rafmacalaba/data-use-mentions-extended huggingface dataset:
accounts for a quarter of the total SME lending volume. However, 90 percent of this amount is in China where the SME definition is broad and SMEs can employ up to 1,500 people. Excluding China, the total SME lending volume in East Asia and Pacific is comparable to Eastern Europe and Central Asia or about 3 percent of the total. Sub-Saharan Africa, Middle East and North Africa, and South Asia together account for only 1.7 percent of the total SME volume.

# **5. Conclusion**

Expanding access to financial services by SMEs is a critical objective of the financial inclusion agenda. The G-20 has started focusing on financial inclusion in the global policy agenda since 2009 and has identified financial inclusion as a key driver of economic growth, reduced economic vulnerability for individual households, poverty alleviation, and improved quality of life for people around the world. SME access to finance is a fundamental component of this agenda. As part of the global efforts to collect data on SME financing, the _Financial Access_ survey by CGAP/WBG initiated data collection on SME access to finance as a first attempt to identify the feasibility of collecting consistent cross-country data on SMEs from financial regulators.

This paper presents the data set resulting from this initiative, as well as a first analysis of the data. The data set is a first attempt to form a consistent cross-country database on SME access to finance. The main message emerging from the new data is that what constitutes an SME is quite different in different parts of the world. Nevertheless, these differences in SME definitions do not drive the variation in SME lending around the world. Income per capita, private credit to GDP, the legal and business environment, and the efficiency of the banking system are among the factors that influence SME lending.

Based on this data set, the total volume of SME loans around the world is predicted as roughly $10 trillion in 2009. The bulk of SME lending volume—70 percent—is concentrated in highincome countries. The median
information became available. In this section, we summarize some of the topics that have been identified in the course of our own research.

# **7.1 Quantifying CO2 Emissions**

Although satellite-based measures of CO2 concentration anomalies are extremely useful for comparative analyses, the policy community would undoubtedly benefit from conversion of concentration anomalies to physical estimates of CO2 emissions. As we note in the paper, recent research for a few cities has used OCO-2 observations to produce scaling factors (R) for rough adjustment of ODIAC-type emissions estimates (Oda et al. 2018). Future extensions of our econometric work could include the construction of sectoral R-factors that could be used to improve emissions inventories by identifying the sources of their discrepancies from satellitemeasured concentration anomalies. Hopefully, empirical research will ultimately depart from its continued dependence on emissions inventories by developing methods for direct conversion of satellite-measured CO2 anomalies to physical emissions estimates.

# **7.2 Potential Endogeneity Problems**

> 10 Our thinks to the reviewers of this paper for their useful thoughts about future research directions.

33
# **7. Conclusion**

Using a unique new dataset on World Bank adjustment loans that supported financial sector reform from 1992 to 2003, we offer evidence that borrowing countries did better than non-borrowers on multiple measures of banking sector development including M2/GDP, interest spreads, and Cash/M2. These results are found in both panel regressions that incorporate fixed-country effects and in cross-sectional regressions that use average growth in financial indicators over the full period for each country as dependent variables. The cross-sectional regressions indicate that the panel results are not driven by multiple observations from the same country that can artificially drive down standard errors.

Results for private credit/GDP and banking sector concentration are less favorable for borrowers. For concentration, results strongly favor neither borrowers nor nonborrowers. Moreover, recent evidence indicates that concentration measures are not robustly linked to financial development and stability. For private credit, concerns are more valid, in part because borrowing countries appear to have performed worse than non-borrowers.

Because the sample of borrowing countries is non-random, we offer a series of models that try to account for potential selection effects. We use non-linear selection equations to capture concepts from the political economy literature on IFI relations with development countries. In a crude way, we can therefore account for countries that prefer not to borrow from those institutions because they are relatively self-sufficient, and those that the IFI’s prefer not to deal with because reform is unlikely to be successful. When we address selection in this way using treatment effects regressions, borrowing countries had private credit growth rates significantly larger than non-borrowers. Propensity score matching techniques also indicate that private credit grew more quickly in borrowing countries, though the result is not significant. For M2/GDP and cash/M2, borrowers performed significantly better than non-borrowers on at least one of the tests (propensity matching or treatment effects). Borrowers appear to have done less well on interest spreads than the other indicators when we account for selection, but that could be attributable to lost observations.

22
Even though the 41 articles reported in Table 3 surely do not constitute the entire literature, it is notable that only four focus on the experiences least developed countries. Few studies attempt cross-country comparisons of reform experiences or procurement practices—exceptions being Trepte (1998) for Bhutan and Laos and Wittig (1999) for South Africa and Uganda. Moreover, only a few of the articles<sup>11</sup> attempt any form of quantification of the costs, benefits, or effects of existing procurement practices or reform proposals.

Another source of information are the Country Procurement Assessment Reports prepared by the World Bank and other development agencies. These reports provide a diagnostic assessment of the strengths and weaknesses of the procurement systems in individual developing countries.<sup>12</sup> The CPARs that are publicly available are listed in Appendix 2 of this document. The CPAR process has led to recommendations and financing of action plans to modernize or reform existing procurement systems in a number of countries. As is true of the more academic literature, the impact of this work on the performance of national public procurement systems has not been well documented, in the sense that the CPARs tend to focus primarily on legal compliance – whether laws and required procedures have been implemented. There is little focus on quantifying performance, the actual return to having applied processes aimed at enhancing competition, transparency and combating the scope for corruption, or the expected return to adopting new procedures that have such outcomes as objectives.

The only cross-country data that is available on the procurement ‘performance’ of countries are international surveys. An example is the survey of perceptions of

> the WTO Working Group on Transparency in Government Procurement Practices, at <u>http://www.wto.org/english/tratop_e/gproc_e/gproc_e.htm; the World Bank’s Country Procurement</u> Assessment Reports (not all of which are publicly available); and Individual Action Plans of APEC members, at <u>http:/
FIGURE 4. HISTOGRAMS OF SUBMISSION PERIOD LENGTH (DAYS) BY TREATMENT STATUS <!-- Start of picture text --> Manual e-GP<br>20% 20%<br>Mean = 19.5<br>Mean = 25.6<br>15% 15%<br>10%<br>10%<br>5%<br>5%<br>0%<br>0% 0 5 10 15 20 25 30 35 40 45 50<br>0 5 10 15 20 25 30 35 40 45 50<br>8% 8%<br>Mean = 17.8 Mean = 16.1<br>6% 6%<br>4% 4%<br>2% 2%<br>0% 0%<br>0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50<br>Submission Period<br>Signing Period<br><!-- End of picture text --> Note 1: Number of tenders: Manual = 30661; electronic = 43137 Note 2: The red regions of the graph correspond to the stipulated submission and signing periods as per the procurement rules

# 7. CONCLUSIONS AND POLICY IMPLICATIONS

86. In this impact evaluation, we estimate the effects of Bangladesh’s e-procurement system on metrics of access, economy and efficiency of public tenders. Bangladesh’s e-procurement system is advanced compared to most of its comparators around the world in that it not only provides an online advertisement portal, but rather requires that all notable administrative actions are administered digitally, from tender preparation to contract signature. To identify the impact of e-procurement on a wide range of outcomes, we construct a novel dataset, comprising all published electronic public procurement tenders and data extracted from a large sample of paper-based procurement records from there major GOB agencies, which are responsible for the bulk of public works projects.

87. We
CLICROP was run at a 1°×1° resolution (i.e., 111 square kilometer grids in a country measuring 945,000 square kilometers). Separate models were developed for the 9 major crops of Tanzania (i.e., cassava, groundnuts, maize, millet, potatoes, sorghum, soybeans, sweet potatoes and wheat). Predicted yields for each sub-national region are calculated as the sum of overlaid gridded results weighted by geographic area. CLICROP was calibrated to information on soil parameters from the FAO Soils Database (e.g., field capacity, wilting point and saturated hydraulic conductivity) (FAO-UNESCO, 2005). Regional information on crop growing seasons and planting dates were provided by Sacks et al. (forthcoming). Finally, crop locations were based on You et al. (2006) for the year 2000, and crop parameters were drawn from Allen et al. (1998) and Doorenbos and Kassam (1979). Overall, the process is detailed with more than 13 million potential simulation sets.<sup>3</sup>

# _Crop modeling results_

Although CLICROP analysis was conducted for nine crops, we focus on the results for maize for purposes of exposition. Maize is the principal food crop in Tanzania, representing 35 and 45 percent of calories consumed by poor urban and rural households, respectively (Pauw and Thurlow, 2010). Tanzanian farmers allocate about one-third of their crop land to growing maize, mainly without the use of irrigation. Deviations in dry-land maize yields therefore provide a first-cut indicator of food availability in Tanzania (Thornton et al. 2009). In addition, the implications of climate outcomes for maize are likely to be similar for sorghum and millet, making the coverage of caloric sources and land use, particularly for more vulnerable populations, even broader.

Table 2 summarizes the deviations in mean maize yields from a “no climate change” baseline scenario for the 10-year period 2041-2050. The administrative regions are grouped into similar agro-climatic zones based on Fan et
Notes: (1) Panel Fixed Effects; All regressions include year fixed effects; (2) Anchored Relative Poverty line (60% of median at 2011); (3) Data Source: EU-SILC, Eurostat; (4) Symbols: ** P<0.01, * P<0.05, +P<0.1 Although diversifying the sources of income, between agricultural and non‐agricultural, may prove useful to alleviate poverty in households over time, further results suggest that households who receive agricultural income may be better off by specializing in a particular agricultural activity. Table 14 shows that an increase in the share of the area used in specialized crop production seems beneficial to poverty reduction, as regions with higher areas of specialization holdings show higher poverty reduction. However, an increase in the share of land used for specialized livestock shows no significant effect on poverty reduction. Hence, households who turn to agriculture as a source of income do better when they specialize, particularly in crops. Thus, for poverty alleviation, it is useful to have different sources of income. Nevertheless, households should not diversify across agricultural income sources.

**Table 14.** Regions with higher farm holdings specialization have higher poverty reduction

||(1)<br>Povertyrate|(2)<br>Povertyrate|(3)<br>Povertyrate|
|---|---|---|---|
|Agricultural area of specialized holdings share of total|-0.02|-0.02||
|Agricultural area of holdings specialized in crop share of total|||-0.05+|
|Agricultural area of holdings specialized in livestock share of total|||-0.02|
|Ratio of agricultural area to total||-0.03|-0.02|
|Agricultural employment share|0.22|0.22|0.22|
|GDP per inhabitant|-0.
countries and world regions for the year 2014. Country mapping between the I2D2 and GTAP data shows that only 70 developing countries are represented in both databases and thus are included in the sample for estimations. Among the GTAP indicators, the capital-labor ratio is computed by dividing the payment to capital by the payments to labor and emissions include the carbon dioxide (CO2) (mega metric). This CO2 emission data comprises carbon emissions from fossil fuel combustion by sectors.<sup>12</sup> Finally, the GTAP data are aggregated into nine sectors to combine with the I2D2 data set, omitting the public administration and defense sector which is assumed to have no informal employment.

> 12 Detailed description of CO2 emission data calculations can be found in Lee, H.L. (2008), “The Combustion-based CO2 Emissions Data from GTAP Version 7 Data Base,” Center for Global Trade Analysis, Purdue University: West Lafayette.

11
Lippe, Tanja van der and Liset van Dijk (2002), “Comparative Research on Women’s Employment,” _Annual Review of Sociology_ , 28: 221-241.

Meyer, Lisa B. (2006), “Trade Liberalization and Women‘s Integration into National Labor Markets: A Cross-Country Analysis.” _Social Indicators Research_ , 75: 83-121.

Morrison, Andrew, Dhushyanth Raju and Nishtha Sinha (2007), “Gender Equality, Poverty and Economic Growth,” Working Paper 4349, World Bank, USA.

Muravyev, Alexander, Oleksandr Talavera and Dorothea Schäfer (2009), “Entrepreneurs’ Gender and Financial Constraints: Evidence from International Data,” _Journal of Comparative Economics_ , 37(2): 270-286.

Nelson, Robert L. and William P. Bridges (1999), Legalizing Gender Inequality: Courts, Markets, and Unequal Pay for Women in America, Cambridge University Press.

Noguer, Mart and Marc Siscart (2005), “Trade Raises Income: A Precise and Robust Result,” Journal of International Economics, 65(2): 447-460.

Ozler, S. (2000), “Export Orientation and Female Share of Employment: Evidence from Turkey,” _World Development_ , 28(7): 1239–48.

<mark>Pagano, Patrizio and Fabiano Schivardi (2003), “Firm Size Distribution and Growth,”</mark> _<mark>The Scandinavian Journal of Economics</mark>_ <mark>, 105(2): 255-274.</mark> Pearson, Ruth (1995), “Male Bias and Women’s Work in Mexico’s Border Industries,” In Diane Elson (ed.) _Male Bias in the Development Process,_ 2nd edition, pp. 133–63. Manchester, U.K.: Manchester University Press.

Pearson, R. (1998), _Feminist Visions of Development: Research Analysis and Policy_ . London and New York: Routeledge.

Penner, Andrew M. and Harold
order to create a virtuous cycle where increasing take-up shifts norms and encourages more take-up. The fact the social norms treatment’s effects were primarily to be found in subsets of the survey sample (particularly women) suggests there is scope for developing more effective normative appeals. It is possible that different types of information may be more effective in transmitting a norm-based message, and there is a strong case for further research in this area. However, it may well be that some sectors of the population will ultimately need to see firsthand their family, friends and others in their community receiving the vaccine before norms become truly effective in shaping attitudes. Although this would be a discouraging finding for those who hope to use information interventions to help increase vaccine uptake, it would be a positive finding in another sense: vaccinating those who are already willing to be vaccinated may itself help to change the attitudes of others as they see their fellow community members safely taking the vaccine.

Thirdly, health workers have a prominent role to play in increasing people’s willingness to receive a vaccine for COVID-19. The phone survey illustrated that around 80 percent of respondents said they would be more willing to get the vaccine if health workers recommended it, which is a much higher share of respondents than those that would listen to the recommendations of others, such as family and friends or religious leaders. Importantly, the phone survey showed that the way people would like to receive this information is face to face, suggesting that communication from local health workers will have the biggest influence on people’s take-up of the vaccine. In addition, the online experiment’s results, and the failure of Treatment A, which presented the views of “national and international experts”, strongly suggest that generic advice from experts will not serve as an adequate substitute for specific matter of fact advice received from trusted local health workers. One potential complicating factor facing authorities is that vaccine hesitancy is comparatively high among health workers (Pogo et al., 2021). This suggests
creditors.

Table 2: Creditor rights **and** contract **enforcement in Brazil relative to other countries**

||Creditor rights|Contract enforceability|
|---|---|---|
|Brazil|2|2.02|
|Sample mean|2.32|2.50|
|Latin America|1.38|1.92|
|Of which: Argentina|1|2.07|
|Chile|2|2.42|
|Mexico|0|1.83|
|Upper middle income<br>countries|2|2.14|
|OECD countries|1.82|2.99|

Credito **r** ights: index of secured creditor rights, with values between 0 (worst) and 4 (best). Source: La Porta et al. (1997). Contract enforceability: indicator of the degree to which contracts are honored, with values between 1 (worst) and 4 (best)-. Source: Business International Corporation.

Regardless of the presence of creditors rights, these rights need to be enforced, and in this dimension, Brazil's judicial system scores considerably below countries in the upper middle income group and OECD countries, as evidenced by a cross-country indicator of contract 3
|‐6.8|4.7|‐6.8|‐7.1|0.1|‐4.0|‐2.0|6.4|
|||Type|of househ|old(HH)||||||
|HH without children|4.1|‐1.0|2.4|‐7.0|‐7.8|0.3|‐3.6|‐4.2|10.1|
|Retiree HH|11.8|‐5.1|5.0|‐8.3|‐6.6|0.7|‐3.3|2.3|15.9|
|HH with children|7.1|‐1.6|8.8|‐0.6|‐6.8|‐3.4|‐2.8|‐2.0|7.0|
|1 child|4.3|‐7.3|12.0|‐2.0|‐6.5|‐6.3|‐5.2|‐2.3|7.4|
|2 children|7.2|8.7|2.0|‐3.0|‐7.6|2.2|‐1.2|‐2.7|7.0|
|3 and more children|10.9|14.1|13.3|0.9|‐5.5|5.0|‐8.3|6.0|0.7|
|singleparent|12.8|‐12.0|6.9|0.8|‐3.2|‐2.1|2.9|‐2.4|1.9|

Source: Authors’ estimates based on HSS data.

37
euros in 2008, while total assets of foreign intra-EU subsidiaries of EU banks were 4.6 trillion euros in that year. Foreign subsidiaries thus represent 58% of the assets of intra-EU foreign bank establishments. Our data do not appear to undermeasure the overall importance of foreign subsidiaries in the EU. The ECB data for the EU, in particular, allow us to calculate a foreign subsidiary assets share relative to the total assets of credit institutions of 14.1%, while our Bankscope data yield a comparable foreign subsidiary assets share relative to the consolidated assets of banks of 13.1%. Our focus on banks also implies that we ignore the non-bank subsidiaries that banks tend to have (see Herring and Carmassi, 2010, p. 209).

> 8 The calculated value of the foreign liabilities share potentially exceeds 1, as some foreign subsidiaries may have internal debt to the parent bank. Indeed, there are very few observation of the foreign liabilities share exceeding 1, and this variable reaches a maximum of 1.64 in our sample.

8
*<br>(0.15)||
|Distance-discounted commercial bank branches (global)|||2.99***<br>(0.70)|
|Observations|2,155|2,155|2,157|
|R-squared|0.99|0.99|0.99|
|Kleibergen-Paap rk LM statistic|22.59***|23.70***|17.66***|
|Cragg-Donald Wald F statistic|26.31|31.17|18.29|

Notes: Standard errors clustered at the country level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Here common controls and constant terms are omitted for brevity. All regressions include both country fixed effects and year fixed effects. The data are for the period 2004-18. Commercial bank branches are in per 100,000 adults. GDP per capita is in constant 2010 U.S. dollars. The dependent variable at the second stage is DGE-based output informality (in percent of GDP). See sections 2 and 3 for detailed data descriptions and empirical methods. Data are for 2004-18.

22
Figure 33: National Level Shelter Deprivation

# **AGGREGATE SHELTER DEPRIVATION -GHS AND MICS**

<!-- Start of picture text --> child child north child south child rural child urban<br>W A VE 1 W A VE 2 W A VE 3 MIC S 2016/2017 W A VE 4<br>51% 53%<br>35% 43% 43% 34% 44% 44% 36% 45% 45% 44% 32% 39% 39%<br>23% 17% 19% 15% 20% 17% 22% 23% 18% 14%<br><!-- End of picture text --> Source: Authors’ estimates based on GHS Wave 1-4 and MICS 2016/17 Note: Individuals with missing age were dropped from the analysis. Child refers to children aged 0-17.

There are noteworthy differences though across regions.

Irrespective of the survey, a higher share of children in the rural North East and North West are severely shelter deprived than in the South East and South South. The share of deprived children in the rural South West, initially second highest and at par with those in North West clearly decreased over time but is in levels still more comparable to other northers rather than southern regions. Urban shelter deprivation estimate are less than half compared to the ones in rural areas and lie around 10 percent, slightly lower according to the GHS and slightly higher according to the MICS estimates, with only the North East and North West facing higher levels of urban shelter deprivation. In terms of levels both surveys rank regions in the same order except for the MICS finding contrary to the GHS urban shelter deprivation levels in the North East exceeding that of the north West, which might be due to non-random accessibility differences of sample areas in the North East. Further, it is worth noting, that whereas over time the share of severely shelter deprived children clearly decreased in the South West, there appears to be a