rafmacalaba/datause-encoder-data huggingface dataset:
Administrative data offer a representative measure of disease incidence in each district, enabling the estimation of the broader impacts of the campaign, including indirect or spillover effects on individuals not directly exposed to it. As a result, this district level analysis also facilitates more precise evaluations of the campaign’s cost-effectiveness.

# **4.3.3 Outcome Measures**

We evaluate the impact of the social media campaign on three outcome categories: (1) behaviors associated with malaria prevention and response as reported in surveys, (2) malaria incidence through self-reported surveys, and (3) objective malaria incidence from the health facility HMIS data. Below we list the primary behavioral and health outcomes and explain their construction. The questions underlying the outcomes are reported in Appendix B.

**Self-reported outcomes** **_during_ the campaign.** Respondents in the experience survey were asked every 17 days a series of questions that captured the adoption of preventative behaviors and response actions related to malaria. From these questions, we constructed the following outcomes: (i) the share of times a respondent reported to use a bed net or other protection measures, (ii) the average share of household members who used a bed net, (iii) any fever/malaria case in the household, and (iv) medical seeking and testing behaviors. We aggregated the responses for every individual across survey waves by considering the _mean_ for outcomes (i-ii) and the _maximum_ for outcomes (iii-iv). In the analysis, we weight observations by the number of survey waves answered by the respondent.

**Self-reported outcomes** **_after_ the campaign.** In the cross-sectional survey, we inquired about the occurrence and number of individuals who tested positive for malaria since August 2020 (the campaign launch date), households’ concerns about contracting COVID-19 or malaria in the upcoming year, intentions to purchase mosquito nets, and whether mosquito nets had been treated with insecticide.

**Health facility recorded outcomes.** These data were used to construct objective measures of malaria incidence. Specifically, for every subdistrict, we considered the monthly number of recorded malaria cases and tests conducted, as well as the total population counts.<sup>27</sup> From these, we constructed malaria incidence rates (number of recorded cases per million people), and test positivity rates (number of positive cases per test conducted).

> 27We collected population data from WorldPop (www.worldpop.org), which produces various gridded population count datasets. We used the 2020 1km-resolution data, which were adjusted to match United Nations national population estimates. We used GIS software to compute the total population in each subdistrict.

16
|P117274|2010|2013||0 Closed|Sudan|
|---|---|---|---|---|---|
|P117275|2010|2010||0 Active|Gambia,The|
|P117275|2010|2011|8/29/2010|1 Active|Gambia,The|
|P117275|2010|2011|4/8/2011|2 Active|Gambia,The|
|P117275|2010|2012|7/20/2011|3 Active|Gambia,The|
|P117275|2010|2012|1/3/2012|4 Active|Gambia,The|
|P117275|2010|2013|7/2/2012|5 Active|Gambia,The|
|P117275|2010|2013|3/23/2013|6 Active|Gambia,The|
|P117275|2010|2014|10/4/2013|7 Active|Gambia,The|
|P117275|2010|2014|5/25/2014|8 Active|Gambia,The|
|P117275|2010|2015|12/11/2014|9 Active|Gambia,The|
|P117275|2010|2015|3/15/2015|10 Active|Gambia,The|
|P117275|2010|2016|10/3/2015|11 Active|Gambia,The|
|P117275|2010|2016|3/31/2016|12 Active|Gambia,The|
|P117275|2010|2017|9/18/2016|13 Active|Gambia,The|
|P117275|2010|2017|4/26/2017|14 Active|Gambia,The|
|P117275|2010|2018|11/28/2017|15 Active|Gambia,The|
|P117275|2010|2018|6/21/2018|16 Active|Gambia,The|
|P117275|2010|2019|12/27/2018|17 Active|Gambia,The|
|P117275|2010|2019|6/28/2019|18 Active|Gambia,The|
|P117275|2010|2020|1/15/2020|19 Active|Gambia,The|
|P117275|2010|2021|7/23/2020|20 Active|Gambia,The|
|P117278|2010|2010||0 Closed|Burkina Faso|
|P117278|2010|2011||0 Closed|Burkina Faso|
|P117279|2011|2011||0 Closed|Liberia|
|P117279|2011|2019|5/9/2019|1 Closed|Liberia|
|P117281|2010|2010||0 Closed|Cote d'Ivoire|
|P117281|2010|2011|1/29/2011|1 Closed|Cote d'Ivoire|
|P117282|2010|2010||0 Closed|Togo|
|P117282|2010|2011|2/17/2011|1 Closed|Togo|
|P117286|2011|2011||0 Closed|Niger|
|P117286|2011|2012|12/17/2011|1 Closed|Niger|
|P117286|2011|2013|7/11/2012|2 Closed|Niger|
|P117287|2010|2010||0 Closed|Benin|
|P117287|2010|2011|12/5/2010|1 Closed|Benin|
|P117287|2010|2011|6/13/2011|2 Closed|Benin|
|P117287|2010|2012|12/30/2011|3 Closed|Benin|
|P117293|2011|2011||0 Active|Peru|
|P117293|2011|2012|9/10/2011|1 Active|Peru|
|P117293|2011|2012|4/17/2012|2 Active|Peru|
|P117293|2011|2013|10/14/2012|3 Active|Peru|
|P117293|2011|2013|6/23/2013|4 Active|Peru|
|P117293|2011|2014|12/2/2013|5 Active|Peru|
|P117293|2011|2014|6/27/2014|6 Active|Peru|
|P117293|2011|2015|12/10/2014|7 Active|Peru|
|P117293|2011|2015|6/4/2015|8 Active|Peru|
|P117293|2011|2016|1/3/2016|9 Active|Peru|
- 3 -

(Figure 1).<sup>2 3</sup> Why is agricultural development lagging behind? One reason may be that agricultural production tends to be inefficient; the total factor productivity growth in agriculture is usually low, particularly in developing countries (e.g., Bravo-Ortega and Lederman, 2004).<sup>4</sup> It could also be attributable to large migration of labor force from agriculture to nonagriculture, as often expected as the economy develops. A decline in agricultural labor input directly restrains the agriculture sector growth.

<!-- Start of picture text --> Figure 1. GDP growth and agricultural growth, 2001-2005<br>(Percent per annum)<br>20<br>15 Share of agriculture in GDP 45 degree line<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-10 -5 0 5 10 15<br>Average GDP growth rate<br>Source: World Development Indicators.<br>Average agricultural growth rate<br><!-- End of picture text --> Lack of adequate infrastructure might be another reason for stagnant agricultural productivity improvement. Infrastructure stocks have normally been found conducive to economic development because infrastructure improvements could reduce transportation and transaction costs for producers (e.g., Canning, 1998; Fay and Yepes, 2003; Calderón and 2 Notably, there may be a causality issue between agriculture and nonagricultural growth. Bravo-Ortega and Lederman (2005) indicate that agricultural growth Granger-causes nonagricultural growth, and vice versa.

3 The figure also reflects the significance of the agriculture sector in each economy. It is natural that in countries with higher dependency on agriculture, GDP growth is more easily affected by the agricultural growth rate. But there is no systematic trend associated with the size of the agriculture sector.

4 Notably, Schultz (1964) argued that traditional economies were “efficient but poor.” Farmers and other producers in these economies don’t have much, but they make good economic use of what they have. In the growth accounting context, however, recent empirical agriculture economics has found that physical inputs, particularly capital would explained the majority of growth in agriculture, along with human capital investment (i.e., schooling). Bravo-Ortega and Lederman (2005) and Tiffin and Irz (2006) also find the evidence that agricultural value added (Granger-)causes economic growth in developing countries. As far as total factor productivity is concerned, however, it seems to be very limited on the order of 10 percent of the total agriculture growth (e.g., Mundlak, 2000).
continue to have lower wages than in other sectors, real wages remained stable over this period and actually increased in the last year of the study period.

<!-- Start of picture text --> Figure 31. Cumulative change in log real wage, Figure 32. Real wage by overall sector,<br>2014-2022 (base year: 2010) 2014-2022 (thousand 2010 ZMK)<br>0.2 160.0<br>0.1 140.0<br>0 120.0<br>-0.1 100.0<br>-0.2 80.0<br>-0.3 60.0<br>-0.4 40.0<br>-0.5 20.0<br>-0.6 2014 2015 2016 2017 2018 2019 2020 2021 2022 -<br>2014 2015 2016 2017 2018 2019 2020 2021 2022<br>Within-firm Between-firm Structural transformation<br>Entry* Exit* Total Agriculture, forestry, and fishing Mining and quarrying<br>Rest of industry Non-industrial services<br>Note: Wages are deflated by Consumer Price Index (CPI).<br>Source: World Bank and ZRA staff calculations based on PAYE database<br>Log-changes<br><!-- End of picture text -->

**Net formal job creation has generally been in lower paying subsectors.** At the ISIC 1-digit level, the largest net formal job creators have been agriculture, forestry and fishing (about 28,000 net new jobs between 2014 and 2022), retail trade (28,000), manufacturing (21,000), and other service activities (23,000),<sup>12</sup> all relatively low-wage sectors. By contrast, there has been limited new formal job creation in financial services (5,000 new jobs), the highest-wage subsector, and there has been net formal job destruction in mining (5,000 fewer jobs in 2022 versus 2014), the third highest paying subsector (see Figure 33). As a result, although not quite as stark as the impact of within-firm real wage declines, across-sector shifts in the formal workforce have also contributed to average real wage declines for formal workers in Zambia. In general, formal job creation in lower-paying subsectors may still translate into better livelihoods on a net basis for Zambians as a whole insofar as workers are transitioning from lower-paying informal jobs. However, this trend highlights the limited remaining job-creating potential in mining—which is constrained by natural resource endowments—as well as the still untapped potential for job creation at scale in higher value-added service subsectors such as financial services and information technology.

> 12 In Zambia, this mostly accounts for personal care and service jobs.

18
- Saatchi, S, M. Harris, S. Brown, M. Lefsky, E. Mitchard, W. Salas, B. Zutta, W. Buermann, S. Lewis, S. Hagen, S. Petrova, L. White, M. Silman, A. Morel. 2011. “Benchmark Map of Carbon Forest Carbon Stocks in Tropical Regions across Three Continents.” _Proceedings of the National Academy of Sciences_ , Vol. 108 (24): 9899-9904.

- Shaye, G, B. K. Sharma and J. S. Pal. 1984. “Cookstove Testing, Gorkha VORC,” Memo prepared for Save the Children Nepal. July 20.

- Smith, K.R., S. Mehta, M. Maeusezahl-Feu. 2004. “Indoor air pollution from household use of solid fuels, in: M. Ezzati, A. D.  Lopez, A.Rodgers, C. J. L.  Murray (Eds.). _Global and Regional Burden of Disease Attributable to Selected Major Risk Factors_ . World Health Organization, Geneva, pp. 1435 -1494.

- Smith, K.R., K. Dutta, C. Chengappa, P. P. S. Gusain, O. Masera, V. Berrueta,  R. Edwards, R. Bailis, K. N. Shields. 2007. “Monitoring and evaluation of improved biomass cookstove programs for indoor air quality and stove performance: conclusions from the Household Energy and Health Project,” _Energy for Sustainable Development._ Vol. 11: 5 -18.

- Thakuri, M. B. M. 2009. “Revising the Need of Improved Stoves: Estimating Health, Time and Carbon Benefits,” SANDEE Working Paper No. 44-09.  Kathmandu.

- van der Werf, G. R.; D. C. Morton; R. S. DeFries; J. G. J. Olivier; P. S. Kasibhatla; R. B. Jackson; G. J. Collatz; and J. T. Randerson. 2009. “CO2 emissions from forest loss,” _Nature Geoscience_ 2: 737 – 738.

van Kooten, G.C. and E.H. Bulte, 2000. _The Economics of Nature_ , Oxford: Blackwell.

- UNFCCC, 2012. “Default Values of Fraction of Non-Renewable Biomass for Least Developed Countries and Small Island Developing States.” 67th Meeting Report, Annex 22. United Nations Framework Convention on Climate Change, Executive Board of the Clean Development Mechanism, Bonn. http://cdm.unfccc.int/DNA/fNRB/index.html.

- Weitzman, M. 2011. “Fat-Tailed Uncertainty in the Economics of Catastrophic Climate

- Change,” _Review of Environmental Economics and Policy_ , 5 (2): 275–292

- WHO 2014. Burden of disease from household air pollution for 2012. Available at www.who.int/phe. (Accessed 20 October 2014): World Health Organization.

- Wilcoxon, F. 1945. ‘Individual comparisons by ranking methods’. _Biometrics Bulletin_ **1** (6): 80–83.

- Wood, T. 1987. _Assessment of Cookstove Programs in Nepal_ , Report Submitted to the US Agency for International Development, Kathmandu, Nepal.

- Wood, T. 1983. “Field Evaluation of Renewable Energy AIP Project No. 625-0937.7 Administered by USAID/Dakar, Senegal,”

- Zimmerman, D. W. 1997. "A Note on Interpretation of the Paired-Samples t Test". _Journal of Educational and Behavioral Statistics_ 22, 349–360.

25 | P a g e
<mark>Table 2b: Transition Matrices - Synthetic vs. Actual Panel Data Nicaragua 2001 and 2005</mark>

|Status in 2001, 2005|Lo<br>|wer Bou<br>Estimate|nd<br>s|Truth|Up<br>E|per Bou<br>stimate|nd<br>s|
|---|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|[7]|
|Poor,Poor<br>|40.63<br>|39.44<br>|37.34<br>|35.68<br>(32.08,39.26)|31.70<br>|31.17<br>|30.73<br>|
|Poor,Non-poor<br>|0.00<br>|0.00<br>|3.31<br>|3.35<br>(2.00,4.70)|11.86<br>|9.84<br>|10.52<br>|
|Non-poor,Poor<br>|21.17<br>|22.36<br>|24.46<br>|26.12<br>(22.82,29.41)|30.10<br>|30.63<br>|31.07<br>|
|Non-poor,Non-poor<br>|38.20<br>|38.20<br>|34.89<br>|34.85<br>(31.27,38.42)|26.34<br>|28.36<br>|27.68<br>|
|R-squared|0.30|0.45|0.53|.|0.30|0.45|0.53|
|Residual Correlation|0.58|0.53|0.52|.|0.58|0.53|0.52|
|Observations|684|684|684|684|684|684|684|

<mark>Data source: EMNV, 2001-2005.</mark> <mark>Note: R-squared is calculated for opposite halves of the total 2001 sample. Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. Columns [1] and [5] show a simple model with household time invariant characteristics. Columns [2] and [6] add sub-national controls from census and region fixed effects. Columns [3] and [7] add interactions between household time invariant characteristics and sub-national controls. Results in column [4] show actual panel mobility. 95 percent confidence interval between parentheses. Upper bound estimations are based on 50 repetitions.</mark> 24
-21-

opted for a monetary reform also experienced high inflation rates.'<sup>3</sup> That experience also indicates that inflation resulted because the authorities let the excess money stock feed into prices by failing to adopt anti-inflationary policies. In fact, this experience illustrated how quickly the monetary overhang problem could result in a hyperinflation. Studies on hyperinflation experience shows that it develops quickly in economies with features similar to those prevailing in Bulgaria: a monetary overhang problem, large fiscal deficits, indexation of nominal wages to past price levels, and underdeveloped financial markets.

A:though until 1991, when the Stabilization Program was undertaken, the authorities lacked a clear strategy of how to deal with the monetary overhang problem, there were some important monetary policy developments that happened in the late 1980s (see Table 11). First, the authorities took measures to reduce the inflationary pressures. In late-1 980s, they reduced the large inherited monetary base by improving the clearing system; and in end-1 990 they introduced a 5 percent reserve requirement on bank deposits. These measures reduced somewhat the inflationary pressures by reducing the level of outstanding bank deposits (outstanding bank balances) and by reducing the money multiplier.

Second and in contrast, the authorities increased the inflationary pressures by continuing their accomodative monetary policy. The authorities continued to use the expansion in money base as a source of revenue. For instance, on average in 1987-89, NBB expanded the monetary base with the public (excluding banks deposits with the clearing system) by more than 3 percent of GDP and by 6 percent in 1990. Most of these resources were used to finance the General Government deficit.

The net effect of these two measures resulted, however, in an increase in the inflationary pressures. This was indicated by the money multiplier and the income veincity of money estimates (see Table 11). While both of these indicators improved in 1986-89 when the authorities reduced the excess base money, in 1989-90 they began to deteriorate as the inflationary pressures emerged. In 1987-89 the income velocity of MI feil by about 20 percent, while in 1989-90 it increased by more than 20 percent. Also, as inflationary pressures grew, NBB's non-inflationary sources of revenue from money creation started diminishing and more revenue was obtained from inflationary sources. Seignorage fell from 3 percent of GDP in 1988 to -23 percent in 1990, and inflation tax increased from -0.1 percent in 1988 to 28 percent in 1990.

_jU/_ **See Dornbusch and Wolf** O 990); **and Dornbusch, Sturzonegger and Wolf (1990).**
# **References**

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28
from negative two to positive four.<sup>19</sup> For the indicators of subsequent deworming, I set _CH_ to four, and as _C_ increases, the number of terms in the summation of later deworming indicators decreases.

Several regularities appear across the first seven columns of the table. First, the coefficients on deworming before age _C_ cannot be statistically distinguished from one another for the first five columns ( _before age -2_ through _before age 2_ ), but after that, the coefficients lose significance and fall in magnitude. Either _β_ 4<sup>3or</sup><sup>_β_</sup> 4<sup>4(columns6and7)canbe</sup> statistically rejected as being equal to any of the coefficients from earlier columns. Second, the latest exact age indicator to be statistically significant is always age zero (in columns 1 through 3); conversely, in the first four columns of the table, the earliest coefficient to be statistically insignificant because of its lower magnitude is always that for deworming at exactly age one.

By including as many later deworming indicators as I do in columns 1 through 7, however, I sacrifice statistical power by reducing the size of the omitted group. Because of the two patterns described above, I repeat the specification from column 4 ( _C_ = 1 year) in columns 8 through 11, but decreasing _CH_ across the columns, until the specification in column 11 is simply that of Equation 3. The specification in column 10 yields the coefficients shown earlier in Table 2.

The evidence here corroborates the results in the earlier sections: community deworming prior to age 1 brings about a 0.2-standard-deviation improvement in performance on Raven’s Matrices later in life; deworming at age 1 may have some positive effect, but smaller, though this could simply be due to noisy measurement of child age; deworming after age 1 cannot be statistically distinguished from deworming much later.

# **5.3 Discussion of results**

Others have also found effects of deworming on cognition, though typically only in the short term. An observational study by Jukes, _et al._ (2002) investigated the relationship between cognitive function and helminth infections among Tanzanian schoolchildren, and found that

> 19Deworming “before age 0” means deworming before birth; “before age -1” means more than one year before birth; and so on.

17
(v)

# Table of Contents

||Pare|
|---|---|
|Chapter 1. INTRODUCTION: TRADE AND AFRICAN DEVELOPMENT PROSPECTS|1|
|Chapter 2. THE DESTINATION AND COMPOSITION OF AFRICAN EXPORTS|4|
|Chapter 3. OECD TARIFF BARRIERS FACING AFRICA|13|
|A. Tariff Protection Against African Exports|14|
|B. The Influence of Tariffs on African Commodity Processing|20|
|C. The Influence of the Uruguay Round on Tariffs|23|
|D. Regionalism and African Exports|27|
|E. Tariffs: The Potential for Positive OECD Policy Action|28|
|Chapter 4. OECD NONTARIFF BARRIERS FACING AFRICA|32|
|A. Pre-Uruguay Round NTBs Facing Africa|32|
|B. The Achievements of the Uruguay Round|42|
|C. Implications for African Exports: The Round's Effects on NTBs|51|
|D. Protection Levels from 'Tariffied" NTBS|56|
|E. Appropriate OECD Policy Action on NTBs|58|
|Chapter 5. ISSUES RELATING TO TRANSPORT COSTS|60|
|A. The Evidence from National Customs Statistics|62|
|B. The Magnitude of Cross-Product Variation in Freight Costs|66|
|C. Effective Transport Protection Facing African Exports|70|
|E. Transport Costs: The Potential Policy Dimension|75|
|Chapter 6: SUMMARY AND CONCLUSIONS|78|
|Statistical Annex 1: Transportation Costs for Regional Groups of African Countries Exports|87|
|Statistical Annex 2: Uruguay Round Concessions Received and Profiles of Protection Facing<br>Sub-Saharan African Countries in the European Union, Japan and the<br>United States|96|