rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9441

# **Abstract**

This paper empirically examines the demand and supply sides of bribery using World Bank Enterprise Survey data on 18,005 firms in 75 developing countries. It assesses the determinants of firms’ bribe paying behavior and examine how bribe behavior affects two main sectors where corruption is rampant: taxation and government contracts. The paper shows that corruption in tax administration tends to be mainly a demand-side phenomenon. Paying a bribe requested by a public official is associated with a 16 percent increase in the share firms’ sales not reported for tax purposes. In public procurement, the results suggest, on the contrary, that corruption is a supply-side phenomenon, with bribe transactions generally initiated by firms to secure public contracts. Firms supplying a bribe without a previous request by officials is associated with a 17 percent increase in the bribe paid to secure a government contract, more than three times the effect observed on the demand side of bribery.

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 wkouame@worldbank.org.

_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 findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research
In addition to the performance tests, PIRLS provides a rich set of background information. This background information was gathered through a student questionnaire, a home questionnaire (completed by parents), a teacher questionnaire and a school questionnaire (completed by school principals). By merging these data with the student achievement data, the database used in this paper contains extensive information on the family background of students, on the resource endowment and teacher characteristics of the class and school and on schools’ institutional features.

A problem immanent in any empirical work based on survey data is non-response and thus missing data. Table A1 in the appendix reports the share of missing values for each variable used in this paper. While there are only very few missing values on such variables as student age and gender, kindergarten attendance and teacher gender and education, the amount of missing data on some variables in some countries is quite substantial, reaching more than 30 percent in Argentina in the case of books at home and parental education, working status and occupation. In such a situation, dropping any student that misses data on at least one variable in multivariate analyses such as the ones conducted in this paper means that one is left with relatively small samples. Furthermore, since data points are unlikely to be missing at random, such a procedure could introduce severe biases and reduce the representativeness of the results. In effect, when sticking to the original data, there is an inescapable trade-off between controlling for many variables and keeping many observations. Even more, dropping all observations with some missing values always means dropping relevant information, because there is abundant information on other variables for each student that misses data on one or a few of the numerous considered variables.

Thus, to ensure the usage and exploitation of all the information available in the dataset, we imputed missing values in the data using a simple median imputation method. This method assigns the median of the available observations at the lowest possible level, i.e. either the class, school or country median,
of both the government and the private sector, the internet can bring major benefits in the provision and delivery of services. Digital identification can also improve participation of and help for the disadvantaged groups in the country to become integrated. The advantages of a well-developed ICT sector are manifold and transformational.

In this paper, we use data from the Fourth Djiboutian Household Survey (henceforth referred to as EDAM4) to study the effects of introduction of competition in the telecom sector in Djibouti on the welfare of its residents. EDAM4 is a nationally representative survey conducted in 2017 in Djibouti. The sampling strategy of the EDAM4 was designed to produce representative indicators at several levels: national, urban, rural, by regions and by 5 districts in Djibouti city. In a first for the Djiboutian Statistical Office (DISED), the survey collected information on sedentary ordinary households and nomads, with a total of 4,474 households interviewed. Findings from the survey 4
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but also quality of roads has been low. About 40 percent of roads in Nepal remain to be paved (Figure 2).

**Figure 2. Road density in South Asia**

<!-- Start of picture text --> 180 166<br>160 148 Road densityPaved road density<br>140<br>120<br>120 112<br>100<br>80<br>60 53<br>40 34<br>20 16 22 1711 12 7 6 2<br>0<br>Km per 100 km2 of land area<br>Bangladesh Sri Lanka India Pakistan Bhutan Nepal Afghanistan<br><!-- End of picture text --> Note: Figures are the latest available data in 2002-2007. Source: World Development Indicators.

In Nepal there are several important rural road reconstruction and rehabilitation projects assisted by multi- and bi-lateral donors. While the World Bank has been assisting the Rural Access Improvement and Decentralization Project (RAIDP) in 20 districts mainly in the Terai area, other donors, such as Asian Development Bank and UK Department for International Development (DfID), have been supporting other 45 districts under the Rural Reconstruction and Rehabilitation Sector Development Project. The remaining 10 districts are planned to be covered by scaling up the RAIDP.

In the RAIDP, the 20 districts were selected according to the predetermined performance evaluation framework composed of a wide range of criteria, such as project preparedness, existing infrastructure gap and poverty profile. The framework aimed at creating institutional incentives to compete for resources among districts. The funding allocation was also designed on a performance basis. However, the original performance criteria were found unsuitable, given the prevailing conflict unstable political conditions, and low implementation capabilities at the district level. Therefore, the criteria were somewhat simplified, focusing on financial and physical progress of the works carried out.

Three entities share responsibility for the rural road development and maintenance in Nepal: the Ministry of Local Development (MOLD
<!-- Start of picture text --> 60<br>z@@@°<br>2S e) (@)@9)<br>ro eo eo ee<br>s ° @ © @deo@ @<br>3 40 @ @ @ eo<br>= e@ 0 @ @@ o]<br>oO [Se,e) @ @<br>Q e@ @ a [o<cere}<br>D @ e000 @2 00<br>by ee @ @ @ O@W O<br>bo] @o e@@ @@e<br>& eo ° @oe @ @<br>oO @ a @ @d0o<br>= @@e colo we)<br>© 20 @ @<br>2 e® © @¢0<br>==o= e@ 1} °®®e 6@@ e@ °<br>* “ said y = -3.25 + 0.57 x, R2=0.56<br>Pi (2.03) (0.03)<br>0<br>0 25 50 75 100<br>Availability of All SDG Indicators (%) (SPI Pillar 3 Score)<br>@ East Asia & Pacific @ Latin America & Caribbean @ NorthAmerica @ Sub-Saharan Africa<br>region°9 @ Europe & Central Asia @ Middle East & North Africa @ South Asia<br>Source: SPI Data Products Score from the World Development Indicators (IQ.SPI.PIL3). The SPI Data<br>Products score is created by calculating the availability of all SDG Indicators, based on the UN<br>Global SDG Database. The availability of Gender SDG Indicators based on authors calculations based<br>on the UN Global SDG Database.<br><!-- End of picture text
’s population is growing at a higher rate, than the growth elasticity of poverty needed to result also in the reduction in the absolute number of poor people.

However, the United Nations definition of poverty extends beyond monetary poverty that may be directly affected through growth, also reflected in the SDGs 2, 3, 4 and 6. During the 1995 world summit in Copenhagen absolute poverty was defined as “Absolute poverty is a condition characterized by severe deprivation of basic human needs, including food, safe drinking water, sanitation facilities, health, shelter, education and information. It depends not only on income but also on access to social services.” (UN,1995, p.38).

While Nigeria is working on improving its human capital through different programs and policy interventions, there is a need for evidence on where to find the neediest children along these dimensions, to allow for more targeted and evidence-based policy making and interventions, given limited resources.

> 1 See World Bank Indicators Population ages 0-14 (% of total population) <u>https://data.worldbank.org/indicator/SP.POP.0014.TO.ZS?locations=NG</u>

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by a local trained volunteer.<sup>1</sup> The sessions educated women on optimal nutrition practices and prevented malnutrition through growth monitoring. Women started attending the monthly group sessions in September 2012.

The second part of the program entailed public works. All households participating in the nutrition sessions were eligible for public works and eligibility was not targeted based on income or assets. In the absence of a national poverty targeting mechanism and very limited social assistance programs, the government rolled out a public works program that, in theory, would reach the poorest households through self-targeting. In practice, however, self-targeting does not occur due to the wage rate, the low rate of the participants’ other foregone income, and the work site’s design features (discussed below).

The public works program included labor-intensive services (e.g., garbage collection, particularly plastic bags), light labor-intensive community works (e.g., street rehabilitation to improve traffic and expand access to surrounding areas), and small group-based artisanal projects. Communities were assigned to the type of project based on the preferences that their community leaders reported. The public works projects commenced, on average, within two weeks of the offer to households. This emergency support project also laid the foundations for developing a social registry system that could be used to identify the beneficiaries of future social assistance programs.

For equity reasons, public works programs often include gender quotas. For example, India’s NREGA reserves one-third of the work opportunities for women and offers equal wages to both men and women (Afridi et al., 2016). Ghana’s labor-intensive public works (LIPW) program requires at least half of the beneficiaries to be women (Dadzie & Ofei-Aboagye, 2021). In both programs, however, the share of participating women has exceeded these quotas. The program in Djibouti had an even more aggressive gender target: women in the first 1,000 days of pregnancy and motherhood. Employment offers in the public works program
# 3. Data

In this section, we discuss the data used to measure the depth of a trade agreement and describe in detail our source for bilateral investments at the sector and activity levels.

## a. Preferential trade agreements

We first estimate overall the effects of deep trade agreements by using a novel measure of depth. Then we move to the estimation of the effect of the specific disciplines included in PTAs.

Our variables of depth and disciplines come from the World Bank database on the content of deep agreements. It covers all 279 PTAs that are in force and notified to the WTO as of 2015. (Hofmann, Osnago, & Ruta, 2018) give a thorough description of the database and the way the mapping of PTAs has been implemented. The methodology is based on the work of (Horn, Mavroidis, & Sapir, 2010), which was also used in the World Trade Report 2011 (World Trade Organization, 2011). This comprehensive data informs us on the specific disciplines, or policy areas, that are covered in each agreement. In total, 52 disciplines are identified across the 279 PTAs (see Table A.1 for the list of disciplines). On top of the area coverage, the data provide information on the legal enforcement of each discipline within an agreement. The enforcement coding is based on the analysis of the legal language of the treaty text and the possibility of recourse to dispute settlement.

For our baseline measure of depth, we follow previous works that use the content of this data set or other similar sources, by counting the number of disciplines included in a PTA. We focus on the number of legally enforceable disciplines as they are expected to be more impactful than disciplines that are just mentioned in treaties but without any sign of substantial commitment from the parties. We define the variable T T T T i = ∑52 i - i.e. the simple count of legally enforceable disciplines kk ( i ) included in the agreement between country TTh ii kk=1
in scope than equivalent indexes used in the papers described above. For instance, in addition to restrictions on the cross‐border flow and domestic use of data, we now also include restrictions related to IPR for digital sectors, intermediate liability and content access for online platforms, as well as regulatory policies regarding the telecommunication market. We expect these data restrictions to be negatively correlated with the extent to which firms innovate, particularly in sectors that are more reliant on data. In assessing this hypothesis, we use the identification strategy as developed in Ferracane _et al._ (2018b).

# **3. Empirical Strategy**

This section sets out the empirical strategy. We develop a composite indicator following the works of Ferracane _et al._ (2018a) and Ferracane and van der Marel (2018). In these two works, a data linkage variable is developed that interacts their data policy index with an industry‐level measure of data‐ intensity. In our case, the composite indicator is comprised of the index that covers for data restrictions, including the ones related to IPR and telecommunication, which is interacted with variable that measures the extent to which sector are intensive in the use of software. In our view, this latter proxy crudely specifies how much each a sector employs data. Some sectors are more dependent on data than others and we expect that data‐intensive sectors are proportionately more affected by changes in restrictive data policies. To reflect this consideration, we therefore weight the data policy index with our measure of software use that signifies data‐intensity at the industry‐level.

In a second step, we present our baseline specification for the regressions in which we use different firm‐level variables that measure innovation and regress them on our composite indicator of data restrictiveness. We perform regressions using two types of firm‐level data, namely one at the aggregate cross‐country and industry level using the World Bank Enterprise Survey database, in addition to three country‐specific firm‐level data sets for
A first use of this survey was to be able to compare the management practices of the auto parts sector in our sample to that of Colombian manufacturing as a whole. Figure A7.1 shows that the distribution of management practices in our firms is similar to that of all SME manufacturing firms in Colombia. A second purpose was to enable comparison of Colombia to the rest of the world. Figure A7.2 shows Colombia’s average management practices, with a score of 2.54, are poorly managed by global standards, but typical for many developing countries, just below that of India and just above Kenya. The mean management practices score for the auto parts firms of 2.38 is similar.

**Figure A7.1: Comparison of WMS Management Practices Distribution of our Auto Parts firms to a Representative Sample of the Colombian Manufacturing Sector**

Source: WMS surveys conducted of 180 Colombian manufacturing firms and 77 auto parts firms conducted by the LSE WMS team in 2013.

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literacy is lagging behind, given the level of knowledge necessary to effectively participate in this economic system.

It is likely that most young Russians did not grow up with parents who had bank loans (i.e., they did not learn financial skills at home), did not receive formal financial literacy courses in school (there is no curriculum requirement for financial education in Russia), and do not have long personal banking relationships or experience with financial products.<sup>2</sup> Additionally, there is known to be a widespread perception among the young of ubiquitous unfairness in economic processes and a lack of trust in the rule of law and institutions (Gächter and Herrman, 2006; EBRD, 2007).

Our paper extends the extant literature in a new direction, analyzing results of a detailed survey of financial literacy administered to a nationally representative sample of about 1,400 Russian individuals. The survey includes questions on financial literacy, retirement planning, and the use of various financial products as well as detailed demographic and socioeconomic information. We address some novel questions: for instance, what is the level of financial literacy in a country without a legacy of consumer credit or a precedent of financial education? What is the level and asset mix of retirement planning in Russia, in view of the demographic situation, the fears for the future, and the recent pension reforms? Is financial literacy linked to the use of different types of pension funds, and, importantly, are higher levels of financial literacy related to participation in individual private pension plans? Finally, because Russia is a country with pronounced regional inequalities and gender gaps, we are very interested in examining whether there are significant differences between certain population segments with respect to financial literacy and retirement planning.

We find that even though consumer borrowing is increasing very rapidly in Russia, only 36.3% of respondents in our sample seem to understand the workings of interest compounding and only half of the sample was able to answer a simple question about inflation.
.7 5.4<br>5.1 5.8 6 4.9 4.8<br>5 4 3.0<br>0 2<br>0<br>2004-08 2009-17<br>2004-08 2009-17<br>GDP Exports Imports GVA Services<br>Consumption Capital Formation Agriculture Manufacturing Construction<br>Other Industries<br><!-- End of picture text --> Note _:_ Central Statistics Office data. Data are in constant Indian rupees (INR). Years are fiscal years.

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use of the full sample of countries, i.e without the NTM data restriction. Taken together our results suggest no significant differences across categories of goods.<sup>31</sup> Finally, when including a full set of origin, destination group and sector interactions (columns (5) and (10)), although the coefficient on tariffs decreases in size and loses significance, the results are globally consistent with our baseline.

**Endogeneous trade policy.** A second potential concern is that trade policy may be endogenous to market structure. That is, importing countries might decide to set their levels of NTMs or tariffs depending on the market power of the foreign firms serving their markets. While this concern is mitigated by the fact that we are looking at trade policy in the destination countries, where firms from the set of developing countries in our sample are typically small players, we still provide two robustness checks to ensure that endogenous trade policy does not affect our results. We show that we obtain similar results when we drop from the sample exporting countries with high market shares in their destination-product (i.e. exporters that could affect foreign trade policy). Our results are also similar when we use an instrumental variable approach in which NTMs and tariffs in a product-destination are instrumented by the level of NTMs/tariffs set by the same destination on other products. These results are reported and discussed in Section C.6 of the online appendix.

**Further robustness.** The online appendix Sections C.7 to C.13 contain additional robustness exercises. These show that the results of Tables 4 to 6 are robust to: (i) the use of an alternative cleaning method of unit values (Section C.7); (ii) using export-currency denominated specifications (Section C.8); (iii) using alternative clustering strategies (Section C.9); (iv) boot-

> 31While this might appear surprising at first, these results are consistent with
al. (2019) develop a model to highlight the differences between demand- and supply-driven structural change. In their model, supply-driven structural change is captured by a positive productivity shock to the modern sector (in this case, say manufacturing) allowing it to draw labor from other, less productive sectors of the economy. To the extent that structural change is supply-driven, we would expect to see an expansion of modern sector (or formal) activity in the manufacturing sector. By contrast, demanddriven structural change was likely a result of positive aggregate demand shocks possibly due to some combination of factors like public investment, external transfers, or increases in rural incomes. Demanddriven structural change is more likely to be accompanied by the entry of less productive smaller manufacturing firms.

# _Employment Growth Is Dominated by Small and Less Productive Firms_

To explore this hypothesis, we again use employment data for manufacturing from two sources: the Economic Transformation Database (ETD) (de Vries et al. 2021) and the manufacturing employment data from the INDSTAT2 2020 database produced by the United Nations Industrial Organization (UNIDO 2020). The manufacturing employment data from the Economic Transformation Database is largely based on population census data, and so covers manufacturing in both the formal and informal sectors (Timmer et al. 2015). By contrast, INDSTAT2 records manufacturing employment data for formal firms in the manufacturing sector. Although country statistics sometimes vary in terms of the size of establishments covered, typically INDSTAT2 covers firms with 10 or more employees. For several countries, we compute small and informal sector employment in the manufacturing sector as the difference between total employment (from the ETD data) and formal sector employment (from the INDSTAT2 data). We then plot total, small and informal and formal sector manufacturing employment for these countries.<sup>1</sup> 1 We gauged the accuracy of these data with comparisons to other data sources. For the recent total manufacturing employment numbers reported in the Economic Transformation Database, we looked at estimates of manufacturing employment based