rafmacalaba/data-use-mentions-extended huggingface dataset:
in Mexico. In fact, this was the focus of an evaluation by Catteno et al. (2009). With these caveats, Linn (2010) selected the following research issues.

# 1. <u>Slum improvement</u>

The prevalence and growth of slums remains a major challenge, as reflected in the fact that slum improvement is the only urban target included in the MDGs. While much has been learned over the years, there remain major unresolved issues, including whether or not to focus on the urban poor where ever they are, or specifically on slum areas; whether slums are predominantly a problem or an opportunity; whether targeted interventions to improve or reduce slums are worth pursuing; whether one should focus on improving existing slums or on preventing the growth of new slums; whether to focus on large or rather on small and medium cities, etc. Linn (2010) explains how the slum improvement research agenda could be shaped to consider

- different challenges (e.g., gender issues, crime and violence, employment creation and access to transport services),

- different instruments (comprehensive versus narrowly focused slum improvements, relying on community participation and multi‐stakeholder alliances, or employing conditional cash transfer programs),

- different analytical tools (happiness analysis, impact evaluation, longitudinal analysis, GIS and poverty mapping, and different planning tools),

- different institutional constraints and opportunities (fragmentation of governmental responsibility, the role of international partners, the effectiveness of aid), and

- different geographic dimensions (a special focus on Africa, or on South Asia; or learning from the decade‐long experience of Latin America)

Among the highest priorities from these choices for research would in our view be an assessment of African slum growth and improvement, bringing to bear the international experience with comprehensive slum improvement programs, combining various tools of impact analysis, longitudinal studies, and life‐satisfaction approaches, with a special focus on the role of participatory planning. Of particular interest is the impact of
**Table 1. Serbia general government revenue, 2016**

||Fiscal data|<br>|Included<br>(SILC|in analy<br>and HBS<br>|sis<br> )<br>|
|---|---|---|---|---|---|
||bn RSD|%<br>GDP|Yes/No|bn<br>RSD|%<br>GDP|
|**Total revenue**|1,765.90|42.5||||
|**_Direct taxes_**|235.5|5.7||||
|Personal income tax|155.1|3.8|Yes|101.5|2.5|
|Wages/Salaries|113.0|2.8|Yes|89.7|2.2|
|Self-employment income|6.5|0.2|Yes|9.0|0.19|
|Capital income|11.9|0.3|Yes|0.2|0.0|
|Rental income|3.8|0.1|Yes|2.2|0.1|
|Capital gains|0.7|0.0|No|||
|Other income|13.0|0.3|No|||
|Annual income tax|6.0|0.1|Yes|0.4|0.0|
|Corporate income tax|80.4|1.9|No|||
|**_Social contributions_**|527.5|13.0|Yes|455.1|11.3|
|Pension and disability insurance|354.7|8.8|Yes|313.2|7.7|
|Health insurance|132.7|3
# **The Impact of Corruption on SMEs’ Access to Finance: Evidence Using Firm-Level Survey Data from Developing Countries**

By: Mohammad Amin<sup>a</sup> and Victor Motta<sup>b</sup> Keywords: Corruption, Finance, SMEs, Bribery JEL Codes: D22, D73, G30, O16

> a Corresponding author. Senior Economist, Enterprise Analysis Unit, DECEA, World Bank, Washington, DC. Email: mamin@worldbank.org

b Assistant Professor, Sao Paulo School of Business Administration, Fundação Getúlio Vargas. Email: victor.motta@fgv.br The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

<mark>We would like to thank Jorge Luis Rodriguez Meza for providing useful comments. All remaining errors are our own.</mark> <mark>We thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the data available.</mark>
Dykstra, Dykstra, and Sandefur (2014), by automatizing the sourcing of results from the World Bank website, collected 10,000 points from the underlying distribution of income or consumption for each country and each year in the _PovcalNet_ database. The resulting data set is, using the words of the authors, _a data set of distributions, not the original micro survey data_ (Dykstra, Dykstra, and Sandefur, 2014, p.1).<sup>5</sup> We use data from the most recent year available for 129 countries featured in the database. Only for 10 countries in the data set is the available survey older than 2000, while for 44 countries it is from 2010 or more recent. The countries represented in the data set cover all income groups and regions in the word. Appendix A summarizes the list of countries and corresponding years.

# **5 Is poverty reduction feasible through DRM? The facts**

## **5.1 Arithmetics**

The interaction between poverty level, optimal marginal tax rate required to eliminate poverty, and the political feasibility is not straightforward. The empirical relation between headcount poverty and the optimal marginal tax rate on the non-poor to eradicate poverty is positive and increasing: the higher the share of population below the poverty line, the higher the marginal tax rate that needs to be levied on the excess-poverty gaps of the nonpoor in order to eradicate poverty. Nevertheless, the same poverty rate may correspond with very different patterns in the Poverty Eradication Capacity Curve. Take, for example, the cases of Namibia and Senegal (Figure 4).

The two countries have about the same share of individuals living below the poverty line of 2 dollars per day in 2005 purchasing power parities (51 percent in Namibia and 55 percent in Senegal). On the other hand, they differ very much with respect to the arithmetics of their redistribution capacity. Namibia’s poverty eradication capacity curve lays above Senegal’s regardless of the redistribution rule (whether the
# **<mark>4.2 Costs and Benefits</mark>**

Certain regenerative agricultural practices involve investments in equipment and labor, which can be costly for farmers. For instance, no-till planters and seed drills are needed to plant seeds without disturbing the soil. The costs of obtaining necessary equipment vary by location and size of the farms. In particular, the prevalence of small-scale agriculture, coupled with policies that discourage farm consolidation, can reduce the cost-effectiveness of investing in equipment. Moreover, low farm gate prices and limited market access can make the adoption of regenerative agriculture less appealing due to associated costs. Thus, despite the potential positive impact of regenerative agriculture on productivity and resilience, these practices may not necessarily be profitable for farmers to adopt.

Moreover, accessibility is another key factor influencing adoption. For instance, in Malawi and Zimbabwe, the adoption of regenerative agricultural practices is negatively associated with access to fertilizers (Makate, et al. 2019). Applying fertilizers is more profitable when they are accessible in this context.

The potential for carbon mitigation through regenerative agriculture, though supported by mixed evidence, presents a promising avenue to increase the benefits through soil carbon credits. Farmers engaged in carbon markets can receive financial incentives or credits for adopting and maintaining these sustainable practices. This introduces an additional source of income for farmers, which can make regenerative agriculture more attractive.

# **<mark>4.3 Lack of Local Knowledge Support and Information</mark>**

The effectiveness of regenerative agriculture is intricately tied to specific local climate and soil conditions, which are subject to change over time. Hence, there is a crucial need for local knowledge support to assist farmers in adopting the most suitable practices based on their evolving conditions.

With the help of the growing accessibility of satellite data and advancements in machine learning techniques, the costs associated with local knowledge support can significantly diminish, enabling widespread availability. For instance, e-extension services can utilize predictive machine learning technology to offer insights based on
Spatial correlation is instead modeled entirely through the area random effects, which are conditioned on the sample residuals for that area.

As noted above, it is not clear that Empirical Best methods are preferred in the traditional poverty mapping setting where a household census serves as the auxiliary data. While EB methods benefit from incorporating information from the sample, the sample is typically a small share of the census 14 Omitting the cluster effect also can lead to bias by simultaneously overestimating the variance of epsilon and the variance of eta, but the resulting biases act in opposite directions and therefore partially cancel each other out.

13
bus stop or bus station, create parallel landmark with word removed

2. If word contains certain word/phrase, swap with another

- (a) (stage, bus stop, bus station) – make interchangeable. So if someone says “X stage”, create “X bus stop” and “X bus station”

1 We treat flyovers and roundabouts as landmarks, even though they are roads, as they represent a unique location 2 Other geoparses such as LNEx only add the n-grams and skip-grams if the name does not already exists in the gazetteer. Our algorithm differs, and we add all n-grams and skipgrams. However, in the algorithm to locate events, we preference locations where the landmark name associated with the location was not a derived n/skip-gram, but still consider the n- gram/skip-gram version as the nonderived landmark location may be removed from consideration if it is not near a mentioned road.

- 3 For example, from the original land-

- mark ‘Prestige Plaza Shopping Mall‘, this generates ‘Prestige Mall‘, ‘Prestige Plaza Mall‘, and ‘Prestige Shopping Mall‘

3. Different spellings of words

- (a) British/English spellings (Eg,: center vs centre, theater vs theatre)

S5
istrations to make timely and data-based decisions such as forecasting revenues, measuring progress, and monitoring staff performance. However, these internal functions have limited empirical evidence in the literature, and we do not include them in the scope of this paper. Instead, we focus on technology applications that directly affect interactions between taxpayers and the tax administration. The rest of this paper proceeds as follows. Sections 2, 3, 4 and 5 provide an overview of consumption taxes, real estate taxes, taxes on trade and income taxes, respectively, in Africa and discuss the possible role of technology in improving the administration of these taxes. Section 6 provides insights from interviews with revenue administrators in Africa on their practical experiences and challenges in implementing technology solutions. Section 7 concludes.

# **2 Technology and Consumption Taxes**

Consumption taxes include sales tax, value added tax (VAT) and excise taxes. VAT is becoming increasingly common in Africa, as in the rest of the world, accounting for about 40 percent of total tax revenue (ATAF, 2019). Albeit quite sophisticated and complex to administer (Slemrod and Velayudhan, 2022), the VAT holds great potential to improve compliance, mostly due to its in-built self-enforcing mechanism as buyers must report their input purchases to be able to deduct the VAT they paid from their own VAT liability from their sales. This generates opposite incentives for buyers and sellers in reporting the same transaction: buyers have an incentive to maximize the amount paid, whereas sellers have an incentive to minimize the amount received, thereby creating less room for evasion. Importantly, the VAT generates a paper trail or a record of transactions from the different trading partners in the value chain. Especially when digitized, such a trail provides a wealth of information to tax administration which they can use in their monitoring function.<sup>4</sup> Technology can significantly help in the correct administration of such a complex tax

> 4Another crucial use of VAT data is that it can be very informative about the state
.47|
|10|31,368|14.39|0.50|69,165|14.55|0.51|
|15|32,793|16.93|0.56|72,309|17.40|0.63|
|20|34,219|19.50|0.59|75,452|20.18|0.64|
|25|35,645|22.06|0.62|78,596|23.03|0.68|

The combination of (a) the sensitivity of measured price inflation to the changes food share, (b) the change in real expenditures to changed inflation estimates, and (c) the sensitivity of poverty rates to expenditure changes can give us some rough rules of thumb as to what to expect from various food shares (o) in price deflation embedded in the poverty calculations. The formula is:

> **6** This 9.75 percent is produced by an iterative procedure for defining the poverty rate outlined in Pradhan _et al_ (2000) using Susenas 1996 data set.

10
) are the most comprehensive in that regard. One drawback of most returnee modules, however, is that they typically collect parsimonious information on past migration experience overseas. This prevents an in-depth analysis of the linkages between past migrant experience and current labor outcomes. In addition, the number of return migrants picked up by nationally-representative surveys in origin countries can be small if the incidence of temporary migration is limited, especially in surveys that restrict the recall period of past emigration episodes.<sup>8</sup>

> 6This is typically the case for low-skilled migrants who move to the Gulf States or to Southeast Asia.

> 7Available surveys of this kind include the Nepal Labor Force Survey (LFS) 2017/2018, the Albania Living Standard and Measurement Surveys (LSMS) 2005, 2008 and 2012 used by Piracha and Vadean (2010) and Carletto and Kilic (2011), the 2009 Nepal Migration survey, the 2017 Comprehensive Survey of the Migration of Armenia Population, the Egypt Labor Market Survey (ELMS) used by McCormick and Wahba (2001), McCormick and Wahba (2003), Wahba and Zenou (2012) and Wahba (2015), the Bangladesh Household Income and Expenditure Survey (HIES) 2016 and the Listening to the Citizens of the Kyrgyz Republic 2021, among others.

> 8Some surveys ask whether the household member has ever lived abroad in the past 5 years, or, in some cases, in the past one year. Those surveys thus capture only a small number of returning temporary migrants.

14
data with broadly-calibrated emissions factors (Crippa et al. 2020; Solazzo et al. 2021). Policy analysts can use spatial inventory data to identify emissions-intensive areas, trace the emissions to specific subsectors, and perform benefit-cost assessments of alternative technologies for emissions reduction.

Satellite-based methane measurements have the potential to inform priority-setting by supplementing “bottom-up” emissions estimates with direct observations. The value added by the satellite-based approach depends on the comparative accuracy of the estimated sectoral outputs and emissions intensities used by existing inventories. This paper initiates a round of empirical assessments with a comparative study for irrigated rice production. It combines atmospheric CH4 concentrations from our Sentinel-5P database with georeferenced data on paddy areas, production yield and planting seasons from IFPRI (2019) and RiceAtlas (Laborte et al. 2017). The results are compared with agricultural CH4 emissions estimates from the EDGAR database.

The remainder of the paper is organized as follows. Section 2 describes our global methane database, and Section 3 introduces the application to irrigated rice production. Section 4 describes the computation of CH4 concentrations over irrigated rice production areas. Section 5 incorporates IFPRI and RiceAtlas data to study the relationship between the scale of irrigated rice fields and local methane concentrations, and to identify outlier areas where methane concentrations deviate significantly from scale-based expectations. Section 6 incorporates EDGAR emissions estimates and analyzes their relationship to the scale of irrigated fields. Section 7 assesses the “value added” of S5P by identifying outlier areas where S5P methane concentrations deviate significantly from expectations based on field scale and EDGAR estimates. Section 8 integrates the S5P concentration data and IFPRI production data to develop an index of local methane intensity (methane emissions per ton of rice produced). Section 9 summarizes and concludes the paper.

# **2.  The Atmospheric Concentration of Methane**

# **2.1 Global Warming Potential**

The concentration<sup>2</sup> of atmospheric methane (CH4) was 1,857 ppb in 2018, about 2.6 times greater than its estimated level in 1750
suggestive.<sup>12</sup> If particular interventions are especially effective in relaxing the constraints to these “high-potential entrepreneurs”, these policies could be more broadly targeted in middleincome countries where these types of self-employed are plentiful. Conversely, in this case, targeting entrepreneurship interventions carefully would be more important in low and lowermiddle income contexts. Future research can complement this ongoing evaluation agenda, with the help of observational data that combines data on entrepreneurs’ outcomes with data on constraints to their growth such as access to credit, infrastructure, governance, and ambition, to better understand the relative importance of different constraints to entrepreneurial success.

> 12 See Cho and Honorati (forthcoming). Their analysis also finds that training tends to be more effective for younger than older entrepreneurs, suggesting that high-potential entrepreneurs do not necessarily benefit more from all types of interventions.

16
# **Cost-benefit estimates**

We use a subsample of 97,050 workers aged 15-65 years who had positive earnings from dependent employment. Annual earnings were converted to $US using the 2017 exchange rate in June, which was equal to 3.52 TL. Table 4 gives their mean earnings by level of education.

|**Table 4: Mean Ann**|**ual Earnings by Educ**|**ational Level**|
|---|---|---|
|Educational level|Mean annual<br>earnings ($US)|Number of<br>observations|
|None|3,714|1,528|
|Primary|5,278|24,850|
|Secondary|5,974|38,567|
|Higher|10,756|32,105|
|All|7342|97,050|

Source: 2017 HLFS We observe a sharp rise in mean annual earnings for those with higher education. This is consistent with the very high demand for university education as well as the restriction on the university admissions by the highly competitive university entrance examination. There is very high demand for university education for several reasons. First, in addition to high income, the university degree confers a prestigious position in Turkish society. Secondly, men with university education serve in the army with a higher rank. These factors are behind the high demand for higher education. The age-earnings profiles by level of education show a very sharp earnings premium for higher education graduates.

**Figure 1: Age-earnings profiles by level of education**

<!-- Start of picture text --> 18000<br>16000<br>14000<br>12000<br>10000<br>8000<br>6000<br>4000<br>2000<br>0<br>15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55
One central aspect of the study depends on the classification<sup>5</sup> of the more than 8,000 food items present in the POF database using the NOVA classification, described in section 2. Table 2 presents, for each consumption decile, the food consumption pattern of households following the classification describing the four food types: in natura; culinary ingredients; processed; and ultra-processed foods. The statistics show that in natura foods represent the most important food group for every decile and, with the exception of the top consumption decile, in natura represents over 50% of the total food consumption. More importantly, ultra-processed consumption is smaller for lower deciles and grows with household consumption decile, with the top decile having a 65% larger consumption than the bottom decile. This pattern is different from countries such as the United States, where consumption of ultra-processed foods is generally associated with poorer households (Baraldi et al, 2018). Consumption of processed foods follows an opposite pattern to ultra-processed, with consumption being higher at the bottom of the distribution. This group includes foods such as bread, cheese, canned food and in general foods that contain no more than three or four ingredients.

Table 2 - Consumption statistics of food

|||Share of household food co<br>|nsumption by type<br>||
|---|---|---|---|---|
|Decile|In natura|Culinary Ingredients|Processed|Ultra-processed|
|1|54.0%|3.9%|24.4%|17.7%|
|2|58.5%|3.9%|19.3%|18.3%|
|3|58.6%|4.0%|17.0%|20.5%|
|4|58.2%|4.0%|17.3%|20.5%|
|5|
Policy Research Working Paper 10633

# **Abstract**

This paper investigates the relationship between disruptive technologies and access to finance for digital tech firms in Africa. Through textual analysis of data from Crunchbase and Pitchbook, the study explores how firms across different age cohorts incorporate disruptive technologies into their offerings in e-commerce, fintech, and information technology services. The findings reveal three key insights for African digital tech startups. First, African startups are less likely to incorporate disruptive technologies into their offerings compared to other regions, except for mobile payments. Second, incorporating these technologies is associated with more funding, but this link is weaker in Africa than in other regions. These results hold when excluding mobile payments and addressing potential endogeneity using instrumental variables. Third, firms that do incorporate disruptive technologies tend to secure funding earlier, with lower initial amounts, but are more likely to succeed in terms of exit or valuation growth than their peers.

This paper is a product of the International Finance Corporation. It is part of a larger effort by the World Bank Group to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at marciocruz@ifc.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

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