rafmacalaba/data-use-mentions-extended huggingface dataset:
<br>0.533|<br>0.502|

Note: Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. The table shows the results of the consumption model used for survey-to-survey imputation purposes. Data are from the 2018/19 NLSS and comprise a series of non-monetary indicators that are comparable between the baseline (NLSS) and target survey (GHS). Household consumption is spatially and temporally deflated and expressed in 2011PPP.

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April, respectively, from a 0.7 percent decline in February. Similarly, Brazil is expected to decline by nearly 7 and 3 percent, while Chile is expected to decline by 1 and 2 percent; and Colombia 0.4 and 2 percent, respectively. In all regressions the optimal number of lags is 3, while the polynomial degree varies from 3

> 1In Table 3 in the Appendix section, various consistency checks with different combinations of the Google Index and the air quality data are compared. The differences between the various explanatory variables are insignificant.

11
# **References:**

Abeyasekera, S. 1997. "Developing a Sampling Strategy for the Estate Land Utilisation Study in Malawi." University of Reading. Ali, D., K. Deininger and M. Duponchel. 2016a. "Using Administrative Data to Assess Impact and Sustainability of Rwanda’s Land Tenure Regularization." _World Bank Policy Research Paper 7705_ , Washington DC.

Ali, D. A., K. Deininger and M. Goldstein. 2014a. "Environmental and Gender Impacts of Land Tenure Regularization in Africa: Pilot Evidence from Rwanda." _Journal of Development Economics_ , **_110_** (0), 262-275. Ali, D. A., K. Deininger, M. Goldstein and E. La Ferrara. 2014b. "Determinants of Participation and Transaction Cost in Rwanda’s Land Markets ", _World Bank Policy Research Paper_ , Washington, DC.

Ali, D. A., K. Deininger and A. Harris. 2015. "Using National Statistics to Increase Transparency of Large Land Acquisition: Evidence from Ethiopia." _Policy Research Working Paper 7342_ , Washington DC.

Ali, D. A., et al. 2016b. "Small Price Incentives Increase Women's Access to Land Titles in Tanzania." _Journal of Development Economics_ , _forthcoming_ . Ali, D. A., K. W. Deininger and C. A. P. Harris. 2016c. "Large Farm Establishment, Smallholder Productivity, Labor Market Participation, and Resilience: Evidence from Ethiopia." _Policy Research Working Paper 7576_ , World Bank, Washington DC. Atwood, D. A. 1990. "Land Registration in Africa: The Impact on Agricultural Production." _World Development_ , **_18_** (5), 659-671. Berge, E., D. Kambewa, A. Munthali and H. Wiig. 2014. "Lineage and Land Reforms in Malawi: Do Matrilineal and Patrilineal Landholding Systems Represent a Problem for Land Reforms in Malawi?"
|_Private financing_: Dummy variable indicating whether the project is funded<br>entirely through private funds (without any financial investment of the state,<br>whether local or national)or not.|160 (52.1%)|147 (47.9%)|
|_Bidding process_: Dummy variable indicating whether there was or not a bidding<br>process to award the concession.|272 (88.6%)|35 (11.4%)|
|_Award criteria_: Dummy variable classifying award criteria (1 for highest price,<br>highest canon or lowest subsidy,0 for all others)|106 (34.5%)|201 (65.5%)|
|_Minimum income guarantee_: Dummy variable indicating whether there is or not a<br>governmentguarantee in terms of minimum incomepromissory.|63 (20.5%)|244 (79.5%)|
|_Arbitration process_: Dummy variable indicating whether there is or not a formal<br>set of arbitration processes stated in the contract providing for the settlement of a<br>dispute between the concession holder and the government, should such a situation<br>arise.|179 (58.3%)|128 (41.7%)|
|_Election_: Dummy variable indicating whether there were or not national elections<br>(legislative or presidential) in any given year. Source: Political Database of the<br>Americas. Georgetown University/Organization of American States. Center for<br>Latin American Studies.|n.r.|n.r.|
|_Political Change_: Dummy variable indicating whether there were or not a change<br>in power following national elections (legislative or presidential) in any given<br>year. Source: Political Database of the Americas. Georgetown<br>University/Organization of AmericanStates.Center for Latin AmericanStudies.|n.r.|n.r
</sup> was also used to complement the survey data and provide more detailed population information.

Kinshasa is the capital and economic center of the Democratic Republic of Congo (DRC) with a population in excess of 14 million (World Bank, 2019) and population growth rate of 5.1% per annum. It is expected to become the most populous megacity in Africa by 2030 (World Bank, 2018). Similarly to Rio, economic activity is concentrated in a small area, in Gombe, with some hyper-localized areas in Ndjili and Matete. However, despite Rio’s relatively high density, the population of Kinshasa lives in an even more compact area with very high densities located relatively close to most economic activity. There are also smaller, slightly fragmented communities located further North-East along the river. The legacy of colonial spatial planning followed by extremely high rates of urbanization with insufficient investment in infrastructure have resulted in a few concentrated affluent areas (such as Gombe/Socimat and Mont Ngafula) and a high incidence of poverty (approximately 60% in 2012 (World Bank, 2018)), with large slums across much of the remainder of the city and many residents being unable to afford transport services (Beeckmans & Bigon, 2016). The lower earning residents are concentrated in Selembao and the Eastern edge of the city proper (Ndjili, Kisenso and Masina) with poorer residents also located along the Northeastern corridor towards Bombala.

In this study, all socio-economic data was aggregated to a hexagonal grid using Uber’s H3 grid system (Brodsky, 2018). Each hexagon has a diagonal of approximately 900 meters and a total area of 0.7 km<sup>2</sup> .

# 2.1.3 Operational costs and fare structure

In order to assess the impact of a fuel price increase on the fares of different transport services, we investigate existing operational costs, fare structures and mechanisms for increasing fares in each city.

> 8 Available at https://www.worldpop
Canada and the United Kingdom. Thirty-five responses were received of which 33 were usable, a response rate of 67 percent. Supplementary interviews (both in person and on the telephone), were conducted to seek further insight and clarification to the survey results.

In the first section of this paper, after providing a summary of the role of legislatures in financial oversight, we present a general concept of public financial accountability. We underline the critical role that PACs play, noting their origin in the 19<sup>th</sup> century and their widespread use throughout the Commonwealth and elsewhere.

Through the data collected by the WBI, we are able to deepen our knowledge about how PACs work and, in particular, what – in the views of the chairs of these committees – constitutes successful performance and what factors influence such performance. The second section of the paper presents the survey instrument and summarizes the broad findings of the survey.

Section three investigates the success factors (and constraints) for PAC effectiveness, based on the survey results. In section four, we identify possible benchmarks for measuring the effectiveness of PACs, and discuss how developing countries may be able to think about strengthening their PACs.

In the fifth and final section of the paper, we draw some conclusions and propose what an ideal PAC might be like.

# <u>Section 1: Legislatures and Public Financial Accountability</u>

# _i) Role of legislatures_

Legislatures perform three functions - representative, legislative, and oversight. They perform a representation function in that they represent the will of the people, which is the legitimate source of authority in democratic countries. They perform a legislative function because, in addition to introducing legislation on their own, they have the power to amend, approve or reject government bills. And they perform an oversight function, ensuring that governments implement policies and programs in accordance with the wishes and intent of the legislature. They undertake this oversight function in two ways: they oversee the preparation of a given policy (
_Figure 15: Factors of well‐being worsening by vulnerable and non‐vulnerable groups, % of respondents_ <!-- Start of picture text --> 40%<br>35%<br>other<br>30% inflation<br>25% loan servicing<br>20% health<br>change of HH composition<br>15%<br>loss of additional income<br>10%<br>retirement<br>5%<br>lower salary<br>0% job loss<br>vulnerable average vulnerable average vulnerable average vulnerable average<br>2014 2015 2016 2017<br><!-- End of picture text --> Source: Authors’ estimates based on HSS data.

There were differences in the way the crisis affected labor income of different social groups:

- Job losses were more commonly reported as a factor of well‐being deterioration by individuals with high education levels rather than those with secondary specialized education or lower education levels. On the contrary, the latter faced a higher risk of falling nominal wages.

- Those identified as partly employed faced a higher risk of well‐being reduction due to loss of job or nominal wages reduction than other social groups. On the contrary, retirees were mostly affected by real decreases in their income (pensions) that took place in 2016 and 2017.

- Households with children appeared to be more exposed to the risk of nominal wages reductions, on average, while single parent households were also vulnerable to inflation, as they have fewer possibilities to optimize their consumption structure compared to larger households with more diversified income sources and larger scale effect.

The economic recovery of 2017 allowed to moderate some of the losses for affected households. Well‐being improvements were reported by 11.8 percent of households ‐‐ significantly higher than in 2016 (Figure 16). In particular, a larger share of the population reported well‐being gains from improved terms of employment and higher wages.
Policy Research Working Paper 10649

# **Abstract**

This paper assesses the performance of Romanian stateowned enterprises with various degrees of ownership (minority owned with 10 to 24.9 percent stakes, minority owned with 25 to 49.9 percent stakes, and majority owned with at least 50 percent ownership stakes) and control levels (central versus local state-owned enterprises and directly versus indirectly owned state-owned enterprises) relative to privately owned enterprises. The paper uses the Romanian firm-level data from the Ministry of Finance covering enterprises of all sizes from 2011 to 2020, combined with the new World Bank Businesses of the State dataset, which tracks ownership of state business entities with at least 10 percent stake in Romania. The paper analyzes whether various degrees of state ownership and levels of control matter for state-owned enterprises’ performance.

The paper also assesses whether Romanian state-owned enterprises were able to act as stabilizers during the early period of the COVID-19 pandemic, and how the presence of state-owned enterprises in markets correlates with market outcomes. The findings show that relative to private firms, Romanian state-owned enterprises, particularly those that are majority owned, directly owned, and local ones, employ more people, pay higher wages, but are less productive. In addition, Romanian state-owned enterprises cushioned the job and wage losses associated with the COVID-19 pandemic better than private firms, especially in competitive sectors. Finally, there is evidence that the presence of state-owned enterprises may limit private firm entry and allocative efficiency.

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. The authors may be contacted at sdauda@worldbank.org, gpop@worldbank.org, and miootty@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage
quality might be misleading in two ways. First, reporting faults might depend on the rules, regulations and dynamic of the sector in the country. Second, after reforms are implemented, companies may simply improve their reporting systems which may result in a larger number of the faults being measured. It is thus impossible to predict ex-ante the correlation between reform policies and reported faults because the improvements in reporting and measurement from regulatory reform may actually lead to a negative correlation. The net correlation is then the result of an actual performance change and a measurement change.

Finally, productivity is approximated by labor productivity simply because there is not enough data on capital and other inputs to get a better grasp of total factor productivity of the sector for a large enough number of countries and for a long period of time. Labor productivity data are also generated from the ITU database and approximated by the number of telephone mainlines per employee. This indicator is calculated by dividing the number of mainlines by the number of staff (with part-time staff converted to full-time equivalents) employed by telecommunications operators. Basic data reported in table 2 show that labor productivity is (on average) 2.5 times higher in developed than in developing countries. However, for both country groups, the expected correlation between productivity and reform policies is positive.

To provide a visual sense of performance at different scenarios of reform policies, we provide in table 3 a basic statistical summary that compares countries that have committed to reforms to those that have not as of 2003. We report the means, and below in parenthesis, we include the associated standard deviations and the number of
, Aprajit Mahajan, David McKenzie, and John Roberts (2020). “Do management Interventions Last? Evidence from India.” _American Economic Journal: Applied Economics_ 12(2):198– 219.

Bruhn, Miriam, Dean Karlan, and Antoinette Schoar (2018). “The Impact of Consulting Services on Small and Medium Enterprises: Evidence from a Randomized Trial in Mexico.” _Journal of Political Economy_ 126(2): 635-687.

Burkart, Mike and Fausto Panunzi (2006). “Agency conflicts, ownership concentration, and legal shareholder protection.” _Journal of Financial Intermediation_ 15(1): 1-31.

Chauvet, Lisa and Helene Ehrhart (2018) “Aid and Growth: Evidence from Firm-Level Data.” _Journal of Development Economics_ 135: 461-477.

EBRD-EIB-WB (2022). Unlocking Sustainable Private Sector Growth in the Middle East and North Africa: Evidence from the Enterprise Survey. © Luxembourg City: European Investment Bank; London: European Bank for Reconstruction and Development; Washington, DC: World Bank.

Ellul, Andrew Marco Pagano, and Fabiano Schivardi (2018). “Employment and Wage Insurance within Firms: Worldwide Evidence.” _The Review of Financial Studies_ , 31(4): 1298–1340, 13
**Figure 4:** Quarterly Year-over-Year Changes in Electricity Consumption

<!-- Start of picture text --> Percent<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4<br>FY20 FY21<br><!-- End of picture text --> Electricity consumption can also be used to track short-run economic activity during natural disasters. To show this, we average growth in monthly electricity consumption over the following natural disasters: Cyclone Roanu in 2016, the landslides and floods in 2017, the floods in 2018 and 2019, and Cyclone Amphan in 2020. Looking at average growth in electricity consumption (figure 5) during the month of a disaster, there is an average decline of approximately 10 percent. Electricity consumption then picks up gradually and overshoots in the second month after the disaster, presumably due to reconstruction efforts and to make up for some interrupted economic activity during the month of the disaster.

**Figure 5:** Average Year-over-Year Growth in Electricity Consumption around Natural Disasters

<!-- Start of picture text --> Percent<br>post-disaster<br>25<br>pre-disaster<br>20<br>15<br>disaster<br>10<br>5<br>0<br>ND (-3) ND (-2) ND (-1) ND (0) ND (+1) ND (+2) ND (+3)<br>Disaster month ND (0) and three months before and after<br><!-- End of picture text --> 8
IFLS. In panel B, Rural and Urban are defined based on the location of the household in the survey. The summary statistics of Panel B are based on the merged sample of first four waves of the IFLS. Average monthly expense was calculated using the inflation adjusted educational expenditure of the first four waves. In Panel C, Rural and Urban are defined based on household location in the first wave (1993). Father’s permanent income was calculated using the inflation adjusted household consumption expenditure in first three waves (1993, 1997, and 2000). In panel D, Rural and Urban are defined based on location of birth (same as in Panel A). Children’s permanent income was calculated using the inflation adjusted household consumption expenditure in last two waves (2007, 2014). Inflation rates are calculated using the price of rice separately for rural and urban areas. The school expense and permanent incomes are expressed in 10,000 rupiahs.

33
⎯ political clout, and governance ability in determining which states are able to attract funds and use them effectively. This needs to be studied carefully in further research.

There is little evidence of ICDS program impact on overall child nutrition status. However, these results need to be interpreted with caution. As we indicated above, using cross-sectional data can lead to various kinds of biases in estimations of the effectiveness of the program. A conclusive evaluation of program impact will have to wait until panel data are available. Yet many studies have highlighted problems with the implementation of the ICDS program, which would limit its impact and are consistent with our result.

In sum, we find limited evidence that the ICDS program is meeting its goals of reducing child malnutrition in India. Some modifications are needed towards this end. Firstly, program coverage and fund allocation needs to be shifted towards states with the highest prevalence of child malnutrition. Secondly, efforts have to be made to ensure that funds are fully utilized in the few states where this is not the case. Thirdly, the impact of the program on recipients can be enhanced by changing some aspects of program design and implementation. With such changes, the substantial resources allocated to the ICDS can be used more effectively for raising future generations of healthy children.

# **Bibliography**

- Alderman, Harold, Jesko Hentschel, and Ricardo Sabates. 2003. With the help of one’s neighbors: externalities in the production of nutrition in Peru, _Social Science and Medicine_ , 56: 2019-2031.

- Allen, Lindsay H. and Stuart R. Gillespie. 2001. What Works? A review of the efficacy and effectiveness of nutrition interventions, Manila: UN Administrative Committee on Coordination Subcommittee on Nutrition, in collaboration with the Asian Development Bank.

- Anderson, Mary Ann, James E. Austin, Joe D.Wray, and Marian F. Zeitlin. 1981. _Nutrition Interventions in Developing Countries, Study 1: Supplementary Feeding_ . Cambridge MA: Oelgeschlager, Gunn and Hain.

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(i.e., NSPF, ASIF, and CSMAR) to make comprehensive comparisons among firms across sectors, sizes, and ownership types.

The fourth data set is the firm-level survey conducted by the World Bank in 2005, which includes 12,400 firms located in 120 cities covering all Chinese provinces except Tibet. The provincial capital of each province, usually the most populous city, is automatically covered, as well as additional cities according to the province’s economic size. One hundred firms from each city are included, except for the four provincial level cities of Beijing, Shanghai, Tianjin, and Chongqing, where 200 firms each are selected. The responses given during the survey 15
4 district to visit. In our approach, by contrast, inefficient units are compared with real-life efficient units. We can tell a policymaker of an inefficient government or a manager of an inefficient delivery unit which real-life country or unit achieves similar outcomes at a lower cost.

The second common shortcoming of DEA and SFA we try to address is their vulnerability to special pleading—policymakers or managers of poorly performing service delivery units claiming that there are legitimate factors explaining their poor performance that are ignored by the analysis. The usual response to date in the DEA and SFA literatures has been to address this issue through a two-stage approach: (1) construct or estimate a frontier to calculate efficiency scores; and (2) regress efficiency scores on factors thought to influence them. This practice has been criticized, not least because the process by which the efficiency scores is generated is ignored in the second-stage regression exercise (Burgess 2006). Instead, we build exogenous constraints into our analysis and allow different groups of units to have different frontiers. This multiple-frontier approach could, of course, be used in DEA and SFA studies too.

We illustrate our approach throughout using examples from the education and health sectors. Our examples are chosen with a view to data quality and variety in terms of level of decision-making. In both education and health, there is growing realization of the need to look beyond the number of people passing through a facility to the difference that the facility makes to the lives of the people passing through it. In the two education examples, therefore, we look at test scores to get at the quality of the education process rather than at student numbers which capture just quantity. One example comes from the level of the local government: our data come from California where school districts control the day-to-day running of public schools. The other education example relates to national education systems and makes use of data from the OECD‟s Program for International Student Assessment (PISA) study; while undertaken by the OECD, the geographic coverage of