rafmacalaba/data-use-mentions-extended huggingface dataset:
defined as loans that are due to be repaid in less than one year, including therefore long term loans with less than a year to maturity. This portion of long term debt is not included in short term debt in Italy. However, the latter contains also, intra-group financial debt, which in principle one would want to keep separate. Aggregate information from a smaller sample of 972 private large firms, suggests that financial intra-group debt represents over the period an average of 13.2% of total financial debt.

In Table 2 we present the evolution over time of the mean, median and standard deviation of the ratio between long term and total debt for both the UK and Italy. Although cross countries comparisons must be treated with some caution because the definition of maturity is not exactly identical for the two countries, it appears that the maturity composition of debt is not dramatically different in the UK and in Italy. Also, the UK data suggest that, whatever measure of centrality is used, the maturity composition of debt becomes shorter during the recession years at the beginning of the 80's, while it lengthens during the prolonged expansion that follows. This trend continues also at the onset of the recessionary period at the end of the 80's, beginning of the 90's. The movements for Italy are less pronounced and are sensitive to the measure of centrality adopted. The only clear trend is the increase in the average value of maturity from 1984 to at least 1988, which is also a period of fast economic growth.

# 4. **Empirical Evidence**

In this section we present descriptive and econometric evidence 12
, achieving an average annual growth rate of 10.7%. Brazil’s performance was above most of the selected comparators, and only Colombia showed slightly higher growth rates of value‐added embodied in gross exports (11%).

Brazil’s performance differs substantially across sectors, including those that are important for Brazil’s participation in GVCs. The selection of sectors that we will investigate Brazil’s GVC performance are those GVC‐intensive sectors in which Brazil is relatively competitive.<sup>4</sup> The data support that Brazil is most competitive in agribusiness (excluding primary agriculture), wood and paper, basic metals and the automotive industry. Brazil also performs relatively well in the chemicals sector, and since this sector in Brazil is one of the largest in the world, we selected it

> 2 These comparators were selected based on similarities in size (GDP and population) and economic development (GDP per capita). They include South Africa, another emerging economy within the BRICS that is commodity dependent, as well as other large regional economies including Mexico, Colombia and Argentina. Thailand and Canada were selected as aspirational countries, and Indonesia as a comparator outside the region that is also integrating into GVCs.

> 3 This analysis is based on van der Marel, E. (2016), “Making GVCs Work for Development: The case of Brazil,” mimeo. 4 We use the indicator of revealed comparative advantage (RCA) based on domestic value added (as opposed to gross terms) to assess Brazil’s relative strength of the sectors in which its relative competitiveness is high. The RCA measures are presented in Table A1 in the appendix.

5
|Survey year dummy (1 if 2016) # Counties severely affected||||||
|---|---|---|---|---|---|
|by EVD (dummy)<br>0.077|-0.053|0.043|0.092|-0.055|0.053|
|(0.042)*|(0.078)|(0.044)|(0.041)**|(0.079)|(0.043)|
|Constant<br>10.609|9.422|10.948|10.609|9.422|10.949|
|(0.028)***|(0.066)***|(0.030)***|(0.028)***|(0.066)***|(0.030)***|
|Mean of dependent variable<br>5.160|0.426|0.550|0.448|0.230|0.366|
|Observations<br>5365|5365|5365|5365|5365|5365|
|Number of clusters<br>123|123|123|123|123|123|
|Fstat<br>39.942|51.174|90.213|36.386|47.367|82.968|
|Prob > Fstat<br>0.000|0.000|0.000|0.000|0.000|0.000|
|Adjusted R-squared<br>0.183|0.155|0.231|0.185|0.155|0.232|

_Source_ : Authors based on HIES 2014 and 2016. Note: Standard errors in parentheses clustered at the district level. *
Fratscher (2012) focuses on the correlates of various types of capital flows in 50 emerging markets, and specifically on the collapse in capital flows during the 2008 global financial crisis. He finds that common shocks had a large effect on capital flows both during the crisis and in the subsequent recovery phase. However, these effects varied across countries, a pattern that he attributes to differences in the quality of domestic institutions, country risk, and the strength of domestic macroeconomic fundamentals.

Forbes and Warnock (2014) observe that investment outflows from emerging markets have become larger over time and have different dynamics than inflows (insights that we build on here). They distinguish several episodes of large increases and declines in the inflows and outflows of capital: surges, stops, retrenchments and flight, analyzing them separately. Using quarterly data from 1980-2009 for 50 developing and advanced economies, they further separate out events primarily driven by the decline in debt flows (portfolio debt and banking flows) and those driven by the decline in equity flows (FDI and portfolio equity).

The authors find that episodes dominated by equity-related flows are not associated with the standard explanatory variables (global risk and liquidity; contagion; domestic growth, etc.). In contrast, the standard measures of market risk are important for the incidence of debt-led episodes: for example, when risk aversion is high, debt-led surges are less likely and debt-led stops are more likely. Contagion at the regional level is also important in debt-led episodes. Country-specific variables are largely insignificant, with the exception of domestic growth shocks: debt-led stops are more likely in countries experiencing negative growth shocks, and debt-led surges are more likely in countries with positive growth shocks.

A few studies focus on particular types of capital flows, FDI flows for example. Lipsey (2001) points to the fact that in some prominent financial and exchange rate crises in the past – the Latin American crisis in 1982, the Mexican crisis in 1994, the East Asian crisis in 1997 – FDI inflows were more stable
Policy Research Working Paper 10444

# **Abstract**

This paper sets out to measure and analyze corruption risks, patterns of favoritism, and state capture in public procurement in Bulgaria. It draws on two main types of data: large-scale administrative data on public procurement and the list of politically exposed persons. The analysis rests on calculating individual corruption risk indicators (or red flags), such as single bidding in competitive markets, and creating a composite Corruption Risk Index based on these indicators. It maps the distribution of these red flags over time, across different regions and markets. The analysis finds that Bulgaria shows high corruption risk among other examined countries in the European Union, with weak institutions contributing to slow gross domestic product per capita convergence to Western European countries. The results point out that corruption risks have deteriorated over time. Combining suppliers’ political connections information with public procurement corruption risk data shows that connections are associated with higher risks, in particular connections to local government members and state-owned enterprises. The large-scale analysis of buyer-supplier contracting networks points at state capture patterns where groups of buyers and suppliers repeatedly connect in high corruption risk procurement contracts. Such groups have gained more power and control over a larger share of contracts since 2016. Finally, policy recommendations are provided in three areas: enhancing data scope and quality, introducing a data-driven approach to corruption risk assessment, and improving public procurement policy and practices to reduce noncompetitive tenders.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 misi.fazekas@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the
<mark>across countries (cf. Jilke, Meuleman, and Van de Walle 2015). Particularly culturally sensitive concepts – such as public service motivation – may suffer from scalar noninvariance (Mikkelsen, Schuster, and Meyer-Sahling 2021). They thus require more care when interpreting cross-country differences than factual questions in our questionnaire (such as whether respondents had a performance evaluation in the last two years).</mark> <mark>While these are important limitations to keep in mind, they of course equally affect many other approaches to constructing cross-country governance indicators. In an expert survey, for instance, experts cannot be randomly sampled, respond to questions in different languages and might exhibit different social desirability biases, among others. We thus believe that our approach nonetheless adds to the available landscape of cross-country</mark> <mark>governance indicators, and with more granularity than most existing indicators. Moreover, as the initiative develops and the core module is included in a wider range of surveys, some of the abovementioned concerns will be reduced.</mark>

# **<mark>Uses of the Global Survey of Public Servants</mark>**

<mark>How can the Global Survey of Public Servants enrich the study of comparative public administration? To illustrate their utility, this section sketches out six possible uses of the indicators.</mark> <mark>First, the indicators can be used to nuance popular global stereotypes – at times reflected in the literature – about bureaucracies. One such stereotype is that bureaucrats around the world lack performance incentives: their work effort is not rewarded with, for instance, better pay or greater promotion prospects (see, classically, Osborne and Gaebler 1992). The data from the Global Survey of Public Servants suggest important nuances to this stereotype. In line with conventional wisdom, only a minority of public servants – 31% on</mark> 13
How often respondent went to bed hungry at 14 years of age|
|Marriage duration|Completedyears of marriage(if married)|
|Assets into marriage|Dummy variable =1 if respondent brought assets into marriage (if<br>married)|
|Age at marriage|Age - Marriage duration|
|Husband works in gar-<br>ment industry|Second respondent works in garment industry<br>**Panel C: Variables from diagnostic tests**|
|Numeracy score|Nominee’s score on 10 numeracy questions, score out of 1 which<br>represents aperfect score|
|Literacy score|Nominee’s score on 20 literacy questions, score out of 1 which rep-<br>resents aperfect score|
|Processing speed score|Nominee’s score on 193 processing speed questions, based on the<br>Wechsler Adult Intelligence Scale, score out of 1 which represents a<br>perfect score|

61
62.54|
|||[55.53; 55.85]|[55.81; 56.28]|[56.09; 56.65]|[56.34; 57.02]||
|Share of youth NEETs, %|30.11|30.20|30.26|30.32|30.38|31.40|
|||[30.17; 30.23]|[30.22; 30.31]|[30.26; 30.39]|[30.32; 30.47]||
|Change in real earnings, %||0.51|0.31|0.10|-0.11|-4.37|
|_Note: Note: Kosovo LFS 2018_|_is used. Wages_|[0.46;0.56]<br>_are imputed as describ_|[0.23;0.40]<br>_ed in Section 2. Low_|[0.01;0.19]<br>_labor demand elast_|[-0.23;-0.00]<br>_icity value is set at_|_-0.1 (-0.4) for_|

_Note: Note: Kosovo LFS 2018 is used. Wages are imputed as described in Section 2. Low labor demand elasticity value is set at -0.1 (-0.4) for skilled labor and -0.3 (-0.6) for unskilled labor in the short (long) run. High labor demand elasticity value is set at -0.7 (-1) for skilled labor and - 0.9 (-1.2) for unskilled labor in the short (long) run. Only employees with wages between the old and the proposed minimum wage are assumed to be affected. Aged 15-64. 95 confidence intervals are in brackets._
Financial Regulation and Development and deposit rates with their respective minimum and maximum rates as published by the National Bank of Ethiopia (2013). Although some changes occurred in mid-2009, it is noticeable that over the period of the policy (2011–2013), rates are generally constant, at least in the first moment of their distributions and the respective supports. This is in line with the theoretical model, in which market prices were left constant over the policy change.

Figure 11: Climate and Policy Change <!-- Start of picture text --> 0 50 100<br>Time<br>Ethiopia Addis Ababa<br>26<br>24<br>22<br>20<br>Celsius Degree<br>18<br>16<br><!-- End of picture text --> _Note:_ This figure reports the monthly average temperature in Ethiopia (blue) and Addis Ababa (red) between January 2005 and August 2013. The policy change occurs in April 2011, Time 75, and there does not seem to be any response to weather changes. The data come from the Berkeley Earth project (http://berkeleyearth.org/). Alternative measures of temperatures were used from the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC), which are highly correlated with the current values (0.72 for Addis Ababa and 0.6 for Ethiopia) and highlight similar differences.

Thirdly, climate might be considered problematic, if the policy change occurred over periods of extensive temperature fluctuations, which might affect the industrial productivity and hence financial markets. Although Ethiopia is a country with a strongly heterogeneous climate, close to the equator and with strongly diverse altitudes, all of these characteristics make it suitable for important temperature fluctuations, which might be related to our study. From an analysis of average monthly temperatures for Ethiopia (blue) and Addis Ababa (red) between 2005 and 2013, as shown in Figure
Nathan Kapata and Dr Muzala Kapina from the Zambian National Institute of Public Health (ZNPHI). The household water and sanitation survey data collection was made possible with then Field Coordinators Sensio Banda and Gertrude Namitala; survey firms Fibonacci Engineering and Palm Associates.

From inside the World Bank, the work was made possible with the support of operational Task Team Leaders (TTLs) Josses Mugabi, Odete Muximpua, Ruth Kennedy Walker and Ai-ju Huang from the Water Global Practice of the World Bank as well as Collins Chansa, Senior Economist, Health Global Practice. Comments were gratefully received from Solomon Sitinadziwe Kagulura, Senior Health Specialist, Health Global Practice. This work was financed by the World Bank Global Water Security and Sanitation Partnership (GWSSP).

The findings, interpretations and conclusions expressed in this paper do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank or the governments whom they represent. The World Bank does not guarantee the accuracy of the data included in this work. Official delimitation of areas and borders might not reflect the official position of the World Bank Group. Country borders or names do not necessarily reflect the World Bank Group's official position. These maps are for illustrative purposes and does not imply the expression of any opinion on the part of the World Bank, concerning the legal status of any country or territory or concerning the delimitation of frontiers or boundaries.
one additional practice. Furthermore, data from our control group show that only about 36 percent of firms that receive the six-page report get back in touch with SEBRAE during the following year to obtain more business advice or participate in training or mentoring.

We tested whether providing information about business practices during the second NaN visit in a more succinct and compelling format can increase take-up of SEBRAE services. If attention is limited, as may well be the case among small firms in developing countries, providing simple information may be more effective than providing full information (Kremer, Rao and Schilbach 2019). Together with SEBRAE-PR, we designed an information sheet that was added to the six-page report in 2018. Appendix 3 contains a sample information sheet. We used four different versions of the information sheet.

The basic version adds several pieces of concise information to the six-page report. It uses a scale of business practice adoption (labeled “scale of excellence”) to visualize how many out of the 29 business practices a firm is currently using. The information sheet also benchmarks business practices relative to other firms in the same sector, stating which percentage of firms adopt more practices than this firm and which percentage adopt the same number of practices. Further, it lists five simple actions the firm can take to start adopting more business practices. At the bottom of the page, the firm is invited to a free training session. The page gives the title, time, date, and location of the event and asks the firm to enroll by calling a phone number.

The second version, pre-enrollment, includes the same information as the basic version, but the invitation to training lets the firm know that it was already enrolled in the session. The intention here was to overcome procrastination that firms may face when enrolling in a SEBRAE service.

The third version, motivation, includes the same information as the basic version, but at the top of the page provides an additional sentence asking: “Did you know that being one level below your competitors (
**Figure 1:  Correlations among country rankings by CPI vs. aggregate indicator from Enterprise Surveys**

CPI vs. ES CPI adjusted with “fitted variables” vs. ES <!-- Start of picture text --> 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160<br>ES Overall Payments Ranking1 ES Overall Payments Ranking1<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Perhaps the CPI measures something akin to an overall “culture of corruption”, whereas the ES captures particular patterns of bribes for certain transactions. In any case, the data about aggregate corruption remain noisy. They may capture different facets of corruption and they may contain other types of information that are not really observations of corruption. This may help to explain why there is such a weak relationship between the CPI and outcomes in infrastructure once we control for income (Kenny, 2008).

While our “results” support a certain amount of skepticism as to the information content of aggregate corruption perception measures, this does not imply that one should discard or disregard indicators like the CPI. They do seem to capture important information at the least about perceptions that may alter investment behavior and they are a way of giving prominence to an important topic. What may, however, be questioned are statistical methods that attempt to place confidence intervals around current corruption indicators. The statistical methods to derive such confidence intervals need to assume some random process that relates actual corruption to the observation expressed by the respondents. If there are other unidentified observations in the data that are not about corruption, it is not
_Brazilian spatial labor dynamics Page 20_

**Table 7B,  Summary statistics, Non-metropolitan Brazil**

|**Variable**|**Obs**|**Mean**|**Std.Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Delta ln Wage (2000/1991)|3532|-0.0233|0.3097|-1.5242|0.8495|
|ln Teacher qualification in 1991|3532|1.1990|2.1519|-4.6052|4.2370|
|Years of Schooling in 1991|3532|3.6414|1.3197|0.4425|7.1707|
|Total rainfall|3,531|1,312.3340|436.0881|368.6636|3,361.7090|
|Government Accountability|3,532|0.9023|0.2969|0.0000|1.0000|
|Delta ln Employment (2000/1991)|3532|0.1231|0.2173|-0.6577|1.3689|
|lnTransport Cost to São Paulo in 1995|3531|7.1172|0.7733|4.1589|9.2603|
|ln Transport Cost to State Capital in<br>1995|3531|5.9358|0.6736|2.1972|8.6910|
|Delta ln Transferences (2000/1991)|3532|0.8316|0.3262|-1.1823|2.4240|
|Delta ln Market Potential (2000/1991)|3523|0.4053|0.1330|-0.4691|2.0949|
|ln Population in 1991|3,532|9.2629|0.8171|6.6214|
the net effects of various policies and programs in the country's fiscal framework. Building on the existing  CEQ 2018 (Vaughan & Cabrera, 2022), the World Bank updates the analysis using the latest available data on the COVID-19 pandemic, including the 2021 Survey on Income and Living Conditions (SILC) and the 2021 Household Budget Survey (HBS)<sup>7</sup> . This analysis not only updates previous work using the CEQ framework but also expands the analysis of social protection transfer. We evaluated changes over time by comparing the pre-COVID CEQ results with the updated CEQ, providing insights into how fiscal policies have evolved and adapted to the challenges posed by the pandemic. This comparative analysis offers valuable information on the effectiveness of fiscal measures in addressing poverty and inequality during the COVID-19 crisis. In addition, the standardized CEQ framework allows Bulgaria's fiscal system to be benchmarked against other countries. By refining and updating previous work, this analysis takes advantage of the most up-to-date household survey data, administrative data, and macro data on fiscal accounts, and improved modeling techniques on the social transfer side, allowing a more comprehensive and updated evaluation of social protection transfers.

**In addition to updating the CEQ, we expanded the analysis by incorporating an assessment of the impacts of fiscal policy on child poverty and using microsimulation techniques to simulate the potential**

> 6 The analysis cited only considered the statistical association of child poverty reduction of cash mean-tested, cash non-meanstested, in-kind mean-tested, and in-kind non-means-tested benefits. Coefficients are more significant for non-means-tested cash and in-kind benefits compared to means-tested, all of them being positive. It does not consider when the benefits are recaptured via income tax payments. This effect could be relevant depending on the cash benefits if they are part of taxable revenue for income tax purposes.

> 7 The 2021 Survey on Income and Living Conditions (SILC) captures income in the year 2020. Unfortunately, the 2020 HBS is not available, so we use the 2021 HBS to approximate consumption in
. The 2018 Enterprise Survey data shows that a formal firm (with five or more employees) in the 75<sup>th</sup> percentile of productivity is between 6 to 12 times more productive – as measured by sales per employee – than a formal firm in the 25<sup>th</sup> percentile. The corresponding figure in higher income economies is much lower. In the United States, a firm in the 75<sup>th</sup> percentile is less than twice as productive than a firm in the 25<sup>th</sup> percentile (Syverson 2004). The figure in Mozambique is also higher than the dispersion found in India or China (Hsieh & Klenow 2009). Taking into account that many enterprises in Mozambique operate in the informal sector and are 2