rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure 7: Changes in renewable natural capital components**

# **(a) Growth in planted forest area**

Planted forests refers to “Forest predominantly composed of trees established through planting and/or deliberate seeding” (FAO, 2020). These values therefore do not include naturally regenerating forests. Percentages on the y-axis refer to average annual growth rates between 2009 and 2020, obtained from FAOSTAT. The size of the bubbles reflects the GDP value-added of agriculture, forestry and fishing.

<!-- Start of picture text --> 6% GDP Value Added (Agriculture, Forestry, Fishing) ROU<br>1% 5% 10% 15%<br>5%<br>BRA<br>MEX<br>4%<br>3%<br>CAN MYS<br>2% B-rated COL<br>AUS FRA CHN A-rated (1.3%) THA PER IDN TUR<br>1% SWE USA FIN (0.6%) PHL<br>GBR IRL IND<br>0% NLDDNK DEU AUT KOR JPN SVK POL ESP ITA ZAF RUSBGR HUN PRT<br>1% NOR CHE<br>0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0<br>Credit quality step (0=AAA, 2=BBB+, 4=BB)<br>Percentage change<br><!-- End of picture text -->

# **(b) Growth in area under organic agriculture**

Organic agriculture refers to “area certified organic and/or in conversion to organic” (Willer et al., 2020). Percentages on the y-axis refer to average annual growth rates between 2009 and 2020, obtained from FAOSTAT. The size of the bubbles reflects the GDP value-added of agriculture, forestry and fishing.

<!-- Start of picture text -->
not yet evident on an economywide basis. The recession and losses from government debt exposures have taken a deep toll on the banking system, undermining financial stability.

# **Portugal**

45. The genesis of Portugal‘s crisis was different from that of the other countries in that it did not reflect the burst of a real estate bubble or fiscal profligacy, but mainly a broad deterioration of the business environment and competitiveness, seemingly accelerated by the country‘s accession to the Euro Zone. In the context of the recession brought about by the global financial crisis, this poor competitiveness resulted in unsustainable debt indicators and triggered the crisis of confidence.

46. Prior to the crisis, Portugal had begun consolidating its finances; in fact, between 2005– 07 the government succeeded in reducing the structural balance by over 3 percentage points of

> 17 Recent corrections of government statistics on the fiscal position showed that Greece had misreported the fiscal deficit prior to the adoption of the euro.

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</sup> approach contrasts with other approaches in the sense that the vulnerability line is not set arbitrarily, for example at 1.5 times the poverty line, but derived empirically from the vulnerability index. In practice, this requires a recursive algorithm that goes over the synthetic panel data to arrive at the vulnerability line that yields the desired vulnerability index.<sup>15</sup> We acknowledge that setting the vulnerability index is still an arbitrary decision, but it is based on a transparent weighing of risks. We argue that anchoring the definition of vulnerability in this way evokes the insecurity that is inherent to vulnerability.

We mostly present results for poverty and vulnerability dynamics in figures that do not indicate the level of precision of estimates. The main reason for this is that the results already consist of bounds

> 14 There are no generally agreed upon names for these categories. In some applications, the non-poor who are not considered vulnerable have been labeled ‘middle class’. We opt for the more neutral ‘economically secure’. One reason is that some of these households will be quite rich, so that middle class would be a misnomer for them. 15 See Rongen (2021) for a manual that describes the procedure in detail.

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27 the pre-FRL period is measured as the difference between the debt/GSDP in year t-1 and the debt/GSDP in year t-5, before the passing of the FRL in year t. Similarly the growth of debt/GSDP in the post-FRL period is measured as the difference between the debt/GSDP in year t+5 and the debt in the year t when the FRL is passed. To leave out the impact of the global financial crisis of 2008-2009, the post-FRL data will cover up to end 2007.<sup>23</sup> In **Australia** , the growth of debt/GSDP is negative for all the states in the sample in the pre-FRL five-year period as well as in the post-FRL period (Table A3.1). The debt/GSDP of Western Australia and Northern Territory continued to decline at faster pace and that for Victoria, Queensland and New South Wales continued to decline, although at a slower pace in the post-FRL period. The states in the table passed FRLs from 2000-2005, but fiscal consolidation started in the 1990s (e.g., South Wales committing to long-term fiscal targets in 1994, and Victoria’s Financial Management Act in 1994). As noted before, the combined state and Commonwealth general government net debt had not exceeded 30 percent of GDP in the 1990s (Simes, 2003).

In **Brazil** , although the growth of debt/GDP for SNGs was positive for both the pre and post-FRL periods, the growth slowed down from 5.0 percent to 1.3 percent (Table A3.2). The slowdown also happened to the federal government.

In **Canada** , all the provinces had declining debt as share of GSDP after the passing of the FRLs (Table A3.3). In British Colombia and Nova Scotia, this decline reversed the trend of rising debt as share of GSDP in the pre-FRL period, with British Colombia experienced the largest turnaround. The other three provinces already had
Bank and **OECD** for most developing countries starting in **1970,** which report debt at face value (after adjusting for debt forgiveness and reduction as well as changes in exchange rates). In addition, we have also the international investment positions (IPs) of the IMF's Balance of Payments, which cover the majority of industrial countries over a varying number of years since the 1980s as well as a handful of developing economies. The valuation methods underlying the BoP's IIPs vary across countries, as well as over time for a given country?

In view of these facts, we take as primary data sources the IMFs **IFS** and BoP and the World Bank's Global Development Finance, complemented in a few cases **by** country-specific documents, typically from the respective central banks, plus the data on international investment positions constructed **by** Rider (1994) primarily for industrial countries.

From these sources, we construct our foreign asset and liability stocks as follows (see Kraay _et. al._ **1999** for more details). For reserves of all countries, as well as developing-country debt liabilities, we simply take the values reported **by** the IMF and the World Bank, respectively. For all other assets and liabilities, we construct stock series from the flows reported **by** theBoP, using the earliest available stock (if one exists) to tie down the level of the series.o From these initial values, stock series are obtained using the recursive formula:

# _S(t)_ **_=_** _S(t_ **_Q-_ 1Xl-** **_b)+_** _F(t)_ **_-_** _Q(t_ **1)**

where **S** denotes the dollar value of the stock at the end of the period,F is the net flow during the period
to maintain comparability across countries, we use only the number of newly registered firms (i.e. flow rather than stock). This exclusion prevents us from calculating entry rates (new firms normalized by total firms) or firm turnover (new plus closed firms normalized by total firms).

_c. Limited Liability Companies:_ Partnerships and sole proprietorships are not considered in our analysis as these types of entities differ substantially with respect to their definition and regulation worldwide. We therefore focus solely on limited liability companies.

_d. Exclusion of off-shore financial centers_ : Data collected from countries categorized as offshore financial centers by the IMF<sup>3</sup> are excluded from our analysis since registered entities may not fit our definition of “entrepreneurship”. The information provided by these countries likely reflects a nontrivial number of shell companies, defined as companies that are registered for tax purposes, but are not active businesses. The case of the British Virgin Islands provides a sharp illustration of the distorting effects of offshore financial centers. In 2007, the country had 77,022 newly registered corporations, which translates to approximately four new firms for each working age individual.

# **_2.3 Data Sources_**

Data were collected from official government sources around the world. Thus, our data are not based on surveys or estimations. In 70 percent of the countries, data were collected directly from the Registrar of Companies, which is generally the government entity responsible for recording and maintaining information on new and existing firms. Data for other countries were collected from national statistical offices and chambers of commerce. For two countries

> 3 The complete list of excluded countries can be found at: http://www.imf.org/external/pubs/ft/wp/2007/wp0787.pdf, Table 10

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directions of the estimates broadly align with our base results, although they are weaker in statistical significance. We believe that this difference in results may be related to the fact WBL indicators reflect the _de jure_ situation, whereas WVS and FLFP capture _de facto_ realities. In particular, the aggregated WBL index captures several dimensions of gender that are not directly related to access to credit for women-led firms.

# **4. Conclusions**

Using firm-level data for manufacturing firms in 61 countries, we show that female-managed firms are not credit constraint on the extensive margin: they are equally likely to apply for credit as their male counterparts and face lower rates of credit rejection and a higher likelihood of opening credit lines. This may be explained in part by the fact that female-managed firms are not any riskier or less productive than their male counterparts. In fact, they are more profitable in our sample of formal firms with over 5 employees, suggesting the possibility of a strong selection of more capable women as managers. However, women-managed firms receive lower levels of credit either because they apply for lower amounts or are granted less than they request. Our analysis suggests that these disparities are more pronounced in traditional countries, indicating that social norms may play a significant role. Our research also provides suggestive evidence of capital misallocation driven by gender differences in credit amounts received.

The findings of this research have important implications for policymakers and the development community more broadly.

_First_ , the results highlight the need for policy makers to prioritize gender-inclusive approaches in financing strategies. While female-managed firms do not face credit constraints on the extensive margin, they receive lower credit amounts on the intensive margin, indicating the presence of credit constraints. In fact, many financial products do not consider gender-specific constraints or risk preferences and therefore are de facto biased against women-led firms. For instance, peerto-peer lending platforms have been shown to replicate gender bias (Chen et al., 2020). Policy makers and Development Finance Institutions (DFI) can encourage financial institutions to design targeted financial products
# Figure 11.  Country correlations for CE and level of bureaucracy quality

<!-- Start of picture text --> Low bureaucracy quality High bureaucracy quality<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>Low bureaucracy quality = 0.47***<br>High bureaucracy quality = 0.33***<br>-0.4<br>-0.6<br>-0.8<br>Azerbaijan Yemen Côte d'Ivoire Ukraine<br>Oman Congo, Rep. Nicaragua Ecuador Uruguay Guyana Liberia Argentina<br>Brazil Namibia Niger Bolivia Kenya Zimbabwe Angola Qatar<br>Sudan Senegal Mali Burkina Faso<br>Ghana Saudi Arabia Mozambique The Gambia Colombia Tanzania Libya Gabon Iceland Ethiopia Mongolia<br>Algeria Uzbekistan Myanmar South Africa Paraguay<br>Iran, Rep. of Madagascar<br>Uganda Cameroon Indonesia Honduras Suriname Togo<br>Russian Federation Papua New Guinea<br>Bahrain Congo, Dem. Rep.<br>Nigeria Peru Iraq<br>Sierra Leone Kazakhstan Guinea-Bissau Costa Rica Botswana Armenia<br>Guinea<br>Chile<br>Zambia<br>Kuwait<br>New Zealand<br>Norway Malawi<br>Australia Guatemala<br>Brunei Darussalam United Arab Emirates<br>Canada<br>government spending and real GDP 1980-2020<br>Correlation between cyclical components of real<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with high (low) bureaucracy quality, defined as bureaucracy quality above (below) the sample median. The difference between the two averages is not significant at the 10 percent level (p-value = 0.16). *, *
example replacement of gas water heaters with solar water heaters would provide higher potential for GHG reduction than through increased efficiency of gas water heaters. Therefore, the study does not include efficiency improvements in ranges and water heaters.

Other measures we considered in reducing GHG mitigation from the residential sector include lightbulbs, TV, refrigerator and washing machines. The average number of bulbs per household is 8, and they operate around 5.26 hours daily. Almost 70% of households use incandescent bulbs, 8% efficient bulbs, and 22% have both. Almost all households have TV, from which around 11% is new LED type TVs. 84% of household own refrigerator and among them 48% state that their refrigerator is energy efficient. Around 68% have the washing machine, 43% of which is purchased after 2010. As for the cooling systems the typical cooling system in Georgia is local split system, used to cool one or two rooms. The central cooling systems are very rare in country, and would result in increase of consumption.

The GHG mitigation potential is sensitive to the assumptions made on their future penetration. Historical rate of penetration could be a guidance. Government policies and incentive mechanism available also affect the rates of penetration. Based on these factors we have made assumptions on the penetration rates of measures and options with potential to reduce GHG emissions.

Like in the residential buildings, space heating is the main energy end-user in the commercial and public buildings. Therefore, improved building insulation provides the highest potential to save building energy consumption and GHG mitigation. The selection of these options in Georgia are based on energy audits (Sustainable Development Centre Remissia, 2015) and Sustainable Energy Action Plans (SEAP) of cities (Zugdidi SEAP 2014, Batumi SEAP 2014, Kutaisi SEAP 2014). Other technologies, such as refrigerators, cooling systems, etc. could also be considered but required data were not available.

The discount rate used in the analysis is a social discount rate, which is assumed to be 7.5
access to systematic assessments of labor demand or the supply of workforce skills. Moreover, despite the large size and economic importance of the informal sector in many developing countries and emerging markets, assessments of supply and demand in the informal labor market are often minimal or wholly absent. As a result, strategies to improve skills matching and keep training providers apprised of evolving demand conditions in the labor market remain important challenges.

There is emerging evidence that analyses of online job-search engines and other employment services can be used to address information asymmetry and coordination failures in the labor market. Global online job portals such as LinkedIn, Indeed, Monster and Career Builder are bridging the gap between employers and job seekers in many countries. In areas where the use of such websites is still limited, local online job portals often provide vacancy announcements for both the formal and informal sectors and help expand access to job information for those with internet connectivity. These online platforms, whether global or local, provide a rich and continuous stream of labor market data that has remained largely untapped by policy makers.<sup>2</sup> In the field of labor economics, the prevalence of big data from online job portals is increasing, and various researchers have explored different methodological approaches to analyzing this data. For instance, Kureková et al. (2015) conducted a literature review in an effort to identify the advantages and disadvantages of using online job data and concluded that it represented a rich source of information that should be exploited despite issues with the representativeness of online job-portal data. Labor economics has traditionally relied on sample survey data, such as workforce and household surveys. Such surveys are often infrequently conducted, and their scope and coverage can entail important limitations. By contrast, big data has the potential to generate multidimensional, granular, real-time information, which can be used to provide new insights into longstanding economic questions, enhance economic research and aid public policy development.

# **2. Objectives**

This paper discusses potential applications of online job-portal data
# **A1.8 World Economic Forum (GCS, GCSA)**

The World Economic Forum (WEF) is an independent, not-for-profit organization bringing together top leaders from business, government, academia and the media to address key economic, social and political issues in partnership. The WEF was founded in 1971 and is headquartered in Geneva, Switzerland. As of May 1999, the WEF can be reached at: httD:l/www.weforum.org.

Since 1996, The WEF has sponsored the Global Competitiveness Report, an annual publication produced in collaboration with the Harvard Institute for Intemational Development (HIID). As background for this report, the WEF conducts a survey of about 3000 enterprises in 60 countries, the Global Competitiveness Survey. Since 1998, the WEF has also sponsored the Africa Competitiveness Report, also produced in partnership with HIID. As background for this report, the WEF conducted a survey of 650 enterprises in 23 African countries, the Global Competitiveness Survey Africa. In this paper, we rely on data from the 1998 editions of the Global Competitiveness Survey and the Global Competitiveness Survey Africa.

# **Global Competitiveness** Survey (GCS)

This survey measures the perceptions of business executives about the country in which they operate. The survey asks top managers to rank on a 1 to 7 scale their opinion on several issues that can not be measured otherwise. The survey covers eight broad topics: 1) Openness, 2) Government, 3) Finance, 4) Infrastructure, 5) Technology, 6) Management, 7) Labor, and 8) Institutions.

# **Global** Competitiveness Survey Africa (GCSA)

The GCSA follows the same methodology as the GCS, although there are some minor differences in the questionnaires of the two surveys. Table A1.8 describes the questions from this survey included in the governance database.

44
measure education service delivery and learning outcomes on a large scale, enabling performance measures to be computed at disaggregated jurisdictional levels such as districts. Such disaggregated measurement could potentially enable attribution of performance in delivering education services to specific government agents and politicians. The Uwezo effort was partly inspired by a similar effort in India, called ASER (Annual Status of Education Report), run by the organization Pratham<sup>15</sup> . Although media stories would suggest that such initiatives contribute to strengthening political accountability for education outcomes, because of the information generated and collective pressure exerted by CSOs, there is as yet no rigorous evidence of how this changes key politically motivated distortions in education allocations. In the case of India, five years of ASER reports has played an important role in raising public awareness of the education quality, but has essentially found no overall increase in test scores for rural children in the period to 2010 (ASER 2011). There remains substantial untapped potential for examining whether such disaggregated information can indeed generate sufficient political 14 http://www.uwezo.net/ 15 http://www.pratham.org/M-20-3-ASER.aspx
Candidates and Potential Candidates|Albania|9|5|-7|-22|-33|-42|
||Bosnia and Herzegovina|-17|-1|-18|-12|-35|-39|
||Croatia|-12|-29|-39|-18|-73|-47|
||Macedonia, FYR|-8|2|-9|-32|-49|-57|
||Serbia|||||||
||Turkey|-30|-17|-19|-7|-31|-26|
|Eastern Partnership Countries|Armenia|-36|4|-6|-29|-27|-42|
||Georgia|-38|-19|-34|-26|-65|-87|
||Moldova|-55|-20|-27|-38|-42|-49|
||Ukraine|-14|5|5|-15|-27|-45|

_Source_ : Updated EWN dataset based on Lane and Milesi-Ferretti (2007). Note: This table reports net debt which is international debt assets plus foreign exchange reserves minus international debt liabilities, and net equity which is international equity assets minus international equity liabilities.

.

Table 3.2a compares the evolution of net debt and net equity for Emerging Europe to the Euro Periphery, Emerging Latin America and Emerging Asia. There are some striking differences across these regions. While emerging economies in Latin America and Asia accumulated positive net debt positions between 2002 and 2009, only the Eastern Partnership bloc in Europe saw a reduction in net debt liabilities. In contrast, the growth in net debt liabilities was most rapid for the Euro Periphery and the new member states.

**Table 3.2a. Net Debt and Equity IIP, Percent of GDP**

|Country||Net Deb|t|N|et Equit
||||consumption per capita<br>from NA;unitpass-through|
|---|---|---|---|
|Hamati (2018)|Lower national PL (LPL)<br>Upper national PL (UPL)|2009: 14.9%; 2015: 85.8-75.9%<br>2009: 29.4%; 2015: 93.1% - 87.5%|Dynamic microsimulation<br>model projections that<br>introduce price,<br>demographic, labor market,<br>and income shocks.|

# 2.2 Post-conflict estimates based on HNAP 2021 Demographic and WASH survey

The scale of Syria’s humanitarian crisis and the necessity to inform operations on the ground has prompted UN agencies to develop an articulated system of welfare monitoring: the _Humanitarian Needs Assessment Programme_ (HNAP). The HNAP is a joint UN program established in April 2018 with the objective of tracking displacement and return movements, conducting multi-sectoral assessments, and monitoring humanitarian needs inside the country.<sup>10</sup> In addition to collecting data at the community level through key informant interviews, the HNAP also implemented sample-based household surveys whose frame relies on community level population data.<sup>11</sup> The HNAP Demographic and WASH survey is a large-scale survey aimed at providing national and subnational-level estimates of demographic and basic socio-economic conditions of the population living inside Syria (resident, IDPs and returnees). The survey is primarily used to inform the programming of assistance of various UN agencies and humanitarian actors. The questionnaires of this survey have evolved over time.<sup>12</sup> In 2021, in order to assess the monetary deprivation of Syrian households, a module on household income and household expenditures was included in the survey. Based on this information, _the HNAP Socio-Economic situation report of the summer 2021 estimated that 98 percent of the Syrian population was living in extreme poverty_ (Figure