rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 8886

# **Abstract**

Current measures of fiscal impoverishment and gains are not consistent with the law of diminishing returns. This paper proposes new measures of fiscal impoverishment and gains that are consistent with the law of diminishing returns, based on a methodology that gives more significance to greater income gaps, and more importance to the experience of the poorest individuals within the fiscal system. The new indicators are decomposable and cover the incidence, intensity, and severity of fiscal impoverishment and gains. An empirical illustration using the 2014 household consumption data reveals that, overall, in Niger the fiscal system is improving the welfare of the population: only 33.2 percent of the population has become poorer due to the fiscal system, while the remaining 66.8 percent has become richer because of it. Moreover, the mean relative fiscal loss (0.014), is 11 percent lower than the mean relative fiscal gain (0.126).

This paper is a product of the Poverty and Equity 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 mcoulibaly2@worldbank.org/asanoh@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.
Policy Research Working Paper 10820

# **Abstract**

This paper studies the performance differentials between privately-owned enterprises and businesses of the state. Businesses of the State (BOS) are firms with 10 percent or more direct or indirect state participation. By analyzing firm-level data across 16 European countries between 2011 and 2020, the paper finds evidence that state ownership matters for operational and financial performance and sheds light on how and when it matters. The analysis disentangles the multiple forms of state participation and its effects on firms’ performance by exploring the heterogeneity across sector type, levels of state participation, and degree of separation (direct versus indirect shareholding). It also analyzes the early response of businesses of the state to the COVID-19 shock. The results suggest that businesses of the state underperform in terms of labor productivity, profitability, and return on investments, although the effects are heterogeneous depending on the level of state participation, degree of separation, and type of sector. Businesses of the state appear to be more financially leveraged vis-à-vis their private counterparts, suggesting potential soft budget constraints. Wider differentials in profitability and return on investments are evidenced when the state operates in fully competitive sectors that are viable for private participation, underscoring the opportunity costs of state ownership in those sectors. Furthermore, the findings show that mixed ownership with the private sector can drive better results when compared to fully owned businesses of the state. Similarly, a higher degree of separation from the state seems to improve performance, highlighting the role of corporate governance and ownership reforms to foster independence. Finally, businesses of the state demonstrated greater resilience in preserving jobs in the short term during the COVID-19 pandemic in 2020.

This paper is a product of the Finance, Competitiveness and Innovation 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.
were out of school. The census identified a population of 3,419 active members across the 149 clubs. The female baseline survey occurred from September to December 2016, 2–5 months before any interventions were implemented, and resulted in a final sample of 3,178 females.<sup>10</sup> The males’ baseline survey took place from December 2016 to February 2017. The sequential nature of the female and male baseline surveys allowed for the female baseline data on social networks to serve as the sampling frame for the male sample. During the baseline survey, females were asked to list males with whom they were friends, males to whom they were attracted, and males with whom they were currently or historically having sex. All of the males listed as sexual partners in _Boys_ communities were selected for survey, and, in _Supply_ and control communities, a random sample of males were selected from these lists. In total, 1,466 males were surveyed at baseline, 787 of whom were in the _Boys_ arm, 376 were in the _Supply_ arm and 303 were in the control communities. The male sample was intentionally split so that half of the male sample would be in the _Boys_ arm and the other half of the sample would be equally split between the _Supply_ and control communities. Figure 1 presents the sample distribution across study arms.

Prior to endline data collection, another census of ELA members was conducted during May 2018. Endline data collection took place between June and August 2018 for both males and females, six months after the end of all interventions. Figure 3 shows the timing of data collection relative to the interventions.

Baseline and endline adolescent surveys collected information on the adolescent’s household (e.g., roster, dwelling characteristics, asset ownership) and about the adolescent’s sexual behavior, SRH knowledge and attitudes, education and time use, health, and socio-emotional skills. STI and HIV testing was also conducted, but prevalence was unexpectedly low at baseline, around 1
take advantage of these datasets for targeting strategies, such as proxy means testing or geographic targeting. Another important limitation is that HBS often only collects limited information on disposable income, which makes it difficult to deduce reliable poverty indicators from these types of surveys.

**The current data environment in Bulgaria imposes serious restrictions on studying energy poverty.** Detailed information on income, living conditions, and access to social protection programs in Bulgaria is mainly gathered as part of the European Union Statistics on Income and Living Conditions (EU-SILC). Expenditure data, on the other hand, is primarily collected by HBS. Only relying on HBS to study energy poverty would result in very limited analyses due to the following reasons. First, the information on household income is less reliable and rigorous in the case of the HBS, as the survey is mostly designed to capture expenditure patterns, not income patterns. Figures A.1 and A.2 in the Appendix show that the distribution of equivalized household income, as reported in the HBS and EU-SILC, differs in the case of Bulgaria. Second, the HBS does not contain information on social protection programs. Consequently, it is not possible to study if energy poor households are sufficiently covered by social protection schemes. Third, the HBS only has very limited information to study potential drivers behind energy poverty. Detailed information on housing conditions, for example, is only available in the EUSILC. Ideally, to achieve efficient and effective policy interventions, one would want to observe all this information together. In addition, the current data environment also imposes limitations on identifying individuals who might be vulnerable to energy poverty.<sup>3</sup>

**Given that the sample of households between different types of surveys does not overlap, one cannot combine both datasets based on common household identifiers.** Due to the data environment described previously, one can only observe energy spending shares and energy poverty indicators in the HBS, while we can only observe reliable monetary poverty indicators as well as
research on the Niger cash transfer program that shows positive effects of complementary livelihood interventions on social well-being and social cohesion, with no evidence of increased community tension (Bossuroy et al. 2022). Premand and Schnitzer (2021) also analyze the application of household targeting methods and find no effect on the selfreported likelihood of conflict, which—if anything—seems to decrease in the villages over time between the start and the middle of the program.

> 27As such, the paper illustrates that very large samples are required to analyze the impacts of policy interventions on rare outcomes such as conflict events. The sample size of typical RCTs for which micro-data is collected would not be sufficient in our setting. On a related note, the unavailability of micro-data across a very large number of villages also limits our ability to analyze a wide range of intermediary outcomes to tease out mechanisms.

15
Figure 2 presents time-period averages of the normalized FDI and exports variables before and after the devaluation episodes, based on data from sub-samples of countries with data for all periods before and after the devaluation, and the list of countries for each sub sample appears in the table in the Appendix.<sup>9</sup> Tables from 1990-2007, the correlation between the two series is 0.73. Putting the frequency of the data aside, the Penn World Table variable could be more desirable for future work, because the REER depends solely on prices of consumer goods (in each country’s basket of goods used to calculate CPI indexes), whereas in principle the PWT index includes consumer goods as well as investment goods and inputs purchased by governments. On the other hand, not all countries in the PWT database conduct price surveys, and the PWT includes observations derived from imputing techniques.

8 In addition, the estimation of exchange-rate misalignment is itself a complex undertaking, replete with potential pitfalls, because it requires a well specified model of equilibrium exchange rates. For details, see, for example, Levy-Yeyati and Sturzenegger (2007), Rodrik (2008), among others. Our sample of episodes includes crises that could be driven by sudden stops of capital inflows, such as Mexico’s crisis of 1995 and East Asian devaluations during the late 1990s, as well as more recent and less sharp devaluations of high-income country currencies during 2009-2010. Consequently the magnitude of the devaluations vary greatly across countries, but what matters for the FDI fire sale effect is that the devaluations are large relative to the behavior of each country’s REER over the longer time horizon of 1990-2010. See section

> 9 Since the date of each episode varies across countries, and some occurred towards the beginning of the sample period or the end, the sample used to calculate these averages changes over the 16 quarters portrayed in the graph. In addition, the coverage of the data for each indicator varies across countries. The series averages with the
|4.70||
|Vaccination card at origin|1.60||||||
|Various fees and taxes at<br>destination|12.50|15.00||11.80|16.00||
|Police/military at origin (“droit de<br>passage”,“commisse”etc.)|||4.70|||9.80|
|Police/military at destination (“droit<br>depassage”,“commisse” etc.)|||5.00|||7.60|
|_Total_|_35.90_|_47.00_|_12.50_|_32.90_|_48.20_|_20.20_|

Source: confidential survey of 57 trading firms in Kinshasa and Brazzaville. Prices converted using exchange rates of 910 Congolese francs per USD and 510 CFA francs per USD

**Trading firms consider the existing transport infrastructure grossly inadequate.** Only two ageing ferries are in operation: ONATRA’s “Matadi” and a smaller vessel operated  by CNTF, both of which make two daily return trips. Normally, the 120-ton “Matadi” carries some 2,000 passengers a day, of which 600-700 pay a fare, and a small number of vehicles. However, the “Matadi” had to be taken out of service by the end of June 2010 and replaced with the smaller vessel “Ikanda”, because of safety concerns, further reducing capacity. CNTF’s ferry is considerably smaller still. In recent years, the two ferry operators have granted a fixed number of concessions to private transport firms, who run small passenger speed boats (“canots”) between the two cities for a fee paid to the ferry operators (for which, they claim, they receive no services in return). As evident from Table 10, the prices of travel in fast boats are prohibitive for most residents.

**Traders are frustrated by tight timetables and
we add to the model to reflect more accurately the interplay between context and grievances. The figure differentiates between grievance-based factors, which could be economic, social or political in nature and represent demand-side reasons for protest, and context-specific factors, which could be viewed as supply-side factors enabling or deterring conflict, including political opportunities and resource mobilization.

The political-opportunity theory underscores the importance of the costs of mobilization and the probability that an uprising will be successful (Meyer, 2004). While this approach emphasizes the relationship between the willingness to participate in an uprising and more mainstream institutional politics, it has been criticized for focusing on factors that are often static (e.g. regime type) or are the result of an uprising (e.g. political terror) (Ackerman, 2007). The resource-mobilization theory emphasizes the importance of human, financial, and informational resources and organization capacity that can be used to mobilize the population. It assumes that people will mobilize whenever they have the means to do so, but it does not factor in the government’s ability to create opportunities for and constraints to collective action (McAdam 1999). The rest of the section provides details on the variables in the regressions and the data used to proxy them.

# **Dependent variable: Nonviolent uprisings**

Reliable and comparable cross-country time series data on uprisings are hard to come by. Preferably, we would have a panel with data on subjective well-being and civil resistance at the individual level, allowing us to study the relationship between an individuals’ propensity to engage in nonviolent conflict and their level of life satisfaction. However, asking individuals about their engagement in conflict is problematic, as respondents in many countries run the risk of being prosecuted for their engagement in these types of activities. Additionally, by asking respondents about their participation in nonviolent conflict retrospectively, we would have to make the assumption that the level of subjective well-being is constant in the period between 9
human capital and the capacity of firms to absorb technology account for 23 percent and 20 percent, respectively, of the model’s goodness of fit.

More interestingly, however, is the fact that horizontal factors that in principle affect overall firm performance and the accumulation of any type of capital (not only knowledge capital) also matter as determinants of innovation performance: results from Figure 7 suggest that _Institutions, Infrastructure_ and _Market Sophistication_ pillars account, together, for a substantial part (34.7 percent) of the explained variance of the model.<sup>9</sup> This reinforces the idea presented by Cirera and Maloney (2017) that complementary factors of the business environment - that go beyond specific barriers to innovation - are key to improve the performance and returns to innovation. These factors are normally neglected in the advanced country literature because markets in high-income countries function normally well, but this is not necessarily the case in middle- and low-income countries.

Table 1 below displays how developing East Asian countries covered by the GII data set perform in terms of all enabling factors. The country’s performance in each pillar is measured relative to the pillar’s median value in the global sample (of the GII data) and the expected level of the country’s performance in that pillar given its income level (measured in GDP per capita). Countries whose observed values of pillars are below both the median and the predicted value are marked as one. The enabling factor where this set of countries mostly underperform is _Institutions_ ; of the seven developing East Asian countries included in the above-mentioned data set, six underperform in this domain; the exception is Malaysia.<sup>10</sup> After _Institutions_ , _Business Sophistication_ and _Human Capital_ stand as the worst performing pillars: four of the seven developing East Asian countries covered by the GII data set present a deficit in these two domains.

**Table 1. How developing East Asian countries perform in terms of overall innovation enabling factors, 2017*
_47|44_._04|114_._64|135_._14|
|SRHS|Sorghum|5_._47|40_._36|129_._42|242_._49|
|SRHS|Wheat|5_._47|14_._02|53_._08|102_._22|

Notes. In this table we present the results of the SD approach using the groundwater supply or volume measure using the aquifer dataset constructed by World Bank. Each row represents one simulation. Column (1) specifies the choice of model. Column (2) specifies the crop for which the analysis was run. Column (3) presents the fraction of all croplands where the constraints were met. Column (4) presents the share of croplands where we see production gains, as compared to the all the croplands where the constraints were met. Columns (5) and (6) present the median and mean of the simulated gains in production. All the results presented imposed constraints on groundwater depth _≤_ 7m, an extraction recharge ratio _≤_ 0.7, and aquifer productivity _≥_ 0.05.

28
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WPS 3388

# **Tools for Legislative Oversight: An Empirical Investigation**

## **By**

## **Riccardo Pelizzo**<sup>**1**</sup>

## **and Rick Stapenhurst**<sup>**2**</sup>

## Abstract

Parliaments are the institutions through which governments are held accountable to the electorate. They have a wide range of tools with which to carry out this oversight function, but until recently little analysis had been undertaken on the characteristics or use of such tools. This paper uses data for 83 countries that was collected in 2001 to investigate whether the oversight potential relates to three variables, namely the form of government (presidential, semi-presidential, or parliamentary), per capita income levels, and the level of democracy.

The paper finds that oversight potential is greatly affected by the form of government, per capita income levels, and levels of democracy. Countries with parliamentary forms of government, higher income levels, and which are more democratic have a greater number of oversight tools and greater oversight potential. While the oversight potential follows this general trend, the use of committees of enquiry, interpellations and ombudsman offices follows a different pattern. The use of interpellations as an oversight tool is most common in high income countries, less common in low income countries and least common in middle income countries while the presence of committees of enquiry and of the ombudsman offices is most common in middle income countries, less common in high income countries and least common in low income countries.

World Bank Policy Research Working Paper 3388, September 2004 _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
technologies can affect jobs and wages is a useful starting point (Acemoglu and Restrepo (2019).<sup>9</sup> First, computerization and other digital automation replaces labor with capital in completing routine, codifiable tasks, thus eliminating jobs (the “displacement” effect). Automation can also allow for more flexible allocation of tasks, thereby raising labor productivity and the demand for labor in non-automated tasks (the “productivity” effect). Finally, new technologies can also create new tasks that complement digital technologies, potentially creating jobs (the “reinstatement” effect).<sup>10</sup> On net, the negative displacement effect of automation may be outweighed by productivity gains that increase the demand for labor, including in complementary tasks.

Evidence suggests that the adoption of ICT associated with the first wave of digitalization has not reduced aggregate employment in advanced economies. Based on data for the 2000s from 19 advanced economies, including those of the European Union, Australia, Canada, Japan, the Republic of Korea, and the United States, Autor and Salomons (2018) find that own-industry employment losses attributable to ICT-related automation were reversed by indirect gains in customer-facing industries and induced increases in aggregate demand.<sup>11</sup> In the United States, the expansion of automated teller machines (ATMs) is a much-cited example. Despite the number of ATMs quadrupling between 1995 and 2010, the number of bank tellers increased by about 10 percent between 1980 and 2010. By reducing the cost of operating a bank branch, ATMs indirectly increased the demand for tellers. Further, as routine cash-handling tasks receded, computerization enabled tellers to become involved in new “relationship banking” tasks (Autor 2015).

The few empirical studies on developing economies broadly find that the net effect of the first wave of digitalization on jobs has been positive, albeit with differences according to type of employment. Hjort and Poulsen (2019), for example, conclude that the reinstatement effect outweighs the substitution effect