rafmacalaba/data-use-mentions-extended huggingface dataset:
Our second measure of entry and startup costs, tries to capture the actual number of procedures, time, and costs to initiate operations in a country. Data on the procedures required to open banks across countries come from Barth, Caprio, and Levine (2001). They construct an index of bank entry requirements that takes values from 0 to 8 depending on whether banks are required to submit none, some, or all of the following to issue a bank license:

- (1) Draft by-laws.

- (2) Intended organizational chart.

- (3) First 3-year financial projections.

- (4) Financial information on main potential shareholders.

- (5) Background/experience of future directors.

- (6) Background/experience of future managers.

- (7) Sources of funds to be used to capitalize the new bank.

- (8) Intended differentiation of new bank from other banks.

This index is combined using the method of principal components with two other measures of the costs of starting a business that come from the World Bank _Doing Business Indicators._ In particular, we include survey estimates of the actual monetary costs (in dollars) and, separately, the time (in number of days) involved in opening a business across countries. The first measures all identifiable official expenses in setting up any business (not necessarily banking), namely fees, costs of procedures and forms, fiscal stamps, legal and notary charges, etc.<sup>9</sup> The second, the

> 9 As stated in the Doing Business Report “the text of the Company Law, the Commercial Code, and specific regulations and fee schedules are used to calculate the costs [of starting a business]. If there are conflicting sources and the laws are not clear, the most authoritative source is used. The constitution supersedes the company law, and the law prevails over regulations and decrees. If conflicting sources are of the same rank, the source indicating the most costly procedure is
particular equivalence scale depends on technical assumptions about economies of scale in consumption as well as value judgements about the priority assigned to the needs of different individuals such as children or the elderly” (OECD, n.d.). The modified-OECD equivalence scale appears to be the most widely used among the different equivalization methodologies for household income. It assigns a weight of 1 point to the first adult in the household, 0.5 points to each additional person aged 15 years or older, and 0.3 points to each child under the age of 15 (OECD, n.d.). Equivalized household income is then derived by dividing total household income by the sum of the equivalence points allocated to the household.

However, for the purpose of housing affordability calculations using the progressive EIR and the RIM or REM, two sets of equivalence scales will be needed: (i) one based on housing _plus_ non-housing costs for the progressive EIR, and (ii) another based on only non-housing costs for the RIM or REM.<sup>16</sup> In countries where there are no equivalence scales, perhaps a standard scale could be developed and applied. Given the variations in the definitions of equivalized and non-equivalized income, it is recommended that results of affordability analyses be presented in both forms, where possible.

4.4 Summary of Application of the REM in Urban Pakistan Using data from the Pakistan Household Integrated Economic Survey (HIES) 2018/19, the REM was used to estimate housing unaffordability for households in urban areas of the country (see Annex 1). The results from the application of the REM were juxtaposed with the results from the conventional EIR approach to compare robustness of the two methods.

As described in previous sections, the RIM approach is often limited in its ability to measure housing affordability in low income and developing country contexts due to the lack of reliable income data as well as appropriate budget standards (i.e., the minimum food and non-food expenditure that is necessary
Mobile phones have been used to collect data using three approaches: (1) _Face-to-face interview using phone, laptop, or tablet computer with electronic form._ The mobile phone essentially takes the place of paper in a traditional household survey, with enumerators conducting a “live” interview but capturing data provided by the respondent using an electronic device.<sup>2</sup> (2) _Direct response to phone using SMS, USSD, IVR, or electronic form._ Respondents interact with their own devices to answer questions digitally using the mobile’s keypad. The interface and data transfer technology employed could be SMS, USSD, or electronic forms. An alternative approach is to link the respondent to an automated interactive voice response system (IVR) in which the respondent dials a number and is prompted by a computer to record answers using the keypad.

(3) _Interview with enumerator via phone._ Respondents receive a call from an operator at a call-center, are asked questions and dictate their answers which are then recorded by the operator on a computer.

Few systematic studies are available that compare the data quality and data collection efficiency of these alternative approaches. In one India-based study evaluating the accuracy of data collection using electronic forms, voice, and SMS through mobile phones, MIT researchers found that a voice interface (in a setting where respondents dictated answers through the mobile phone to an operator in real time) provided the highest level of accuracy, leading them to favor the voice-operator combination for the implementation of a tuberculosis program (Patnaik et al. 2008). In Tanzania, a research team from Twaweza, a citizen-centered initiative in East Africa, favored a voice-operator interface to collect weekly data on provision of public services, due to implementation challenges associated with USSD, electronic forms, and IVR (MobileDataGathering 2011). For reasons explained below, SSEPS also employed a voice-operator approach.

> 2 The face-to-face, device assisted approach has also been used with devices other than mobile phones, such as tablet and notebook computers
Arai et al. (2020) : The Hidden Potential of Call Detail Records in The Gambia.

# **1. Introduction**

# **A. Unlocking the potential of mobile phone data**

The COVID-19 pandemic has highlighted the value of real-time, high-resolution data to inform decision making in a crisis situation. External shocks, such as climate change, conflicts, and pandemics trigger population movements and displacement. In response, decision makers require information about origins and destination of refugees and migrants to inform a rapid policy response. Yet, survey and administrative data exhibit severe shortcomings which complicate any crisis assessment: traditional data is likely to be outdated during a quickly evolving crisis situation, and rapid data collection is expensive, slow, and often incompatible with a lockdown situation. By contrast, new data sources that are readily available have gained prominence, notably satellite and location data. These offer real-time snapshots at a high level of spatial resolution.

Call Detail Record (CDR) data offers the potential to document population dynamics in near real time. CDR data is available as high-frequency, highly localized data which can be collected and processed in real-time and at relatively low cost. In developing countries where smart-phone penetration is low, CDR is likely to have much more coverage than GPS data. The analysis of CDR data involves investments in technical capacity and IT infrastructure. One defining characteristic of such data is that it is updated in near real time and requires terabytes of storage capacity, either on the cloud or servers on premise. A so-called data pipeline is required to automate the data flow. The raw data from the Mobile Network Operators (MNO) is encrypted and shared with the regulator, who in turn aggregates it into indicators available for analysis. It is also essential to build the technical capacity of analysts in managing and analyzing data, ensuring the sustainability of the initiative.

The use of CDR data raises privacy concerns and requires a strong institutional framework to regulate access and ensure confidentiality. Researchers and governments have worked closely with regulatory
|0.44|
|Pct. ofpeers employed at least one month beforegraduation/entry|0.65|0.18|0.26|0.25|
|**Panel D.Outcomes**|||||
|Average monthly wage conditional on working (USD, PPP)|1,031.40|577.56|876.06|381.36|
|Average monthly wage (USD, PPP)|715.62|678.84|300.29|472.02|
|Percent of time employed after graduation|53.37|43.97|24.01|38.23|
|Employed at least one month aftergraduation|0.70|0.46|0.35|0.48|
|_Number of observations_|7,843||1,239||

_Sources_ : Own calculations using individual-level administrative data for Brazil and Ecuador. For more details on data sources and variable definitions, see Section 3.3 and Appendix 1.

_Notes_ : In this table, the unit of observation is a program graduate. Statistics are weighted using WBSCPS sampling weights. A higher value of the socioeconomic index indicates a higher socioeconomic status. For a given student, her peers are the other students in her program and cohort; cohorts are defined in Section 3. Means of PCA scores are different than zero due to the use of sampling weights. In Panel C, previous labor market outcomes are pre-graduation (Ecuador) and pre-enrollment (Brazil). Average monthly wage conditional on working corresponds to individuals who are employed at least one month after graduation. For the remaining individuals, average monthly wage equals zero; therefore, the “Average monthly wage” row shows unconditional average monthly wages (which equal zero for individuals who do not work formally at all). Wage statistics trim off the 1<sup>st</sup> and 99<sup>th</sup> percentile. Purchasing power parity (
.021|0.028|0.023|
|Baseline scenario, +3.6% in Gini|0.036|0.0355|0.043|0.040|0.037|
|Median break-even ̃_α_, no interven|tion and|saturati|on|||
|Baseline scenario, distribution-neutral|0.609|0.491|0.323|0.259|0.387|
|Baseline scenario, +3.6% in Gini|1.089|0.869|0.642|0.354|0.646|

Yet, we cannot rule out no-intervention as a plausible policy in some low-income countries. Their median break-even _α_ ̃ are slightly larger than one under one of the two ̃ scenarios shown in Table 3. At least some low-income countries have break-even _α_ larger than two (see Figure 5). For values of _α_ ̃ in that range, the welfare comparison in the poorest countries remains theoretically ambiguous, although only for observers placing a low normative value on an additional year of life relative to a year in poverty.

Given an observer's xed normative choice of _α_ , the lower _α_ ̃ the greater the welfare losses associated with the no-intervention scenario, relative to the actual early June estimates. The downward-sloping relationship in Figure 5 thus indicates that the welfare losses from policy inaction relative to the responses observed until early June 2020 increase sharply with GDP per capita. This eect is emphasized when using a common poverty line for all countries, such as the international extreme poverty line (IPL) of $1.90 a day (Ferreira et al., 2016). Figure 6 provides a scatter plot of _α_ ̃ for all countries using the IPL. The slope of the tted line is very steep. When using extreme poverty in all countries, we obtain _α_ ̃ values well below 0.1 in high-income countries.<sup>25</sup> This is because even in
Murphy et al. (1991) in using engineering density derived from Maloney & Valencia (2022) and census data. Figure 4 shows the evolution of the logarithm of copper revenue by Chilean nationals, and US nationals in the US, and Figure 5 shows the evolution of the number of domestic engineers for both countries. Note Chile has a higher initial revenues, but is overtaken by the US within the first decades. On the other hand, US has a higher initial entrepreneurial capital and keeps investing in it at a higher pace than Chile.

To replicate the observed data with our model we start, for each country, with an initial _τ_ 0 equal to the inverse of the number of engineers in the population in 1850, and an initial _μs_ equal to the logarithm of copper revenue in US in 1850. The initial _μr_ is also set equal to the initial _μs_ , the second _μr_ is set equal to the initial logarithm of copper revenue for each country, and _t_ is set to infinity, that is the signal reflects the true expected return of the risky project. The depreciation parameter _δ_ is set to 8 and the cost of investing in entrepreneurial capital _c_ is set to 0 _._ 5 for both countries. The process for the risky projects arising each period follows a normal distribution with mean equal to _μs_ and variance equal to the inverse of _τ_ given the perfect informativeness of the signals.

We use the same seed for the realized returns of the risky project for both countries. Figure 6 shows the generated evolution of copper revenue, while Figure 7 shows the implied evolution of entrepreneurial capital. Note the numerical exercise replicates the observed paths in Figures 4 and 5. Despite Chile having a higher initial return, US quickly surpasses it because of a higher initial entrepreneurial capital and a lower initial return.

To gain further clarity, we decompose the effect of each by assigning the U.S. initial value to each as a base and then varying them individually. Figures 8 and 9 show the evolution of
_Five,_ our work confirms that better management practices is associated with superior firm performance. Firms with more structured management practices have higher sales per employee, profitability, are more innovative and more likely to adopt sophisticated technology. This result is highly robust to a numerous checks and controls as well as varying measures of firm performance.

_Finally_ , while explaining the differences in management practice across firms, it becomes clear that among the firm characteristic that explain at least a portion of the variation in management practices, firm size, foreign linkages and the education level of non-managers are the most relevant. Surprisingly firm age is negatively associated with better management practices, especially in services. This implies that unlike in the United States and Mexico, firms do not learn to be better managed as they age. Thus, it is possible that a lack of pro-competitive forces in Croatia and more so in the services sectors do not promote learning and possibly hinder market selection.

If management matters for firm performance, then why firms do not adopt better management practices? Our survey also includes a question relating to the firm’s own perception of its management practices, which was originally a part of WMS survey questionnaire. More specifically, we ask the following question: “ _Excluding yourself, how well managed is your firm on a scale of 1 to 10, where 1 is worst practice, 10 is best practice and 5 is average_ ”. Prior studies using WMS data that compared self-perception of management capabilities with the objective management score found a huge divergence between observed management capabilities and perceived one. In line with these findings, we uncover that firms in Croatia also have a higher perception on their management practices ( **Figure 9** ). Given that firms do not realize that there is a problem with respect to their management practices, it does not get the required attention. Our survey objectively measures management capabilities among firms, and also provides an understanding of the capabilities that these firms are weak at.

_Figure 9: Firms perceive their management to
juvenile catch). Status quo tons landed are 2.1% lower than in my counterfactual projection because anchoveta growth exceeds natural mortality within the support of the data. FOB export revenues were $1.79 billion in 2017 (PRODUCE, 2018 _a_ ). I therefore calculate that the closures policy reduces exports by $75 million per year

> outcome variable and control variables using third-party inspector data.

> 34Larger closures could cause larger increases in juvenile catch because congestion costs are lower or if larger closures are a stronger positive signal of fishing productivity.

44
12 but this perception does not prevent them from engaging in the SME business, possibly reflecting their mandates to fill the SME financing gap, regardless of the associated risks.

GCC banks seem to adopt stricter selection criteria for engaging in SME lending, relative to nonGCC banks. As shown in Figure 14a, in selecting potential SMEs, a larger share of GCC banks considers specific factors such as the growth prospects of specific SME sectors, the size of their exposure to these sectors, and the existing clientele. The apparently stricter selection criteria adopted by GCC banks is consistent with the higher perception of risk in the SME business among these banks, and with the smaller volumes of SME lending as well. Interestingly, GCC banks do not target exporting SMEs, a result which can be explained by the limited number of exporting SMEs in oil economies (Figure 15c).

At the same time, the apparently lesser relevance of selection criteria among non-GCC banks disguises large differences between state and private banks. As shown in Figure 14b, a much lower share of state banks adopts selection criteria to identify and screen their clients and build up their SME portfolios than private banks. This again probably reflects the broad mandates of state banks to fill the SME financing gap and serve the SME sector.

Less than half of GCC and non-GCC banks have developed internal scoring models to assess the risk of current and prospective clients (Figure 15a). An even smaller share makes use of external scoring. Since most public registries and private credit bureaus still do not provide scoring models, the respondent banks are referring to models developed by external consulting firms, frequently non-customized models. Almost all the banks are making an effort to develop internal rating systems, combining credit scores of the owner with other information from the SME, both qualitative and quantitative. It is noticeable that very few banks use automated application processing. This result reflects to some extent the weak state of development of credit reporting systems in MENA, and the limited reliance on the quality of internal credit scores. However
future, high-resolution DTMs based on technologies such as LIDAR might enable such an approach even at the country-level, but this is beyond the scope of this work.

# <mark>4.2.</mark> Flood exposure of agriculture and aquaculture production

The exposure of agricultural production to flooding is estimated based on the land-use maps described in section 3.1. Specifically, a distinction is made between areas where there is farming of rice, orchards and other crops. As the resolution of the datasets differs, the flood maps are scaled up to match the detailed resolution of the land use data. This can be conceptualized by dividing a square into four new squares that occupy the same area. For each agricultural land use type and each flood return period, the amount of flooded agricultural area per province is extracted using a spatial overlay. This is done separately for coastal flooding and for riverine flooding. Agricultural area is considered to be flooded if it is affected by flooding to a depth of at least 25cm, a threshold chosen as most plants cannot withstand flooding above this height. Finally, the results of flood-exposed agricultural area are corrected to account for existing flood protection infrastructure in each province, as described in section 4.1.

Figure 4.1 and Figure 4.2 give an overview over the exposure of crop and rice production to flooding of three different return periods while accounting for protection infrastructure.

**Figure 4.1: Agricultural crop production is particularly exposed in the Red river and Mekong delta, especially for coastal flooding**

_a) Coastal flood risk: exposure of all agricultural land (sum of rice, crops and orchards)_ <!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>Return period: 1-in-25 1-in-100 1-in-500<br>97%<br>67%<br>88%<br>89% 43%<
respect to _퐸푡,푗_ , the event dummy. The number of lags for the dependent variable and the event dummy is denoted by p and set to 2 for estimation. This specification controls for country specific trends, lagged event dummies and past values of the dependent variable.

For our purpose, the potential endogeneity bias caused by contemporaneous interaction between financial sector variables and climate and environmental related natural disasters could reasonably be dismissed. It is unlikely that changes in banking sector asset quality, profitability and capital adequacy, for instance, can trigger natural disaster events, at least in the same year and several years after. In the longer run, when one’s focus is on transition risks, this direction of causality becomes more likely. Nonetheless, to guard against possible reverse causation between financial sector variables and disasters, lagged financial sector variables are used as controls.

# **5 Empirical results**

## **5.1 Non-performing loans**

We find that severe climate and environmental related natural disaster episodes lead to an increase in the level of system-wide NPLs to total gross loans, and the effects can be persistent (figure 3 and table 3). We consider two types of severe disaster episodes: a country-year identified by the EM-DAT database as associated with an official declaration of a state of emergency(as calls for international assistance are relatively rare) and a country-year consisting of multiple types of disaster episodes. As shown in figure 1, for the entire sample, the system-wide NPLs are estimated to have increased by 0.37 percentage point on impact after a disaster episodes associated with an official declaration of emergency. Two to three years after an episode with multiple types of disasters, the cumulative increase in system-wide NPLs stands at 0.56-0.6 percentage point on average. The magnitudes may seem to be modest, but at the country level, our estimates are probably biased against finding results of larger magnitude as most disaster episodes tend to affect selected regions within a country. This effect is likely due to
GNI per capita to the operational cutoff and (the log of 1 plus) the number of consecutive years in which GNI per capita has exceeded the operational cutoff. If income falls below the cutoff in any given year, this indicator resets at 0. The number of consecutive years is capped at 9, and a separate variable captures (the log of 1 plus) the number of years in excess of 9 that a country has been above the cutoff but not graduated. This captures the notion that a country that has been above the cutoff for so long may become less likely to graduate due to other factors. For example, there may be a negative element not included in the model that explains why a country has not graduated despite its income status.

We proxy for IBRD creditworthiness with the Institutional Investors Creditworthiness Index. It is based on surveys of international bankers, with scores potentially ranging from 0 (highest risk of default on sovereign debt) to 100 (lowest risk). Creditworthiness and income per capita have been historically the