rafmacalaba/datause-encoder-data huggingface dataset:
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43
funds to cover the estimated cost of $120 million (Adepoju [2019]).<sup>2</sup> By 2016, 33 out of 47 African countries had committed to a health financing strategy for universal health coverage (Barroy et al. [2016], Cotlear and Rosemberg [2018]). Although there is uncertainty about the total number of CHW, the One Million Community Health Workers Campaign estimates over 300,000 CHW in Sub-Saharan Africa alone.

Despite these developments, the existing empirical research provides only a partial picture of the effect of these policies. Systematic reviews cite some support for positive effects of community health workers on a range of outcomes including child mortality and morbidity, but note the low quality of the evidence and need for further research (Lewin et al. [2010], Gilmore and McAuliffe [2013], Scott et al. [2018]). Similarly, there is a relative scarcity of rigorous evidence on the health outcome effects of removing user fees (Ridde and Morestin [2012], Dzakpasu et al. [2013], Lagarde and Palmer [2011]). Some exceptions exist: for example, a randomized control trial in Ghana found positive health effects on children who were anemic at baseline, and a difference-in-difference study using variation in access due to apartheid policies in South Africa found positive effects on children’s nutritional status (Powell-Jackson et al. [2014], Tanaka [2014]). We know of no study that studies how CHW visits and health care subsidies interact.

Given that CHW visits to households combined with free care for children under five are cornerstones of current global health policy, and the population receiving both interventions is rapidly growing, it is crucial to learn more about both the individual effects of these two interventions as well as their interaction in different beneficiary populations. Here, we study the individual and combined effects of CHW visits and subsidies on measures of preventive and curative care for diarrhea and related health outcomes in peri-urban areas of Bamako, Mali.<sup>3</sup> We analyze data from a randomized control trial of the Action for Health program by the NGO Mali Health. Action for Health combines biweekly CHW visits with a subsidy

> 2The reforms have been delayed due to the Covid-19 pandemic and political disruptions.

> 3 While CHW programs were originally intended for rural areas, many CHW work in urban or peri-urban areas. For example, 6% of Malian CHW are estimated to work in Bamako (Saint-Firmin et al. [2018]).

3
methods of cultivation, increasing productivity and profitability. Further, changes in credit availability affected the cost of financing fixed capital production costs. Overall, these changes in prices, in access to inputs, and in efficiency of advice on crop husbandry led to changes in land allocation to cotton and in cotton yields. Our objective in this paper is to quantify these impacts.

The empirical analysis exploits unusual farm surveys, the Post Harvest Surveys (PHS) of the Zambian Central Statistical Office. These are repeated cross-sections of Zambian farmers covering the 1997-2002 period. Information on land allocation, yields, input use, and household characteristics across farmers in rural Zambia is collected. We use these data to set up an empirical model of cotton crop choice and cotton productivity. Our identification strategy relies on a modified difference-in-differences approach. First, we take differences of outcomes (i.e., cotton productivity) across the different phases of the reforms. Second, we use maize productivity to difference out unobserved household and aggregate agricultural year effects. Finally, since more productive cotton farmers are also more likely to allocate a larger fraction of their land to cotton production, we use cotton shares, purged of observed covariates, as a proxy for unobserved cotton-idiosyncratic productivity.

A simpler difference-in-differences model, without the correction for selection and thus without accounting for entry and exit into the agricultural cotton sector, would lead to biases in the estimates of aggregate productivity. Exit of low productivity farmers in the failure phase may bias productivity up, whereas entry of low productivity farmers in the success phase may lead to downward biases in measured productivity. The importance of these compositional effects has been emphasized in the industrial productivity literature (Olley and Pakes (1996), Pavcnik (2002)). We propose a different dynamic approach to take care of these effects when measuring productivity in agriculture and which can be applied to repeated cross-sections of farm-level data.

Our analysis provides valuable lessons on the interaction between export crops and the adoption of domestic policies in Sub-Saharan Africa. Further, by affecting market agricultural participation and cotton yields, our results have important implications for household income and consumption. These are critical issues in rural Zambia, where poverty 2
# **Reference:**

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- Beaglehole, R., Bonita, R., Horton, R., Adams, C., Alleyne, G., Asaria, P., et al. (2011). Priority actions for the non-communicable disease crisis. _The Lancet, 377_ (9775), 1438-1447, doi:10.1016/s0140-6736(11)60393-0.

- Been, J. V., Nurmatov, U. B., Cox, B., Nawrot, T. S., van Schayck, C. P., & Sheikh, A. (2014). Effect of smoke-free legislation on perinatal and child health: a systematic review and meta-analysis. _The Lancet, 383_ (9928), 1549-1560, doi:10.1016/s01406736(14)60082-9.

- Bonu, S., Rani, M., Peters, D., Jha, P., & S, N. (2005). Does Use of Tobacco or Alcohol Contribute to Impoverishment From Hospitalization Costs In India? _Health Policy and Planning 20_ (1), 41-49.

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- Chapman, S., & Richardson, J. (1990). Tobacco excise and declining tobacco consumption: the case of Papua New Guinea. _Am J Public Health, 80_ (5), 537-540.

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- Jha, P., Joseph, R., Li, D., Gaurvreau, C., Anderson, I., & Moser, P. (2012). Tobacco tax administration: a win-win measure for fiscal space and health. Asian Development Bank. .

- Ng, M., Freeman, M. K., Fleming, T. D., Robinson, M., Dwyer-Lindgren, L., Thomson, B., et al. (2014). Smoking Prevalence and Cigarette Consumption in 187 Countries, 19802012. _JAMA, 311_ (2), 183-192.

- PNG, G. o. (2014). Papua New Guinea 2015 National Budget: Volume 1 Economic and Development Policies

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- The World Bank (2014). The NCD Roadmap Report is available at <u>http://www</u>

<u>wds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2014/07/10/ 000456286_20140710094818/Rendered/PDF/893050WP0P13040PUBLIC00NCD 0Roadmap.pdf.</u>

- Townsend, J. L. (1987). Cigarette tax, economic welfare and social class patterns of smoking. _Applied Econ, 19_ (2), 355-365.

- Vallance, P. J., Anderson, H. R., & Alpers, M. P. (1987). Smoking habits in a rural community in the Highlands of Papua New Guinea in 1970 and 1984. _P N G Med J, 30_ (4), 277280.

- Windham, G. C., Hopkins, B., Fenster, L., & Swan, S. H. (2000). Prenatal active or passive tobacco smoke exposure and the risk of preterm delivery or low birth weight. _Epidemiology, 11_ (4), 427-433.

- World Health Organization (2004a). "The Millennium Development Goals and Tobacco Control." accessed at <u>http://www.who.int/tobacco/publications/mdg_final_for_web.pdf</u> on April 2, 2015

17
- ~~~~~~~- 55 -

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Two previous studies are similar to our analysis here. These studies focus not on various types of trade facilitation aid, however, (which affects both imports and exports), but on projects that focus on export promotion only. Brenton and von Uexkull (2009) evaluate whether technical assistance in export development programs have been successful. The authors find that on average these programs have induced a stronger export performance in the targeted products. With respect to the effectiveness of export promotion agencies (EPAs), Lederman, Olarreaga, and Payton (2006) find that EPAs, on average, have a positive and statistically significant impact on exports. Nevertheless, they find that the impact is heterogeneous across regions: larger effects were found in Latin American and Asian agencies, while agencies in Sub-Saharan African, the Middle East and North Africa lag behind.

To summarize, while there have been efforts to analyze the mechanisms behind trade facilitation and to show how aid and trade are related, to our knowledge no study as yet has made the link between the two. Our study shows how aid directed towards trade facilitation measures may be related to trade flows. Given the increased focus on aid in the trade negotiating context, as well as the increased interest in aid effectiveness, it makes sense to take a look at these links. The next section discusses the data and methodology.

# **_III. Data and Methodology_**

## **III.1. Data**

The key ingredient of this paper is the disaggregated data on aid flows. Data on aid flows for the years 1990 to 2005 come from the OECD Creditor Reporting System (OECDCRS) database, which documents Official Development Aid (ODA) flows from donor to recipient countries starting in the year 1973. This database is very comprehensive and covers almost every country of the world. It includes aid extended by about 40 individual country donors as well as multilateral agencies. Each entry contains the value of the aid flow from donor to the recipient including a description of the aid according a specific classification system (both in words and five digit code). According to the WTO/OECD (2006) almost US $ 8.7 billion (in 2005) or 21.3 % of all ODA (excluding debt relief) was directed towards the Aid-for-Trade agenda, which is the focus of our analysis.<sup>22</sup> The OECD-CRS reporting system map data into three categories that match the Aid-forTrade agenda.<sup>23</sup>

- 1) _Trade policy and regulation_ (US$ 296 million in 2005)<sup>24</sup> : Comprises aid flows to facilitate the participation in multilateral trade negotiations and to improve the

> 22 The Aid-for-Trade agenda originated in the single-undertaking of the Uruguay Round, developed in the context of the Integrated Framework (IMF, ITC, UNCTAD, UNDP, WTO, OECD/DAC as an observer), and then received further impetus in the Gleneagles G8 summit and WTO ministerial in Hong Kong in 2005.

> 23 See Appendix for the full CRS classification for the three functional areas.

6
**Table 1. Poverty Rates (2008-2016)**

|**Year**|**Full Sample**|**Restricted Sample**|**Diffe**|**rence**|
|---|---|---|---|---|
|2008|42.2|43.3|-1.16|***|
|2009|40.4|41.4|-1.01|***|
|2010|37.3|38.0|-0.72|***|
|2011|34.2|34.9|-0.70|***|
|2012|32.9|33.2|-0.30|***|
|2013|30.7|31.0|-0.33|***|
|2014|28.6|28.7|-0.04||
|2015|27.9|27.8|0.16||
|2016|28.1|27.6|0.45|***|

**Note** : The table shows estimates of the moderate poverty rate in Colombia using the _full sample_ of the GEIH and a _restricted sample_ based on head of households aged 25 to 60 years in the first survey (GEIH-2008) and adjusted accordingly for later waves of the survey.

**Source:** Own estimations based on GEIH from 2008 to 2016.

20
Mwamba et al took a broader look using a cholera risk in Zambia between 2008 and 2017, identifying 16 districts at higher risk using a discreate Poisson-based space-time scan statistic to allow for variation over the ten-year study period (Mwaba et al. 2020). However, this study provides a unique contribution in terms of the combination of data sources that it brings together, considering multiple known environmental risk factors, and its granular focus on Lusaka.

# **A2 MATERIALS AND METHODS**

Studies of previous epidemics in Lusaka and elsewhere have implicated a range of different factors as contributing to cholera risk, including inadequate provision of safe water and sanitation (Mengel et al. 2014; Adedire et al. 2015), climatic conditions (Luque Fernández et al. 2009), inadequate drainage (Sasaki et al. 2009) and food-borne contamination (Dubois et al. 2006; Sinkala et al. 2004). We also see associated environmental factors where cholera outbreaks could be more prevalent such as areas with higher levels of poverty (Cowman et al. 2017) and densely populated peri-urban settlements (Chimusoro et al. 2018). In this work multiple data sources that reflect these covariates as far as possible were brought together to identify where the strongest correlates with risk were in the case of the 2017-18 Lusaka outbreak. Table 1 provides an overview of all the data sources that went into the analysis. They include the cholera case data itself, data on poverty rates by ward, population estimates, household size information, groundwater vulnerability data, distance from water and sewer networks and burial grounds and household reported access to treated water, improved sanitation, and handwashing facilities. Finally, data on the risk of flooding; frequency of complaints regarding sewer network; water supply/quality and prevalence of E. coli in drinking water sources were all input into the model.

**<u>Table 1- Data sources used to generate the covariates in the analysis</u>**

|Data Description|Data Type, Resolution, Dates, Obs,<br>samplingtechnique|Source|
|---|---|---|
|Cholera case data<br>Pre-existing geospatialgrids|De-identified GPS coordinates for<br>October 2017-May 2018 (5,444<br>cases). Sampling must have followed<br>the ZNPHI and GTFCC protocols for<br>identifyinga cholera outbreak.|Zambia National Public<br>Health Institute (ZNPHI)<br>http://znphi.co.zm/|
|Poverty rates|World Bank Group Small Area<br>Estimations – ward level|de la Fuente, A., Murr, A.<br>and Rascon, E.<br>(2015)_Mapping_<br>_Subnational Poverty_<br>_in Zambia_. Lusaka:<br>World Bank Group<br>and Zambia Central<br>Statistical Office.|
<!-- Start of picture text --> 10 10<br>gS gS<br>g~8 ga 8<br>2 2<br>a= BE<br>SB 6 SB 6<br>Lv Lv<br>Ze 2S<br>Be Be<br>==<br>00<br>0 2 4 _6., 8 10 0 20, 40 60 80<br>Extrapolation time True poverty rate (%)<br>— GDP growth w. passthrough rate by inc./cons. — GDP growth w. passthrough rate by inc./cons.<br>— Best model overall — Best model overall<br>— Best model that predicts poverty directly — Best model that predicts poverty directly<br>10 10<br>gS gS<br>2. 8 sn 8<br>2 2<br>a= BE<br>326 Sh6<br>Lv Lv<br>as =e<br>ge Se 4<br>ga §&<br>o~ 2 ao~ 2<br>==<br>00<br>1980 1990 2000 2010 202 -.1 -,05 0 05 Al<br>Year Annualized growth in real GDP/capita<br>— GDP growth w. passthrough rate by inc./cons. — GDP growth w. passthrough rate by inc./cons.<br>— Best model overall — Best model overall<br><!-- End of picture text -->

- Best model that predicts poverty directly

- Best model that predicts poverty directly
**Annex ‐ Table 3 ‐ SSA: Fiscal Regression for Model B**

|**_Dependent variable_**|||**Real GDP**|**Central**<br>**African**<br>**per capit**|**Cote**<br>**a**||||||**Real**|**GDPper c**|**apita**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Benin**|**Botswan**|**a Cameroon**|**Rep. **|**d'Ivoire**|**Ethiopia**|**Gabon**|**Gambia**|**Ghana**|**Kenya**|**Mozambique**|**Nigeria**|**Rwanda**|**Senegal**|
||_Column 1_|_Column 2_|<br>_Column 3_|_Column 4_|_Column 5 _|_Column 6 _|_Column 7_|_Column 8 _|_Column 9_|_Column 10_|<br>_Column 11_|_Column 12 _|_Column 13_|_Column 14_|
|Constant|6.693<br>(2.199)**|‐21.985<br>(‐8.323)*|7.531<br>**(11.312)**|22.642<br>*(11.415)*|18.079<br>**(16.239)**|4.495<br>*(3.494)***|16.511<br>(8.647)**|8.034<br>* (9.394)***|2.459<br> (6.486)**|7.315<br>* (3.529)***|‐12.439<br> (‐2.502)**|1.881<br>(0.297)|7.117<br>(1.991)*|11.119<br>(3.737)***|
|Government expenditure per|||||||||||||||
|capita (in constant US$)|0.004<br>(0.152)|‐0.098<br>(‐3.27)**|0.04<br>* (2.092)*|0.007<br>(0.213)|0.025<br>(1.108)|0.276<br>(3.886)***|0.014<br>(0.671)|‐0.012<br>(‐0.241)|0.009<br>(0.671)|0.018<br>(0.53)|0.172<br>(1.414)|0.014<br>(1.757)*|0.287<br>(4.22)***|0.02<br>(0.47)|
|Net lending per capita (in|||||||||||||||
|constant US$)|0.001<br>(1.246)|‐0.001<br>(‐2.964)*|‐0.004<br>**(‐3.334)***|0.002<br>(1.172)|0<br>(‐0.379)|0.019<br>(3.758)***|0.002<br>(1.214)|‐0.003<br>(‐0.793)|‐0.001<br>(‐2.047)*|0.003<br>(1.435)|0.001<br>(0.35)|‐0.008<br>(‐1.778)*|0.007<br>(2.686)**|‐0.002<br>(‐2)*|
|Private investment (in GDP)|0.059<br>(2.81)**|0.193<br>(3.111)**|0.347<br>* (19.39)***|0.075<br> (1.54)|0.06<br>(2.726)**|0.012<br> (1.055)|0.049<br>(1.17)|0.121<br>(2.072)*|0.004<br>(0.784)|0.064<br>(2.065)*|0.02<br>(1.789)*|0.086<br>(2.075)*|0.06<br>(0.84)|0.032<br>(0.932)|
|Log of labor force|0.344<br>(1.674)|2.342<br>(11.046)*|0.078<br>**(2.233)**|‐0.789<br>(‐6.34)***|‐0.384<br> (‐5.814)**|0.053<br>*(0.673)|‐0.184<br>(‐1.444)|‐0.031<br>(‐0.431)|0.725<br>(22.514)*|0.147<br>**(1.243)|1.563<br>(4.328)***|0.46<br>(1.262)|0.064<br>(0.259)|0.067<br>(0.333)|
|AR(1)|0.853<br>(10.969)**|0.679<br>* (3.514)**|0.392<br>* (1.55)|0.602<br>(1.574)|0.108<br>(0.428)|‐0.144<br>(‐0.613)|0.251<br>(1.001)|0.236<br>(0.919)|‐0.228<br>(‐0.632)|0.759<br>(3.29)***|0.631<br>(3.118)***|0.709<br>(3.588)***|0.791<br> (4.589)***|0.844<br> (4.599)***|
|MA(1)|0.151<br>(0.608)|0.997<br>(13.184)*|‐0.936<br>**(‐6.548)***|‐0.161<br>(‐0.327)|0.94<br>(9.27)***|0.941<br> (36.716)**|0.937<br>*(7.217)**|0.33<br>* (0.901)|0.769<br>(3.068)**|0.328<br>* (1.174)|0.282<br>(1.062)|0.641<br>(3.364)***|‐0.018<br> (‐0.058)|‐0.092<br>(‐0.3)|
|**Adjusted R2**|0.952456|0.996762|0.982125|0.931985|0.930368|0.787928|0.714775|0.601661|0.996591|0.766513|0.959573|0.863187|0.770766|0.822966|

The estimation technique is ARIMA. t‐statistics are given in paranthesis. *** stands for 1% significance level, ** 5% significance level, and * 10% significance level. AR(1) is autoregressive variable. MA(1) is moving average variable.

29