rafmacalaba/data-use-mentions-extended huggingface dataset:
averages mask significant variation across countries. Concerns about vaccine efficacy were greater or equal to concerns about safety in five Latin American countries (Argentina, Chile, Dominica, Dominican Republic, and Mexico). Respondents in the Democratic Republic of Congo and the Republic of Congo were hesitant because of dislike for vaccines in general (40% and 44% in March 2021 of hesitant respondents, respectively) and distrust for government, pharmaceutical industry, and the international community (40% and 22% in March 2021, respectively).

Supply-side concerns for not getting vaccinated such as “health facility too far or too hard to get to,” “there is shortage of vaccines in the country,” and “I don’t know how to access the vaccine” (see Appendix Table 2b for full list) only accounted for 6% on average across all surveys with the single answer option. However, over 10% of respondents in several Latin American countries reported the concerns “Health center too far or hard to reach” or “I don't have time to go to get vaccinated.” These included Antigua and Barbuda (22%), St. Lucia (18%), Jamaica (16%), Guyana (13%), Belize and Nicaragua (both 12%). The relatively incidence of supply-side concerns in the HFPS data may be influenced by the framing of the question, however, such as the access issues being queried in the context of other concerns, or because respondents were asked if they would get vaccinated “when the vaccine becomes available”, and access constraints may be equated in people’s minds with vaccines not being available presently, and thus vaccines becoming available signifying the alleviation of existing supply constraints. We are not able to test this formally, however.

Among the seven countries with multiple answer options, a similar pattern was observed: concerns about safety were the most common concern cited in five countries (Mali, Nigeria, Kenya, Sudan, Uganda). In
17

# Appendix: Sources of Population Data

|-ntry,<br>conomy, or<br>erritory|Last wublis<br>P<br>Date|hed census<br>opulation<br>(1000)*|<br>Source for population<br>(mid-1985)|Source for fertility<br>(1985-90, unless indicated)|Source for mortality<br>(1985-90, unless indicated)|
|---|---|---|---|---|---|
|anistan|Jun 79|13051 F|Bank projection from census,<br>adding refugees, assumed<br>to be abroad teaporarily|Bankest.|Bankest.|
|ktbania|Oct 60|1626 F|UNPVSR<br>4/87 (official<br>est.)|Based on UNPVSR<br>4/87|Based on UNPVSR<br>4/87|
|geria|Sep 87|22971 J|U.N. 1984 assessment|Based on UNPVSR<br>4/87|Based on U.N. 1984<br>assessment|
|uerican Sam|oa Apr 80|32 J|USBOC<br>1985|--|--|
|kndorra|Nov 54|6 F|UNPVSR<br>7/86||--|
|ingola<br>|Dec 70|5646 F|U.N.1988 revision (pretim.)|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|
|inguilla|--|--|UNPVSR<br>1/88|||
|kntiguaand<br>Barbuda|Apr 70|66 F|UNPVSR<br>1/87 (officiat<br>est.)|1980-85, based on UNPVSR<br>7/86; 1985-90, Bank<br
# **_Based on the evidence currently available, to what extent and for what reasons is the use of OGD associated with higher levels of accountability and improved service delivery in developing-country contexts?_**

To begin to answer this question, this research will first attempt to determine whether a statistical association actually exists to support the purported relationships between the variables. In doing so, the research will rely on a unique data set that operationalizes OGD, government accountability, and service delivery—including both provision and outcomes—while operationalizing other intervening variables that capture critical conditions for improved accountability and service delivery. Given the paucity of _crosscountry_ comparative studies on this topic, this research will utilize data for over 25 countries in Sub-Saharan Africa over a time horizon for which data currently exist.

# **_Significance and Contributions_**

The present research aims to investigate the relationship between OGD, accountability, and service delivery in order to provide one of the first statistical assessments of the available evidence in a cross-country comparative context. Importantly, it is not the intent of the present research to provide a _definitive_ or _causal_ assessment of the relationship among OGD, accountability, and service delivery. Rather, given the limitations of the data available, the empirical contribution of this research is to flesh out potential correlations between variables in order to provide an _initial_ assessment of OGD’s efficacy. In doing so, this research attempts to identify key indicators—which operationalize a conceptual framework gleaned from the literature—to test what associations may exist between variables. Moreover, the empirical contribution of this research seeks to tease out and test the effects of a number of intervening variables, as suggested by the literature, in order to have a fuller sense of the political economy dynamics at play.

In addition to this empirical contribution, however small, this research also seeks to make a contribution to the theoretical literature by providing a conceptual framework, which captures current discussions and debates on OGD, transparency, accountability, and service delivery. In addition, this research seeks
amount and quality of available public goods and services. Inflation is expected to increase nonviolent uprisings, because increases in food and energy prices are generally regarded as an important source of dissatisfaction with regimes (Chenoweth & Ulfelder, 2017). We also include a measure tracking the views of informed public opinion experts and business people of corruption in the public sector from Transparency International. Widespread governmentrelated corruption has been identified as a major source of dissatisfaction with the regime, including more recently during the Arab Spring (Arampatzi et al., 2018). However, the individual grievances inflicted by corruption might be more accurately captured by the subjective well-being variable.

In our regression models we control for political-opportunity factors by including a measure of the extent to which a country is an institutionalized autocracy. Autocracies tend to be less tolerant to public dissent, which increases the cost of mobilization while simultaneously reducing the probability that they will be successful (Chenowetz & Ulfelder, 2017). On the other hand, the tendency of autocracies to restrict civil rights and use repression might also motivate people to take to the streets. Our measure of democracy is derived from the Polity data set and ranges from 0 to 10, where 0 indicates that there are no autocratic policies in place, whereas a 10 indicates that a regime is a formal autocracy (Marshall & Jaggers, 2002). Because we include country fixed effects in our regression models, the autocracy variable merely captures variation in autocratic policies over time rather than variation between countries.

In addition, the degree of repression and state-inflicted political terror increases the perceived costs and decreases the probability of success of uprisings, and it may accordingly discourage participation in such events (Carey 2006; Davenport 2007; Francisco 2004). Therefore, we include a measure of state-inflicted _political terror_ derived from the politicalterror scale in Gibney et al. (2015). The scale ranges from 1 to 5, where a 5 indicates that terror (e.g. murders, disappearances, and torture) has expanded to the whole population.
# Who Uses Electricity in Sub‐Saharan Africa? Findings from Household Surveys

Masami Kojima, Xin Zhou, Jace Jeesun Han, Joeri de Wit, Robert Bacon, and Chris Trimble<sup>1</sup> Key words: access, electricity, affordability, subsidies, poverty gap, households, gender, expenditure surveys JEL codes: Q41, Q48, D19, H29 1 Masami Kojima, Joeri de Wit, and Chris Trimble are at the World Bank, and Xin Zhou, Jace Jeesan Hun, and Robert Bacon are consultants. The paper benefited from useful comments provided by Pedro Antman, Sudeshna Banerjee, Malcolm Cosgrove‐Davies, Vivien Foster, and Wendy Hughes, all of the World Bank. The authors owe special thanks to Rose Mungai of the World Bank for providing survey data and information for many countries, and to Xavier Daudey for helping with tariff information. The authors are grateful to Prospere Backiny‐Yetna, Yele Batana, Nadia Belghith, Tom Bundervoet, Andrew Dabalen, Kristen Himelein, Dean Jolliffe, Roy Katayama, Talip Kilic, Vasco Molini, Djibril Ndoye, Clarence Nkengne, Aly Sanoh, Victor Sulla, Ayago Esmubancha Wambile, Quentin Wodon, and Precious Zikhali, all of the World Bank, and Harold Coulombe, Zakaria Koncobo, Paul Saumik, Ilana Selff, and Xiao Ye for various data files and answers to questions. The financial support from the Africa Renewable Energy and Access Program (AFREA), part of the Energy Sector Management Assistance Program (ESMAP), is gratefully acknowledged.
where ���� is the probability of household _j_ , in stratum _h,_ and PSU _i_ to be included in the sample; kh is the number of PSUs selected in stratum _h_ ; mhi is the number of households selected in PSU _hi;_ �� is the total number of households in stratum _h_ ; ��� is the number of households to be selected in PSU _i_ and stratum _h_ based on the sampling frame; and �′�� is the number of households in PSU _i_ and stratum _h_ interviewed, listed, or consistent with the official population statistics.

For non-response corrections, the ���/�′�� term corresponds to the number of households to be selected divided by the actual number of households interviewed in each PSU. For corrections to the PSU size based on the listing exercise, the ���/�′�� term corresponds to the number of households in the PSU based on the sample frame divided by the number of households in the PSU based on the listing exercise.<sup>29</sup> Lastly, for post-stratification adjustments to make sampling weights add up to official population statistics, the ���/�′�� term corresponds to the number of households based on the official population statistics divided by the number of households based on the sample frame.

The sampling probability weights for the HIES 2016/17 have been adjusted _ex-post_ for two things: (i) to match the official share of urban population reported in all official publications produced by BBS after the 2011 Census of Population and Housing, and (ii) to match the official 2016 population projections estimates produced at the distict ( _zila_ ) level (BBS 2015). More specifically, the 2011 Census of Population and Housing calculated the share of urban population in Bangladesh at 23 percent. A month after completion of the Census, the Bangladesh Institute of Development Studies (BIDS), the independent agency in charge of

> equal the reference population of interest at the time
Capture of Decentralization: Vote Buying through Grants to Local Jurisdictions. In _Is Decentralization Good for Development? Perspectives from Academics and Policy Makers_ . Oxford University Press. 5

- Khemani, S. (2019). What Is State Capacity ? Tech. Rep. 8734, The World Bank. Publication Title: Policy Research Working Paper Series. 5, 6, 13, 28

- Khemani, S. (2020). Legitimacy and Trust in the Times of COVID-19. Tech. Rep. Research & Policy brief No. 32, World Bank, Washington DC. 6

- Knack, S. (2001). Aid Dependence and the Quality of Governance: Cross-Country Empirical Tests. _Southern Economic Journal_ , _68_ (2), 310–329. Publisher: Southern Economic Association. 5

- La Ferrara, E. (2016). Media as a tool for institutional change in development. 28

- Legovini, A., & Jones, M. R. (2020). Administrative data in research at the World Bank: The case of Development Impact Evaluation (DIME). In _Handbook on Using Administrative Data for Research and Evidence-based Policy_ . https://admindatahandbook.mit.edu/. 28

- Levy, B., & Kpundeh, S. (2004). Building state capacity in Africa: New approaches, emerging lessons. 7

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the extent to which the relationship between income inequality and future income growth is channeled through inequality of opportunity.

We find that it is inequality of opportunity that is negatively correlated with future income growth of the poor. Using weak instrument-robust inference confirms the robustness of this finding. While inequality of opportunity is also found to be positively correlated with growth at the top end of the income distribution, this effect is not robust. Our research advances the earlier studies by Marrero and Rodríguez (2013) and van der Weide and Milanovic (2014). The relationship between inequality of opportunity, inequality of effor ~~ts~~ and growth in average income uncovered by Marrero and Rodríguez (2013) is found to describe the income growth of poor households better than growth of the average household. The relationship van der Weide and Milanovic (2014) found between total inequality and growth of the poor versus growth of the rich appears to be driven mostly by inequality of opportunity.

The present paper also provides additional robustness checks to Marrero and Rodríguez (2013) and van der Weide and Milanovic (2014). Marrero and Rodríguez (2013) used the PSID database which provides detailed information on individual circumstances (including parental education) but for a relatively small sample of households, and for a limited period of time (1970-2000). They opted to disregard states with fewer than 50 observations for any given decade, which ultimately limited their database to 26 states. Despite the extensive robustness analysis they carried out, the smallness of their survey samples makes their analysis vulnerable to sampling error. Instead, we use an entirely different database (the US community survey) that covers between 1 and 5 percent of the US population which reduces sampling error to a minimum and allows us to consider all US states and the larger time period 1960-2010. Our study also extends the analysis of van der Weide and Milanovic (2014) by considering a different measure of inequality (MLD based on individual income at reference age – aged 30 to 50 – rather than the Gini
Saharan Africa).<sup>1</sup> Due to data availability issues, the analysis is limited to 16 countries. This reduction stems primarily from misreporting in the sales variable but is also due to incomplete data for control variables, and electricity prices; the latter is, for example, not available for Tajikistan. Furthermore, self-reported energy efficiency measures are not available in all countries. Consequently, regressions that include energy efficiency measures are based on data from 13 countries. The sample covers micro, small, medium, and large businesses across all main sectors (agriculture, manufacturing, retail, and other services, including construction).<sup>2</sup> <mark>Table A1 (a-c) in the Annex describes the sample distributon across years, sectors, and frm sizes at the country level. This breakdown is instrumental for categorizing sectors by their energy usage intensity. The sectors are metculously disaggregated to the most granular level the sampling methodology allows. Within the sample, agriculture accounts for 6 percent of the frms, manufacturing for 25 percent, wholesale and retail for 24 percent, and a diverse array of other services for 45 percent. Within the service sector, food services and accommodaton represent 23 percent, transportaton and informaton and communicaton technology (ICT) account for 13 percent, and constructon for 12 percent. The remaining frms are engaged in fnancial actvites, real estate, educaton, health, and other sectors.</mark> Table A2 in the Annex shows the summary statistics of the main variables used in the analysis. The dependent variables used in the estimations are employment, sales, and sales per worker as a measure of labor productivity.<sup>3,4</sup> The median firm has sales of $12,312, 6 workers,<sup>5</sup> with a labor productivity of $1,950 per worker, and has been operating for 15 years. Figure 1 depicts the trends of our outcome variables, labor
Unfortunately, since one does not observe all relevant individual circumstances in practice, this measure of IO serves only as a lower bound.<sup>6</sup> Finally, IO can be estimated either non-parametrically (Lefranc et al., 2008, and Rodríguez, 2008) or parametrically (Bourguignon et al., 2007, and Ferreira and Gignoux, 2011). The non-parametric approach makes minimal assumptions, but requires a sufficient amount of data. When the data are limited there is a premium for imposing additional structure by assuming a functional form for the function _g_ . This would be the parametric approach. We will adopt the former, the non-parametric approach, as this takes full advantage of the fact that the database covers between 1 and 5 percent of the total population.

# **3. The database and variables**

# **3.1. The IPUMS USA database and sample design**

We use the IPUMS USA database, which contains the largest sample of US   population between 1 and 5 percent of the total population that covers a period of 50 years at regular decennial intervals: 1960, 1970, 1980, 1990, 2000 and 2010.<sup>7</sup> The data are representative at the US state level. The obvious advantage of working with such a large data set is that sampling errors are reduced to a minimum. For their special features we do not consider the states of Alaska, Hawaii, District of Columbia and Puerto Rico in this analysis.

Total yearly income is obtained by aggregating incomes from 8 different sources: ( _i_ ) wages, salary, commissions, bonuses or tips; ( _ii_ ) self-employment income; ( _iii_ ) interest, dividends, net rental income, or income from estate/trusts; ( _iv_ ) social security or railroad retirement; ( _v_ ) supplemental security income; ( _vi_ ) public assistance or welfare payments; ( _vii_ ) retirement, survivor or disability pensions; and ( _viii_ ) other regular sources of income
Comin and Gertler (2006), stress the significance of R&D in generating medium-run fluctuations. They consider an endogenous growth model where R&D generates new specialized intermediate goods which enhance the production of final goods. They allow for R&D in both the capital good and the consumption good sectors. Their model is impressively successful in capturing the fluctuations of basic macroeconomic variables but does less well in generating the fluctuations of R&D observed in the data.<sup>2</sup> In our model, we decompose aggregate production into two sectors, the R&D sector and the non-R&D or consumptiongood sector. We incorporate the stock of R&D as a distinct input in the production function adopting Griliches’ (1979) proposition. Physical capital is mobile between sectors but with a cost. Fluctuations are driven by three types of shocks: two types of sectoral productivity shocks and capital investment-specific shocks. To quantify the impact of R&D on aggregate fluctuations we first estimate a VAR using seven post-war annual time series. Following Fisher (2006), the shocks are identified by imposing long-run restrictions which are justified by the theoretical model. Data on R&D are only available at the annual frequency. Thus, following Comin and Gertler (2006), we focus our analysis on those frequencies. As shown by Comin and Gertler, information extracted from annual data regarding medium-run fluctuations is virtualy the same as that extracted from quarterly data. The plausibility of the empirical impulse responses are assessed by comparing them with the theoretical ones which are generated by the simple equilibrium model.

Previous work by Butler and Pakko (1998) calibrates an endogenous growth model where R&D drives the level of labor augmenting technology which in turn a↵ects the production of

> 2As noted by the authors, this could be due to measurement errors in the data.

4
While the simplified definition overlaps with self-employment, it is less linked with sectoral distribution and is preferred for the analyses in this paper (See Annex 2 for details).

11. **From a firm’s perspective, past informality studies tend to use the following three criteria to identify informal firms (ILO 2018).** First, it is not a legal entity separate from its owners, has its own complete set of accounts, and is not owned or controlled by one or a few household members. Second, it is a market enterprise that sells its goods or services. Third, it falls into one of the following categories: it keeps the number of workers employed below a threshold determined by business regulations; it is not registered; or it hired workers informally. Other studies provide an alternative definition of informal firms as a continuum depending on size, registration, accounting standards, tax payments, mobility of workplace, and access to bank credit (Benjamin and Mbaye 2012; Mbaye, Benjamin, and Gueye (2017). In firm surveys such as World Bank Informal Sector Enterprises Surveys, informal firms are typically defined as a business without the possession of a firm registration (or tax) number.

12. **Indonesia follows the global standard in defining formal and informal businesses.** Specifically, a formal enterprise is defined as a business in possession of a firm registration number ( _TDP-Tanda Daftar Perusahaan_ ) or firm unique ID ( _NIB-Nomor Induk Berusaha_ ), whereas an informal enterprise is a business that does not possess either of these.

> 3 The BPS moved from the old to the new definition of informality in 2016.

5
registry improve the accuracy of prediction models using geospatial indicators? (3) How do predictions generated using extreme gradient boosting, which are more robust to outliers, compare to those generated using post-LASSO linear models?

The main result is that introducing the simulated partial registry yields predictions of village welfare that are vastly more accurate than the other methods considered. When using a partial registry, the rank correlation, Area Under the Curve (AUC) coefficients, and R<sup>2</sup> are 0.75, 0.89, and 0.57 respectively. In contrast, the rank correlation for the other three methods ranges from - 0.02 to 0.2, the AUC scores range from 0.5 to 0.6, and the R<sup>2</sup> of the predictions range from 0.001 to 0.04. These are huge differences in predictive accuracy.

These results are based on a simulated partial registry of approximately 450 villages, about 10 percent of the total number of villages with available data. However, the accuracy of the predictions does not substantially improve when the size of the simulated partial registry increases to 675 or 900 villages, which is 15% or 20% of the census extract. In other words, a partial registry of 450 villages is sufficient to train a high-performing predictive model in this context.

Finally, this exercise provides a useful opportunity to compare the predictive performance of extreme gradient boosting against a post-LASSO model, which has also been used in the literature but imposes a linear functional form on the model. We find that using post-LASSO for the geospatial model – the second step of the partial registry approach -- makes little difference to the accuracy of the predictions. However, using LASSO rather than gradient boosting for the census model used to construct the benchmark village welfare measure greatly reduces the explanatory power of the geospatial model, whether it is estimated using gradient boosting or LASSO. This is because extreme gradient boosting is a tree-based method that