rafmacalaba/data-use-mentions-extended huggingface dataset:
on measuring profits has recommended summary questions as potentially more accurate noisy measures ( **?** ).

Expenses reported in the weekly monitoring surveys were as follows: wages, outsourcing fees, inventory, rent, taxes, electricity bills, furniture, machinery, tools, repairs, and other. In the specifications presented in this paper, profits are calculated as total sales less wages, outsourcing fees, and inventory costs, each of which are recurrent variables operating expenses. Rent, taxes, and bills (including electricity bills)<sup>11</sup> are lump-sum costs that it would be inappropriate to include in weekly measures (in Ghana, these are most often paid on an annual or biannual basis). The study categorizes the purchasing and repairing of equipment as investment, and thus does not deduct furniture, machinery, tools, and repair expenses from sales in the profit calculations.

> 10In the analysis sample of 342 firms, the panel survey targeted a total of 2,394 surveys (342 by 7 weeks). 32 of these are missing, primarily due to travel by firm owners towards the end of the data collection period (near Easter).

> 11At the time of the survey Hohoe electricity consumers were on a post-pay meter system with intermittent billing. Most ECG consumers have since been transitioned to a pre-pay metering system that uses scratch cards and smart phone apps to purchase power piecemeal.

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The application of the “emerging” theories to housing and particularly to tenure issues belongs to a work program of the future, and is beyond the scope of this study. However, there are two relevant bodies of literature dealing with rental housing supply within a rudimentary equilibrium theory approach: (i) on the theory of rent control; and (ii) on the theory of residential filtering process.

Rent control and its distorting effects, including politics, populism and implications for housing market equilibria, have been extensively researched. The literature survey (Annex 1) gives an overview of these issues. The economists’ verdict against first-generation “hard” rent controls and ceilings is universal; however, more indirect, softer forms of second-generation rent controls that cap only usurious (short-term monopolistic) practices have been found to exercise a demand-stabilizing effect. Again, the theories are not sufficiently rich in structure to explain partly diverse empirical outcomes.

The theory of residential filtering (trickle-down) seems more useful, so far. Consider several submarkets of the housing sector that compete for consumers. Consumers gain in housing consumption capacity over their life cycle and face financial and transaction cost hurdles when trying to realize their perpetual desire to upgrade their housing status.<sup>12</sup> Filtering assumes not only that households change submarkets over their life cycles, but also that supply changes too, as older units filter down in the rent hierarchy, until they are modernized or exit the market through dilapidation and demolition. No rigorous specification of this model with modern economic theory tools is known to us; however, its rich structure has the potential to integrate both complex supply and tenure choice elements in a consistent equilibrium theory. As discussed later (Annex 1) in more detail, numerous conclusions can be drawn for tenure policies and need to be researched further. The most important one is that price controls or subsidies in a specific sub-market may impede the entire filtering chain, leading to a decline in overall housing investment.

Summarizing the small body of applicable
ily. Firewood and water collection are women’s tasks and often consume more than half the day. The remoteness of the region, lack of good roads, and stringent social norms mean that, once married, women are unable to visit friends or even parents regularly. As many as 47 percent of Uttarakhandi women reported not having the final say on visits to family and friends [IIPS and ORC Macro, 2007]. Focus group interviews and our survey data suggest that women’s lives are defined by their husbands, children, and in-laws, and women seldom participate in the political process, even at the village level. Constrictive social norms thus restrict women to the narrow spheres of family and housework. In our qualitative field work, most women reported being in contact with fewer than five people outside their family.

_Mahila Samakhya_ is a women’s empowerment program that started in 1995 in the study area. The program is centered around weekly meetings of women’s support groups, which are often the first time participants have had the chance to talk about themselves and think about their place in the household and society [Janssens, 2010]. Nussbaum [2000, p. 281] describes the program as attempting to “reconstruct the family by altering social norms and perceptions” and that by creating women’s support groups, the program “transforms the family profoundly, making it no longer the sole source of personal affiliation.” She adds that such support groups have transformative power because although getting the groups going can be an iterative process, once women start thinking about their lives, “it is difficult to go backward” [Nussbaum, 2000, p.290]. In Nussbaum’s words, these tightly-knit support groups are “communities of equality and agency, rather than hierarchical communities that define women as in crucial ways passive before their destiny.” In 8
education, with 11% having a postgraduate degree (at managerial (non-managerial) grades, 17% (10%) have a post-graduate degree). As in other state organizations, bureaucrats enjoy stable employment once in service: the average bureaucrat has 13 years in service, with their average tenure in the current organization being 7 years.

Across tiers, bureaucrats are similar in that they are in their mid-thirties, have been in their current post for roughly similar amounts of time, and have all worked in roughly 3 service organizations. However, officials at regional organizations are more senior and more likely to be a manager. Officials at district organizations are less likely to have a university education, and only 1% of district officials have a master’s degree. They are also more likely to be male, with 18% of district officials female compared with 23% of regional officials and 27% of federal employees.

To develop and enumerate a questionnaire that was relevant for the Ethiopian setting, we worked closely with the Ministry of Public Service and Human Resource Development and employed ex-civil servants within our enumeration teams to facilitate navigation of the public service. As will be described in more detail below, we used the survey to ask a range of questions about citizen characteristics and the public service rules to capture the knowledge of public officials with regards their working environment. Beyond our focus on collecting bureaucrat knowledge, we surveyed officials on their basic characteristics and the nature of management practices utilized at their organization. The implementation of the survey was successful across the organizations we visited, with 99.5% of public officials sampled agreeing to be interviewed and 98.2% of interviews being classified by the enumerator as having gone ‘somewhat well’ (26.4%) or ‘very well’ (71.7%).

In conjunction with the survey, we also collected data on the performance of public officials as assessed in their annual appraisal. Each year, public officials are evaluated by their direct manager on the tasks
## **Box 3:  Micro Housing Finance in Colombia**

During the banking crisis, banks suffered considerable losses from residential mortgages with non-performing loan ratios exceeding 20%. As a result, banks remain reluctant to lend to the sector. The Government has taken a number of measures to promote lending to the sector, these include: (i) a rediscounting program with a state owned second-tier financial intermediary (FINDETER) for banks, credit cooperatives and finance companies to offer mortgage and real estate micro-credits to lower income communities; (ii) a voluntary agreement between Government and the banking sector to allocate a small portion of bank funding to the microhousing finance sector (alternatively, banks can purchase bonds issued by FINDETER whose proceeds are used jointly with FINDETER funds for FINDETER’s micro-credit and social housing programs); and (iii) an insurance program established by the Government’s National Guarantee Fund to cover financial institutions against losses from housing micro-credit and social mortgage loans. Under the FINDETER social mortgage program, mortgage loans at subsidized interest rates are available for households which earn less than four minimum legal monthly salaries. Social mortgage loans are only available for the construction of new housing.

# **2.2 COST BARRIERS**

10. The demand survey has identified lack of resources and high costs of bank accounts as key reasons why people do not use formal financial services. The following pages take a look at the costs of different banking services in the formal sector based on the limited material available to date. Sources include a survey by the Superintendencia Bancaria of financial services of 26 banks as of June 2002 and subsequent update, as well as interviews with five financial institutions in Bogotá, including four banks (Bancolombia, Colmena, Megabanco and _Caja Social_ ) and one financial corporation (Finamerica). A more comprehensive supply side analysis is recommended to validate the initial findings presented here.

11. **Access to financial services for low income clients increases transaction costs.** Serving low income clients increases transaction costs
.470<br>0.136<br>0.810*<br>0.204<br>(0.342)<br>(0.198)<br>(0.434)<br>(0.235)<br>Observations<br>9,887<br>34,005<br>6,864<br>24,013<br>Outcome mean<br>0.018<br>-0.013<br>0.046<br>0.013<br>State fxed efects<br>x<br>x<br>x<br>x<br>District characteristics<br>x<br>x<br>x<br>x<br>Individual characteristics<br>x<br>x<br>x<br>x<br>_Notes_: Data are from the 2015-16 National Family Health Survey (NFHS) of India. All columns present 2SLS estimates a<br>close elections as those in which the victory margin is less that 3 percent of votes. The dependent variable is the physical <br>by husband’s desire to have more sons than what the couple currently has. Panel B examines heterogeneity by the discorda<br>of sons. All regressions include state fxed efects and control for the share of seats in the district that had close elections b<br>include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls incl<br>of urban population, and the share of SC-ST population in the district, and the female and male literacy rates. Robust stan<br>***, **, and * denote signifcance at the 1, 5, and 10 percent levels.|

23
, where appropriate, marketing margins. Annex 3 discusses the details of the NTB tariff-equivalent estimation for selected industries for which we have more detailed cost-share estimate from the Nathan study from 1999. It is important to point out that although the price wedge approach used here gives an estimate of the tariff equivalent of NTBs, it says nothing about the exact policies or policy instruments acting as NTBs, nor does it reflect their respective contributions.

The NTB estimates, as seen in Annex Table 3, cover a heterogeneous group of goods, but only where a CIF price is available. The reader will note that some products have explicit marketing margin estimates, some without. Even in the case of explicit marketing margin estimates, the NTB ad valorem estimates might be implicitly incorporating domestic marketing costs, including inventory costs, distribution and maintenance costs, advertising cost, and so on. If one takes all products in Annex Table 3, the average of all NTB ad valorem estimates is slightly less than 26 percent. Perhaps more representative for inputs used in manufacturing is to take the average NTB ad valorem estimates over the products that are of lower value added. That is, to take an average excluding jewelry, autos, soaps and cosmetics, table spreads, drinking glasses and cups, hand drills and refrigerators, all of which have high estimated price wedges due to NTBs. After excluding these high value-added goods, the average NTB ad valorem is slightly more than 9 percent.

What the tariff equivalent estimates of the NTBs emphasize is that there are sectors for which the ad valorem equivalents of barriers are apparently very high, and that there is a large dispersion among all NTB estimates. On the surface, this is puzzling, but certainly 10
# <u>Description of step</u>

It is suggested that when the level of the IU has changed in the last ten years, with limited data at the new IU-level, the calculation procedure outlined in sections 2, 3, and 4 is directly applied to data at the level of the new IU, where any missing new-IU level data for the last ten years is filled in with the corresponding data for the old IU. For example, for groundnut in Gram Panchayat GP1 of Andhra Pradesh, yield data was collected at the mandal level from 2000 to 2006, with GP-level yield data recorded from 2007 to 2009 (see Table 16). If products are to be offered at the level of the GP, the data to be used in de-trending, calculation of the threshold yield and pure premium rate is therefore the concatenation of mandal-level data from 2000 to 2006 and GP-level data from 2007 to 2009.

Table 16. Groundnut in one gram panchayat in Andhra pradesh: data for threshold yield and pure premium rate calculations

|**District**||**H**|**istorica**|**lyield**|**data, bef**|**ore de-**|**trendin**|**g (kg/h**|**a)**||
|---|---|---|---|---|---|---|---|---|---|---|
||**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|
|New insurance unit:<br>village cluster (gram<br>
The second contribution of this paper is the descriptive analysis of a wide range of topics covered in this cross-city comparable data. This analysis has two main purposes. First, many general patterns found in our data match the existing knowledge about how the informal sector operates, thus providing validation of the database itself. Second, we have highlighted features of the informal sector and patterns in the data that warrant further research, which can be conducted using this data, potentially supplemented with other data sources. Importantly, the World Bank's Enterprise Analysis unit continues collecting similar data in other cities and countries, making such analysis possible with increasingly wider coverage.

An initial analysis of the data from the seven countries yields the following stylized observations. Informal businesses operate almost all year round and on a full-time basis but make meager earnings. Informal businesses are generally very small and young, started out of necessity rather than as an opportunity for growth, typically operating in the retail sector, often from the owner's household premises, and are frequently owned by women, who experience a sizeable productivity gap compared to businesses owned by men. Even within the small scale at which informal businesses operate, they considerably under-perform formally registered microenterprises, consistent with the existing literature (La Porta and Shleifer 2014), with the data suggesting an interesting and sizeable cross-country variation.

The informal sector appears to be poorly integrated into the formal financial sector (as expected due to their lack of registration and thus lack of access to formal financial products) as well as formal supply chains. <mark>The data on management practices suggests a considerable gap between informal businesses and the formally registered micro-enterprises, especially in terms of record-keeping.</mark> The aspects of management practices related to planning for the subsequent year appear among the most challenging for informal businesses, suggestive of uncertainties that they may face, preventing them from adopting longer-term horizons.

The data suggest that operating informally does not seem to be associated with high chances of harassment, bribery, or crime; neither
2 patients per day in Malawi (Column 4, Table 1). The median provider in the average country saw on average 10.9 patients per day, with country-level medians ranging from 1.2 (Nigeria) to 20.7 patients per day (Malawi). A provider who saw 10.9 patients per day would face a one hour and forty minute workday in addition to other duties if providers were spending nine minutes per patient, which is the _highest_ mean consultation time observed in LMIC settings.<sup>6</sup> Given that the mean consultation time across LMIC settings is usually closer to five minutes, it could well be the case that the median SSA provider spends no more than one hour a day seeing outpatients.

In the bottom panel of Table 1, we compared these estimates to WHO statistics, using the combined number of physicians, nurses, midwives, and CHWs reported per 1,000 population.<sup>19</sup> We assumed 6 visits per person per year and 250 working days for each provider. These assumptions indicated that the mean provider (physician, nurse or midwife) would be responsible for 30.6 outpatients per day, ranging from 12.4 to 96.0 across countries, as these countries had generally lower availability of PHC providers than the African Region as a whole. Across all countries, the mean provider outpatient caseload we estimated from the SDI data tracked these estimates in a range from 9% (in Niger) to 114% (in Kenya) of the WHO-based calculation, with an average 49% lower

**7/30**
Inequality Database (WIID) (Azzarri and Signorelli, 2020; Cappelli _et al._ , 2021; Paglialunga _et al._ , 2022).<sup>7</sup> Still, a major limitation with household surveys is that such surveys are often not frequently collected, particularly in poorer countries due to resources and logistical constraints. A survey by Beegle _et al._ (2016) indicates that just slightly more than half (i.e., 27) of the 48 countries in Sub-Saharan Africa had two or more comparable household surveys for the period between 1990 and 2012. Dang _et al._ (2019) find a 10 percent increase in a country’s household consumption is associated with almost one-third (i.e., 0.3) more surveys, indicating the paradoxical fact that poorer countries with a stronger need for poverty reduction also face a more demanding challenge of poverty measurement given their smaller numbers of surveys.

Furthermore, many studies exploring the impacts of natural disasters on poverty and inequality traditionally rely on household surveys that collected data on extreme events (Datt and Hoogeveen, 2003; Khandker, 2007; Bui _et al._ , 2014; Arouri _et al._ , 2015; Karim, 2018). While these surveys, usually based on respondents’ recollections, provide direct insights into how households are affected by and respond to disasters, the accuracy of such data can be subject to recall bias and other inherent errors associated with self-reported information. In addition, many (older) surveys do not provide GIS information that accurately geo-locates households. Consequently, recent studies have increasingly turned to other sources of data that offer the geographical and temporal granularity needed to provide better and timely analysis of climate change and natural disasters that we discuss below.

 Are panel data often used?

The analysis of climate change and natural disasters’ effects on poverty and inequality has evolved in tandem with advancements in data collection and processing. Early studies often

> 7 For more details of the PIP database
) for firm _i_ from 2003 to 2008.<sup>28</sup> _Size_ , is expressed in terms of number of employees on the company’s payroll, and is divided into five previously mentioned categories. Microenterprises are mostly family-owned and have a limited division of tasks. Flexibility in labor usage and minimal overhead costs allow microenterprises to reach a baseline level of efficiency. However, limited access to capital

> 24 World Economic Forum. _Global Competitiveness Report_ . http://www.weforum.org/issues/global-competitiveness.

> 25 World Bank. _Doing Business_ . http://doingbusiness.org/.

> 26 For a discussion of principal component analysis, see Appendix V.

> 27 Given that the principal component analysis is built on the basis of indicators, it shares the indicators’ methodological limitations. To verify the quality of the principal component analysis indicator, we compare it with an alternative measure of the quality of business regulation, the Product Market Regulation indicator constructed by the Organisation for Economic Co-operation and Development. The correlation between the comprehensive principal component analysis index of _Doing Business_ indicators and the economy-wide Product Market Regulation indicator is very high (-0.74) using 2008 data (World Bank 2009) for the 39 countries for which both indicators are available.

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complexity. Population size for 2023, used for the weighted analysis, is sourced from the “wbopendata” module in Stata (Azevedo, 2020), with data for Taiwan Province of China obtained from the Statista website. All countries with an observed AIPI score exceeding 0.7 (the weighted average and median score for HICs) are highlighted in the grey quadrant. Among these, only countries with AIPI scores surpassing their predicted values (above the linear fit line) are marked in yellow and identified as global overperformers.

Both charts highlight the top performers in AI preparedness, concentrated in the northeast quadrants. Consistent with previous BMA findings and the critical role of initial conditions in AI readiness, these top performers are exclusively HICs. To identify global overperformers within this

> 18

8
a reasonably high proportion of women, at 63 percent of total microcredit borrowers (although only 31% employees of Sanabel member MFIs are female). The proportion of women microcredit clients ranges from as low as 18 percent in Iraq, where female workforce participation has declined since the most recent conflict, to as high as 81 percent in Yemen and 83 percent in Jordan where many MFIs target women as part of their mission. Table 4 compares 24 larger and better performing MFIs from across MENA with their counterparts globally, on outreach, gender, efficiency and portfolio quality.

**Table 4. Comparative Outreach, Cost and Portfolio Quality Statistics by Region**

||**Outreach**|**Percent of**|**Operating**<br>**Expense/Loan**|**Portfolio at Risk**|
|---|---|---|---|---|
||**(Income)**|**Women clients**|**Portfolio**|**(>30 days)**|
|**MENA **|**15.8**|**65.4 **|**21.4 **|**2.4 **|
|Africa|67.8|57.2|32.7|4.7|
|Asia|17.8|93.8|17.2|1.5|
|ECA|68.3|43.1|15|2.0|
|LAC|25.5|59.6|22.3|4.2|

Source: 2008 MFI Benchmarks MIX Micro Banking Bulletin. Median values. Outreach is average loan balance per borrower/GNI per capita.

> 7 The Bulletin has a database of 600 microfinance institutions from 84 countries, including most of the leading institutions by size and performance as recorded by the