rafmacalaba/datause-encoder-data huggingface dataset:
India, improving teacher attendance by increasing the frequency of school monitoring is ten times more effective at increasing the effective teacher-student ratio, as compared to hiring new teachers.

# **Figure 5: Barriers to Access**

Percentage of respondents that “ _Needed and Did Not Apply_ ” for a Government delivered good or service <!-- Start of picture text --> Driving License<br>Register Land/Property<br>Utilities<br>Public School<br>Healthcare<br>Aadhaar Card<br>Voter ID Card<br>0 0.05 0.1 0.15 0.2 0.25 0.3 0.35<br>Documents Know-How Affordability<br><!-- End of picture text --> _Note_ : The variable “ _Needed and Did Not Apply_ ” is defined in Section 3.1.

# **4. Inequality in Access to Government Provided Goods and Services**

Unequal access may be unintentional, resulting from poor service design or weak state capacity. Or it may be intentional, resulting from deliberate and thus discriminatory practices. Exclusion resulting from poor service design can be remedied by tweaking policies or strengthening service delivery systems. Discriminatory exclusion, however, requires a more involved approach. More importantly, exclusion, discriminatory or not, is seldom random — the weakest sections of society are often excluded at a higher rate versus the rest of the population. For example, the poor and the uneducated are less likely to have access to government run schools or hospitals. Thus, unequal access may not only contribute to socio-economic inequality, but may also reinforce or exacerbate inequality. Governance measures, such as absenteeism, or outcome measures, such literacy rates, are agnostic to horizontal inequalities in the provision of public goods and services. Good 19
Table 5: Accuracy across countries and methods

|||Mean abs|olute error|||Mean squ|ared error||
|---|---|---|---|---|---|---|---|---|
||EBP|Cubist|XGB|BRF|EBP|Cubist|XGB|BRF|
|**Panel A: Assets**|||||||||
|Burkina Faso|0.303|0.285|0.237|0.466|0.143|0.119|0.081|0.273|
|Madagascar|0.320|0.395|0.336|0.355|0.152|0.197|0.154|0.163|
|Malawi|0.440|0.369|0.348|0.462|0.373|0.294|0.254|0.532|
|Mozambique|0.278|0.198|0.200|0.191|0.115|0.065|0.067|0.066|
|Sri Lanka|0.159|0.134|0.135|0.164|0.043|0.031|0.033|0.058|
|Tanzania|0.413|0.345|0.336|0.366|0.224|0.156|0.154|0.179|
|Vietnam|0.236|0.215|0.204|0.228|0.106|0.092|0.076|0.102|
|Average|0.307|0.277|0.257|0.319|0.165|0.136|0.117|0.196|
|**Panel B: Poverty**|||||||||
|Malawi|0.180|0.123|0.142|0.180|0.047|0.028|0.037|0.049|
|Tanzania|0.085|0.069|0.073|0.090|0.015|0.009|0.010|0.013|
|Average|0.132|0.096|0.108|0.135|0.031|0.019|0.024|0.031|

Note: The first four columns present the simple mean of the mean absolute error (MAE) across simulations for each country. The last four columns present the simple mean of the mean squared error (MSE) across simulations for each country. The results are based on 100 simulations for each country and method.
# **<mark>Appendix I.2: Exporter participation rate and export intensity by industry</mark>**

________________________________________________________________________________________________________________________________

|Country|Year|Variable||||||In|dustry||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|___________|_______|___________________|DA<br>_______|DB<br>______|DC<br>______|DD<br>_______|DE<br>_____|DF<br>_______|DG<br>______|DH<br>_______|DI<br>______|DJ<br>_______|DK<br>______|DL<br>______|DM<br>_______|DN<br>________|
|Austria|1999|Participation rate 36.0|89.3|83.8|60.5|77.1|NA|92.7|89.1|53.6|71.6|90.7|82.1|87.5|63.1||
|||Export intensity|16.2|50.2|63.3|36.6|24.0|NA|44.1|43.6|26.3|40.0|54.7|54.6|56.5|31.5|
|||<br>Number of firms 626|224|37|319|310|NA|109|193|239|612|440|273|88|398||
||2005|Participation rate 38.8|93.4|91.3|67.1|79.2|NA|91.7|89.4|55.4|71.3|91.2|84.3|90.3|67.9||
|||Export intensity|22.5|56.6|61.9|41.5|26.1|NA|54.6|50.4|32.4|41.8|58.3|56.9|58.4|32.6|
|||Number of firms 662|152|23|283|284|NA|108|208|224|669|489|299|103|336||
|Belgium 1996|Partici|pation rate 82.9<br>84.3|81.8|78.1|75.3|66.7|96.2|95.8|80.9|75.9|88.4|84.9|89.7|85.9|||
|||Export intensity|37.5|56.7|43.8|33.2|29.7|16.7|44.3|45.8|28.3|34.6|42.0|39.2|35.7|42.0|
|||Number of firms 480|383|11|96|299|12|239|167|209|494|242|186|107|185||
||2005|Participation rate 79.8|88.0|85.7|85.9|71.1|73.3|96.0|94.7|72.0|66.9|83.9|84.5|88.1|83.2||
|||Number of firms 43.7|62.0|55.6|39.4|31.9|32.1|46.8|52.3|38.1|41.5|46.5|43.5|41.5|42.5||
|||Export intensity|371|183|7|71|235|15|224|152|186|441|168|148|101|101|
|Chile|1990|Participation rate|16.7|12.0|22.4|20.8|18.0|47.1|41.0|23.3|18.0|41.9|11.8|20.6|9.1|17.6|
|||Export intensity 34.5|3.7|10.5|18.5|3.3|1.3|9.3|1.7|1.6|36.0|2.8|4.4|16.4|15.7||
|||<br>Number of firms 1,509|591|156|409|178|17|205|206|122|62|490|63|88|34||
||1999|Participation rate 18.9|20.9|24.5|23.5|29.4|53.8|55.4|32.5|20.8|47.2|17.4|40.6|29.1|23.3||
|||<br>Export intensity 34.2|6.0|2.9|24.0|4.2|2.3|14.0|4.8|3.2|27.7|7.4|7.0|26.6|19.2||
|||<br>Number of firms|1,410|421|94|387|170|13|166|212|130|53|534|64|55|30|
|China|1998|Participation rate 13.1|49.2|56.2|20.4|9.8|8.1|20.2|28.4|10.1|21.4|21.0|33.3|14.7|46.2||
|||<br>Export intensity|55.1|73.1|81.8|66.3|50.6|30.2|41.1|64.2|51.4|57.7|36.0|60.3|36.9|72.7|
|||<br>Number of firms 18,060|16,846|3,023|2,149|7,591|939|13,835|7,084|13,347|12,372|14,854|10,853|5,995|4,551||
||2005|Participation rate 22.1|44.8|59.8|25.7|13.4|7.6|22.0|31.2|19.0|23.2|27.5|49.2|23.9|61.3||
|||Export intensity 47.9|71.7|81.7|67.2|42.4|19.6|37.7|62.5|46.8|57.2|50.4|65.7|44.6|80.5||
|||<br>Number of firms 22,232|33,758|6,127|5,235|11,822|1,796|23,440|14,451|19,643|24,363|43,953|12,160|10,990|11,356||

62
<!-- Start of picture text --> 100% ——<br>FAer<br>80%60% hj<br>w7<br>Q<br>oO iy<br>40% /,<br><!-- End of picture text --> <!-- Start of picture text --> 20% J,<br>weaVA<br>0% =<br>N w fe) - wo oO oO Oo o<br>°o ° - w N © °<br>Per capita welfare (PPP$ 2011)<br>——— Children(0-4) Children(5-9)<br>—-—-— Children(10-14) Children(15-17) ———— Adults<br><!-- End of picture text -->
# **Table 6. Stock market performance and structure of bank capital over the financial cycle: Alternative definitions of large bank**

The dependent variable is the quarterly stock return. See Table 4 for a detailed description of the estimated model. In this table we report results for a group of large banks defined as banks with operating income above 1 billion US $. The sample period for the stock return is Q1-2006 to Q1-2009. All control variables are lagged one year. Crisis is a dummy that takes value one from Q3-2007 to Q1-2009. Tier1 (Tier2) is Tier1 (Tier2) capital scaled either by risk-weighted assets (RWR) or total un-weighted assets (LR). Tier1 capital includes shareholder funds plus perpetual, noncumulative preference shares, plus retained earnings. Tier2 capital includes subordinated debt, hybrid capital, loan loss reserves, and valuation reserves. Common equity is shareholders fund and other capital total regulatory capital minus common equity. Standard errors clustered by bank are reported in brackets and the p-value for the test of significant difference between the pre-crisis and crisis coefficients in parentheses.

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||RWR|||LR|
|Tier1*PreCrisis|-0.058|-0.064|0.041|0.081|
||[0.104]|[0.123]|[0.111]|[0.126]|
|Tier2*PreCrisis||-0.11||-0.171|
|||[0.167]||[0.183]|
|Tier1*Crisis|0.282**|0.398**|0.673***|0.674***|
||[0.121]|[0.172]|[0.235]|[0.222]|
||(0.022)|(0.004)|(0.001)|(0.001)|
|Tier2*Crisis||0.444||0.531|
|||[0.303]||[0.388]|
|||(0.034)||(0.120)|
|Controls*Crisis|x|x|x|x|
|Controls*PreCrisis|x|x|x|x|
|Country*Year FE|x|x|x|x|
|Nber. Obser.|803|803|745|707|
|R squared|0.34|0.34|0.34|0.33|

Note: (*), (**) and (***) stand for statistically significant at the 10%, 5 % and 1 % level, respectively. Robust standard errors clustered by bank reported in brackets. In parentheses we report the pvalue for the test of equality of effects during crisis and pre-crisis.

23
Table C7. Distribution of Manufacturing Establishments Motorcycles by Employment Size, Indonesia <!-- Start of picture text --> NOTORCYC (0lI MOTORCYCLES FOR MESSENGER) FIENSIZE (EMPLOYEES per FIRM)<br>Frncmy<br>Percent<br>noi Pct<br>CotPet 1-19 120-49 150"99 100-199 1200-499 1500-999 i1W0 A Total<br>~~~~~~~,, ,,,,,, , ,,,,,, ,, ........ ....... ........ . I I I ........ I ........ I ........... I ++... ... [~~~~~~~~over------<br>YES 1 I 722 4.14 4.14 14 1 4 73<br>0.341 7.591 4.831 4.831 4.831 1.38 1.38 25.17<br>-1 37 301 14 t 1 19 19.18 tS 1 5.4<br>2o.00o 23.40 2S.W00 22.58 t31.82 21.05 40.00<br>................ ........ ........ ............. . ++....... .............. + ..... ..........<br>no I 41 721 42 1 U 301 15i 6 1 217<br>1.38 24.83 1448' 1655' 10.34 5.171 2.07 74.83<br>1.84 33.181 19:351 22.12 I 13:821 6.911 2.76,<br>W80.00 76.60 75.001 77.42 68.18 78.95 60.00W<br>.. . ........... .. .. . .. . . . . . . . .. .. . .. .. . .. .. . .......<br>Total 5 94 56 62 44 19 10 290<br>1.72 32.41 19.31 21.38 15.17 6.55 3.45 100.00<br><!-- End of picture text -->
# TABLE 1

# GATT/WTO Membership and World Trade

# 1982-1997

|Countries/Territories||G|ATT/WT|O Members|hip||||World E|xports|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Number|||Per cent||Valu|e in US $ B|illion|Per cen|t of World|Exports|
||1982|1987|_1997|1982|1987|1997|1982|1987|1 1997|1982|1 1987|1 1997|
|Developed|24|24|25|27.3|25.3|19.1|1,168|1,734|3,554|62.5|69.7|69.4|
|Developing|58|65|97|65.9|68.4|74.0|211|348|972|11.3|14.0|19.0|
|Others|6|6|9|6.8|6.3|6.9|57|84|97|3.0|3.4|1.9|
|Total GATT/WTO<br>Members|88|95|131|100.0|100.0|100.0|1,436|2,166|4,623|76.8|87.1|90.3|
|Non-Members|||||||434|322|495|23.2|12.9|9.7|
|Total World||l||||.<br>._|1,870|2,488|5,118|100.0|100.0|100.0|

Source: WTO, Statistical Yearbook 1997; WTO Directory, 1982, 1987, 1997.
Thus, in the first quintile, where only 60 percent of homes have access to safe water, 40 percent did not. The rapid expansion of piped water supply can at least partly be contributed to the so-called _Bahia Azul_ program, a state program that aims at expanding access to clean water in Bahia.

<!-- Start of picture text --> Figure 6.3: Access to Infrastructure Services in Bahia (2001)<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>piped water sewerage system<br>10%<br>collected waste electricity<br>0%<br>1st (poorest) 2nd 3rd 4th 5th (richest)<br>Income Quintile<br>Source: Calculation based on PNAD 2001.<br>Access to Service<br><!-- End of picture text --> Public sewerage connection reach from 17 percent for the poorest quintile to 58 percent for the wealthiest quintile. However, the access to sewerage service by the poor increased by 6 percentage points during 1993-01. In the same period, more extremely poor obtained access to waste collection, reaching 57 percent in 2001 (37 percent in 1993) compared to slightly below 70 percent of the total sample. Still the access is regressive, slightly more than 54 percent of the poorest quintile have waste collected compared to around 90 percent of the richest quintile. Electricity has also expanded rapidly among poor households and 76 percent now has access compared to 86 percent of the total sample.

# **SOCIAL PROTECTION**

Recognizing that economic growth and social investments in education (and health) will still leave many in extreme poverty, the state and federal governments have a variety of programs aimed at reducing economic insecurity and targeted poverty reduction. The programs fall into three categories: (i) those dealing with life-cycle considerations such as social security and pensions; (ii) those dealing with income volatility such as unemployment insurance; and (iii) those dealing with social protection, aimed at improving the well-being of specific vulnerable groups.

The social protection programs in Bahia include for example conditional cashtransfer schemes, employment and income generation programs, and programs to combat child labor and sexual abuse. In addition, there are health and education 37
performance of those approaches that strongly rely on data from previous surveys, e.g. adjusting poverty lines and cross-survey imputations.

# **3. Results**

We assess the performance of the rapid approach vis-à-vis alternative approaches (Table 1). The long questionnaire of the full-consumption approach can increase unit and item non-response but is nevertheless used as benchmark due to its _de facto_ standard for consumption surveys. The rapid approach compromises on the long list of items by introducing a subset of core items and distributing remaining items in optional modules, reducing the questionnaire length with beneficial impacts on unit and item non-response but at the cost of additional estimation error. The reduced approach further decreases questionnaire length by simply dropping items altogether, creating substantial bias in the resulting estimates. The bias can be minimized by adjusting the poverty line based on data from a previous consumption survey, called the adjusted reduced approach below. Finally, the largest time savings are generated by completely abandoning consumption data and using a structural model to impute consumption from a baseline survey using common co-variates. The time gap with the baseline survey as well as shocks and other structural changes, for example from the implementation of a project to reduce poverty, invalidate the structural model leading to substantial bias in the estimates.

**Table 1: Comparison of consumption methodologies and sources of error.**

||**Unit non-response**|**Item non-response**|**Implementation issues**|**Error inherent to method**|
|---|---|---|---|---|
|**Full**<br>**consumption**|Elevated levels of<br>non-response,<br>particularly<br>among<br>urban and wealthy<br>households (Korinek,<br>Mistiaen,<br>and<br>Ravallion 2006; Osier<br>2016)|Long list of items<br>increase item non-<br>response<br>and<br>measurement<br>error<br>(Fiedler and Mwangi<br>2016;<br>Finn<br>and<br>Ranchhod 2015) as<br>well as unfinished<br>interviews (A. Deaton<br>and Grosh 2000)|Relatively limited issue<br>as<br>questionnaire<br>is<br>straightforward though<br>length may be issue|Theoretically<br>unbiased<br>if<br>implemented correctly with<br>full response|
|**Rapid**<br>**approach**|Non-response is an<br>issue but not as<br>much<br>as<br>in<br>full<br>consumption due to<br>the<br>shorter<br>questionnaire|Less of an issue than<br>full consumption due<br>to<br>shorter<br>questionnaire|Could be substantial<br>issue<br>with<br>paper<br>questionnaires<br>but<br>almost<br>completely<br>mitigated with CAPI|Trade-off between length of<br>questionnaire and accuracy of<br>poverty<br>and<br>inequality<br>estimates<br>due<br>to<br>underestimation of variance<br>attenuated by less core items<br>and more optional modules<br>(Table 2; Figure 1; Figure 2<br>and Figure 3).|
|**Reduced**<br>**consumption**|Non-response is an<br>issue but not as<br>much<br>as<br>in<br>full<br>consumption due to|Less of an issue than<br>full consumption due|Limited<br>issue<br>as<br>questionnaire<br>is<br>straightforward|Substantial<br>bias<br>in<br>total<br>consumption<br>attenuating|

9
**Table C.2: List of Major Investment Incentives Available for Manufacturing Sector**

|Tax Incentives|Tax Concessions|
|---|---|
|Pioneer Status|An exemption of 70% of statutory income for five years with the balance of 30% of the statutory income taxable at<br>current corporate tax rate.<br>An exemption of 100% of statutory income for five years will given to pioneer companies located in promoted area<br>(Sabah,Sarawak,Perlis,Labuan,Eastern Corridor of Peninsula Malaysia and high technology)|
|Investment Tax Allowance|An allowance of 60% (100% for Sabah, Sarawak, Perlis, Labuan and designated Eastern Corridor of Peninsula<br>Malaysia) of qualifying capital expenditure incurred during the first five years. The allowance can be utilized to offset<br>against the 70% of statutory income for each year of assessment.(100% for Sabah, Sarawak, Labuan, designated<br>Eastern Corridor of Peninsula Malaysia and high technology)|
|Incentive for Industrial Adjustment|Not applicable anymore|
|Incentives to strengthen the Industrial<br>Linkage Scheme Incentive for Large|Tax deduction for expenditure incurred for training for employees, product development.|
|Companies - Incentives for Vendor|Pioneer status or an ITA status for five years with 100% exemption on the statutory income.|

**Table C.3: Incentives Specifically for MSC Status Companies**

|Financial|Pioneer Status with a tax exemption of 100% of the statutory income for a period of 10 years or Investment Tax Allowance of 100% on the<br>of 5years to be offset against 100% of statutoryincome for eachyear of assessment.|
|---|---|
||Eligibilityfor R&Dgrants(for majorityMalaysian-owned MSC Status companies)|
||Duty-free import of multimedia equipment|
||Globallycompetitive telecommunication tariffs|
||Freedom to source capital for MSC infrastructuregloballyand the right to borrow fundsglobally.|
|Other|Intellectualproperty protection and a comprehensive framework of cyberlaws|
||No censorshipof the Internet|
||World-classphysical and IT infrastructure|
||Globallycompetitive telecommunication services|
||Consultancyand assistance bythe Multimedia Development Corporation to companies within the MSC|
||Highquality, planned urban development|
||Excellent R&D facilities|
||Green andprotected environment|
||Freedom of ownership.|
||Unrestricted employment of local and foreign knowledge workers|

Source: MIDA (http://www.mida.gov.my/beta/view.php?cat=3&scat=6&pg=129); Cyberport (http://www.cyberport.cc/download//MSC%20Cyberport%20Brochure.pdf and http://www.cyberport.cc/institute-incentives.php).

54
# **_4.4. Plot level input use_**

In this section, we use the rich data on plot level characteristics to further explore input use variability at that level (recall footnote 4). Through both the household survey and georeferencing, we now explore the association between modern agricultural input use and a range of biophysical, institutional, and governance variables at the plot level.

# <u>Crops on plot</u>

Because nutrient needs, the incidence of pests, and the need for improved seeds will vary by plant type, we would expect farmers would allocate modern inputs differently across crops. In the LSMS-ISA data, we observe most input use, apart from seed type, at the plot (not crop) level. Instead of making assumptions about how inputs were distributed among the crops grown on a given plot, we lump plots into categories based on the types and portion of land under particular crops. While our categorization is rough, it attempts to isolate the “most important” crop on the plot, defined as comprising at least 50 percent of the plot area under cultivation (see note for Table 19 for more details). These categorizations offer an admittedly-imperfect attempt to categorize plots by the types of crops grown on them, but are arguably no more arbitrary than other classification schemes used in the literature.

A purposively chosen mix of crops for each country in the LSMS-ISA surveys can be found in Table 19 alongside the number of plots in each category and average input use values on those types of plots. Notice that in many countries the “other” category is the largest, pointing to the high degree of mixed and intercropping in these countries. Further, in Niger, most plots contained some mix of millet, sorghum, and cowpea on over half of the plot, so we created one category to lump those instances together.

One striking pattern is that plots with mostly maize are among those most likely to receive a modern input and with the highest application amounts. The two cases where maize plots are not always the most intensively cultivated – although still among the highest – are Ethiopia and Malawi where teff and tobacco plots, respectively, receive more inputs (Minten et al. 2013). Contrary to much prevailing prior belief, agro-chemicals do not appear confined to plots with horticultural or cash crops (which would fall into the “other” category), with relatively high percentages also on plots containing mostly grains. This finding is similar to Williamson, Ball, and Pretty (2008) who observed a very high rate of pesticide use not just on cash crops and vegetables, but also on staple crops. This observation in the LSMS-ISA data is especially important given the significance of maize as a food security crop for many households in SubSaharan Africa.

27
**Access to support or assistance was low across all households but slightly more common among families owning both types of businesses.** Approximately 22 percent of households enjoyed some kind of support. Families owning both types of businesses had a slightly higher propensity (23 percent) to receive assistance from any source, as compared to households with only farm or non-farm (or no) enterprises (Table 4). Families owning any type of business or owning both were more likely to receive assistance from a third party --i.e., the government, family, friends, whereas families not owning a business coped by drawing down (selling) their own assets or government assistance.

**Table 5. Income change and use of coping strategies since the start of the pandemic (Household Head Definition)**

||All|Self-employed<br>agricultural<br>enterprise|Self-employed<br>non-agricultural<br>enterprise|Wage<br>employee|<br>Countries<sup>31</sup>|
|---|---|---|---|---|---|
|_Share Reporting an Income_|_change, by sour_<br>|_ce of income_<br>||||
|Ttl i dli|0.75|0.68|0.89|0.55|4|
|oa ncome ecne|(0.078)|(0.126)|(0.060)|(0.190)||
|Farm income decline|0.51|0.47|0.51|0.42|7|
||(0.052)|(0.100)|(0.050)|(0.097)||
|f i li|0.83|0.74|0.85|0.84||
|Nonarm ncome decne|(0.024)|(0.046)|(0.040)|(0.033)|8|
||0.70|0.60|0.79|0.73||
|Own business decline|(0.044)|(0.075)|(0.050)|(0.071)|8|
|Wage income decline|0.46<br>(0.056)|0.36<br>(0.095)|0.51<br>(0.060)|0.29<br>(0.071)|8|
|_Share reporting Coping stra_|_tegies, by type o_|_f coping strategy_||||
|Sold assets|0.06|0.08|0.06|0.09|4|
||(0.025)|(0.062)|(0.030)|(0.062)||
|Private assistance (cash|0.06|0.06|0.07|0.08|6|
|or in-kind transfer)|(0.019)|(0.032)|(0.050)|(0.037)||
|Public assistance (cash|0.02|0.02|0.02|0.04||
|or in-kind transfer)|(0.007)|(0.010)|(0.010)|(0.012)|6|
|Any assistance from any|0.17|0.20|0.21|0.18|8|
|source|(0.052)|(0.119)|(0.119)|(0.096)||

Source: Own calculations based on HFPS-Wave 1.

Notes: Sample limited to urban household heads. Nonfarm (farm) income is reported for agricultural (nonagricultural) enterprises since income is reported at the household level while the source of income is only defined for the household head. Similarly for reported changes in income for wage employees.

> 31 Countries included: **Total income decline** (Ethiopia, Malawi, Nigeria, Senegal), **Non-farm income** (Ethiopia, Malawi, Nigeria, Sudan, Senegal, Sierra Leone, South Sudan, Zimbabwe), **Farm income** (Ethiopia, Malawi, Nigeria, Sudan, Sierra Leone, South Sudan, São Tomé and Príncipe), **Business revenue** (Burkina Faso, Ethiopia, Malawi, Nigeria, Sudan, Sierra Leone, South Sudan, Zimbabwe), **Wage income** (Ethiopia, Malawi, Nigeria, Sudan, Senegal, Sierra Leone, South Sudan, Zimbabwe), **Sold assets** (Ethiopia, Nigeria, Sudan, South Sudan), **Private assistance** (Burkina Faso, Ethiopia, Malawi, Nigeria, Sudan, Zimbabwe), **Public assistance** (Ethiopia, Malawi, Nigeria, Sudan, South Sudan, Zimbabwe), **Any assistance from any source** (Burkina Faso, Ethiopia, Malawi, Nigeria, Sudan, Sierra Leone, South Sudan, Zimbabwe).

15