rafmacalaba/datause-encoder-data huggingface dataset:
|Industrial Credits Project||
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|Industrial Credits Project||
|Port Development Project||
|ForestryProject||
|ForestryProject||
|ForestryProject<br>||
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|ForestryProject||
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|HighwayProject(06)||
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|Education Project(03)||
|Education Project(03)||
|Education Project(03)||
|Education Project(03)||
|Power Project(03)||
|Power Project(03)||
|Urban Development Project||
|Urban Development Project||
|Urban Development Project||
|Urban Development Project||
|Urban Development Project||
|Urban Development Project||
|Urban Development Project||
|Urban Development Project||
|Rural Development Project(02)||
|Rural Development Project(02)||
|Rural Development Project(02)||
|Rural Development Project(02)||
|Rural Development Project(02)||
|Rural Development Project(02)||
|Education and TrainingProject(04)||
|Education and TrainingProject(04)||
|Education and TrainingProject(04)||
|Education and TrainingProject(04)||
|Education and TrainingProject(04)<br>||
|Education and TrainingProject(04)||
|Education and TrainingProject(04)||
|Education and TrainingProject(04)||
|Power Project(04)|Marcelo Amador Osorio|
|Power Project(04)|Marcelo Amador Osorio|
|Power Project(04)|Marcelo Amador Osorio|
|Power Project(04)|Marcelo Amador Osorio|
|Power Project(04)|Marcelo Amador Osorio|
|Power Project(04)|Marcelo Amador Osorio|
|Port Au Prince Water SupplyProject|Emmanuel N. Njomo|
_Figure 8b: Reason for vaccine hesitancy, countries with multiple responses_ <!-- Start of picture text --> Kenya<br>Uganda<br>Sudan<br>Mali<br>Nigeria<br>Gambia<br>Georgia<br>0 20 40 60 80 100 120 140 160<br>Safety (includes side effects)<br>Efficacy<br>Distrust (of government/pharmaceutical industry/international community)<br>Dislike vaccines in general<br>Prefer natural immunity<br>Lack of knowledge/access<br>Religious<br>Not eligible<br><!-- End of picture text --> Notes: The X axis represents share of vaccine hesitant households. In some cases, the share is larger than 100% because multiple responses were allowed For the construction of harmonized response categories, see Appendix Table 2b.

# **4. Discussion**

# _4a. Principal findings_

Data from the HFPS were used to investigate COVID-19 vaccine hesitancy. On average across 53 countries, one in five adults are hesitant about getting a COVID-19 vaccine. The highest levels were observed in Eastern European and Central Asian countries and the lowest levels in Latin American and Caribbean countries. Among population groups, female respondents, younger adults, and those with less formal education reported higher levels of COVID-19 vaccine hesitancy than their respective counterparts. Between October 2020 and August 2021, there tended to be little change in levels of hesitancy except in Iraq, Malawi, and Uzbekistan, where hesitancy increased. The main self-reported reason for being hesitant was concerns over safety, especially worries about side effects, and to a lesser extent, concerns over vaccine efficacy and dislike of vaccines in general.

# _4b. Strengths and comparisons with other studies_

This study has several advantages over the existing literature. It is based on a very large sample of respondents from national surveys in 53 developing countries, a part of the world that is underrepresented in the literature. Over half of the surveys used random digit dialing, typically in upper middleincome countries in which a large share of the population uses mobile phones. In lower income contexts, most surveys, 35 percent overall, sampled based on previous face-to-face surveys which were in turn drawn from a census frame. In the remaining 10 percent of cases, sampling was carried out from lists provided by mobile phone operators. In each case, surveys drawn from pre-existing face-to-face surveys were reweighted using the baseline data to become more representative, while many random digit dialing surveys were also reweighted to make those samples more representative of the universe of phone numbers. While it is impossible to eliminate issues of representativeness in phone surveys, the high17
26

# _Table A-1 (continued)_

# _Panel C - Decentralization varaibles added to both models_

|Varia|bles|Definition<br>|Cross-<br>Sect.|Obs.|Mean|Max.|Min.|Std. Dev.|Data<br>Source|
|---|---|---|---|---|---|---|---|---|---|
||Subnational<br>Tax<br>Autonomy|<br> <br>Ratio of subnational tax<br>revenue on which subnational<br>governments have control<br>over total subnational<br>government revenue|10|30|0,078|0,252|0,000|0,075|OECD|
|<br> <br>Decentrali-<br>|Subnational<br>Tax Sharing|<br> <br>Ratio of subnational tax revenue<br>on which subnational<br>governments have no control on<br>total subnational government<br>revenue|10|30|0,476|0,910|0,144|0,181|OECD|
|<br>zation (Ebel<br>and Yilmaz)|Subnational<br>Fiscal<br>Dependen-<br>cy|<br>Ratio of subnational grant revenue<br>excluding general purpose grants<br>with objective criteria and<br>unconditional specific grants over<br>total subnational government<br>revenue|10|30|0,155|0,531|0,000|0,140|OECD|
||Subnational<br>Non-Tax<br>Autonomy|<br> <br>Ratio of subnational non-tax<br>revenue over total subnational<br>government revenue|10|30|0,177|0,363|0,040|0,076|OECD|
||Subnational<br>Revenue<br>Autonomy|<br>Ratio of own source revenues<br>over total revenues of<br>subnationalgovernments|10|30|0,370|0,657|0,071|0,158|OECD|
|<br>New<br>decentrali-<br>zation<br>|Subnational<br>own<br>revenue<br>ratio<br>|<br>Ratio of subnational own<br>source revenue over<br>aggregate government<br>revenue<br>|10|30|0,068|0,212|0,016|0,040|OECD|
|measures|Subnational<br>dependent<br>revenue<br>ratio|<br>Ratio of subnational<br>dependent revenue over<br>aggregate government<br>revenue|10|30|0,121|0,204|0,028|0,053|OECD|
agricultural production and incomes of smallholder farmers (Chirwa & Dorward, 2013). The program provides two 50 kg bags of inorganic fertilizers to farmers and a choice of improved maize and legume seeds; however, the successor program AIP no longer makes legume seeds available due to fiscal challenges. The AIP has also introduced alternative cereals such that farmers have a choice of improved seeds for maize or rice or sorghum. With growing concern about climate change, there is a greater need than ever for drought tolerant crops specified as sorghum, millet, cassava, and potatoes to increase diversity (CIAT and World Bank, 2018). Small ruminants, mainly goats, are also featured in the AIP for selected districts. Thus, opportunities exists to move the program away from the bias on subsidies for maize in line with government agricultural transformation agenda (GoM, 2020). Other changes aim to improve beneficiary targeting, with a focus on productive poor farmers—unlike in the past, when target groups included resource poor and vulnerable households (Holden & Lunduka, 2012; Logistics Unit, 2017). It remains to be seen if the program successfully includes the productive poor and excludes those farmers unable to effectively use the inputs. In the same vein, possible program graduation was not stated or included in the program design. There is limited private sector involvement in the program, which could cause input market disruption (Mason & Ricker-Gilbert, 2013; J Ricker-Gilbert, Jayne, & Chirwa, 2011). Other challenges relate to the late distribution/ delivery of inputs and network connectivity issues at market outlets since the national registration system is used for identifying farmers (Centre for Development Management (CDM), 2017).

# **2.2 Social cash transfer programs**

Malawi’s government implements an unconditional social cash transfer program (SCTP) with the objective of increasing school enrollment and alleviating poverty and hunger. The program targets ultra-poor households – those unable to meet basic needs and labor constrained. The SCTP started as a pilot project in Mchinji district in 2006. By September 2017, the program had expanded to 18 out of the 28 districts in the country (Pace et al., 2017). Currently, the program has national coverage and benefits 1.3 million people from 300,800 ultra-poor households (GoM, 2022). The government estimates an average household benefit of MK9000 (equivalent to 5 USD; 1USD=MK1700) that has since increased to MK14,919 per month.<sup>4</sup> The transfer amount is

> 4 <u>https://mtukula.com/content?view=18&pageName=Cash%20Transfers</u>

5
relating to a household’s assets (including consumer durables) and amenities, including materials used for housing construction and its access to water and sanitation. These variables are then aggregated into an index using factor-analytic methods, with the wealth index being identified as the first principal component of the data. The DHS wealth index comes as a z-score, i.e., standardized with mean zero and standard deviation of unity. So the index is country specific—not intended to be comparable across countries.

We focus on the poorest 20% and 40% of households based on the wealth index. These are arbitrary choices, although the 40% figure does coincide fairly closely with the overall poverty rate found for SSA using the World Bank’s international line.<sup>27</sup> The 20% figure allows us to focus more on the lower part of the wealth distribution. We also provide key results for the full range of the distribution.

It should not be forgotten that the DHS wealth index is a proxy, not a direct measure of wealth. The index focuses on durable and productive asset wealth rather than labor or education wealth, arguably the main assets of many among the poor. When compared to the results of a full-blown consumption survey, the DHS index will undoubtedly count as poor some who are not (often called “inclusion errors”) and count as non-poor some of those who are in fact poor (“exclusion errors”). In practice, policy makers targeting poor households almost never have access to accurate measures of wealth or consumption for the population as a whole, and must rely instead on a relatively small number of indicators, such as those embodied in the DHS index. Nonetheless, we also conduct the analysis using household consumption per capita for the sub-set of countries for which this is feasible. Surveys that contain detailed household consumption data as well as anthropometrics for women and children are not common, but some do exist including within the LSMS (specifically the LSMS Integrated Surveys on Agriculture) as listed in Table 1.<sup>28</sup> The consumption variable is spatially deflated and expressed in per capita terms.

In an attempt to test whether controlling for additional information, including education and labor assets, enhances predictive power, we draw on household and individual covariates from both surveys. Variables based on the consumption surveys are constructed to be as similar as possible to those used in the DHS data.

> 27 Using the World Bank’s international line of $1.90 a day at 2011 purchasing power parity, 43% of the population of SubSaharan Africa are found to be poor in 2013 (based on <u>PovcalNet).</u>

> 28 Only the consumption survey from Ghana is not one of the Integrated Surveys on Agriculture within the LSMS.

14
- 46 -

Table 18: Average Annual Hours of Instruction at the Primary Level (Glades 1-6) in the 1980s by Per Capita Income of the Country and Level of Primay Enrollment Expansion

**Per Capita Income Level:**

|||Develo|ping Countrie|s|||
|---|---|---|---|---|---|---|
||Low-<br>Income|Lower<br>Middle-<br>Income|Upper<br>Middle-<br>Income|High-<br>Income|Developed<br>Countries|World<br>Totals|
|Mean value|800.9|926.6|834.7|884.1|868.3|866.1|
|Standar Deviation|<br>_127.8_|129.9|107.2|223.7|132.9|141.1|
|Number of Cases|(N=18)|(N=24)|(N=13)|(N=6)|(N=9)|(N-70)|

||**Gross Primary**|**Enrollment Rate**||
|---|---|---|---|
||(only devel|oping countries):||
||Less than_Q_|Greater than 80 %|lsaw,|
|Mean value|881.0|855.4|862.3|
|Standard Deviation|172.4|130.0|141.6|
|Number of Cases|(N=15)|(N40)|_(N=55)_|
employment is the lowest. This implies a trade-off: a very high (low) immediate employment effect of given spending is unlikely (more likely) to be very beneficial in the longer run, in terms of spurring technological development and growth, since largely low- (high-) quality labor is stimulated. Secondly, the import content of demand may vary. Domestic employment will then be stimulated most when the import share is the smallest; with a high import share much or most of the stimulus will rather spill over to foreign economies. While having a low import share is unambiguously favorable from a domestic demand management perspective, stimulating demand in sectors with low labor productivity is not unambiguously favorable. Ideally, we wish to allocate any free labor power to activities that yield the highest possible returns; which is often likely to clash with our aim to induce a maximal employment effect.

# **6.2 Results for Developing Countries**

We next examine more specific (but so far, scant and spotty) information on costs of short-run “green” job creation in developing countries. Table 6.2 below provides projected figures for the South Korean green stimulus proposal.<sup>27</sup> The assessed employment effects of this package per US$ billion of spending are similar to those for the US, which may seem surprising as wage levels and labor productivities are lower in South Korea. Note however that the two tables are not fully comparable, as only direct employment effects are counted in Table 6.2, whereas indirect and multiplier effects are counted in the US-based assessment in Table 6.1. The sector that stands out as having the most impact on employment per unit expenditure is, perhaps not surprisingly, forest restoration. We see otherwise that “vehicles and green energy” have relatively poor employment-generating effect, while “energy conservation” and “environmentally friendly living space” generate relatively many jobs per dollar spent.

**Table 6.2: Employment Effects of Green Spending Items in South Korea’s “Green Stimulus” Package**

|Spending item|Total<br>Employment<br>Increase|Total Planned<br>Spending (US$ m)|Employment<br>Increase/US$ Bn<br>Added<br>Expenditure|
|---|---|---|---|
|Mass transit|138,000|7,005|19700|
|Energy conservation|170,000|5,840|29100|
|Vehicles and clean energy|14,300|1,490|9600|
|Env friendly living space|10,800|350|30900|
|River restoration|200,000|10,500|19000|
|Forest restoration|134,000|1,750|76600|
|Water resource management|16,000|685|23400|
|Resource recycling|16,000|675|23700|
|Green information|3,000|270|11100|
|Total|703,000|28,600|24600|

Source: Barbier (2009).

> 27 South Korea is not a typical developing country (it is also an OECD member). We include it here rather than above, as it is a “lower-income” country compared to the others considered above.

19
# TABLE _5_

# **Reasons why the Girls thought they were not in School**

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|---|
|.PS:CC'<br>"<br>S:.'<br>**'-.**<br> <br> <br> <br> <br>|
|P.akistanInstitutea<br>of Deeomnt<br>Edunatics.<br>Ilme.ad<br>1986.|
**Figure 3: Yield trends in major smallholder crops, 1990 to 2007**

<!-- Start of picture text --> 4000<br>3000<br>2000<br>1000<br>0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006<br>hybrid maize local maize burley tobacco<br>crop yield estimate (kg/ha)<br><!-- End of picture text --> Source: Ministry of Agriculture and Food Security, Annual Crop Estimates.

# **Food prices**

11. **Food prices in general and maize prices in particular are much higher and highly variable compared to the trends experienced in previous years.** Although the reasons for the high food prices are not fully understood, it appears that they are triggered partly by speculative behavior by traders and cautious response by farmers to the escalating commodity prices on the international market. As can be seen in Table 2, in February 2008, the price of maize had more than doubled from MWK21 per kg in the previous year (Feb 2007) to MWK43 per kg<sup>4</sup> . From January 2008 to March 2008, the price had increased by 10 percent, with March representing the peak of the lean season. The maize harvest period which started in March in some parts of the country did not result in the weakening of the prices, as expected. As such, maize prices have kept on an upward trend, currently reaching over MWK60 per kg. The high volatility of prices between the harvest (March – June) and lean season (October – February) is typical of thin maize markets (see Figure 4). Farmers are compelled to engage in distress selling at low prices during the harvest period, only to buy back the maize during the lean season at much higher prices during the lean season. Over 80% of the smallholder farmers end up being net food buyers. Except for the warehouses that belong to the Government parastatal, the Agricultural Development and Marketing Corporation (ADMARC), private warehousing is limited. Private sector investment is curtailed by Government interventions in the maize market.

> 4 For most of this period, the exchange rate was fairly stable and the domestic inflation remained in single digit, as such, maize price increase is not attributed to domestic inflation.

6
in a linear model when compared to legal origin and religion, we constructed a five point scale for the variables, based on their quantiles, to ensure the same number of categories as legal origin. In this section we provide further evidence of the non-linearities by adding the square and cube of each of the institutional variables to see if it makes a difference to the adjusted R-squares of the model. In Table 7 we present results using the raw data as well as higher order terms and compare it to the contributions from the rescaled variables. The results are shown for the full sample of 80 countries as well as smaller sub-samples of non-transition economies and former colonies. The first column of Table 7 presents results using raw data (when ethnic fractionalization and latitude are not rescaled) for the full sample of countries. We see that Legal Origin explains the most variation (3.89%) in firms’ perceptions of property rights protection. In the second column we include higher order terms of the institutional variables in the regression model. Note that the higher order terms are included only for the continuous variables and not for country dummies, legal origin dummies or religion dummies. The ethnic diversity and endowment theories increase in explanatory power by as much as 411% and 94% respectively when we use higher order terms of these variables but Legal Origin still dominates. The third column repeats the results from Table 3 where we used rescaled values for latitude and ethnic fractionalization. The third column also shows that there is a substantial increase in explanatory power of the ethnic fractionalization (by a factor of 7) and latitude variable (by a factor of 2.2) on rescaling. The table suggests that the results in current literature may be biased by ignoring non-linearities present in several of the institutional theories.

# **Insert Table 7**

We repeat the above analysis for a sample of 56 non-transition countries and a sample of 40 former colonies. In both cases we observe substantial increase in explanatory power when we use higher order terms of some institutional variables or rescale them. Our results from Tables 4 and 5 that the dominance of Law and Finance view is not robust to sample specification are confirmed again.

22
Hand, D.J., Anagnostopoulos, C., 2013. When is the area under the receiver characteristic curve an appropriate measure of classifier performance? Pattern Recognition 80, 400–414.

Krzanowski, W.J., Hand, D.J., 2009. ROC Curves for Continuous Data. CRC Press—Chapman and Hall, New York.

Gigliarano, C., Figini, S. and Muliere P. (2014) Making classifier performance comparisons when ROC curves intersect, Computational Statistics and Data Analysis 77, 300-312.

Lusted, L.B., 1971. Signal detectability and medical decision-making. Science 171, 1217–1219.

Martin-Shields, C. and Stojetz, W. (2017) Framing the Relationship Between Food Security and Violent Conflict Data: A Clustering and Typology Approach, World Development, forthcoming.

Maxwell, D. Young, H., Jaspars, S., Burns, J. and Frize, J. (2011) “Targeting and Distribution in Complex Emergencies: Participatory Management of Humanitarian Food Assistance.” Food Policy Vol. 36(4), pp. 535–543.

Morris, S., Levin, C., Armar-Klemesu, M., Maxwell, D. and Ruel, M.T. (1999). “Does Geographic Targeting of Nutrition Interventions Make Sense in Cities? Evidence from Abidjan and Accra.” World Development Vol. 27(11), pp. 2011–2019.

Thomas, L.C. (2009). Consumer Credit Models: Pricing, Profit, and Portfolios. Oxford University Press.

van Weezel, S. (2017) Food, Drought and Conflict: Evidence from a Case-Study on Somalia.

Verme, P.; Gigliarano, C.; Wieser, C.; Hedlund, K.; Petzoldt, M.; Santacroce, M.. 2016. The Welfare of Syrian Refugees : Evidence from Jordan and Lebanon. Washington, DC: World Bank. World Bank. https://openknowledge.worldbank.org/handle/10986/23228 License: CC BY 3.0 IGO.” Wodon, Q. (1997) Targeting the Poor using ROC Curves. World Development, Vol. 25, Issue 12, pp. 20832092.

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about the relevance of debt aversion. Two recent empirical studies from selective colleges in the United States provide evidence for the presence of debt aversion.

In a recent study Field (2006) reports on an experiment in which law students at New York University were randomly assigned to one of two financial aid conditions. The first, standard, condition is a loan repayment assistance program (LRAP) which forgives all graduates who choose careers in the public sector or other low paying fields of law the majority of educational loans incurred during law school through quarterly prospective funding for up to ten years following graduation. The second, innovative, condition consist of public service scholarships (PSS) that provide grants of two-thirds tuition that converted to a loan in the event that a recipient did not pursue a public interest law career. The two schemes are identical in terms of their financial consequences, and differ only in the duration of indebtedness. Under the LRAP scheme people have a debt position right from the start. Under the PSS scheme people only carry a debt burden when they do not pursue a public interest law career. Field’s (2006) results show differences in outcomes between the two treatments. In classes for which the lottery was announced prior to enrolment, those in the PSS treatment are twice as likely to enroll. Moreover, those in the PSS treatment are also substantially more likely to have a first job in public interest law. Fields (2006) interprets her findings as evidence of debt aversion: behavior is consistent with utility being negatively affected by carrying debt loads.

Rothstein and Rouse (2007) analyze data from a highly selective university that introduced a “no-loans” policy under which the loan component of financial aid awards was replaced with grants. They use this natural experiment to identify the impact of student debt on 23