rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9370

# **Abstract**

The 19<sup>th</sup> International Conference of Labour Statisticians (in 2013) redefined labor statistics standards. A major change was to narrow the definition of employment to work for pay or profit. By the revised standards, farming that is only or mainly intended for own use is no longer considered employment, and such a farmer is no longer considered to be employed or in the labor force. This paper analyzes the implications of the revised standards on measures of employment in Sub-Saharan Africa obtained from multitopic household surveys. It shows that, in some contexts, 70 to 80 percent of farmers produce only or mainly for family consumption and are therefore, based on this activity, not considered employed by the revised standards. However, there is wide variation across countries and regions. Moreover, farmers are more likely to report intending to produce for sale at the end of the growing season of the main local crop than earlier in the season. Men are more likely than women to produce for sale. The revised standards lead to significantly lower employment-to-population ratios in rural Africa and change the sectoral composition of the employed population toward non-agricultural sectors. The paper concludes with recommendations for data producers and users.

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 igaddis@worldbank.org and apalacioslopez@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
in Africa and other developing countries.<sup>3</sup> The source of these variables is the World Bank Financial Development and Structure Dataset (Beck and Demirgüç-Kunt, 2009).

# **2.3. Financial Inclusion Measures**

In this study, we employ five measures of financial inclusion. The first is the percentage of adults that have an account at a formal financial institution. The second is the percentage of adults that had a loan from a financial institution in the year prior to the survey. The last three variables are related to the use of mobile telephones in financial transactions. They are the percentage of adults using mobile telephones to send money, to receive money, and to pay bills. We use these variables to explore whether mobile banking has exhibited deeper penetration in Africa than elsewhere.

All variables related to financial inclusion are taken from the World Bank Global Financial Inclusion (Global Findex) Database, which measures how people in 148 countries save, borrow, make payments, and manage risk. This database was recently released and therefore only covers the year 2011.

# **2.4. Explanatory Variables**

In the choice of the explanatory variables for financial development and financial inclusion, we rely on previous studies, in particular those on the finance-growth nexus (e.g., Levine, 2005) and from other studies that analyze the determinants of financial development (e.g., Beck et. al, 2008; Cull and Effron, 2008). These studies regress indicators of financial development on a set of variables that describe the environment in which such development takes place, but that are exogenous to that process such as population size and density, natural resources and a dummy variable for offshore financial centers. They also include per capita income as an exogenous regressor, claiming that its effect on financial development is contemporaneous while the effect of financial development on income is lagged. We expand further the set of regressors by including macroeconomic variables and broad measures of

> 3 For most countries of Sub-Saharan countries, stock exchanges are just a recent phenomenon.  The number of stock exchanges has, in fact, proliferated to over two dozen in the last decade.  While this is encouraging, the stock exchanges (except South Africa) are thin and malfunctioning, although liquidity provision has improved (Senbet and Otchere, 2008).  With more robust stock market development in Ethiopia it would be worth expanding the domain of financial development to include stock markets in future analyses.

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A2.4. South-east Asia

# A2.4.1 Japan

Index: Nikkei 50

||**Tokyo**<br>**Electric**<br>**Power**|**Chubu**<br>**Electric**<br>**Power**|**Kansai**<br>**Electric**<br>**Power**|**Matsushita**<br>**Electric**<br>**Works**|**Mitsubishi**<br>**Electric**|**NTT**<br>**(telecoms)**|
|---|---|---|---|---|---|---|
|Five-year|0.28|0.29|0.34|0.57|0.69|0.62|
|beta|||||||
|1990|0.42|0.52|0.54|0.70|0.89|0.48|
|1991|0.32|0.25|0.37|0.60|0.75|0.65|
|1992|0.26|0.32|0.30|0.53|0.62|0.60|
|1993|0.36|0.30|0.37|0.62|0.65|0.98|
|1994|0.20|0.15|0.17|0.52|0.67|0.68|

# A2.5 **Australia and New Zealand**

# A2.5.1 Australia Index: Australian All-share Index

||**AGL**|
|---|---|
|Five-year beta|0.38|
||_.25_|
||0.39|
||0.28|
||0.28|
||0.45|

# A2.5.2 New Zealand Index: NZ Capital 40 Index

||**Telecom Corp.**|
|---|---|
|Five-year beta|0.97|
|1990|n/a|
|1991|0.52|
|1992|0.60|
|1993|1.20|
|1994|1.06|

_Note:_ NZ Capital 40 Index is not available prior to 1991.

56
Knudsen et al. (2006) review evidence that early life disadvantage translates into cognitive heterogeneity of the adult workforce. Vogl (2012) documents this in Mexico.

Our findings suggest that even a low capacity government can implement a relatively inexpensive program that will cause an important improvement in cognitive skills, given the context of widespread open defecation. The program we study was imperfect: much open defecation remains even after the end of a decade of the TSC. However, open defecation may be so harmful for early-life physical and cognitive development that even an imperfect and incomplete improvement in sanitation can have important effects.

# **References**

- **Adukia, Anjali.** 2013. “Impacts of School Sanitation on Student Outcomes in India: Evidence from a School-Latrine Construction Initiative.” working paper, Harvard Graduate School of Education.

- **Alok, Kumar.** 2010. _Squatting with Dignity: Lessons from India_ , New Delhi: Sage. **ASER.** 2010. _Annual Status of Education Report (Rural) 2009_ : Pratham.

- **Barham, Tania.** 2012. “Enhancing Cognitive Functioning: Medium-Term Effects of a Health and Family Planning Program in Matlab.” _American Economic Journal: Applied Economics_ , 4(1): 245–273.

- **Barnard, Sharmani, Parimita Routray, Fiona Majorin, Rachel Peletz, Sophie Boisson, Antara Sinha, and Thomas Clasen.** 2013. “Impact of Indian Total Sanitation Campaign on Latrine Coverage and Use: A Cross-Sectional Study in Orissa Three Years Following Programme Implementation.” _PLOS ONE_ , 8(8): e71438, August.

- **Beckett, Celia, Barbara Maughan, and et al.** 2006. “Do the Effects of Early Severe Deprivaton on Cognition Persist Into Early Adolescence? Findings from the English and Romanian Adoptiees Study.” _Child Development_ , 77(3): 696–711.

- **Black, Maggie and Ben Fawcett.** 2008. _The Last Taboo: Opening the Door on the Global_

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Table 9: Education <u>policy:</u> school-attendance subsidy <u>(</u> _D_ = 1)

|Year|1990|2000|2010|2020|2030|2040|2050|
|---|---|---|---|---|---|---|---|
|_e_<sup>1</sup><br>_t_|1.000|1.000|1.000|1.000|1.000|1.000||
|_e_<sup>2</sup><br>_t_|0.000|0.127|0.194|0.329|0.455|0.605||
|_λ_<sup>2</sup><br>_t_|3.69|4.29|4.33|4.71|5.09|5.69|6.42|
|_λ_<sup>3</sup><br>_t_|4.52|3.69|4.81|5.30|6.34|7.53|9.19|
|_yt_|2647|2277|2430|2592|2863|3218||
|_Yt_(10<sup>9</sup>)|29.97|36.04|48.49|58.64|68.94|82.70||
|Population (10<sup>3</sup>)|23475|29818|33327|34617|35588|37349||
|Subsidy<sup>_a_</sup>||0.94|1.29|1.82||||

> _<u>a</u>_ in % of the AIDS GDP level without any intervention.

product 0 _._ 5 _· σ_ 90<sup>2</sup><sup>_e_</sup> _t_<sup>2</sup><sup>_N_2</sup> _t_<sup>(lastrowofTable9).Forthepurposesofcomparinginterven-</sup> tions in the two domains, the alternative is to allocate the same stream to measures designed to combat the epidemic, as summarised in the function _qt_ ( _Gt_ ; _D_ = 1), where ( _G_ 100 _, G_ 110 _, G_ 120) is suitably normalised to ( _A_ 100 _, A_ 110 _, A_ 120) in order to allow for the difference in the unit of observation (family and entire cohort of youth, respectively).

As can be seen from a comparison of Tables 7 and 9, the programme of subsidies to secondary education produces a fairly substantial effect on _e_<sup>2</sup> _t_<sup>,andhenceindirectlyon</sup> _λ_<sup>2</sup> _t_<sup>.In2040,theproductivityofbothprimaryandsecondaryschoolgraduateswilllie</sup> almost exactly half way between their respective levels in the reference cases _D_ = 0 _,_ 1. The same holds for GDP per adult. Put somewhat differently, the programme reduces the delay in attaining the counterfactual levels from ten years to five.

If these funds were allocated instead to combating the epidemic, not only would there be a substantial fall in mortality – which is desirable in itself – but the associated improvement in expectations would also promote investment in secondary education. The latter effect is small at first, but then gathers pace, with a startling jump between 40
Inequality among provinces makes a larger contribution to inequality among households in rural China than in urban China. Using the CHIP surveys, Gustafsson et al. (2007b) found that the proportion of household inequality in urban China due to between-province inequality fell from 29 percent in 1988 to 19 percent in 2002. The main gain came from within eastern China, where this more developed economy was becoming more spatially integrated. The contribution of betweenprovince inequality to rural income inequality rose from 22 percent in 1988 to 39 percent in 1995 and remained at 39 percent in 2002 (Gustafsson et al. 2007b). It appears that the initial polarization effects were offset by the spread effects that were created by the growing scarcity of local resources.

# <<A>>Evaluating Inequality

It is well known that absolute income poverty in China has fallen dramatically. For instance, Ravallion and Chen (2007) report that the proportion of households who were under the official absolute poverty line decreased from 53 percent in 1980 to 18 percent in 1988 and to 8 percent in 2001. In each year, the great majority of the poor were rural. However, if poverty is defined in terms of relative income, it did not decrease. For instance, the proportion of households with no more than half of the national median household income per capita edged up from 13.2 percent to 13.3 percent in the five years from 2002 to 2007 (Li et al. 2013). Ultimately, the choice of how to measure poverty requires a value judgment. Concern for income inequality implies the introduction, wholly or partly, of a relative concept. Sen (1983 and elsewhere) has argued that concern for absolute poverty in terms of people’s 13
28

# Figure 10: Balassa-Samuelson Estimation

<!-- Start of picture text --> Balassa-Samuelson Relationship (5-year average 2005 to 2009)<br>Price Level vs. GDP per Capita<br>BMU<br>DNK<br>IRLCHE NOR<br>FIN<br>COG Angola CYPPRT GRCNZLESPITAFRADEUJPNGBRBELSWECANAUTAUSNLDISLUSA LUX<br>KNA ISR<br>VEN PRI QAT<br>ZAR BDILBR SOM NERMWICAFEthiopiaERI MozambiqueTGOMDGGNBSLEGINAFGBFACOMRWAMaliTMPTanzaniaSenegalUgandaNPLGhanaBENKenyaTCDLSOGMBBangladeshCIVHTI STPMRT Z KHMSLBambiaCMRNigeriaSDNKGZUZBDJINICLAOPAKMDAYEMPNGVietnamPhilippinesINDMNGMARCPVSWZFSMHNDBOLPRYLKAIndonKIRSYRGUYBTNIRQ e FJIJORMDVsiaGEOEGYNAMSouth AfricaARMDMACHNTUNALBGTMVUTECUDZABIHTKMSLVVCTPERWSMUkraineMNEMKDColoMHLAZETHATONSRB m BrazilbiaMUSJAMBLZDOMPAROMBWA N IRNSURTurkeyUruguayGABBGRCUBKAZCRIBLRArgentinaLBNMYSChileMLCAGRD e xicoLVARussian FederationLTUPLWPOLHRVATGHUEST N GNQSVKLBYOMNSAUMLCZE T BHRBRBKORSYCSVNTTOBHSTWN HKG SGPMACBRNKWTARE<br>TJK<br>0 1 2 3 4 5<br>Log Relative Per Capita GDP (US=100)<br>Sub-Saharan Africa: Core Comparators<br>Sub-Saharan Africa: Rest Rest of the World<br>Linear fits:<br>All countries Without Sub-Saharan Africa<br>log P = 3.544 + .193 logY log P = 3.05 + .333 logY<br>Specifcation: Rogoff (1996, p. 660).<br>Data: Penn World Tables 7.0 (May 2011), 188 observations, Zimbabwe is excluded.<br>Slope is significantly different at the 95% level.<br>5<br>4.5<br>4<br>3.5<br>Log Price Level of GDP (US = 100)<br>3<br><!-- End of picture text --> Source: Gelb et al, 2013
# **3 Data and spatial methods**

In order to test this model, attention is restricted to a set of 15 _coastal primate_ countries in which the main city is also the main port, so transportation to the primate city is important for trade with both the largest domestic market and the rest of the world (Figure 1).<sup>13</sup> Counterclockwise from the northwest, these countries are Mauritania, Senegal, Guinea, Sierra Leone, Liberia, Cˆote d’Ivoire, Ghana, Togo, Benin, Nigeria, Cameroon, Gabon, Angola, Mozambique, and Tanzania. Further details about all data used are in the Appendix.

## **3.1 City lights**

To date, very little economic data, especially for income and especially as a panel, have been available for individual African cities.<sup>14</sup> In most national household surveys, if any city is individually identifiable, it is only the largest city in a country. The largest program of firm surveys, the World Bank Enterprise Surveys, rarely collects data in more than four cities per African country, and it is typically not clear how the surveyed cities are selected. Censuses often report populations for many cities, but they are almost always carried out at intervals of at least ten years, which limits their usefulness. In order to fill this gap, I propose a novel data source as a proxy for city-level income: satellite data on light emitted into space at night.

Satellites from the United States Air Force Defense Meteorological Satellite Program (DMSP) have been recording data on lights at night using their Operational Linescan System sensor since the mid-1960s, with a global digital archive beginning in 1992.<sup>15</sup> Since two satellites are recording in most years, 30 satellite-years worth of data are available for the 17-year period 1992–2008. Each 30-arcsecond pixel in each satellite-year contains a digital number (DN), an integer between 0 and 63, inclusive, that represents an average of lights in all nights after sunlight, moonlight, aurorae, forest fires, and clouds have been removed algorithmically, leaving mostly human settlements.<sup>16</sup> Figure 2 shows the lights data for one satellite-year for Tanzania. No lights

> 13Five other countries in Sub-Saharan Africa fit this criterion but are not included in analysis. Djibouti, Equatorial Guinea, Guinea-Bissau, and Somalia are excluded because they lack (at least) roads data. Using the city definitions below, The Gambia has only one city, and therefore it provides no information in the presence of country*year fixed effects.

> 14Some administrative data on economic indicators such as employment are collected for subnational regions in some countries, including Tanzania, but assessing their comparability is a challenge, and they are typically only available for large regions and not for multiple years.

> 15These sensors are designed to collect low light imaging data for the purpose of detecting moonlit clouds, not lights from human settlements.

> 16A 30-arcsecond pixel has an area of approximately 0.86 square km at the equator,

8
# **Introduction**

Since the availability of the first COVID-19 vaccines in late 2020, the world has witnessed the largest vaccination effort in history.<sup>1</sup> In early June 2022, 60 percent of the world population had been fully vaccinated for COVID-19. However, large regional disparities in COVID-19 vaccine coverage remain (Figure 1). Sub-Saharan Africa (SSA), in particular, is lagging in its vaccination efforts. As of July 1, 2022, less than 17 percent of the population had received at least two doses. Consequently, all but two countries (the small island states of the Seychelles and Mauritius) in SSA remain adrift of the World Health Organization’s (WHO) goal to fully vaccinate over 70 percent of the African population by June 2022.<sup>2</sup> _Figure 1: Share of the population fully vaccinated over time._ <!-- Start of picture text --> 60<br>Burkina Faso<br>40 Kenya<br>Malawi<br>Nigeria<br>Tanzania<br>Low income<br>Lower middle income<br>20 Sub-Saharan Africa<br>World<br>0<br>1/1/2021 7/1/2021 1/1/2022 7/1/2022<br>Date<br>Source: Our World In Data<br>Percent<br><!-- End of picture text --> A variety of reasons have been cited for why vaccination efforts in SSA are trailing their targets.<sup>3–12</sup> Shortages in vaccine supplies, the accessibility of vaccination sites, and vaccine hesitancy are the main hypotheses discussed to explain the low coverage.<sup>3,13</sup> However, there is a conspicuous lack of systematic, cross-country comparable, and up-to-date evidence on these issues. Using data from five national phone surveys, we provide new evidence on vaccine hesitancy, uptake, last-mile delivery barriers, and possible promoters of vaccine demand in SSA. Our contributions are fourfold. First, unlike many existing studies that examine specific population groups or regions within countries, our data is national in scope and was collected by the respective national statistical agency of each country we study (Burkina Faso, Kenya, Malawi, Nigeria, and Tanzania) with samples drawn from large, nationally representative sampling frames. Second, data collection efforts were harmonized to a high degree between countries. Our insights are therefore cross-country comparable and representative of a population of approximately 369 million people, 2
<!-- Start of picture text --> Non-Revenue Water<br>40<br>3530 -lala oe<br>=wv&525<br>vo 20<br>@5<br>15<br>105 or<br>0)<br>2010 2011 2012 2013 2014 2015 2016<br>= Middle 60% = -==—==Top20% =< Bottom 20%<br><!-- End of picture text --> <!-- Start of picture text --> Staff Productivity<br>250<br>= 200 sssee"*-oorr-” canal<br>a<br>F1502iFSS rrvrvrererwr—s<br>-® 100<br>5 ——"<br>[s) 50<br>ty)<br>2010 2011 2012 2013 2014 2015 2016<br>—— Bottom 20% ———Middle60% === Top 20%<br><!-- End of picture text --> <!-- Start of picture text --> Consumption of Water<br>180<br>160 rien<br>= 140<br>120<br>& 100<br>S 80 =<br>a<br>ao 60<br>=— 40 eee eee eae ew eee ee eee<br>20<br>0<br>2010 2011 2012 2013 2014 2015 2016<br>=——= Bottom 20% ———= Middle 60% —-———=Top 20%<br><!-- End of picture text --> <!-- Start of picture text --> Continuity<br>18<br>16 >oor"<br>14<br>12<br>S 10<br>5 8 oo<br>[-}<br>Doig<br>4<br>eee — -<br>2<br>0<br>2010 2011 2012 2013 2014 2015 2016<br><== Bottom 20% =——=Middle60% === Top 20%<br><!-- End of picture text --> <!-- Start of picture text --> Average revenue<br>0.18<br>0.16 te<br>s3S 0.14 -—<<.-" --""<br>a<br>5 0.12<br><<br>ig<br>E oe<br>5 008<br>= 0.06<br>§ 0.04 oo<br>0.02<br>0<br>2010 2011 2012 2013 2014 2015 2016<br>=—<-= Bottom 20% ———Middle60% === Top 20%<br><!-- End of picture text --> <!-- Start of picture text --> Operational Cost Recovery<br>g 25<br>oi<br>a 2 sS<br>3 ~e<br>s ween ee eee<br>£15<br>& —<br>T4 ennoes 5<br>5 wmmen,<br>2os<br>g<br>eat 0<br>o 2010 2011 2012 2013 2014 2015 2016<br>a<br>°<br>=—=_= Bottom 20% ———Middle60% === Top 20%<br><!-- End of picture text --> <!-- Start of picture text --> Unit Costs<br>0.14<br>3 0.12<br>3<br>38 0.1 —<br>a --"<br>icJ5 0.08 ae”<br>2 0.06<br>Ss<br>E 0.04<br>$=0.02 =<br>0<br>2010 2011 2012 2013 2014 2015 2016<br>——=Bottom 20% =——=Middle 60% ——=Top 20%<br><!-- End of picture text --> <!-- Start of picture text --> Collection Rate<br>100<br>90 —_ ese<br>#0 ee —<br>70<br>%<br>gio@ 6050 ---o"- -a<br>2<br>ao 40<br>30<br>210<br>ty)<br>2010 2011 2012 2013 2014 2015 2016<br>—— Bottom 20% ——=Middle 60% ——=Top 20%<br><!-- End of picture text -->
|**Upazila Name**|**Number**<br>**of**<br>**Villages**|**HH on**<br>**the**<br>**validation**|**Prevalence**|**Average**<br>**prevalence**<br>**within**<br>**villages**|**Number of**<br>**villages**<br>**with 0%**<br>**prevalence**|
|---|---|---|---|---|---|
|Chakaria|6|1,630|1.10%|0.54%|4|
|Cox's Bazar Sadar|10|4,221|2.99%|2.03%|4|
|Pekua|2|501|0.00%|0.00%|2|
|Ramu|3|508|2.95%|3.16%|1|
|Teknaf|7|2,830|1.41%|1.36%|3|
|Ukhia|5|1,467|0.14%|0.08%|4|
|Total|33|11,157|1.80%|1.30%|18|

**<u>Source:</u>** <u>Validation household listing, 2018</u>

This two-step data collection on Rohingya refugee prevalence confirmed that prevalence in host communities was low, and that this was the case not only for newer Rohingya displaced, but for the older cohort of displaced, as well. These figures could still to some extent reflect the displaced population’s mobility, as well as Rohingya’s unwillingness to reveal their identities to surveyors. However, the design of the validation listing questionnaire sought to minimize this effect by asking a series of questions that related to how long the household that been resident in the host community, rather than direct questions on respondents’ selfreported identity as Rohingya. Based on the validation findings, the final decision taken was to use administrative population data available from IOM NPM.

This pattern of refugee prevalence supported having one stratum for the Rohingya displaced living in camps. The sampling strategy for the CBPS therefore focused on generating representative estimates for the camp based Rohingya population in Cox’s Bazar district.<sup>15</sup>

# _Stratification of the host population in Cox’s Bazar district_

For hosts, the sampling strategy was designed to account for the differential implications of a camp-based concentration of close to a million Rohingya displaced for different areas of Cox’s Bazar. As in other displacement settings, the welfare among members of the host population is likely to vary with distance to camps. Hosts living closer to camps may be more likely than those living farther away from camps to compete with the Rohingya in labor and goods markets, as well as markets for natural resources like land and fuelwood. On the other hand, host households closer to camps may gain from camp-based economic opportunities,

> 15 While the original sampling strategy was designed to be representative of all camp-based Rohingya displaced, campsites with older Rohingya displaced refused to participate in the listing due to other political sensitivities. This refusal was maintained despite many attempts. Since the older Rohingya displaced were not a separate stratum, a decision was made to drop these households from the survey.

9