic Review_ 80(2):92-96.

- Mauro, Paolo (2003) Stock returns and output growth in emerging and advanced economies. _Journal of Development Economics_ 71(1):129-153.

- Patel, Dev, Justin Sandefur, Arvind Subramanian (2021) The new era of unconditional convergence. _Journal of Development Economics_ 152:102687.

- Rapach, David, Guofu Zhou (2013) Forecasting stock returns. In Handbook of Economic Forecasting, 2:328-383. Elsevier.

- Renneboog, Luc, Jenke Ter Horst, Chendi Zhang (2008) Socially responsible investments: Institutional aspects, performance, and investor behavior. _Journal of Banking & Finance._ 32(9):17231742.

- Roller, Lars-Hendrik, Leonard Waverman (2001) Telecommunications Infrastructure and Economic Development: A Simultaneous Approach. _American Economic Review_ , 91(4):909-923.

- Sorensen, Morten, Ravi Jagannathan (2015) The public market equivalent and private equity performance. _Financial Analysts Journal_ 71(4):43-50.

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World Bank (2019a) World Development Indicators World Bank (2019b) Global Financial Development Database (GFDD) 29
et al. (2019) also find that similar patterns of measurement error in earnings in the US Current Population Survey (CPS).

# _Sparseness_

Household surveys used to measure the top tail of the wealth or income distribution may have a sample of only a few thousand households. This makes it possible that no very high-income households or individuals are sampled, meaning one has a right truncated distribution. And when such individuals do appear in the sample, they may appear to be incorrect outliers (Jenkins, 2017, Lustig, 2020). This can lead to volatility when measuring the top tail of the distribution over time within countries (Burkhauser et al., 2017) or across countries, meaning it is harder to discern trends or make cross country comparisons. But there may also be incorrect outliers, and discerning which data are real and which are not is thus important.

# _Proxy respondents_

We highlighted above that item non-response can result from proxy respondents answering “don’t know” to questions on incomes of other household members. Many household surveys, including the LSMS, ask most or many questions to the most knowledgeable household member (Kilic et al. 2021, Doss et al. 2008) or, in the US CPS, to any one household member (Bollinger et al. 2019), rather than to each member individually.<sup>4</sup> In discussing the measurement of consumption, Deaton (2005) notes that proxy respondents may also answer incorrectly or under-report and that this is likely to be worse for richer households when the responding household member may not know about all consumption of other household members. This means proxy respondents may introduce additional measurement error, over and above that discussed above.

Hasanbasri et al. (2022) showed that measured wealth inequality is higher when questions about individual asset ownership assets are asked to each household member individually, rather than to a most knowledgeable household member. This research suggests that whether data was collected from each member or from one member in a household
quality of the data and the performance of the underlying algorithm. We processed tweets in real-time and dispatched a motorcycle delivery service (Sendy) to the site of the crash within minutes. The Sendy driver was tasked with verifying and reporting whether a crash actually happened in that location. If a driver could not see the crash, they were instructed to ask a bystander whether a crash had occurred but was cleared or whether a crash occurred nearby. Drivers were able to arrive at the crash location quickly; the median time between being alerted of a crash and arriving at the scene was 26 minutes.

# **Results**

The methods laid out here created a georeferenced RTC dataset that was previously unattainable and produced one of the first real-time maps of RTCs in Nairobi. We classify 52,228 tweets as crash-related out of a universe of 874,588 tweets during 2012 - 2020 (Panel A of Figure 2). This is based on the SVM algorithm, which we find performs better than the Naive Bayes algorithm according to the F1 statistic (see Table S4 in the SI). We geolocate 32,991 time-stamped crash tweets from August 2012 to July 2020 and cluster them into 22,872 unique geolocated crashes (panels B and C of Figure 2 show the unique crashes generated by Twitter daily using the algorithm and clustering). In our truth dataset, where we manually 7