rafmacalaba/data-use-mentions-extended huggingface dataset:
Formal Income" is defined to include wages and salaries, pensions and benefits, rental income, loan income, and aid. "Informal Income" is defined to include income from selling agricultural products and raw materials, income from selling non-agricultural individually made products, income from individual services, profit from trade, and other income. Note that this definition of informal income is different from that of "individual activities" used by the SSO in publications.

23.The reference to "unemployed people" in this section refers to "number of unemployed people whose age is possible for labor," question number 23 on the SSO Household Survey.

24.But as the latter estimate includes moonlighters, some of those counted here as "engaged in informal activity" are already considered employed in the official statistics. Excluding moonlighters and pensioners, it is estimated that informal employment is between 16% and 26% of Ulaanbaatar's officially recorded employment.

# 25.These estimates excluded pensioners.

26.1t should be noted that if one adjusted the estimate for taxicabs using the procedure outlined in Appendix B, then the estimated number of not-employed informals per employed informal would be higher. The range would be between 3 and 6 not-employed informal per employed informal, rather than the more conservative 2 to 6 presented in the text.

27.That is, it was assumed that each of the three types of informals surveyed is representative of some proportion of the population of informals in Ulaanbaatar. If each makes up no less that 20% or the population, then the range of possible values for the true mean of the overall population becomes 3 to 4.6.

28.One may argue that some of those seventeen thousand people are in fact not participants of the informal sector. However, several points need to be made. First, many studies in the literature on the informal sector consider the legal form of organization to be the most important defining characteristic of the informal sector; non-corporate forms of organization, for which there is a legal
Figure 3: One-third of adults living in agricultural households have an account <!-- Start of picture text --> Adults living in a household where growing crops or raising livestock is the main source of<br>household income (%), 2017<br>Congo, Dem. Rep. |<br>RN<br>ai EE<br>a1NN<br>Mozarique|<br>0% 10% 20% 30% 40% 50% 60%<br>@ Has account © Does not have account<br><!-- End of picture text -->

Source: Global Findex database. Note: The total length of the bar represents the share ofadults living in a household where growing crops or raising livestock is the main source ofincome.
corporate governance guidelines and incorporates global emerging trends in corporate governance.<sup>95</sup>

# _Property Rights_

PEVC investors face due diligence challenges in Kenya when evaluating investment opportunities, particularly related to property. Property due diligence in Kenya is generally undertaken through title searches at the lands registry. However, land registries records in Kenya are in a precarious state, and there are documented irregularities which make it difficult to establish legitimate ownership; and ascertain if planning and environmental laws have been complied with, if diligenced properties are subject to multiple title allocations, or whether the records have been forged. On the plus side, most public services in Kenya are now available online, which has made service times more efficient as well as less susceptible to corruption.

# _Intellectual Property (IP)_

PEVC investors in Kenya are assured of international standards of intellectual property (IP) rights protection because Kenya is a signatory to international IP treaties. Kenya is a member of the World

> 92 For example sections 22, 63, 393, 407 on changes to a company that require special resolutions, sections 398 and 780 on the rights of a member to make an application to court, section 34 on the right of a member to bring proceedings to restrain the acts of a director that are beyond the directors’ power, section 277 on the rights of members to require directors to convene a general meeting and section 23 on the effect of an amendment to the articles of association on a member.

> 93 The underlying principle under the Companies Act is that the affairs of a company are managed by its board of directors and that a majority of shareholders can pass most resolutions required to be passed by shareholders. 94 “Securities” is defined in the CMA as including “debentures, shares, bonds, commercial paper, or notes issued or proposed to be issued by a body corporate”.

95 The Code is predicated on an ‘apply or explain’ principle. As such the portfolio companies isuing securities are required to either apply
||2.5|Closure+|
|---|---|---|
|Non-Tradable|19.3|Additive of NTP|
|LFPR, Pay-Gap and Sectoral|11.9<br>9.6|Closure<br>Closure+|
||23.6|Closure|
|Complete Convergence|22.1|Closure+|
||45.5|Additive of NTP|

_<mark>Notes:</mark>_ <mark>Closure represents complete convergence of the respective gap. Closure+ represents</mark> appreciation/depreciation is applied to wages due to the displacement of workers between sectors. SSA and OECD stands for closure of the gap between Liberia and the averages found in SubSaharan Africa and OECD countries, respectively. Additive represents the additional value added by the non-tradable sector. The “additive of NTP” presented in the “Complete Convergence” section is the sum of the closure scenario estimate and the value of NTP.

# **5.1 The Status Quo: Women’s Economic Contribution in 2020**

Liberian women contributed an estimated USD 1.08 billion to the Liberian economy in 2020 (Table 2). The estimate is derived from demographic, educational, and economic parameters. According to mediumvariant UN projection, the population of Liberia was 5.06 million individuals. The working age (20-65) population was 45.4 percent of the entire population and had an average educational attainment of 4.6 years. Economically, GDP was USD 4.05 billion, GDP was USD 800, and per-worker GDP was USD 2,125 in 2020. The additive economic contribution of natural resources is estimated at USD 1.27 billion, or 31.4 percent of GDP. Women’s contribution represented 26.8 percent of GDP and 39 percent of labor’s contribution to GDP. These initial estimates are referred to as the “status-quo” that will serve as the benchmark for the
facilities, cooking fuel, or assets of the household. Individual questionnaires record information on a range of health-related issues such as fertility, family planning, reproductive health, or nutrition. Data files follow a standardized format to maximize comparability across surveys . In contrast to HCES surveys, DHS does not collect information on HHs’ income, consumption, or expenditure. The welfare variable that DHS provides is aggregated based on the various assets that HHs own (Rutstein 2015).<sup>9</sup>

## 3.2.3 Prindex

We also draw on Prindex data which offers comparable data on perceived tenure security – a variable usually unavailable in most multi-purpose surveys. Prindex is a joint initiative of the Overseas Development Institute (ODI) and Global Land Alliance (GLA), carrying out nationally representative surveys of adults in 140 countries measuring how secure individuals feel on their property. In addition, Prindex collects data on a range of demographic and socioeconomic characteristics of respondents, and on land-related variables that may influence the perception of tenure security such as documentation or ownership status. Prindex collects data at individual level and not at household level, therefore, aiming at capturing a fully representative and comparable assessment of individual perceptions on tenure security, not just the heads of households who are most likely to hold official titles.

We impute perceived tenure security reflected in Prindex into HCES based on three variables that are common across both surveys: income (quintiles), location (rural / urban), and ownership of the property (own / rent / other). In both surveys, we assign HHs into groups based on these three variables. We then include the average perceived tenure security of that group to the respective ownership variable available in HCES. As a result, we obtain a tenure security variable at household level that includes tenure perception provided by Prindex and ownership status provided by the most recent household consumption and expenditure survey.<sup>10</sup>

# **_<mark>3.3 Summary Statistics
<!-- Start of picture text --> GVC backward 859.70 64.32<br><!-- End of picture text --> Figure 2: Replication of World Bank WDR 2020 Figure 1.13.

**Figure 1.13** GVCs expanded in both the agriculture and food industries from 1990 to 2015

<!-- Start of picture text --> a. Agriculture GVCs b. Agri-food GVCs<br>60 90<br>80<br>50<br>VNM RWA GHA 70<br>40 ETH KEN 60 HUN TZA<br>50 SRB<br>30 LAO GMB<br>40<br>SSD BGR<br>20 LBN 30 ETH<br>YEM 20<br>10<br>10<br>0 0<br>0 10 20 30 40 50 60 0 10 20 30 40 50 60 70 80<br>Agriculture GVC participation, 1990 (%) Agri-food GVC participation, 1990 (%)<br>East Asia and Pacific Europe and Central Asia Latin America and the Caribbean<br>Middle East and North Africa North America South Asia<br>Sub-Saharan Africa<br>Agriculture GVC participation, 2015 (%) Agri-food GVC participation, 2015 (%)<br><!-- End of picture text --> Figure 3: Replication of World Bank WDR 2020 Figure B2.1.1.

<!-- Start of picture text --> a. Backward integration of<br>electronics and machinery as a<br>share of gross exports<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2000 2005 2010 2015<br>Percent<br><!-- End of picture text --> Throughout the WDR 2020, several figures on GVC-related trade at
Policy Research Working Paper 9466

# **Abstract**

The coronavirus disease 2019 (COVID-19) and the attempts to limit its spread have resulted in profound economic impacts, and a significant contraction in the global economy is expected. This paper provides some of the first evidence on the socioeconomic impacts of and responses to the pandemic among households and individuals in Sub-Saharan Africa. To do so, reduced-form econometric methods are applied to longitudinal household survey data from Ethiopia, Malawi, Nigeria, and Uganda—originating from the pre-COVID-19 face-to-face household surveys and from the novel phone surveys that are being implemented during the pandemic. The headline findings are fourfold. First, although false beliefs about COVID-19 remain prevalent, government action to limit the spread of the disease is associated with greater individual knowledge of the disease and increased uptake of precautionary measures. Second, 256 million individuals—77 percent of the population in the four countries—are estimated to live in households that have lost income due to the pandemic. Third, attempts to cope with this loss are exacerbated by the inability to access medicine and staple foods among 20 to 25 percent of the households in each country, and food insecurity is disproportionately borne by households that were already impoverished prior to the pandemic. Fourth, student-teacher contact has dropped from a pre-COVID-19 rate of 96 percent to just 17 percent among households with school-age children. These findings can help inform decisions by governments and international organizations on measures to mitigate the effects of the COVID-19 pandemic and reveal the need for continued monitoring.

This paper is a product of the Development Data Group, Development Economics. 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 tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas
experimental study, Abay et al. (2021) show that delaying a module on dietary diversity by 15 minutes led to underestimation of the dietary diversity score due to respondents’ fatigue in a phone survey in Ethiopia.

Recall length also contributes to respondent burden and has been found to affect data quality (Beegle et al., 2012a; Wollburg et al., 2020). As discussed in the previous section, phone surveys can be used to shorten recall periods through higher frequency data collection (Arthi et al., 2018; Gaddis et al., 2021). Shorter lags between survey rounds tend to also lower the chances that respondents attrite (Gourlay et 9 Households earning between 0% and 30% of income from agriculture are excluded for presentation purposes. Only countries in the RuLIS database with data on adult literacy rates since 2010 are included. The year of data collection by country is as follows: Armenia - 2013; Bangladesh - 2010; Burkina Faso - 2014; Cameroon - 2014; Ecuador - 2014; Ethiopia - 2016; Ghana - 2013; Guatemala - 2014; India - 2012; Iraq - 2012; Kyrgyzstan - 2013; Malawi - 2017; Mali - 2017; Mexico - 2014; Nepal - 2011; Nicaragua - 2014; Niger - 2014; Nigeria - 2019; Pakistan – 2014; Peru - 2019; Rwanda - 2014; Senegal - 2011; Sierra Leone - 2011; South Africa - 2015; Tanzania - 2015; Uganda - 2016.

19
As Table 7 shows, none of the explanatory variables discussed earlier contribute significantly to explaining urbanization. In particular, initial education does not explain differences across countries in the rate of urbanization. Nor do our urban bias measures (CEREAL or BMP), the crop shock variables, or the food aid data. We do find that SIVARD is significantly negative, indicating that civil strife reduces the growth rate in urbanization (the opposite sign from what we expected). The coefficient on the democracy variable is also significantly negative, suggesting that democracies tend to urbanize less rapidly than non-democracies, all else constant.

Our sample includes developed countries with a mature urban system, which have essentially stopped urbanizing. We checked whether any of our results changed if we excluded them. The results were generally robust to the change in samples (although the coefficient on cereal food-aid became significantly negative).

# Conclusion

Urbanization levels are closely correlated with levels of income. But changes in income do not explain changes in urbanization. Urbanization continues even during periods of negative growth, carried by its own momentum, largely a function of the level of urbanization. From that point of view, Africa's urbanization without growth is not a puzzle.

The finding of a strong positive association in levels between urbanization and income, combined with the absence of such a relationship in changes should be related to similar results obtained by Easterly (1999) on the relation between income and quality of life indicators. Easterly speculates on this patterns of results as (i) the long and variable lag that comes between growth and changes in the quality of life, (2) the possibility that global socio-economic progress is more important than home country growth for many quality of life indicators. Similar explanations could be applied to urbanization, notably the possibility that with globalization comes and inherent push for urbanization.

Factors other than income that help predict differences in _levels_ of urbanization across countries include: income structure, education, rural-urban wage differentials, ethnic tensions and civil disturbances. Also, the relationship between
private returns to capital in emerging countries, more capital flows into these economies to finance the expansion of the government as well as the private sector. As a result,

> 29 Simulations on infrastructure spending in this paper use the findings of a recent study that used panel data for 88 countries to determine that output rises by 0.8 percent for each 10 percent increase in infrastructure stock (Calderón, Moral-Benito, and Servén 2011).

18
role of interest rates in the aftermath of large devaluations that result in an undershooting of the real exchange rate. They ask whether high interest rates following a devaluation increase the likelihood that real exchange rate equilibrium is restored through a nominal appreciation rather than through higher inflation. They find that high interest rates are effective in this sense only in countries with strong banking sectors. Furman and Stiglitz (1998) examine daily data on interest rates and exchange rates in a sample of nine developing countries during the 1 990s to identify episodes of sustained high interest rates, and then ask whether these were followed by an appreciation of the domestic currency. They find little evidence that this is the case. In contrast Dekle, Hsiao and Wang (1 999a,b) study the relationship between interest rates and exchange rates using weekly data for Korea, Malaysia and Thailand during 1997 and 1998, and argue in favour of the conventional view. The main difficulty with all of these papers is that they simply document reduced-form (partial) correlations between interest rates and exchange rates. Without controlling for the endogeneity of the monetary policy response, it is difficult to infer anything regarding the effects of high interest rates from these papers. This paper makes a first attempt to take seriously the identification problem, drawing on a much larger sample of successful and failed speculative attacks.<sup>5</sup> The remainder of this paper proceeds as follows. In Section 2, I describe the data and the methodology used to identify successful and failed speculative attacks. In Section 3, I present some descriptive results, which provide scant evidence of any association between changes in interest rates and the outcome of speculative attacks. In Section 4, I develop a simple model to illustrate the endogeneity problem, and I use this to motivate a set of probit regressions expressing the probability that speculative attacks fail as a non-linear function of policies and fundamentals. After instrumenting for the endogeneity of policy, I again find no evidence of a significant impact of high interest rates on the
# 4. Methodology

## 4.1 Data

Public procurement data underlying the analysis must be high quality, wide scope and high resolution, including information on individual items purchased and their unit prices. We obtained the data sets directly from the official data holders such as national public procurement authorities. Data sets contained structured information on tendering processes such as procedure type used, tendering outcomes such as number of received bids, and information of actors such as buyer and bidder names. More on indicators in section 4.2. Given our goal of looking at health care markets across countries, the biggest challenge of database building was to harmonize data sets coming from different legal and IT systems, for example by matching product classifications from different countries.

When compiling the data set for the analysis, we started from the full public procurement data sets in each country and territory: we compiled a data set of 7 countries and 2 territories (2 states of Brazil). As a first step of database creation, we selected all tenders which relate to the health care sector in the source data sets (Table 2). Second, we narrowed down the list of selected health care-related tenders to those which belong to an overlapping product category across all 9 data sets. This harmonization process resulted in a considerable reduction of our database size from about 128,000 tenders to 36,611 tenders, basically due to incommensurate national product classifications at the very detailed level we needed for the unit price analysis. The data was available to the research team largely for the period between 2012 and 2018, with considerable differences across countries. While each data set was defined on the level of item or purchase, in one country data structure differed. Here, the unit of observation was purchase orders within framework agreements which had to be aggregated to unique items within tenders to make them comparable to the other data sets.

11
_PROGRESA_ was first implemented during the first half of 1998 in 320 randomly assigned rural villages out of 506 villages pre-qualified for immediate participation.<sup>20</sup> The remaining 186 villages were assigned as control villages and received their first monetary transfers in December 2000. Implementation of the program in a phased manner and random selection of program villages for the first phase of implementation were extremely important factors aiding rigorous evaluations of the program.<sup>21</sup> The baseline survey ( _Encuesta Évaluation de los Hogares_ or ENCEL) for _PROGRESA_ was collected in 1997 in all 506 pre-qualified villages, covering 24,077 households in seven states.<sup>22</sup> _PROGRESA_ administrators ran a follow-up survey over the same set of households once every six months, generating a rich household panel dataset. Many evaluations of _PROGRESA_ were conducted on the basis of these surveys, including the previously mentioned studies by Skoufias and Parker (2001), Parker and Skoufias (2000), Schultz (2004), Todd and Wolpin (2006), Attanasio et al. (2005), and Coady (2004). Overall results show an increase in the enrollment rates of both boys and girls at the primary and secondary levels, a reduction in the incidence of illness among children of age 0-5 years, an increase in the annual growth rate of children of age 12-36 months and a reduction in all poverty indices. _PROGRESA_ (Opportunidades) is now covering around 5 million households (18 percent of the country’s total population) with a budget equivalent to 0.4 percent of GDP, contributing around 20 percent of the income of participating families.

In 2003, Ecuador updated _Bono Solidario_ , its existing social program focusing on poverty reduction that had been created in 1999. The new program, _Bono de Desarrollo Humano_ (BDH), is part of the Social Protection Plan of the _Ministerio de Bienestar Social_ and re-targeted the transfers under the erstwhile _Bono Solidario_ program. While the main purpose of BDH is to transfer