rafmacalaba/data-use-mentions-extended huggingface dataset:
license, obtaining operating license, and inspections or meetings with tax officials. Based on this information, the ES compiles two separate measures of petty corruption. The first measure is the incidence of petty corruption defined as a dummy variable equal to 1 if a firm experienced a bribe payment or request for one or more of the six transactions listed above ( _Petty Corruption Incidence_ ). The second measure is the depth of petty corruption defined as the percentage of the 6 transactions for which the firm experienced a bribe payment or a request for one ( _Petty Corruption Depth_ ).<sup>6</sup> We use both these measures of petty corruption as our dependent variables. The mean value of _Petty Corruption Incidence_ equals 0.18 and the standard deviation is 0.39; the corresponding figures for _Petty Corruption Depth_ are 14.1 and 32.1, respectively.

# 2.2 _Main explanatory variable_

Our main explanatory variable is a measure of country size. Following the broader literature, we use the (log of) total population of the country as our main measure of the size of the country ( _Population_ ). We use five-year lagged values (from the date of the ES) of the variable to minimize concerns about the reverse causality problem. The mean value of _Population_ in our baseline sample equals 15.7 log points (or 42 million people) and the standard deviation equals 1.96. The smallest country in our sample has a population of 47,971 (St. Kitts and Nevis) while the largest country has a population of about 1.3 billion (China). The data source for the variable is WDI.

Another measure of country size frequently used is the total surface area of the country. For a robustness check, we use (log of) country’s total surface area as a measure of country size ( _Area_ ). The variable is taken from WDI.

> 6 The two measures of petty corruption are available for only those firms that solicited the public
four quarters after rollout in advanced economies and major EMDEs owing to logistical issues. Caseloads decline only gradually through 2022, mostly due to sustained social distancing.

Economic activity remains depressed in this scenario, as households fear contact-intensive services, including recreation and tourism, and grapple with stringent social distancing measures. Firms—facing pandemic-control policies, a bleak outlook for consumer demand, and elevated uncertainty—curtail investment and hiring plans, keeping employment well below pre-COVID-19 levels. Financial conditions tighten markedly through 2021, as financial market sentiment continuously deteriorates in tandem with a string of unexpected vaccine delays and insufficient control of the pandemic, and as corporate and bank balance sheets deteriorate over prolonged demand weakness and forbearance requirements. While accommodative monetary policy keeps financial crises at bay, fiscal sustainability concerns limit the size of additional fiscal stimulus, leading to insufficient income support to the unemployed and struggling small and medium-sized firms.

> 11 Only about half of the population in advanced economies and major EMDEs is assumed to be amenable to vaccination, a level broadly consistent with the lower bound from global survey evidence (Lazarus et al. 2020).

11
. For each bidding process, we observe data on the type of work being bid on (which makes possible segregating projects by their nature or segment), the duration (in days) of the bidding process (from the opening of the first envelope to the award of contract), the presence (if any) and duration (in days) of legal complaints filed by bidders during the bidding process, the estimated project cost (in R$) provided by each company to the bidders during the bid preparation period, and the actual contract amount (in R$), which is the end result of the procurement process. Note that the period of study purposely excludes 2008, as this was the year that São Paulo state introduced sub-national legislation allowing phase inversion for the procurement of works.

More specifically, data collected from SABESP are organized in two separate datasets: the first describing all bidding processes, the second containing data specifically related to those processes impacted by procurement complaints. Table 1 describes the fields contained in each SABESP dataset. For COPASA, a single dataset was collected, whose detailed contents are described in Table 2.

> 10 Specifically, SABESP provides water to 24 million people and generated 2009 revenues of $4.6 billion; COPASA serves a population of some 13 million, with 2009 revenues of $1.2 billion.

Page | 14
and 39.4 percent of households, respectively, had a mobile phone and a landline.<sup>2</sup> For Saint Lucia, our own calculations based on the 2016 Survey of Living Conditions and Household Budgetary Survey indicate that 95.4 percent of households had a cell phone while 28.0 percent of the households had a landline. For Dominica and Haiti, no information is available on the share of households with a phone. However, related indicators suggest that this share is high. According to the ITU, the share of the population covered by a mobile-cellular network is 100 percent in Dominica. Haiti compares

> 1 World Bank (2022) provides an overview of many resources and findings based on the phone surveys.

> 2 See <u><mark>https://www.itu.int/en/ITU-D/Statistics/Documents/statistics/2020/CoreHouseholdIndicators.xlsx (</mark></u> consulted on February 12, 2024).

3
Policy Research Working Paper 10894

# **Abstract**

Examining one of the world’s largest public business support programs, this paper studies how subsidized credit and partial credit guarantees shape access to finance for micro and small businesses in Indonesia. The analysis uses administrative data on more than 8.4 million borrowers and unique quantitative and qualitative data to show that subsidized credit can enable firms to access formal credit for the first time and boost financial inclusion. However, subsidized credit does not alleviate longer-term credit constraints by serving as a stepping stone to unsubsidized commercial credit in this context. The results highlight the challenge of reaching borrowers without collateral, even in programs that explicitly target them using instruments such as partial credit guarantees. The paper sheds light on how public credit schemes for small businesses can be designed to optimize inclusiveness and additionality.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and the Gender Innovation Lab, East Asia and Pacific Region. It is part of a larger effort by the World Bank 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 at aalibhai@worldbank.org, hjohnson1@ worldbank.org, cniang@worldbank.org, and fstrobbe@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S
<!-- Start of picture text --> Figure 13. Country correlations for CE and<br>level of government stability<br>Low Governance Stability High Governance Stability<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4 Low Governance Stability = 0.45***<br>High Governance Stability = 0.39***<br>-0.6<br>-0.8<br>AzerbaijanYemen Côte d'Ivoire Ukraine<br>Oman Congo, Rep.NicaraguaEcuador UruguayGuyanaLiberia Argentina<br>Brazil Namibia NigerBolivia KenyaZimbabwe AngolaQatar<br>Ghana Saudi Arabia MozambiqueThe Gambia Colombia Tanzania LibyaGabon Iceland EthiopiaMongoliaSudan SenegalMali Burkina Faso<br>AlgeriaUzbekistan MyanmarSouth Africa Paraguay<br>Iran, Rep. of Madagascar<br>UgandaCameroon Indonesia Honduras Suriname Togo<br>Russian Federation Papua New Guinea<br>Bahrain Congo, Dem. Rep.<br>NigeriaPeru Iraq<br>Sierra Leone Kazakhstan Guinea-Bissau Costa Rica Botswana Armenia<br>Guinea<br>Chile<br>Zambia<br>Kuwait<br>NorwayMalawi New Zealand<br>Australia Guatemala<br>Brunei Darussalam<br>United Arab Emirates<br>Canada<br>Correlation between cyclical components of real government spending and real GDP 1980-2020<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) government stability, defined as government stability above (below) the sample median. The difference between the two averages is not significant. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on International Country Risk Guide and
transactions with reported prices for ‘gardens’ (land use categories 01.05, 01.06 and 01.07) that may have other unobserved infrastructure attached to it throughout.

> 9 Data for personal and commercial use used in the regressions are winsorized prices at the 1st and 99th percentiles to reduce outlier-induced bias.

> 10 Crop maps for 2019-24, elaborated using the methodology described in Kussul _et al._ (2017); Shelestov _et al._ (2017), and Shelestov _et al._ (2020) were used and are available at <u>https://ukraine-cropmaps.com/. Soil quality data is at from https://files.isric.org/soilgrids/latest/data.</u> 11 Crop field delineation for 2022 (https://area-monitoring.sinergise.com/docs/markers/field-delineation/) is used, as natural features will not change in the short term.

8
Far more empirical research is needed to go beyond the indicative correlations presented here that are enabled by our cross-sectional data set towards more sure and detailed identification of causal links between elements of innovation and competition policy and resulting advances in growth and poverty elimination. Tracking enterprises over time could create a panel data set with more opportunities for strong instruments from exogenous changes in the economic environments to identify robustly the directions of causality suggested by the system of equations. Ideally, data following the workers as well as following the enterprises and their environments could be analyzed jointly to ascertain with more specificity just what policies best contribute to innovation-driven inclusive growth.

26
|0.39|0.31|0.29|0.33|0.32|0.32|0.31|0.36|0.34|0.37|
|Middle East & N. Africa|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|0.01|
|North America|0.37|0.40|0.41|0.44|0.40|0.37|0.33|0.28|0.29|0.35|0.30|
|SouthAsia|0.00|0.00|0.02|0.02|0.01|0.02|0.05|0.05|0.02|0.01|0.01|
|Sub-Saharan Africa|0.00|0.00|0.00|0.01|0.01|0.01|0.00|0.01|0.01|0.01|0.00|
|Western Europe|0.19|0.16|0.19|0.15|0.18|0.17|0.18|0.20|0.20|0.20|0.19|

Source: Author’s calculation based on the original Colombian customs data. Note: This table displays Colombian exports as a share of total exports and is normalized in 2007.

Since NTM data are only available for LAC countries, the focus of the paper is on Colombian exports to LAC countries. Thus, the following tables explore how export margins have evolved, taking LAC countries as the destination. Table 2 reveals that the number of firms exporting to LAC countries followed a U-shaped pattern over the period under investigation. The number of
PSG program had been implemented in 2023; i is a vector containing levels and squares of weather variables including total precipitation, GDD (>5°C), and the number of days with zero precipitation during XX planting and growing seasons as well as winter crop area to allow for substitution effects between winter and summer crops that was found important elsewhere (Deininger _et al._ 2023a); _λt_ is a time fixed effect that varies with farm size category (< 20, 20-50, and > 50 ha); and _εit_ is an error term. To explore heterogeneity of potential program effects, we also interact _PSGi*Post_ with categorical variables for farms’ size category (<20, 20-50, and >50 ha), distance to the front (< 50 km, 50-100 km, and >100 km);<sup>24</sup> and legal status (legal entity FOP, or individual).

Table 5 presents descriptive statistics for the regression sample of 19,660 PSG recipients (in panel A) and 23,343 eligible non-recipients (in panel B), overall (column 1) and by farm size (columns 2 to 4 ), and legal status (columns 5-7). The first two rows in each panel provide the total area of parcels (or agricultural parcels) to which the registrant has a registered right. With an average area of registered land close to 20 as compared to about 9 ha for non-participants, successful PSG participants are larger (12% as compared to 3% cultivating more than 50 ha) and more likely to be organized as a legal entity (19.2% vs. 6.2%) or FOP (12.7% vs. 7,7%) than the control. Overlaying cadastral with crop map information suggests farmers in treatment and control cultivated 96% and 99% of their registered area, respectively, in the pre-war years (2019-2021); the war triggered a marked decline of cultivated area to 93%
<!-- Start of picture text --> b. Start primary school on time<br>60.0<br>48.8 48.8 50.3 50.3 51.2<br>50.0<br>40.0 35.8 36.6<br>31.0 32.1<br>30.0<br>19.8 20.4<br>HOI<br>20.0<br>11.0 13.5 15.5<br>Coverage<br>10.0 3.3 4.6 5.4<br>0.0<br><!-- End of picture text --> _Source_ : Demographic Health Survey used for African countries, except Côte d’Ivoire, which uses household survey data. All country surveys are from 1998.

# **5. Benefit Incidence Analysis in Côte d’Ivoire**

This section **relates the fiscal policy angle to the new measurement developments of equality of opportunities.** The authors conducted an exercise, expanding one of the most traditional and commonly used distributive analysis methods for fiscal policies, benefit incidence analysis (BIA), to relate it to the concept of equality of opportunities. The authors derived the **Opportunity Benefit Impact Analysis, or Op-BIA** , to present an incidence analysis of public educational spending along the distribution of opportunities and compare it with the traditional BIA based on income distribution. The Op-BIA has two main advantages over the traditional BIA. First, it allows one to analyze directly the allocation of public resources to education against a concept of vulnerability _directly related_ to education, not an indirect concept of per capita household income or consumption as performed by BIA. In other words, it allows a sharper picture of distribution of resources and opportunities directly associated to such resources (in this case, education resources). Second, it provides insights on how multiple factors (all
br>-1.67|||||
|S(LE)*Diversity of employment|||0.0195|<br>1.50|<br>0.0259|<br>2.28|||0.0280|2.41|
|S(LE)*Member<br>of a cooperative<br>or NGO|||-0.0309|<br>-0.91|||||0.0818|2.56|
|S(LE)*Owns land|||-0.0454|<br>-1.56|-0.0473|<br>-1.75|||-0.0798|-2.43|
|S(LE)*Social capital index|||0.0009|<br>0.01|||||-0.0890|-2.18|
|S(LE)*Infrastructure<br>capital index|||0.0497|<br>0.83|||||||
|S(LE)*Commercial<br>capital index|||-0.1001|<br>-1.49|-0.0669|<br>-1.73|||||
|Nuimberofobservations|38710|<br>38710||38710||38710|38585|38585||38585|
|Number of estimated<br>parameters|233|233||263||249|233|233||263|
|R - square|0.5890|<br>0.5869||0.5904||0.5902|0.5890|<br>0.5869||0.5904|
|F test<br>Ptob > F|||||F(14,391<br>0.8|
World Bank for its headline global poverty monitoring. For ease of reproducibility, the calculations reported here are performed on the “binned” version of the dataset created by Mahler `r` et. al. (2022), where the income distribution in each country-year is represented by data grouped into 1000 bins of equal population size per country and year, rather than on the underlying microdata itself.<sup>20</sup> Due to differences in the underlying survey instruments, the PIP combines surveys in which consumption is the main household-level measure of well-being (covering about threequarters of the world’s population) with surveys in which income is the measure of well-being (covering the remaining quarter of the world’s population). This presents challenges, since conceptually consumption and income are very different measures of well-being, and these differences are likely to be more pronounced in the tails of the distribution. For example, it is biologically impossible to survive with zero consumption, but it is possible to survive with zero income if households can draw down stocks of wealth. This conceptual difference is apparent in the data. For example, zero values for reported consumption are virtually nonexistent, while it is common for income surveys to have some mass of observations with zero or negative incomes.<sup>21</sup> Beyond these extremes in the lower tail of the distribution, household saving introduces conceptual and empirical differences between income and consumption throughout the income distribution. We follow the practice of the World Bank’s global poverty monitoring by abstracting from the difference between income- and consumption-based surveys when combining distributions across countries. For terminological convenience we will refer to the measure of well-being from the survey as “income” irrespective of whether it is drawn from an income or a consumption survey and note that it is measured in constant 2017 $PPP, per person per day.

An additional challenge is how to handle the effect of very low or zero measured income when constructing distribution-sensitive welfare measures. Specifically, our welfare measures are
ln(Dia.)*Language sim.||-0.0829<br>(0.0522)|||-0.117*<br>(0.0606)|
|ln(Diaspora)*Colonial|||-0.106**<br>(0.0498)||-0.110**<br>(0.0494)|
|ln(Diaspora)*Religion||||-0.0526<br>(0.0708)|0.00141<br>(0.0758)|
|Controls|Yes|Yes|Yes|Yes|Yes|
|Constant|-6.102|-5.847|-6.941|-5.701|-6.745|
||(6.019)|(6.000)|(5.985)|(6.080)|(6.044)|
|Observations|26,160|26,160|26,160|26,160|26,160|
|Pseudo R2|0.958|0.958|0.957|0.957|0.958|
|Sending FE|Yes|Yes|Yes|Yes|Yes|
|Receiving FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|LogLik|-18490.83|-18482.05|-18478.32|-18490.28|-18459.73|

Source: Authors’ analysis based on data described in the text.

Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ‘_i’ and ‘_j’ stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at
.98|0.99|1.34|0.74|
|**Mean**|1.03|1.04|1.00|0.95|1.06|

Source: Authors’ elaboration based on BOOST data. This table presents the results for the input–oriented Malmquist index and its components for the period 2006 – 2018.

**Figure 20: Mean Deviations from the Malmquist Index**

<!-- Start of picture text -->
1.00<br>0.50<br>0.00<br>-0.50<br>-1.00<br><!-- End of picture text -->

Source: Authors’ elaboration based on BOOST database. Mean deviation is calculated by subtracting 1 from the Malmquist Index presented in Table 5.

24
.01|78.27|
|Jamaica|51.78|51.78|
|Panama|71.14|38.56|
|Bangladesh|87.67|95.45|
|Vietnam|66.51|50.28|

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ Poor households are defined as belonging to the first four deciles of the expenditure (or income) distribution in each country. Error of inclusion is measured by the percentage of all beneficiary households that are rich; error of exclusion is measured by the percentage of poor households that do not get a subsidy. All figures are calculated using sample weights.

# **4 Conclusion**

This paper offers a methodology to both estimate the amount of subsidy resulting from piped water consumption tariffs and assess the resulting subsidy’s efficacy in improving the accessibility and affordability of service for the poor. In an analysis of 10 low- and middle-income countries, we find that piped water is substantially subsidized, as existing tariffs do not fully recover the costs of service provision. Moreover, these consumption subsidies fail to efficiently address service gaps among the poor due to ineffective targeting. Our analysis reveals remarkably consistent findings across countries, despite their great diversity in terms of economic development, national income levels, piped water coverage, and the costs of producing and distributing piped water.

20
industrialized countries.<sup>6</sup> The literature on developing countries has documented that the magnitude of potential knowledge spillovers depends importantly on the existence of technological capabilities allowing the assimilation of know-how by indigenous firms (Pack and Westphal, 1995). A unique feature of many transition economies in comparison to most developing countries is that technological ability is substantially greater. In principle, this should facilitate adoption of new technologies and allow rapid convergence towards "best practice." Much of the econometric literature has focused on productivity measures as a proxy measure of technology diffusion. Early studies such as Blomstr6m and Persson (1983), using industry level data, found that domestic labor productivity is positively influenced by foreign presence in an industry, measured by the foreign share of industry employment. More recent studies using firm level data are less supportive of the existence of spillovers. Aitken and Harrison (1997) and Haddad and Harrison (1993) find that foreign investment has a negative effect on the performance of domestically owned firms. Harrison (1996) suggests that in imperfectly competitive markets entry by foreign investors implies that domestic incumbents lose market share and this impedes their ability to attain scale economies. The negative spillover results contrast with the findings of the case study-based literature and may to some extent reflect the omission of important variables such as the level of R&D spending, expenditures on training and the magnitude of employment of personnel with technical degrees (engineers, scientists).<sup>7</sup>

- 6 See Griliches (1992) for a survey of the literature on R&D spillovers, Nelson and Wolff (1997) for a recent contribution to this literature.

- 7 The literature on technology acquisition and adoption in developing countries is substantial. See, for example, Evenson and Westphal (1987), Lall (1987; 1992), and Pack and Westphal (1986). Westphal, Rhee and Pursell (1981) discuss the case of Korea in some depth..

7
gender balance. These difference between countries are explained by the prevalent type of work (e.g. construction and repair vs. personal and household services) performed by shadow employees in different countries (see below).

<!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>LV BG PL EE SK UA HU SI CZ RO RU FI NO DK SE GR CY IT IL PT ES UK FR IE BE DE AT NL CH<br>East North South West<br>Employees with contracts Informal self-employed Employees without contracts<br>% Male<br><!-- End of picture text -->

**Figure 4 Gender composition of formal and informal employment, 2004-2009.**

_Source:_ Calculation with ESS data.

By contrast, in _all_ European countries the proportion of males among the informally self-employed is higher than among formal employees; the ratio varies between 1.2 and 1.5 in continental Europe, whilst it is 1.6 in the UK and 1.8 in Ireland<sup>20</sup> . Possible explanations include a higher risk tolerance among males (see Ekelund _et al._ (2005), Le _et al._ (2010) and references therein), and the heritage of the social norms which considered business as a ̳nonfemale‘ occupation. The fact that informal self-employment is dominated by males is consistent with the finding of the survey of undeclared work in the EU (Riedmann and Fischer 2008: p. 24) that undeclared work of any type is done mostly by males, both in the EU as a whole and in every country except Spain, France, and Italy.

**_Age._** The age composition of informal workforce is also markedly different for its two components: In all parts of