rafmacalaba/datause-encoder-data huggingface dataset:
# 3.3 Speci...c Program Information

In addition to administrative data, we gathered information from the Ministry on implementation and characteristics of each school participating. The dataset includes information for each full-time school on the year of conversion to FTS (or creation for new schools), the department and school number, as well as school characteristics and the main reason why the school was chosen for conversion. This information was complemented with informal conversations with ANEP’s sta¤ involved in the implementation of the program.

# 3.4 Summary Statistics

As explained above, the full-time school program targeted disadvantaged schools, which implies that full-time and non-full time schools had di¤erent characteristics at baseline. Table 3 presents summary statistics for key variables in 1996, broken down for schools that did not convert to FTS (columns 1-3), and schools that converted to FTS after 1996 (columns 4-6). The variables include test scores, teacher experience, school characteristics and household characteristics including size and parental education. Indices were constructed for “school equipment”and “buildings”using factor components analysis.<sup>5</sup> Column 7 presents t-statistics for the null hypotheses that the mean of the variables for the full-time schools is the same as for non-full time schools.

The two groups of schools di¤er on a number of di¤erent dimensions. First, full-time schools scored signi...cantly lower than other schools on the 1996 achievement tests, and the di¤erence is substantial (about 0.3 of a standard deviation). Second, full-time schools have signi...cantly worse socio-economic indicators than non-full time schools, including parental education and household size. Third, there are notable di¤erences at the school level: full-time schools had worse infrastructure, but better school equipment and smaller class sizes at baseline. Teacher experience does not seem to di¤er signi...cantly. These di¤erences were expected given that the program was targeted towards worse-o¤ schools. These di¤erences persist if we restrict the sample to schools that were tested both in 1996 and 2002.

> 5 The underlying variables were extracted from the 1996 director’s questionnaire, and include the availability of teaching materials (television, video, overhead projector, music) for the factor “equipment” and auxiliary buildings (library, lab, audio-visual room and computer room) for the factor “buildings”. Finally, the variables household size, household number of rooms and parental education try to capture the socioeconomic characteristics of the household and were constructed directly from parental questionnaires.

7
|_Table 7b_**_con_**|**_tinued:33%_**|_replac_|_emen_**_t_**<br>_ra_|_e_||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||I<br>d|Y =0%||l|y=Y<br>- li)|<br>_|d|Y = 1.5|||dY= 2|%|
|Contribution|rate|18|21|25|18|21|25|18|21|25|18|21T|<br>25|
|||Deat<br>Iage_|||||||||||_|_|
|Real i=4%||||__|___|||||||||
|Retire at 55|80|9|10|1<br>-|lO|1||13|11|12|14|12|13|15|
||81|9|_10|11|I1|12|13|12|13|14|13|14|15|
||83|9|_10_|12|11|12|14|12|13|sS|_13_|14|16|
|,|85|10|10|12|11|12|14|13|13|15|14|15|16|
|Retire at 60|80|6|7|8|7 ||<br>8|9|8|9|10|9|10|11|
||81|6|7|8|3|8|9|8|9|10|9|10|1|
||83|7|7|8|8|9|10|9|10|11|10|10|12|
|__________|85|7|7|9|8|9|10|9|10|11|10|11|12|
|Retire at 65|80|4|4|5|5|6|6|6|6|7|7|7|8|
||81|4|5|5|5|6|6|6|6|7|7|7|8|
||83|5|5|6|6|6|7|7|7|8|7|8|8|
||85|5|5|6|6|7|7|7|7|8|8|_8_|9|
|Retire at70|80|3|3|3|3|4|4|4|4|5|4|5|5|
||81|_3_1|<br>3|3|4|4|4|4|4|5|5|5|5|
||83|3|4|4|4|4|5|5|5|S|5|5|6|
||l85|3|4|4<br>_4|||5|5|5|6|6|6|7|

35
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five of the six countries had made a major change in the design of the module collecting consumption data in their surveys.<sup>3</sup> Only South Africa had not changed the consumption survey design across the two years in the database, although they did in 2005. As we will show in this paper, these design changes can result in large changes in both mean consumption and distributional measures.

Our findings come from a study specifically intended to provide more comprehensive evidence on the sensitivity of consumption estimates to varying survey designs. We developed eight alternative consumption questionnaires that were randomly distributed across 4,000 households surveyed in Tanzania. These eight designs all represent common approaches to consumption measurement that have been implemented in numerous settings. We consider the various strengths and weaknesses of each approach to survey-based consumption measurement, including the types of errors that are likely to be more prominent. While in this experiment there are no validation data available for the study households, one resource intensive variant – a personal consumption diary with intensive and frequent supervision – is believed the most accurate and in many ways represents a “gold standard” for field based consumption measurement in survey format. We investigate how alternative survey designs compare with our benchmark, with a focus on the patterns of over or under-reporting of consumption expenditure with respect to household characteristics, and poverty and inequality measures.

The paper is organized as follows. A review of related literature is presented in Section 2. Section 3 describes the experiment, the Survey of Household Welfare and Labour in Tanzania, and construction of the consumption aggregates. Section 4 discusses the results. We compare estimates of total and component consumption for each of the modules, explore how household characteristics affect reporting, and consider the impact of alternative survey designs on inequality and poverty measures. Section 5 discusses the resource requirements for each survey design. The final section reviews the lessons learned herein.

> 3 The variation in the type of consumption survey for these countries included: bounded recall and usual consumption (5 surveys Côte d`Ivoire), bounded recall and usual consumption (3 surveys in Ghana), bounded recall and diary (2 surveys in Malawi), usual consumption and diary (3 surveys in Tanzania), and bounded recall and diary (2 surveys in Zambia). The terms bounded recall, usual, and diary are explained in the next section.
|**Neighborhood Conditions &**<br>**Locations**||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Considers safe|1.196|0.077|0.006|0.917|0.069|0.250|1.071|0.112|0.509|1.377|0.104|0.000|0.886|0.067|0.108|
|Public transport|1.470|0.133|0.000|0.578|0.063|0.000|0.741|0.114|0.051|1.398|0.187|0.012|1.281|0.132|0.016|
|No flooding|1.259|0.091|0.001|1.123|0.104|0.211|1.186|0.150|0.175|1.020|0.088|0.814|1.213|0.097|0.016|
|Informal/slum|0.926|0.092|0.440|0.630|0.073|0.000|0.712|0.121|0.046|0.713|0.084|0.004|0.614|0.070|0.000|
|Informal elec. connections in Nhood|1.382|0.191|0.020|||||||||||||
|**City (Base: EMBU)**||||||||||||||||
|Eldoret|1.520|0.313|0.042|2.271|0.451|0.000|0.421|0.134|0.006|7.645|3.583|0.000|0.674|0.151|0.078|
|Garissa|4.187|1.037|0.000|1.317|0.259|0.162|0.070|0.028|0.000|9.755|4.561|0.000|0.492|0.128|0.006|
|Kakamega|0.607|0.115|0.008|0.560|0.110|0.003|0.858|0.222|0.554|0.918|0.546|0.886|0.726|0.153|0.130|
|Kericho|1.056|0.200|0.776|4.512|0.960|0.000|0.147|0.049|0.000|17.121|8.129|0.000|0.358|0.080|0.000|
|Kisumu|1.528|0.356|0.069|0.290|0.069|0.000|0.403|0.157|0.020|15.492|7.991|0.000|0.912|0.224|0.709|
|Kitui|1.553|0.425|0.107|0.428|0.118|0.002|0.203|0.075|0.000|2.925|1.934|0.104|0.504|0.136|0.011|
|Machakos|1.982|0.506|0.007|0.143|0.043|0.000|0.913|0.346|0.810|18.535|8.719|0.000|0.756|0.215|0.325|
|Malindi|1.794|0.390|0.007|0.850|0.207|0.504|0.532|0.199|0.092|26.261|12.519|0.000|1.430|0.371|0.168|
|Mombasa|4.658|1.064|0.000|0.398|0.110|0.001|0.623|0.195|0.131|79.443|36.976|0.000|0.675|0.173|0.127|
|Nairobi|2.363|0.594|0.001|0.349|0.080|0.000|0.144|0.042|0.000|88.220|40.464|0.000|1.829|0.447|0.013|
|Naivasha|6.778|1.467|0.000|0.266|0.068|0.000|0.238|0.085|0.000|56.055|26.206|0.000|0.500|0.128|0.007|
|Nakuru|1.649|0.370|0.026|0.612|0.136|0.027|0.431|0.127|0.004|131.096|59.161|0.000|0.425|0.096|0.000|
|Nyeri|1.282|0.264|0.227|0.505|0.099|0.000|0.376|0.097|0.000|9.219|4.368|0.000|0.861|0.177|0.465|
|Thika|2.927|0.795|0.000|1.074|0.234|0.743|0.290|0.092|0.000|6.140|2.935|0.000|6.443|1.532|0.000|
|**Tenure**||||||||||||||||
|House secure|1.421|0.106|0.000|1.083|0.095|0.365|1.168|0.146|0.214|0.884|0.076|0.155|1.264|0.110|0.007|
|Stay in unit|1.000|0.000|0.206|1.000|0.000|0.352|1.000|0.001|0.739|1.000|0.000|0.742|1.001|0.000|0.002|
|Renter (vs. owner)|3.738|0.470|0.000|0.529|0.068|0.000|0.837|0.217|0.494|2.274|0.311|0.000|4.532|0.625|0.000|
|**Constant**|0.017|0.004|0.000|0.069|0.019|0.000|0.000|0.000|0.000|0.001|0.000|0.000|0.003|0.001|0.000|
|Number of Obs.||10,982|||11,317|||11,137|||11,137|||11,137||
|R-squared||0.330|||0.371|||0.621|||0.317|||0.384||

29
13 for amenities that are correlated with the island’s economic development, for example, security, transportation, and infrastructure, which are hard to measure directly. ln(Real Exchange Rate) captures the relative price of different destinations and hence the parameter measures the price sensitivity of tourism demand. We note that both these variables perform the valuable function of controlling for macroeconomic shocks.

Since we want to eliminate bias due to unobserved country characteristics being correlated with the regressors of interest, we choose a fixed effects specification, where the country-fixed effects are denoted by _ci_ . They are intended to pick up unobserved effects including, among other things, government policy towards tourism, multiple measures of natural “sun, sea and sand” amenities (for example, opportunities for scuba diving), and attractiveness of the island’s unique culture. In fixed-effects models, the unobserved effects may be correlated with other regressors without inducing bias. Further, we include year fixed effects _dt_ in order to control for common shocks that may be, for example, weather related or changes in economic and non-economic characteristics of American tourists. Finally _uit_ encompasses unexplained tourism shocks. We presume these shocks to have constant variance and to be cross-sectionally and serially uncorrelated conditional on the regressors.

# **4.2 Data**

The Caribbean Tourism Organization tabulates time series on tourist flows by destination and origin from the statistical and tourism offices of its member states in the annual _Caribbean Tourism Statistical Report_ . The CTO has adopted the international definition of “tourist” advocated by the World Tourism Organization of “Any person visiting a country other than that in which they normally reside, for not more than one year, and whose purpose of visit is leisure, business (including meetings, study and research) health treatments, religious pilgrimages, and aircraft and ship crew stayover.” Most statistics are obtained by analysis of embarkation / disembarkation cards completed for the national immigration authorities. In cases where a tourist card is not required because travel is consider domestic (e.g. US to Puerto Rico or US Virgin Islands), greater reliance is put on the analysis of hotel registrations. We focus on stayover tourists which is the larger and more lucrative market.
Policy Research Working Paper 10189

# **Abstract**

This paper uses high-frequency nighttime time lights data to estimate the impacts of the Covid-19 crisis on economic activity during the first year of the pandemic for a global sample of 2,800 cities, covering a total population of 2.5 billion people. Activity is found to be negatively affected by both the spread of the virus and the imposition of nonpharmaceutical interventions, but the negative impacts of the spread are large compared to those of nonpharmaceutical interventions. Large differences in city trajectories are also observed. Cities in low- and middle-income countries faced a significantly larger overall loss of economic activity compared to those in high-income countries. Additionally, cities with higher population densities are found to be more resilient in the face of the global shock as compared to less dense ones, but this difference is only observed in low- and middle-income countries. Taken together, the findings suggest that the Covid-19 crisis gave rise to divergence in urban economic trajectories, both across and within countries.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice and the Water Global Practice. 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 akhan31@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
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 4.2  Benchmarking ICT infrastructure within ECOWAS**

|Access|Benin|Burkina Faso|Cape Verde|Côte d'Ivoire|Gambia,|Ghana|Guinea|Guinea-Bissau|Liberia|Mali|Niger|Nigeria|Senegal|Sierra Leone|Togo|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Broadband||||||||||||||||
|subscribers (per<br>100 inhabitants)|0.02|0.01|0.78|0.11|0.02|0.06|0.00|0.00|0.00|0.03|0.00|0.00|0.34|0.00|0.08|
|Coverage of<br>mobile network<br>(% of population)|43.0|60.3|80.8|57.2|60.0|73.0|40.0|60.0|40.0|40.0|45.0|71.0|85.0|73.0|80.0|
|International<br>Internet<br>bandwidth (per<br>capita)|17.2|16.4|49.5|42.0|19.9|21.2|0.3|1.2|0.1|12.2|2.4|4.6|151.7|0.1|28.1|
|Internet<br>subscribers (per<br>100 inhabitants)|0.10|0.14|1.54|0.08|0.28|0.15|0.13|0.04||0.13|0.03|0.34|0.28|0.02|0.26|
|Mobile telephone<br>subscribers (per<br>100 inhabitants)|20.1|10.9|31.0|36.6|33.4|32.4|14.6|19.2|15.0|20.5|6.3|26.5|36.8|13.2|18.1|
|Price of a three-<br>minute call to<br>United States<br>(US$)|1.06|1.22|3.61|0.91|1.85|0.44|4.61|||1.73|1.93|0.28|1.03||2.43|
|Price of the<br>fixed-line<br>monthly phone<br>basket (US$)|5.34|12.00|8.02|25.02|4.00|6.33|7.10|||11.01|12.64|7.39|10.48|2.11|13.86|
|Prepaid mobile<br>monthly basket<br>(US$)|10.4|15.5|12.9|12.9|6.9|5.7|3.8|11.3||14.7|15.0|16.3|9.1|19.4|16.0|
|Price of 20- hour<br>Internet basket<br>(US$)|43.1|75.0|44.2|47.8|17.8|9.4|26.8|74.9||28.4|51.2|118.9|25.8|9.9|20.3|

Source: Ampah and others 2009.

57
- 77 -

# REPERENCES

Ahluwalia, M.S. (1978), "Rural Poverty and Agricultural Performance in India", Journal of Development Studies, Vol. 14(3), pp. 298-323.

(1985), "Rural Poverty, Agricultural Production and Prices: A Re-examination," in Mellor and Desai (eds.).

- Bardhan, P.K. (1970), "On the Minimum Level of Living and the Rural Poor", Indian Economic Review, Vol. 5, pp. 129-136.

(1973), "On the Incidence of Poverty in Rural India in the Sixties", Economic and Political Weekly, Annual Number.

(1974), "Poverty and Income Distribution in India: A Review" in Bardhan, P.K. and T.N. Srinivasan (ed.), Poverty and Income Distribution in India, Statistical Publishing Society, Calcutta, pp. 264-280.

- Bhalla, S. (1987), "Trends in Employment in Indian Agriculture, Land and Asset Distribution", Indian Journal of Agricultural Economics, Vol. 42(4), pp. 537-560.

- Bhattacharya, N. and G.S. Chatterjee (1974), "Oi. Parities in Per Capita Household Consumption in India", in Bardhar C. and T.N. Srinivasan (1974) op.cit.

- Binswanger, H.P., Khandker, S.R., and M.R. Ro z.zweig (1989), "How Infrastructure and Financial Institutions Affect Agricultural Output and Investment in India", World Bank, Policy Planning and Research Working Paper No. 163.

- Clark, S., R. Hemming and David Ulph (1981), "On Indices for the Measurement of Poverty", The Economic Journal, Vol. 91, pp. 515-526.

- Dandekar, V.M. and N. Rath (1971), "Poverty in India", Economic and Political Weekly, January 2 and 9.

- Fields, G.S. (1989), "Changes in Poverty and Inequality in Developing Countries", mimeo, Cornell University.

- Foster, J., J. Greer and E. Thorbecke (1984), "A Class of Decomposable Poverty Measures", Econometrica, Vol. 52, pp. 761-766.

- Gaiha, R. (1989), "Poverty Alleviation Programmes in Rural India: An Assessment", unpublished paper.

- Hanson, J. and S. Sengupta (1989), "India's Manufacturing Sector and Employment Creation: A Comparison of the 1970s and 1980s", mimeo, World Bank, Asia Country Department.
Paraguay and Ghana, a larger share of households than public officials perceive the health sector as corrupt.

Countries with better institutions should in theory be less corrupt. The World Bank’s Country Policy and Institutional Assessment (CPIA) offers a measure of institutional quality. It includes a subcategory that assesses national policies affecting access to and quality of health care (World Bank OPCS 2007c). The scores range from 1–6 (6 indicating higher quality institutions) and are based on the assessments of health experts working on the country against a sample of regional benchmark countries.

Average regional CPIA scores for building human resources for health are shown in Figure 8. In 2007, Eastern Europe and Central Asia had the highest average regional score followed by South Asia; Sub-Saharan Africa had the lowest average score. But there is of course significant variation across countries within each region. These scores reflect perceptions of policy priorities, execution of policy, and the institutional quality of the entities providing health services and therefore, provide a sense of health sector performance.

# **FIGURE 8: CPIA SCORES FOR BUILDING HUMAN RESOURCES IN HEALTH BY REGION, 2007**

<!-- Start of picture text --> Q9a: Health care and nutrition services<br>4.5<br>Q9c: Prevention of HIV/AIDS and other communicable diseases<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Sub-Saharan East Asia and Eastern Europe Latin America Middle East South Asia<br>Africa Pacific and Central and the and North<br>Asia Caribbean Africa<br>CPIA scores for health questions by region, 2007<br><!-- End of picture text --> Note: Unweighted regional averages of CPIA scores for questions 9a and 9c. Source: World Bank OPCS (2007c).

A study of a road building project in Indonesia tested how closely perceptions of corruption corresponded to missing expenditures in the project (Olken 2006). Villagers’ perceptions of corruption were found to be positively correlated with missing expenditures. However, further examination suggested the potential for corruption perceptions to be misleading. In particular, in more ethnically diverse villages perceived corruption levels were higher but actual levels of missing expenditure lower than in 57
that have received transfers over a longer duration.<sup>34</sup> These insights on the variation of effects across transfer programs are useful for intervention design. In addition, Alderman and Headey (2017) find using multi-country microdata that mother’s education and household wealth are complements, suggesting that policies and programs that succeed in raising household economic status may also raise the nutrition payoffs to education attainment.

The decomposition results do not suggest that other factors are fundamentally less important for child growth than mother’s education attainment and household wealth. Factors included in the decompositions with the largest cross-sectional differences in levels or effects would mechanically contribute the most to the cross-sectional difference in the level of child growth; analogously, in the decompositions of intertemporal changes. Although the results underscore the importance of introducing policies and programs that further promote and leverage mother’s education attainment and household economic status, they do not discourage policies and programs that aim to transform factors with low levels or small effects, or factors with levels or effects that have changed little.

The evidence suggests that the development and improvement of nutrition-sensitive programs is key. Two ways to enhance the nutrition sensitivity of programs are by incorporating explicit nutrition goals and by targeting women and children who are more nutritionally vulnerable from a physiological viewpoint (Ruel et al. 2013). Given that slum residents face multiple disadvantages, it may be necessary to saturate slum settlements with a wide array of nutrition-sensitive programs to generate large, durable gains in nutrition status. Notwithstanding, nutrition-specific interventions should remain part of the policy package for slum residents. In their review of the international evidence, Goudet et al. (2017) find that nutrition-specific interventions in slum settlements—such as micronutrient supplementation, nutrition promotion, school feeding, and treatment of acute malnutrition—tend to have positive effects on child nutrition status.

> 34 In terms of available evidence for Bangladesh, Ferre and Sharif (2014) examine the effects of a cash transfers offered to very poor households under a pilot program in rural Rangpur division and in slum settlements in Narayanganj city, Dhaka division. The transfers were tied to households attending sessions for child growth monitoring, and counseling on child care and feeding, among other conditions. Using nonexperimental methods, the authors find that the program increased household overall, food, and protein consumption. They also find that knowledge on child feeding rose. The authors do not find significant effects on child dietary diversity, stunting status, or underweight status. They however – find a significant negative effect on wasting status for children ages 10 22 months at program enrollment, but not for – children in the older age group of 22 46 months at program enrollment.

34