rafmacalaba/data-use-mentions-extended huggingface dataset:
The subject of digitalization also deserves greater attention given its growing role in the informal economy. Research on the links between the digital economy and the informal economy appears to be at its nascent stages, leaving many questions unanswered. For example, what are the distinguishing characteristics of informal workers and enterprises that use digital technologies? What kinds of digital technologies do they use, and how are they used? Moreover, how does the use of digital technologies shape the outcomes and prospects of both informal enterprises and workers? Finally, what are some of the emerging issues or challenges faced by informal workers and enterprises in the digital economy?

In conclusion, numerous questions remain for future research. Some of these questions may be resolved with the help of existing datasets such as the IMK Survey and SAKERNAS. Where this is not possible, new data will have to be collected through other means to properly address the challenge of informality in Indonesia.

26
Our results appear in Figure 10 and show that the observed change in household income when excluding income from the Auxilio Emergencial followed a U-shaped pattern. This estimate was done with a panel of households (about three-fourths of the original data) that appear in both the 2019 and 2020 PNAD-C household surveys. The largest income loss appeared in quintile 5 (19.6%), followed by quintiles 1 (17.6%) and 4 (17.4%). This is at odds with the simulation results, which show declines of about 12%-13% percent for the bottom 3 quintiles and smaller declines at the top. When including the Auxilio Emergencial as part of household income, households across the income distribution experienced smaller income losses than when excluding this social program. This highlights that the AE program covered households across the income distribution. Income declines, however, were highest for the higher quintiles of the distribution, while there was an income gain for quintile 1, highlighting that the AE program was also more targeted towards the poorest households. Since the microsimulation results did not account for this emergency transfer program (as explained in an earlier section), the results from the 2020 microdata incorporating the AE program cannot be compared with the microsimulations. The rest of the analysis thus focuses on comparing the projected and observed income and poverty changes under the assumption of excluding the AE program.

**Figure 10. Change in household per capita income between 2019 and 2020. Observed and projected values**

Projected and observed excluding AE Observed including AE <!-- Start of picture text -->
-11.9%<br>Q1 Q1 14.3%<br>-17.6%<br>-13.0%<br>Q2 Q2 -1.2%<br>-16.0%<br>-11.6%<br>Q3 Q3 -7.8%<br>-15.0%<br>-
otal Manufacturing<br>|||||
|1990|1.4|0.5|3.7|na||||
|2001|3.7|0.7|8.8|4.3||||

1 Calculated for 1995-2001 _Note_ : Data for 1990 is from SSIC 35220 (Medicinal & pharmaceutical products), and from SSIC 242 (Pharmaceutical products) for all other years _Sources_ : Economic Development Board, Report on the Census for Industrial Production, various years; Economic Development Board, Report on the Census of Manufacturing Activities, various years

**Table 2.4  Growth Trend of Private Sector R&D Expenditure in Singapore, 1981-99**

|Year|Total Private Sector R&D<br>Expenditure (S$mn)|Private R&D Expenditure in Electronics<br>& IT Industries<sup>1</sup>(S$mn)|%|Private Sector R&D<br>Expenditure in Life<br>Sciences (S$mn)|%|
|---|---|---|---|---|---|
|1981|44.2|27.5|62.2|na|na|
|1984|106.7|53.4|50.0|na|na|
|1987|225.6|147.1|65.2|na|na|
|1990|309.5|177.6|57.4|na|na|
|1991|442.0|273.9|62.0|na|na|
|1992|577.5|324.2|56.1|na|na|
|1993|618.9|338.1|54.6|24.8|4.0|
|1994|736.2|385.2|52.3|
properties (reported by 75% of respondents in the LFS but only 4% in Prindex) may be one factor contributing to this result, it highlights the need for careful assessment of Prindex’s conceptual foundations, sample selection, implementation protocol and actual data to help appreciate the type of issues it can address. Limitations imposed by the fact that in more than 50% of cases, data on documentation and perceived tenure security were reported by family members rather than owners suggests that methodological adjustments can help this initiative achieve its potential and make a meaningful input to the land policy debate while preventing contradictory policy recommendations from results that reflect differences in data collection protocols rather than ground reality.

The rest of the paper is structured as follows: Section 2 discusses the conceptual framework and relates it to the evolution of Zambia’s land tenure system. Section 3 presents data and descriptive evidence on genderdifferentiated access to formal documents, transferability, perceived tenure security, and demand for documentation at the household and parcel levels. Section 4 presents regression evidence on factors (including access to different types of documentation) associated with higher levels of tenure security, transferability, and demand for formal documents. It also compares our findings from LFS data to those emerging from the Prindex poll that are comparable and explores reasons that might lead to differences. Section 5 concludes by drawing out implications for policy, research, and future efforts at data collection on land rights as a part of the SDGs.

# **2. Conceptual framework and empirical relevance**

To link the conceptual framework to measurement, we use the literature on land tenure and socio-economic outcomes as well as sources of demand for documented tenure to identify aspects of gender-differentiated tenure security in terms of (i) the ability to transfer via bequest or sale; (ii) access to documented land rights, and (iii) perceived security of land rights. This is complemented with a discussion of demand for secure land rights as well as a description of Zambia’s land tenure system to highlight how
disrupted after round 1, but this is not observed in round 2. Because we attempt to control for selection bias, whether due to non response or firm exit, we still in principle have unbiased impact estimates. Moreover, it does not appear that the inability to model firm exit and entry biases our aggregate results, given that in practice our forward-projected estimates without this aspect modeled match the CMMHH data well (see subsequent sections).

18 First stage regression results are provided in Appendix 2 of the long version of the paper. In Jordan, we found that missing values for key variables were more likely if the respondent was not the same, the firm was not medium-sized, and the top manager had fewer years of experience. In Georgia, having mixed or foreign ownership was associated with lower rates of missing values.

15
# **Figure A1: Event study: lagged, contemporaneous and lead effects of household worker sickness (daily data)**

<!-- Start of picture text --> Event study: daily data<br><!-- End of picture text --> <!-- Start of picture text --> Effect on men Additional effect on women<br>-4 -3 -2 -1 0 +1 +2 +3 +4 -3 -2 -1 -4 0 +1 +2 +3 +4<br>day relative to health shock day relative to health shock<br>.2 .2<br>0 0<br>-.2 -.2<br>-.4 -.4<br><!-- End of picture text --> Graph on left shows coefficient from the Other Worker Ill variable. Graph on right shows the interation term Other Worker Ill X Female.
Policy Research Working Paper 5559

# **Abstract**

Does radio access improve public service provision? And if so, does it do so by increasing government accountability to citizens, or by persuading households to take advantage of publicly-provided services? Prior research has argued that citizens with greater access to mass media receive greater benefits from targeted government welfare programs, but has not addressed these questions for public services such as in education and health. Using unique data from Benin, this paper finds that literacy rates among school children are higher in villages exposed to signals from a larger number of community radio stations. The effect is identified based on a “natural experiment” in the northern communes of Benin where within-commune variation in village access to radio stations is exogenous to observed and unobserved village characteristics. In contrast to prior research, the authors find that this media effect does not operate through government accountability: government inputs into village schools and household knowledge of government education policies are no different in villages with greater access to community radio. Instead, households with greater access are more likely to make financial investments in the education of their children.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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 skhemani@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
# **CHILEAN ENERGY SECTOR: THE INITIAL SITUATION**

<!-- Start of picture text --> NTE ENERGY MATRIX (1994)<br><!-- End of picture text --> <!-- Start of picture text -->
*11%  COAL<br>*41%<br>OIL  U  24%  HIDROELECTRICITY<br>r0I7%  7<br>NATURAL GAS  WOOD & OTHER<br><!-- End of picture text -->

SOURCE: NATIONAL ENERGY COMMISION 17
perspective than the other group members than on her own expected high score. As a result of these skill complementarities, ethnic or cultural diversity within a team can boost productivity, as workers from various backgrounds bring a diverse set of skills and abilities to the table (Alesina and Ferrara, 2005; Hong and Page, 1998; Lazear, 1999; Ottaviano and Peri, 2006; Suedekum et al., 2014).

Several studies find positive effects of greater cultural and ethnic diversity on worker and firm productivity, arguably due to the skills complementarity effect mentioned above. They also discover beneficial effects of ethnic and cultural diversity on firms’ innovation activity, which is a key determinant of firm productivity. Most of these studies are on developed countries, and they focus on diversity in teams and workplaces. There are a few studies that are at the country or local (sub-national) level.

Ottaviano and Peri (2006) investigated the relationship between diversity at the city level in the United States and wage (and rent) distribution in a seminal study in this field. They examine 160 metropolitan areas from 1970 to 1990 using data from the Census Public Use Microdata Sample (PUMS). To assess diversity, the authors employ a fractionalization index inspired by Mauro (1995). Their main findings show that a 0.1 point increase in the diversity index increased natives’ average labor productivity (wages) by 13 percent. Cooke and Kemeny (2017) also discover that greater diversity in employees’ countries of origin has a positive impact on labor productivity. This is especially true for workers who are involved in complex problem-solving activities that require a high level of knowledge as well as participation in creativity, innovation, and STEM fields. Trax et al. (2015) estimate the impact of cultural diversity on total factor productivity (TFP) at the establishment level using data from German establishments. They discover that greater cultural diversity within establishments and in local communities has a 6
ITU collects data on connection fees, broadband wireless charges, broadband tax rates, business telephone installation fees, broadband caps, monthly subscription charges and broadband speeds. From these measures we compute a cost index using simple principal components. An increase in the index implies that costs are rising over time. Note that data are standardized across countries and hence an increase should reflect idiosyncratic increases in costs that imperfectly controls for inflation, quality changes or upgrades. To construct this index, all the input variables should have the same sign - so that the interpretation of an increase implies a higher cost relative to a global mean (see Appendix A, Table 41).

The estimated indices are summarized in Figure 6. Costs have been rising over time for all countries in the sample. These costs, associated with higher fees, and lower comparative speeds indicate entry barriers and hence market concentration. Although rising total costs could also indicate quality improvements, the measures here are mainly associated with regulatory costs.

A second measure to compute gaps in the input market uses the statistic from Bartelme & Gorodnichenko (2015) and computes the deviations from a frontier market using inputoutput tables. This measure tracks distortions in input markets by sector, on the assumption that an undistorted economy functions like a market leader. A further assumption is that all countries use the same intermediate goods technology. The distortion is then computed as a ratio of inputs in LAC countries relative to a frontier country in the provision of ICT goods (here the Republic of Korea). A higher ratio implies that the input distortion has risen relative to the frontier economy.

The undistorted share of intermediates of country _i_ in sector _j_ in period _t_ ( _bi,j,t_ ) is related to the distorted share ( _γi,j,t_ ) via a scalar that captures distortions ( _τi,j,t_ ):<sup>1</sup> Given that input-output tables are based on observed variables (i.e., they do not distinguish between distorted and undistorted states)
a result that is broadly consistent with Barro (2000) and Barro (2008), Keane and Prasad (2002), and Ostry, Berg, and Tsangarides (2014). Our work in this regard is most closely related to Desai and Kharas (2017) who emphasize the importance of the middle class in poverty reduction. While these authors use historical data since 1870 to explore the relationship between the middle class and poverty reduction, we focus on a more recent period that is characterized by growing global integration and use counterfactual simulations to quantify the role of the middle class in sustained poverty reduction.

Third, our study contributes to a literature on the effects of trade policy on poverty (Autor, Dorn, and Hanson, 2016; Topalova, 2010; Harrison, 2007; P. K. Goldberg and Pavcnik, 2004; Winters, McCulloch, and McKay, 2004). We introduce to this literature a novel measure of integrated international market size, which in our model predicts sustained poverty reduction. This measure complements and extends the data sets of Sachs and Warner (1995) and Wacziarg and Welch (2008), which identify the specific years at which economies liberalize. By our treaty-based measure of liberalization, in which a country has access to some international market once it signs a trade agreement, many countries appear open in years when these other datasets consider them closed. Even though many countries are closed according to our measure in 1981, almost none are closed today, given almost complete membership in the WTO by UN member states. Despite this, there is large variation in the relative size of the integrated international market in many regions, driven for instance by China’s entry into the WTO in 2001, which lowered the average income of WTO member states by approximately 18 percent.

Finally, while models of development with firm-level increasing returns assume supply-side constraints, such as capital market imperfections, to explain why countries remain poor (Banerjee and Duflo, 2005), our framework does not rely on such assumptions.
different periods: the UNCTAD data span 2001–12, whereas the CDIS data cover 2009– 18. By combining data from both sources, we obtain FDI data covering the entire 2001–18 period.

> 26 The jump takes the following form. There are 4,511 reported zeros (54 percent of non-missing reported observations) in 2003, 6,364 (60 percent) in 2004, 11,590 (72 percent) in 2005, and 4,808 (49 percent) in 2006.

34
Common law|0.000|0.357|0.101|0.002|0.092|
|Muslim law|1.000|0.714|0.739|0.769|0.774|

_Source_ : Authors’ analysis of the Gwartney, Lawson, and Hall (2017) Economic Freedom of the World (EFW) index. Additional data come from the Polity IV dataset, Freedom House, the Database of Political Institutions (DPI), and the JuriGlobe database. _Note_ : Table presents pre-treatment predictor variables for Jordan, their corresponding pre-treatment sample means, and the weighted averages used to construct Synthetic Jordan. OIC denotes the full Organisation of Islamic Cooperation donor pool; OIC minus PAK denotes the OIC donor without Pakistan; and OIC SAL denotes the donor pool limited to OIC countries that received Structural Adjustment Loans (SALs) from either the World Bank or International Monetary Fund (IMF). ‘Political rights’ and ‘civil liberties’ refer to the respective Freedom House Freedom in the World report indexes. ‘Executive constraints’ reference to the Polity IV measure of executive constraints _xconst_ , ‘Presidential’ and ‘parliamentary’ are dummies equal to one for countries with presidential or parliamentary political systems as recorded in the DPI, respectively. Civil, Common, and Muslim law are dummies equal to one for a country’s legal system belonging to each category as reported by the JuriGlobe database.

Table 2 presents goodness of fit indicators for the Synthetic Jordans. The first row indicates the specification’s Root Mean Square Predicted Error (RMSPE), which measures the distance between the synthetic control unit and the predictors used to construct it during the pretreatment period. The predictor variables and their weighted values are listed in table 3, and the country weights for each specification are listed in table 4.

Table 1 column (1) is the preferred model, as it constructs Synthetic Jordan from the Full OIC list of countries with an EFW score and has the best pretreatment fit with an
(1): 36-41.

- World Bank. 2005a. _World Development Report 2006: Equity and Development_ . The World Bank and Oxford University Press, New York.

- World Bank. 2005b. Development Database Platform. Washington, D.C.. Available online at available at <u>http://www.worldbank.org/data/onlinedatabases/onlinedatabases.html;</u> accessed October 1, 2005.

27
. Additional challenges arise in identifying and counting the displaced in cases where they may not be willing to self-identify.

This paper describes a sampling approach that triangulates different sources of data to design a representative survey in the context of the recent Rohingya displacement from Myanmar to the district of Cox’s Bazar in Bangladesh. The Cox’s Bazar Panel Survey (CBPS) aims to produce representative information of both the Bangladeshi host population living in the district and the Rohingya displaced living in the district. This paper highlights the challenges and solutions in conducting the CBPS sampling strategy. The approach included combining information from the Bangladesh housing and population census, humanitarian data on Rohingya displaced living in camps, geospatial data and listing exercises. The next section explains the context and the core objectives of the survey. Section 3 describes the sampling exercise. Section 4 summarizes the lessons learned and concludes.

2
,020|
|R-squared|0.221|0.225|0.237|0.237|
|Department FE|N|N|Y|Y|

_Source_ : REDATAM INDEC Census Argentina 2010. _Notes_ : Standard errors clustered at the department level in parentheses. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

that if the transmission of education by ancestry would continue as observed in the two generations we study, the distribution of education of Natives and non-Natives would not converge to be the same — a large difference would persist.

15
, Mozambique (2014), Nigeria (2013), Senegal (2010), Tanzania (2010, 2014), Togo (2013), and Uganda (2013).<sup>6</sup> In each country, representative surveys of between 150 and 760 schools were implemented using a multistage, cluster-sampling design. Primary schools with at least one fourth-grade class formed the sampling frame. The samples were designed to provide representative estimates for teacher effort, knowledge, and skills in public primary schools, broken down by urban and rural location. For four of the six non-pilot surveys, representative data were also collected for private primary schools. Across the eight surveys, the SDI collected data on 2,600 schools, over 21,000 teachers and 24,000 students.<sup>7</sup>

> 6 The survey in Nigeria covered only four states (Anambra, Bauci, Ekiti, Niger).  The data for Nigeria are therefore not nationally representative, but representative of these four states.  For simplicity we nevertheless refer to this data set as Nigeria.

> 7 See Bold et al. (2017) for details of the sample.

5
br>0.30<br>0.20<br>0.10<br>0.00<br>Market Income plus Disposable Income Consumable Income Final Income<br>pensions<br>Gini coefficient<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal.

Poverty is reduced only slightly when comparing the poverty headcount rate of pre-fiscal income with that of consumable income. However, the Brazilian fiscal system reduces extreme poverty. Extreme poverty based on consumable income is 5.1 percentage points lower than that of MIPP (Table 3). 31F<sup>32</sup> Direct

> 31 The sample includes Argentina, Brazil, Mexico, Costa Rica, Uruguay, Panama, the República Bolivariana de Venezuela, Colombia, Dominican Republic, Ecuador, Chile, Honduras, Peru, El Salvador, Bolivia, Nicaragua, Guatemala, and Paraguay.

> 32 The thresholds for determining whether a household is poor are BRL 499 for moderate poverty and BRL 249.5 for extreme poverty considering the per capita household monthly income.

21