rafmacalaba/datause-encoder-data huggingface dataset:
**Table A3: Impact of PBF on availability of equipment and consumables**

||**Variable**|**Baseline**<br>**Mean in**<br>**PBF**<br>**districts**|**Trend**|**PBF**<br>**impact**|**p-**<br>**value**|**N**|
|---|---|---|---|---|---|---|
||Clock|0.538|-0.096|0.290|0.501|394|
||Child weight scale|0.858|0.051|0.091|0.194|410|
||Height measurement tool|0.849|-0.010|0.142|0.251|418|
||Tape measures|0.802|0.109|0.070|0.119|414|
||<br>Adult weight scale|0.774|0.059|0.158*|0.076|416|
|Equipment|Blood pressure cuff|0.774|0.038|0.188*|0.082|420|
||Thermometer|0.811|0.048|0.141|0.190|420|
||Stethoscope|0.755|0.097|0.139|0.222|418|
||Fetoscope|0.802|-0.333|0.164|0.929|404|
||Otoscope|0.387|-0.033|0.382|0.148|396|
||Exam bed|0.783|-0.490|0.292|0.116|408|
||Amoxicillin|0.283|0.115|0.526***|0.001|420|
|Essential|Iron|0.066|0.644*|0.214**|0.018|420|
|drugs|Oral rehydration solution (ORS)|0.377|0.452|0.171|0.184|420|
||Paracetamol|0.302|0.490*|0.208**|0.010|420|
||Condoms|0.425|0.462*|-0.056|0.249|420|
|Family|Intrauterine device (IUD)<br>|0.217|0.135|0.158|0.999|420|
|<br>planning<br>|Depot Medroxyprogesterone<br>Acetate|0.179|0.202|0.053|0.875|420|
|products|Implant|0.019|0.029|-0.010|0.813|420|
||Oral contraceptive pills|0.406|0.462|0.038|0.864|420|
||<br>Bacille Calmette-Guérin (BCG)|0.028|0.038|0.037|0.983|420|
||Dyphteria Tetanus Pertussis (DTP)|0.170|0.317|0.192|0.242|420|
||Hepatitis B Vaccine (HBV)<br>Tetravalent|0.094|0.202|-0.060|0.876|420|
|Vaccines|HiB vaccine|0.047|0.154|-0.088|0.440|420|
||Measles vaccine|0.094|0.240|0.194|0.615|420|
||Oral polio vaccine|0.330|0.269|-0.024|0.935|420|
||Pentavalent vaccine|0.274|0.356|0.097|0.303|420|
||Tetanus Toxoid (TT)|0.198|0.250|0.118|0.425|420|
||<br>HIV test kit|0.113|-0.212|0.570***|0.000|420|
|Diagnostic|Pregnancy test kit|0.274|0.356|0.097|0.310|420|
|test kits|Syphilis test kit|0.075|0.010|0.396**|0.030|420|
||Urine protein & glucose testing kit|0.009|0.010|0.009|0.268|420|

Note: data from rural health center assessments. All regressions are the difference-in-differences specification with facility fixed effects and controlling for exposure to the collaborative quality improvement and citizen scorecards interventions. Standard errors are clustered at the district and survey round level with wild bootstrapping. Level of statistical significance: *p<0.10; **p<0.05; ***p<0.01 33
content of its leading industries (table 1 and figure 9). As an example, as much as 70 percent of the total production of non-electrical machinery was exported in 2004-05 compared to only 20 percent in 1990-94.

<!-- Start of picture text --> Textiles Industrial chemicals<br>Machinery, except electrical Machinery, electric<br>Transport equipment<br>Source: Author’s calculations, COMTRADE data.<br><!-- End of picture text --> The Chinese catch-up model was founded on a strategy that gradually distanced the Chinese economy from its original inward-looking (socialist) model of self-reliance. As in South Korea, China’s export-led catch up strategy relied on competition in the global market to serve as a screening mechanism to identify globally competitive industries. This was reinforced with policies that maintained a stable macroeconomic environment while attracting foreign investors.

The timing and pace of foreign competition measured by tariff cuts sheds _some_ light on the differences among the BICs in their pursuit of economic integration with the world economy. China cut its tariff rates drastically from about 32 percent in 1993 to 16 by 1996 and 2 percent by 2006 (figure 10). In contrast, in the late 1980s, the tariffs on imports of Electrical machinery were 81 percent in India and 46 in Brazil. The Chinese Electrical machinery industry continues to grow post liberalization but its relative position signals that the lowest rates of protection does not guarantee a competitive edge forever. Even though its absolute size was larger than the same industry in Brazil, India or Korea, in 2005, China’s distance from the Korean productivity frontier was widening. The latter had begun in 2000 and suggests that the pace of technological innovation in Korea is speeding ahead of the BICs. A new wave of technological innovation is required if China wants to continue to catch up with Korea in this high tech industry.

21
In summary, improving communication and transportation seem to be the means to help decrease the impact of adverse rainfall shocks.

# **Concluding Remarks and Policy Considerations**

Carefully matching three rounds of a large regionally representative household survey with rainfall data from 48 weather stations across the Philippines, we have examined the impact of rainfall shocks on household consumption. We find results that point to an adverse impact of climate change. Negative rainfall shocks decrease total expenditure as well as food expenditure by about 4%. In contrast, positive shocks do not lead to limited improvements in total expenditure and no improvement in food expenditure. Hence, in a scenario where the average level of rainfall is unchanged but where there is an increase in the occurrence of periods with high rainfall as well as periods with low rainfall, then household consumption will be hurt. Indeed, while households will be affected by periods of drought, they will not gain from periods with high rainfall.

The finding that the shock affects mainly food expenditure is contrary to what we were expecting. One possible explanation for this result may be that rainfall shocks affect food prices. This is in line with the fact that we do not find that agricultural households are affected harder by negative rainfall shocks.

One important finding from our analysis is that access to communication, highways, and markets decreases considerably the impacts of rainfall shocks. This suggests that many of the policies that can be effective at reducing the impacts of climate change on poverty in the Philippines are not different from the strategies of sound development agendas aimed at reducing poverty and promoting economic growth. The most important elements of such policies include: i) smoothing the price impacts of regional or country-specific climate shocks through international trade; ii) investing in human capital to increase employment opportunities of the poor, accompanied by policies and incentives that facilitate the migration of the poor to the areas with better economic opportunities; iii) providing credit and developing insurance markets; iv) investing in transportation and communication infrastructure; v) investing in irrigation and/or improved water management to deal with extreme precipitation events; vi) investing in adaptive agricultural research and in information and extension services; vii) improving governance of common-pool natural resources; and creating well targeted and scalable safety nets systems.

Interventions that help improve communication and access to markets should be a critical component of country-level adaptation strategies. In particular, safety net systems that are counter-cyclical, such as conditional and unconditional cash transfers, workfare programs (e.g., food or cash-for work), and social funds (community-level programs in infrastructure, social services, training, etc.) can have immediate pay-offs since they enable countries to deal with economic crises and other shocks that may not be related to climate change and climatic variability.

15
# **School Dropout in Central America: An Overview of Trends, Causes, Consequences, and Promising Interventions**

Melissa Adelman and Miguel Székely<sup>1</sup> The World Bank JEL Classification: I2, I24, I25, I26, I28, O15 Keywords: Dropout, Educational Attainment, Secondary Education, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, Panama, Central America

> 1 The authors thank Pamela Mendoza and José Mola-Avila for their research assistance. The authors thank Pablo Acosta, Raja Bentaouet-Kattan, Laura Chioda, and Rafael de Hoyos for their valuable comments.
Table 3 Regression results, Number of countries exporting to OECD

||_(1)_|_(2)_|_(3)_|_(4)_|
|---|---|---|---|---|
|Time|0.662|0.593|0.583|0.543|
||(17.18)***|(12.71)***|(38.49)***|(29.66)***|
|Time, squared|0.008|0.010|0.008|0.009|
||(9.19)***|(8.52)***|(24.76)***|(20.79)***|
|Post 2000||-0.391<br>(2.63)***||-0.228<br>(3.91)***|
|Constant|15.103|15.481|16.560|16.777|
||(40.80)***|(38.99)***|(113.19)***|(107.19)***|
|Observations|53'770|53'770|53'770|53'770|
|R-squared|0.17|0.17|0.54|0.55|
|Number of SITC4<br>|||1'301|1'301|
|Fixed (prod.) effects|no|no|yes|yes|

Note: Dependent variable: Number of non-OECD exporters to OECD. The time variable is an index starting as 1963 = 1. The post-2000 variable is another index starting at 2000 = 1. The panel is unbalanced.

In order to explore further what might be driving the apparent re-concentration of OECD imports, we now decompose OECD imports by importing country and construct a three-dimensional panel whose unit of observation (the basis for the calculation of our concentration indices) is a product imported by an OECD country in a year (a triplet importer  product  year). Looking at things this way allows us to look for another type of composition effects that would work as follows. Suppose that the OECD has two members, _A_ and _B_ , with _B_ sourcing its imports of a given product more narrowly than _A_ . A rise in B’s share of OECD imports will raise the OECD-wide import concentration index for that product through a pure composition effect, although in our previous regressions this would be a within-product rise in the concentration index. Regression results are shown in Table 4 .

21
and the adjusted headcount ratio. This is natural since these indicators have additive separability properties that make them easy to decompose, as opposed to the multidimensional headcount, which is nonlinear in terms of deprivation scores. Decompositions of the multidimensional headcount ratio, in particular dynamic ones, appear to be lacking in attention.

Overlooking the multidimensional headcount ratio is conspicuous, considering that this indicator is particularly important on its own. From an academic perspective, focusing on the evolution of the adjusted headcount ratio as opposed to the multidimensional headcount ratio variations in that come disregards poverty only from changes in the number of poor and arise only in the identification step. Using datasets form several countries, Apablaza et al. (2010) and Apablaza and Yalonetzky (2013) show that declines in the adjusted headcount ratio are mostly due to changes in the multidimensional headcount and not to changes in intensity. From a policy perspective, the multidimensional headcount is frequently used by governments as ”the rate of multidimensional poverty”—instead of the adjusted headcount ratio— as it is easily communicable and also comparable to the monetary rate of poverty.

In this we an to variations in the multidi- paper, propose approach decompose mensional headcount ratio into changes attributed to different categories of dimensions. Our approach builds on counterfactual simulation approaches, which have traditionally been used to decompose poverty and inequality indicators. These approaches were first proposed by Barros et al. (2006), and then extended in a series of papers by Azevedo et al. (2012b, 2013a,b). The approach relies, first, on expressing the indicator of interest as a function of the distribution of its determinants over a finite population. Then it replaces these distributions by counterfactual ones, where one of the determinants has been altered.

Our approach allows us to estimate the extent to which an observed change in a dimension—or category of dimensions—can explain the observed change in the multidimensional headcount ratio. In the presence of panel data, it allows doing this without separately tracking the transitions in and out of poverty of each individual. In repeated cross-section data—when such tracking is impossible—the approach constitutes a good approximation of how much each dimension contributes to the headcount’s change. In both cases, it summarizes separate information on the headcount by dimension, weights and incidence.

Our paper contributes to the literature on the decomposition of poverty measures, providing a guideline to split up the changes of the multidimensional poverty 3
# Table 4: Savings Functions for the Panel Data of Asian Countries, 1960-90

|||Depen<br>(t-|dent Variab<br>Statistics in|le: Savings rat<br>parenthesis)|e||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|_(5)_|(6)|(7)|(8)|
|GDPpercapitagrowth|8.623<br>(2.93)|11.400<br>(3.01)|8.610<br>(2.96)|11.843<br>(2.97)|9.508<br>(2.98)|9.777<br>(2.55)|6.034<br>(1.81)|10.805<br>(2.57)|
|Lagged GDP growth per capita||0.935<br>(0.52)||0.437<br>(0.24)||3.798<br>(1.87)||0.283<br>(0.14)|
|Dependency ratio|0.629<br>(1.70)|0.865<br>(2.29)|0.574<br>(1.62)|0.839<br>(2.26)|-0.200<br>(-0.42)|-0.060<br>(-0.11)|-0.726<br>(-1.24)|-0.159<br>(-0.25)|
|Lagged Savings Rate||0.182<br>(1.42)||0.203<br>(1.52)||0.050<br>(0.27)||0.264<br>(1.62)|
|Overall Education Stock|-5.550<br>(-3.30)|-5.296<br>(-3.24)|||||||
|Lagged Overall Education Stock|5.792<br>(4.00)|5.443<br>(4.00)|||||||
|Primary Education Stock|||-5.060<br>(-3.46)|-4.999<br>(-3.30)|||||
|Lagged Primary Education Stock|||5.304<br>(4.17)|5.079<br>(4.03)|||||
|Secondary Education Stock|||||-2.015<br>(-2.21)|-2.002<br>(-2.58)|||
|Lagged Secondary Education Stock|||||2.089<br>(2.76)|-2.069<br>(3.26)|||
|Tertiary Education Stock|||||||-0.588<br>(-1.66)|-0.525<br>(-1.31)|
|Lagged Terfiary Education Stock|||||||0.541<br>(2.22)|0.490<br>(1.90)|
|AdjR<sup>2</sup><br>DW<br>Observations|0.449<br>1.70<br>60|0.491<br>1.87<br>60|0.423<br>1.68<br>60|0.468<br>1.89<br>60|0.350<br>1.75<br>60|0.365<br>1.93<br>60|0.217<br>1.75<br>60|0.303<br>2.06<br>60|

All variables are expressed in log (with the exception of the GDP growth rate) and in 5 year average. The TSLS have been used in all regression and fixed- **effects** have been introduced to account for countries' characteristics.
<!-- Start of picture text --> 8 iii<br>g J<br>2833 HHH !ii<br>ae88o888o8o8 HH:H:: y 7 ,<br>i wa<br>8 i oa<br>8 or ~~ ~~ ><br>1980 i 1985 1990yearyear 1995 2000<br>—— MYS ---~-- synthetic MYS|<br><!-- End of picture text -->

<!-- Start of picture text --> 8vv :||<br>38<br>88aaaa iNii Ao<br>ely88“888“8“8 cd Hi:i::<br>a |<br>ween ee ee :<br>3 i<br>3 2000 2005 year i 2010 2015<br>—— ISR -~-~-- synthetic ISR)<br><!-- End of picture text -->

<!-- Start of picture text --> 8gg iii<br>38 H<br>taaoao HHH<br>ag88ee88eeee HH:H:: “ eee~~<br>° Tee<br>8 Lo<br>ei H<br>1990 1995 2000ii year 2005 2010 2015<br>—— TO --~-- syntheticTTOTTO<br><!-- End of picture text --> <!-- Start of picture text --> 288 :iii 88vv :|| 8gg iii<br>g J 38 38 H<br>2833 HHH !ii 88aaaa iNii Ao taaoao HHH<br>ae88o888o8o8 HH:H:: y 7 , ely88“888“8“8 cd Hi:i:: ag88ee88eeee HH:H:: “ eee~~<br>i wa a | ° Tee<br>8 i oa ween ee ee : 8 Lo<br>8 or ~~ ~~ > 3 i ei H<br>1980 i 1985 1990yearyear 1995 2000 3 2000 2005 year i 2010 2015 1990 1995 2000ii year 2005 2010 2015<br>—— MYS ---~-- synthetic MYS| —— ISR -~-~-- synthetic ISR) —— TO --~-- syntheticTTOTTO<br>Sg*g38f28823 ;H:Hi:HHiHii / /f///f///////// /]aN\aN\\ 3 g5Ffcti8g82 :H;HH:HH::iH -//// ’“oo--“oo---- ryee558FE]j32ee558FE]j32558FE]j3258FE]j32FE]j32j32 4J:Jae:iHPoPZ:}|J:Jae:iHPoPZ:}|Jae:iHPoPZ:}|ae:iHPoPZ:}|:iHPoPZ:}|iHPoPZ:}|HPoPZ:}|PoPZ:}|PZ:}|:}|}|| ofONON . --<br>811 H / r=} i “ = H<br>oos8s88 “fetaos.fetaos.aeaten:‘:‘eaten:‘‘ aeaten:‘:‘eaten:‘‘ a 6BSo3&o3&3&& etH:iH:iHH:iHiHH L/Saedaed / /// 6s s88 ~~ yy, H|tHitHiHii<br>8 i 8 i g i<br>1985 1990 1995 year 2000-2005 2010 1980 1985 1990yearyear 1995 2000 2000 2005 year 2010 2015<br>—— DZA ---~-- synthetic DZA. —— COL ~--~-- synthetic COL —— Moz ---~-- synthetic MOZ|<br><!-- End of picture text -->

<!-- Start of picture text --> 823233 /f///f///////// /]aN\aN\\<br>H:Hi:HHiHii:Hi:HHiHiiHi:HHiHiii:HHiHii:HHiHiiHHiHiiHiHiiiHiiHiiiii /<br>811 H /<br>os. a<br>oos8s88 “fetaos.fetaos.aeaten:‘:‘eaten:‘‘<br>8 i<br>1985 1990 1995 year 2000-2005 2010<br>—— DZA ---~-- synthetic DZA.<br><!-- End of picture text --> <!-- Start of picture text --> 822 ’“oo--“oo----<br>///<br>3 ///////<br>H;HH:HH::iH;HH:HH::iHHH:HH::iHH:HH::iH:HH::iHHH::iHH::iH::iH:iHiHH -////<br>r=} i “<br>/ ///<br>BSo3&6BSo3&o3&3&& etH:iH:iHH:iHiHH L/Saedaed<br>8 i<br>1980 1985 1990yearyear 1995 2000<br>—— COL ~--~-- synthetic COL<br><!-- End of picture text --> <!-- Start of picture text --> 322 ofONON . --<br>ryee558FE]j32ee558FE]j32558FE]j3258FE]j32FE]j32j32 4J:Jae:iHPoPZ:}|J:Jae:iHPoPZ:}|Jae:iHPoPZ:}|ae:iHPoPZ:}|:iHPoPZ:}|iHPoPZ:}|HPoPZ:}|PoPZ:}|PZ:}|:}|}||<br>= H<br>s6s<br>s88 ~~ yy, |tHiH|tHitHiHii<br>g i<br>2000 2005 year 2010 2015<br>—— Moz ---~-- synthetic MOZ|<br><!-- End of picture text -->
# **–** **<u>Table 5 Antidumping Initiations as Notified to WTO, 1995 June 2004: Countries included in the study and major users</u>**

|Initiations|1995|1996|1997|1998|1999|2000|2001|2002|2003|Jan.–<br>June<br>2004|Totals|Per<br>year<br>1995–<br>J2004|Per<br>year<br>1995–<br>2001|Per<br>year<br>2002–<br>J2004|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Argentina|27|22|14|8|23|45|26|14|1|7|**187**|19.7|23.6|8.8|
|Brazil|5|18|11|18|16|11|17|8|4|6|**114**|12.0|13.7|7.2|
|Chi<br>le|4|3|0|2|0|5|0|0|0|0|**14**|3.1|2.0|0.0|
|Colomb<br>ia|4|1|1|6|2|3|6|0|0|0|**23**|2.4|3.3|0.0|
|Costa Ri<br>ca|0|4|1|1|0|0|0|0|0|0|**6**|0.6|0.9|0.0|
|Mexico|4|4|6|12|11|7|5|10|14|3|**76**|8.0|7.0|10.8|
|Pe<br>ru|2|7|2|3|8|1|8|12|5|3|**51**|5.4|4.4|8.0|
|Major users,<br>for<br>comparis<br>on<br>|||||||||||||||
|United States|14|22|15|36|47|47|76|35|37|21|**350**|36.8|36.7|37.2|
|European<br>Community|33|25|41|22|65|32|29|20|7|13|**287**|30.2|35.3|16.0|
|South Afri<br>ca|16|33|23|41|16|21|6|4|8|4|**172**|18.1|22.3|6.4|
|India|6|21|13|27|65|41|79|81|46|4|**383**|40.3|36.0|52.4|

_Source:_ WTO **Database** 48
Harding, T. and Rattsø, J. (2005). The barrier model of productivity growth: South Africa. Trade and Industry Policy Strategies Working Paper 5 – 2005.

Joffe, A., Kaplan, D., Kaplinsky, R. and Lewis, D. (1995). Improving Manufacturing Performance in South Africa: Report of the Industrial Strategy Project, University of Cape Town Press, for the Industrial Strategy Project and the International Development Research Centre.

Kerr, A., Wittenberg, M., and Arrow, J. (2014). Job Creation and Job Destruction in South Africa. _South African Journal of Economics,_ 82 (1):1-18.

Kreuser, C. F. and Newman, C. (2018), Total Factor Productivity in South African Manufacturing Firms. South African Journal of Economics, 86: 40-78.

Li, Y. & Rama, M. (2013). Firm Dynamics, Productivity Growth and Job Creation in Developing Countries: The Role of Micro- and Small Enterprises. World Development Report 2013: Jobs.

Mamburu, M. (2017). Defining High-Growth Firms in South Africa. UNU-Wider Working Paper 2017-107. Helsinki: UNU-WIDER.

Matthee, M., Rankin, N., Webb, T. and Bezuidenhout, C. (2018), Understanding Manufactured - Exporters at the Firm Level: New Insights from Using SARS Administrative Data. South African Journal of Economics, 86: 96-119.

National Treasury. (2017). Medium Term Budget Policy Statement. [Online] Available at: <u>http://www.treasury.gov.za/documents/mtbps/2017/mtbps/FullMTBPS.pdf.</u> National Treasury. (2018). Budget Review. [Online] Available at: <u>http://www.treasury.gov.za/documents/national%20budget/2018/review/Chapter%202.pdf</u> Olley, G. S., and Pakes, A. (1996). The Dynamics of Productivity in the Telecommunications Equipment Industry. _Econometrica,_ 64(6): 1263-1297.

Pieterse, D., Gavin, E. and Kreuser, C. F. (2018), Introduction to the South African Revenue - Service and National Treasury Firm Level Panel. South African Journal of Economics, 86: 6- 39.

Roberts, S. and Rustomjee, Z. (2009). Industrial policy under democracy: apartheid’s grownup infant industries? Iscor and Sasol. _Transformation_ , 71, 50-74.

Roberts, S. (2016). An Agenda for Opening up the South African Economy: Lessons from Studies of Barriers to Entry.

30
**Table 9:** Survey to survey imputation results: CC mean yields and differences with ML and SR yields by crops, in the overall sample and by region for 2018..

||Millet|Sorghum|Rice|Maize|Cowpea|Groundnut|
|---|---|---|---|---|---|---|
|||||National|||
|CC<br>ML - CC|928.2<br>-54.7|1167.1<br>-151.7<sup>_∗∗∗_</sup>|3661.7<br>-297.3|1898.4<br>-229.1<sup>_∗∗∗_</sup>|534.6<br>-122.4<sup>_∗∗_</sup>|2033.8<br>103.3|
|SR - CC|-99.0|-278.4<sup>_∗∗∗_</sup>|-698.4|-8.2|19.3|-1013.2<sup>_∗∗∗_</sup>|
|N|519|693|241|644|187|554|
|||||Kayes|||
|CC|949.1|1129.8|8293.7|1201.2|580.8|2603.2|
|ML - CC|-282.0<sup>_∗_</sup>|-282.4<sup>_∗∗∗_</sup>|-2823.2|-245.7<sup>_∗∗∗_</sup>|-218.5<sup>_∗∗_</sup>|-436.6<sup>_∗∗∗_</sup>|
|SR - CC|-18.6|-301.6<sup>_∗∗∗_</sup>|-5520.2<sup>_∗∗_</sup>|320.0|175.4|-1384.6<sup>_∗∗∗_</sup>|
|N|40|257|9|206|31|179|
|||||Koulikoro|||
|CC|1035.8|1214.7|2360.7|1931.8|746.4|1717.9|
|ML - CC|-110.9|-154.6<sup>_∗∗_</sup>|905.8|-215.2|-249.6<sup>_∗∗_</sup>|204.4|
|SR - CC|-358.5<sup>_∗∗∗_</sup>|-260.0<sup>_∗_</sup>|-713.7|-42.2|-145.4|-862.3<sup>_∗∗∗_</sup>|
|N|154|163|37|158|44|138|
|||||Sikasso|||
|CC|1018.4|1246.2|2881.2|2383.5|473.5|1667.7|
|ML - CC|-133.8<sup>_∗_</sup>|-222.0<sup>_∗_</sup>|-330.9|-212.7<sup>_∗∗_</sup>|-110.2|188.0|
|SR - CC|-16.9|-338.0<sup>_∗∗∗_</sup>|-1152.0<sup>_∗_</sup>|-95.8|-47.8|-351.1|
|N|107|109|53|225|41|94|
|||||Segou|||
|CC|1106.4|1308.4|7329.0|2203.7|381.0|2116.1|
|ML - CC|-52.8|-7.9|-2665.6<sup>_∗∗_</sup>|-353.6<sup>_∗∗_</sup>|56.6|493.1<sup>_∗∗∗_</sup>|
|SR - CC|-285.8<sup>_∗∗∗_</sup>|-506.9<sup>_∗∗∗_</sup>|-4928.6<sup>_∗∗∗_</sup>|-686.6<sup>_∗∗∗_</sup>|145.7<sup>_∗_</sup>|-1330.5<sup>_∗∗∗_</sup>|
|N|110|123|23|53|42|122|
|||||Mopti|||
|CC|639.7|780.8|2923.3|786.7|460.1|1469.8|
|ML - CC|195.1<sup>_∗_</sup>|77.4|-235.0|289.2|-90.6|93.9|
|SR - CC|227.7<sup>_∗_</sup>|317.8|955.7|459.4|70.8|-525.3<sup>_∗_</sup>|
|N|70|21|40|2|19|21|

_Notes:_ The training set is the EACI 2017. The mean CC yield and differences with ML and SR yields are computed using survey design weights. Statistical significance are assessed using standard errors clustered at the enumeration area level. In the case of the ML, the standard errors used for statistical significance tests account for the between imputations variance computed with 100 simulations. Significance levels: *p _<_ 0.1; **p _<_ 0.05; ***p _<_ 0.01 25