rafmacalaba/data-use-mentions-extended huggingface dataset:
for ten broad sectors of the economy for 42 countries (both developed and developing) spanning 1950 through 2012.<sup>2</sup> The advantage of the GGDC is that it provides a long-run series of sectoral productivity that is internationally comparable across a broad range of countries. However, the GGDC’s Africa Sector Database (ASD) only includes eleven sub-Saharan African countries.<sup>3</sup> Thus, for other sub-Saharan African countries, we collect manufacturing value added and GDP time series from

> 2This period of data availability is as of September, 2017.

> 3The Sub-Saharan African countries in the ASD are Botswana, Ethiopia, Ghana, Kenya, Malawi, Mauritius, Nigeria, Senegal, South Africa, Tanzania, and Zambia.

3
Nordman, Rakotomanana, and Roubaud 2016). As a result, the informal sector consists of both high-productive and low-productive workers and firms. While the latter stay in the informal sector out of necessity, the former join the informal sector to avoid various costs associated with “being formal”. As a result, the definition of informal workers as well as informal firms is largely context and country-specific (see Annex 1 for more details).

9. **Multiple definitions of informal workers exist for cross-country comparison.** According to the International Labour Organization (ILO), informal employment covers all workers of the informal sector and informal workers outside the informal sector (Perry et al. 2007; ILO 2018). The former comprises all persons who were employed in at least one informal firm. The latter group includes self-employed and workers not employed in formal contractual arrangements or not subject to social protection or employment benefits. In practice, the most frequently used informal employment measure is the share of self-employment in total employment (La Porta and Shleifer 2014; ILO 1993).

10. **In the context of Indonesia, labor force surveys also provide several definitions of informal employment.** For instance, the labor force surveys (SAKERNAS surveys) carried out by Badan Pusat Statistik (BPS) report data on self-employment twice a year. BPS provides three definitions of informal employment: simplified, old, and new.<sup>3</sup> The simplified definition mainly reflects job-based informal employment, covering casual workers, (i.e. those without formal labor contracts), part-time workers, and unpaid household workers. Both new and old definitions use information on sectors and types of employment to classify workers, making sectoral estimates hard to interpret. 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
read|0.66|-0.01|0.30|0.01|
|||(0.02)||(0.03)|
|=1 if has access to mobile phone|0.45|0.03|0.42|-0.01|
|||(0.02)||(0.03)|
|**Panel C. Household wealth**|||||
|Food security index (z-score)|0.00|-0.09*|0.00|0.05|
||[1.00]|(0.05)|[1.00]|(0.06)|
|Total expenditure (monthly)|26.03|-2.13*|65.71|-0.52|
||[24.46]|(1.17)|[47.08]|(2.59)|
|Net value of durables, livestock, and fnancial asset|162.55|4.24|416.43|33.88|
||[235.93]|(11.45)|[823.80]|(51.15)|
|Non-agricultural income (monthly)|10.27|0.96|7.84|0.85|
||[16.73]|(0.81)|[20.52]|(1.13)|
|**Panel D. Empowerment-related outcomes**|||||
|=1 if has her own income source|0.44|0.01|0.31|-0.03|
|||(0.02)||(0.03)|
|Age diference from spouse|2.94|-0.16|4.09|0.72|
||[
L/MICs**

- a. Average share of firms providing training and b. Average share of firms providing training, by share of workers receiving training (in firms where firm size and income group offered), by income group

<!-- Start of picture text --> 60 80<br>70<br>50 70<br>42 41 43 60<br>60<br>40 35 34 51<br>50 44 43<br>30 25<br>37<br>40 34<br>20 27<br>30<br>20<br>10 20<br>0 10<br>LIC MIC HIC<br>0<br>LIC MIC HIC<br>% firms providing training to workers<br>% workers receciving training Small Medium Large<br>% firms providing training<br>% firms, % of workers<br><!-- End of picture text --> Source: Estimates based on World Bank Enterprise Surveys.

**Several market failures may help explain firms’ underinvestment in skills beyond formal education.**<sup>107</sup> Firms may underinvest because training to some extent is a public good, and they risk

102 OECD 2019b. 103 European Commission 2015.

104 Carlsson and Eriksson 2019.

105 Neumark et al. 2015. 106 Deloitte 2018.

> 107 See the framework presented in Almeida et al. 2012.

20
development, such as the provision of basic public goods and the establishment of new institutions to gain support from local communities. The Salvadoran context suggests one particularly relevant feature: the creation of alternative governing authorities that relied on community initiatives and promoted distrust of the state (Pearce, 1986; Binford, 1997; Wood, 2003, 2008). In areas controlled by the FMLN, guerrillas eliminated local state authorities and promoted communitybased organizations to address health, education, and economic problems. These peasant-run participatory institutions were autonomous and ideologically founded on community values, altruistic solidarity, and distrust of the state and elites. These organizations flourished as an alternative to state institutions, distancing local communities from national politicians and further eroding trust in the state. Moreover, guerrillas also substituted subsistence crops for large-scale agricultural production to foster economic self-sufficiency among peasants. In contrast, during the same period, nearby areas remained under state control without changes in governance.<sup>3</sup> We examine the effects of the FMLN’s temporary territorial control by comparing areas around and near the boundaries of its zones between 1985 and 1992, as documented in the United Nations map used during peace talks between the Salvadoran government and the FMLN. Figure 1 illustrates these areas and boundaries. The Salvadoran government and the FMLN jointly approved the map and submitted it to the United Nations during the peace talks from 1990 to 1992. We assess the effects of FMLN territorial control on development 20 years later by examining current changes in economic activity (proxied by night light luminosity), human capital (measured as years of education), and a wealth index that approximates average household living standards.

Our empirical strategy employs a spatial regression discontinuity design and uses geospatial data on night light luminosity for 2013 and census tract data on education and wealth for 2007. Moreover, to disentangle mechanisms, we combine information from multiple sources on measures of social capital, land markets, and trust.

A review of the validity of the empirical design shows that all geospatial and economic variables
no data on rents and location choice are available for other natural resource industries. The BP Statistical Review of World Energy provides a large data set containing information on global oil, gas reserves and prices, obtained from government sources and published data. As a proxy for geographic constraints on location choice, we use proven global reserves of oil and gas that can be extracted from known reservoirs with reasonable certainty in the future. We first standardize the oil and gas reserves data (because oil and gas reserves are measured in different units) and subsequently take the average of the two measures to obtain our _gasoilreserves_ variable. We then interact this score with battle-related deaths 35
48. Most of these sectors meet the pre-screening criteria, i.e., they have some upside potential for growth and meet feasibility criteria. A notable exception is sportswear—unavailability of PVC in the domestic market creates a comparative disadvantage and has already resulted in the closure of domestic production. Its competitiveness may depend on the establishment of a domestic petrochemical industry. Also, in the comparator countries, fertilizer and petrochemical production as well as TV production may not take place in small or medium-size enterprises; however, targeted foreign direct investment may be able to attract investments to establish larger firms, including through joint ventures, provided enabling conditions have been put in place.

49. In a second step, Nigeria’s basic wage competitiveness in these sectors needs to be reviewed to determine whether that would allow Nigeria to reap the advantage of backwardness.

50. The table below summarizes wage data for China, Vietnam and Nigeria on a sectoral basis.

**Table  11: Average wage (incl. benefits) by industry (USD)**

||**skilled**|**labor**|||**unskille**|**d labor**||||
|---|---|---|---|---|---|---|---|---|---|
|**Sector**|**Nigeria**|**Ethiopia**<br>**(ICA)**<br>**Ethiopia**|**Vietnam**|**China**|**Nigeria**|**Ethiopia**<br>**(ICA)**|**Ethiopia**|**Vietnam**|**China**|
|food|135|82<br>89-<br>141|181-<br>363|398
boundary where control was contested. As such, conflict or violence may be the source of the negative effects in the development outcomes.

We test this mechanism in several ways. First, we estimate equation (1), controlling for the segments of disputed areas where the Salvadoran government and the guerrillas usually fought. Second, we use a doughnut-hole approach to exclude all observations within 80 m from the boundary of guerrilla-held territories.<sup>34</sup> Results for the main outcomes of interest from these exercises are in Table 9. In general, the coefficients are negative, statistically significant, and similar in size. This suggests conflict is not the main factor behind the negative effects. We obtain similar results using outcomes of war crimes, including number of deaths, disappearances, and other conflict-related crimes as reported by the Truth Commission. Results in Table D.24 once again support the idea that areas under guerrilla control did not experience disproportionately higher crimes relative to other areas. The negative coefficient associated with the war crime estimates suggests that former guerrilla areas experienced less crimes, leading to lower-bound estimates of our main outcomes.

Finally, we appraise the role of guerrilla control in contemporaneous measures of crime to judge whether the historical presence of guerrillas prevented the development of criminal actors such as gangs. The social capital in former guerrilla areas may have done so (Sviatschi, 2020); tightly knit communities with strong social ties can better prevent crime because they raise detection probabilities and attach shame to criminal behaviors (Buonanno, Montolio and Vanin, 2009). If social capital lasts, we expect fewer crimes linked to non-state armed actors, which are pervasive in El Salvador. But if our results stemmed from violence during or after guerrilla control of these areas, we should expect more violence today. To test these hypotheses, we considered homicide rates during 2017 using police data, and victimization rates from 2004 to 2016 in LAPOP surveys.

Table D.25 in the Appendix presents the results. Consistent with the finding that violence during
R&D which constitute an investment that pays o↵in the long run. Currently, expenditures on R&D are not included as investment in GDP in the official accounts but instead they are treated as current period expenditures. Treating R&D as investment rather than as intermediate expenditures results in important changes to the calculation of GDP. In BEA’s National Income and Product Account (NIPA), _business_ R&D expenditures are included as intermediate rather than final expenditures which means that they are not added up in deriving GDP. Other expenditures in R&D which are included in the calculation of the GDP cannot be separately identified from other components reported in the NIPA tables.<sup>19</sup> Although those expenditures are included in GDP, they are not treated as investment which means that they are not subject to depreciation.

In 2006, the Bureau of Economic Analysis (BEA) jointly with NSF launched an R&D satellite account to explore investment in R&D and its larger economic e↵ects. The BEANSF R&D satellite account provides a measure of the value of R&D output and adjusted GDP by transforming R&D expenditures into measures of real investment.<sup>20</sup> The nominal value of R&D is the sum of the costs of the R&D activity of both private and government organizations. Private organizations consist of businesses such as private universities and colleges, private hospitals, charitable foundations, other nonprofit institutions serving households and most Federally Funded Research and Development Centers (FFRDC). Government organizations consist of the Federal Government, state and local governments (excluding uni-

> 19Expenditures on R&D by government and nonprofit institutions are included in consumption expenditures; Federal purchases of R&D, expenditures on in-house R&D performed by the federal government and state and local purchases of R&D are included in government consumption; Spending on R&D by foundations and non-profit institutions serving households are included in personal consumption
**Table 1: Estimation of the NEG wages equations for the urban and rural sectors, 2007**

|||**Urban**<br>|||**Rural**<br>||
|---|---|---|---|---|---|---|
|**Variable**|**OLS**|**2SLS**|**FGS2SLS**|**OLS**|**2SLS**|**FGS2SLS**|
|Constant|3.042***<br>(3.533)|5.032***<br>(4.219)|3.911***<br>(3.892)|3.471***<br>(11.618)|3.564***<br>(11.332)|3.091***<br>(11.091)|
|ln(_human_)|0.565***<br>(7.744)|0.602***<br>(7.985)|0.597***<br>(8.135)|0.608***<br>(5.854)|0.431**<br>(2.379)|0.407**<br>(2.192)|
|ln(_I pw_)|0.107***<br>(5.637)|0.142***<br>(4.149)|0.112***<br>(5.876)|0.0936***<br>(7.877)|0.093***<br>(7.614
it is used throughout our analysis to deflate nominal R&D investment.

# **6.2 Other Variables Used in the Analysis**

In the empirical analysis we employ US annual data for the period 1959-2007. We use annual frequencies because R&D investment and total employment of R&D performing companies are reported only at annual frequencies. Moreover, data on R&D investment and employment are available only after 1959 and 1958, respectively. The former are obtained from the BEANSF R&D satellite account while the latter are from the NSF annual survey.<sup>22</sup> Our sample excludes the turbulent period after 2007.

Total hours worked in each sector are defined as the number of employed multiplied by average hours worked during the reference year. While data on aggregate average hours worked are available, data on individual hours that correspond to workers employed in the R&D sectors are not reported. In our benchmark specification, _HRt_ is computed as employment in the R&D sector multiplied by per capita hours in the nonfarm business sector divided by a population measure that refers to population over 16 years old (US Census Bureau).<sup>23</sup> To compute hours in the consumption-good sector, we first compute employment in the sector as employment in the nonfarm business sector minus employment in the R&D sector. Then, _HCt_ is computed as employment in the sector multiplied by per capita hours in

> 22The NSF reports data on domestic employment by R&D performing companies which does not include universities and government. Although there are various statistics for employment from NSF surveys, there are difficulties in constructing an aggregate measure of R&D employment series. First, there are no complete data for all years of our sample and second, it is unclear which of the participants in the surveys are actually involved in performing R&D activities. Given those issues and since R&D investment by universities and government constitutes, on average, only 20 percent of
Figure A11: Perception of economic conditions <!-- Start of picture text --> 2017<br>2016<br>2015<br>2017<br>2016<br>2015<br>0 20 40 60 80 100<br>Percent<br>Future onths)<br>economic<br>conditions(3m<br>Present<br>economic conditions<br><!-- End of picture text --> Very good/Much better Fairly good/Better Neither good nor bad/Same Fairly bad/Worse Very bad/Much worse Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017 Figure A12: Perception of living conditions <!-- Start of picture text --> 2017<br>2016<br>2015<br>2017<br>2016<br>2015<br>0 20 40 60 80 100<br>Percent<br>months)<br>Future living conditions (3<br>conditions<br>Present living<br><!-- End of picture text --> <!-- Start of picture text --> Very good/Much better<br>Fairly good/Better<br>Neither good nor bad/The same<br>Fairly bad/Worse<br>Very bad/Much worse<br><!-- End of picture text --> Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017 Figure A13: Feeling in control over own life <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>2015 2016 2017<br>No Control Some Control A Great Deal of Control Complete Control<br>Percent<br><!-- End of picture text --> Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017 Figure
of picture text --> Taobao Villages have a higher share of migrants<sup>7</sup> than other villages in China (Table 1). While a typical rural village population is 5.3 percent migrants,<sup>8</sup> the Taobao Villages in our sample are 29 percent migrants, 19.6 percent come from other provinces. The causal relationship between the inflow of migrants and formation of Taobao Villages is unclear. It may be that villages with a large inflow of migrants, typically those with a strong industrial base, are more likely to become Taobao Villages. It may also be that a village where the business environment is more conducive for e-commerce (or commerce in general) attracts more migrants, who are more likely to be entrepreneurial and move there to start their own businesses or to seize employment opportunities. What is clear is the strong positive association of migrant inflows in Taobao Villages.

> 7 Immigrants are defined as individuals who live in this village but have their hukou, which is the population registration system, registered outside the village.

> 8 The numbers are derived from the Chinese Population Census in 2010. Total rural population is taken from module 1- 1c, and numbers of migrants by origin are taken from module 1-4c.

8
**Table 4.1. Elements for quality assessment at airports**

<!-- Start of picture text --> Passengers | Airlines | Others<br>Subjective approach: Quality surveys monitoring<br>assessment Objective approach: Establishment of standards and<br>measurement of performance<br><!-- End of picture text -->

# 4.2.1.- Services to passengers

As mentioned above, BAA controls the level of quality in passenger services through quality survey monitoring. This constitutes an application of the subjective approach. The survey measures passengers' perception of the service they receive in departures, arrivals and retail areas. Over 250,000 passengers are interviewed each year. The interview takes 8 to 12 minutes, and passengers are asked to assess services on a fivepoint scale from "extremely poor"(1) through "average"(3) to "excellent"(5). At Heathrow, Gatwick and Stansted, information has been collected throughout a six-year period on customers perception of twelve basic aspects of services in the departures area, and seven basic aspects of services in the arrivals area. Departing passengers are interviewed as they enter the gateroom, and arriving passengers as they exit the terminal. In a similar way, perceptions about various aspects of service and value for money provided by retail outlets, car parks and restaurants, are also collected.

Scores for BAA airports are presented at Tables 4.2 and 4.3. However, these are results only for those areas that are common to all three airports. Each table shows the constituent factors for each airport ranked according to the quality survey monitoring. Results show that, on average, passengers perceive most areas to be at least "average." Many areas are ranked between "good" and "excellent," and no area is assessed as "extremdly poor." Overall, Stansted scores consistently well, and Gatwick still scores slightly better than Heathrow, although Heathrow has shown more improvement than
the World Bank likewise notes the following: “Open data are those that are freely and easily accessible, machine-readable, and explicitly unrestricted in use. Open data aren’t necessarily big, and big data aren’t necessarily open.”<sup>29</sup> Drawing on these conceptions, the Open Data Charter, which was first signed in July 2013, further delineates the difference between Open Data, and what constitutes _Open Government Data_ , which is the central concept of the present research. In particular, the Charter “recognizes that the term “government data” includes, but is not limited to, data held by national, regional, local, and city governments, international governmental bodies, and other types of institutions in the wider public sector.”<sup>30</sup> Given the plurality of perspectives on these closely interrelated topics, the present research will rely on—for the sake of simplicity and clarity—a two-part definition of OGD as put forth by the OECD.<sup>31</sup> Such a conception marries two constituent elements: (i) “government data,” which includes “any data and information produced or commissioned by public bodies;” and “open data,” which, similar to other definitions, “can be freely used, re-used and distributed by anyone, only subject to (at the most) the requirement that users attribute the data and that they make their work available to be shared as well.”<sup>32</sup> The current research question also relates to a growing literature on transparency and conceptions of “openness” in government. Similar to what constitutes OGD, transparency has no single definition nor is there a single indicator to measure it. Instead, transparency takes on a variety of different meanings and measures across organizations and within the literature. Khemani (2016) provides one of the simplest and most straightforward definitions of transparency as “citizen access to publicly available information about

> 25 Open Data
Policy Research Working Paper 5895

# **Abstract**

Repeated attempts at uncovering the relevance of country size for various economic factors have produced discouraging results. The present paper sheds new light on the relevance of country size using micro or firmlevel data on firms’ experience with the quality of tax administration, an important but neglected element of the business climate. The analysis finds that the quality of tax administration is significantly better for small compared with large countries. The instrumental variables regression method confirms that this finding is robust to various endogeneity concerns. The paper also finds some evidence that the country size and tax administration relationship is non-linear, and much stronger for small than large countries. Implications of these findings for the broader literature on country size are discussed.

This paper is a product of the Enterprise Analysis Unit, Global Indicators and Analysis. 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://econ.worldbank.org. The author may be contacted at mamin@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 Support Team