rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3 – GPS vs Compass and Rope (CR) measures, by plot size classes**

_<u>Means; Acres</u>_

|Level (CR)|||Ethiopia||Mean<br>Bias /|Difference|||Tanzania||Mean<br>Bias /|Difference|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||N|GPS|CR|Bias|Mean CR|in means|N|GPS|CR|Bias|Mean CR|in means|
|1 (< 0.05 acres)|390|0.02|0.02|0.00|0%|‐|45|0.04|0.04|0.00|‐3%|‐|
|2 (< 0.15 acres)|400|0.10|0.09|0.00|2%|***|631|0.11|0.11|0.00|2%|***|
|3 (< 0.35 acres)|365|0.24|0.24|0.01|3%|***|823|0.23|0.23|0.01|2%|***|
|4 (< 0.75 acres)|328|0.52|0.51|0.01|2%|***|326|0.51|0.49|0.02|4%|***|
|5 (< 1.25 acres)|182|0.98|0.96|0.02|2%|***|63|0.94|0.92|0.02|2%|***|
|6(>= 1.25 acres)|100|1.91|1.89|0.02|1%|‐|20|1.91|1.81|0.09|5%|‐|
|Total|1765|0.38|0.38|0.01|2%|***|1908|0.28|0.27|0.01|3%|***|

|Level (CR)|||Nigeria||Mean<br>Bias /|Difference|||Pooled||Mean<br>Bias /|Difference|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||N|GPS|CR|Bias|Mean CR|in means|N|GPS|CR|Bias|Mean CR|in means|
|1 (< 0.05 acres)|‐|‐|‐|‐|‐|‐|436|0.02|0.02|0.00|‐1%|‐|
|2 (< 0.15 acres)|21|0.11|0.11|‐0.01|‐7%|***|1052|0.10|0.10|0.00|2%|***|
|3 (< 0.35 acres)|73|0.24|0.25|‐0.01|‐4%|***|1261|0.24|0.23|0.01|2%|***|
|4 (< 0.75 acres)|129|0.52|0.53|‐0.01|‐2%|**|783|0.51|0.50|0.01|2%|***|
|5 (< 1.25 acres)|108|0.97|0.99|‐0.02|‐2%|***|353|0.97|0.96|0.01|1%|‐|
|6(>= 1.25 acres)|153|2.86|2.87|‐0.01|0%|‐|273|2.44|2.43|0.01|0%|‐|
|Total|485|1.30|1.31|‐0.01|‐1%|*|4158|0.44|0.44|0.00|1%|***|

*p<.1; ** p<.05; *** p<.01

Note: Results for categories in which there are fewer than 20 observations are not reported. The same is true for all tables presented in the paper.

34
- 3 -

should own infrastructure? Who should operate? Who should invest in public assets? Under what circumstances could those tasks be delegated to different agents? What circumstances call for bundling them together under a single agent? The theoretical approach that views public procurement as a problem of bundling and unbundling multiple distinct tasks could reply to these questions (e.g., Hart, 2003; Bennett and Iossa, 2005). While bundling everything under state ownership means nationalization of infrastructure, unbundling some of the tasks can represent some forms of private sector participation in infrastructure, as in public private partnerships (PPP).<sup>2</sup> <!-- Start of picture text --> Figure 1. Government decisions and theoretical models<br>In-house or contract out?<br>Incentive theory<br>Contract theory<br>Bundle or unbundle<br>construction and operation? Bundling model<br>How to select contractors? Auction theory<br>Ex post adjustment or<br>Ex post adjustment model<br>incentive contract?<br><!-- End of picture text --> Third, when the scope of public tasks being delegated to outside contractors is determined, governments need to make a decision on how to select the best private partner. This is addressed largely by auction theory, which is normally discussed under the totally different setting from (incomplete) contract theory. The selection of agents or contractors raises a series of questions. How should auctioneers design the award mechanism for efficiency purposes? What mechanisms are available to assess potential venders, operators and contractors? How could governments prevent and detect collusion and corruption? Is there any optimal level of competition in public tendering? How could governments encourage

> 2 The economic literature on public procurement has made tremendous progress in recent decades. See, for example, the comprehensive study of Laffont and Tirole (1993). On public–private partnerships, see Harris (2003) for an account of recent trends in developing countries. Also see IMF (2004).
# **Figure 1: Variation in cabinet size in Sub-Saharan Africa**

Notes: The dashed line indicates the pooled sample median (22). N = 1,797. The data are from Arriola (2009) with authors’ updates coded from subsequent editions of Europa Publications, Africa South of the Sahara.

We examine the relationship of cabinet size with a number of plausible outcome measures indicated by the preceding discussion. One principal source is the well-known _Worldwide Governance Indicators_ (WGI) data set produced by the World Bank since 1996, which includes perception-based estimates across several dimensions of governance, aggregating up a number of variables from different sources (Kaufmann et al. 2010). Of these indicators, we use the variables 6
Intervention Size and Persistence<sup>∗</sup> Florence Kondylis<sup>†</sup> John Loeser<sup>†</sup> JEL Classification Codes: C93, D12, D14, E21, O12 Keywords: Cash transfers, Long run impacts, Cost effectiveness

> ∗This research benefited from suggestions and comments from Pierre Bachas, Paul Christian, Emma Frankham, Margaret Grosh, Jonas Heirman, Maximilian Kasy, Erin Kelley, Greg Lane, Arianna Legovini, Jeremy Magruder, David McKenzie, Berk Özler, Patrick Premand, Hee Kweon Seo, seminar audiences at the World Bank and Göttingen University, and Development Impact readers. The authors acknowledge generous funding from the Office of Evaluation of the World Food Programme. Marc-Andrea Fiorina and Eric Jospe provided excellent research assistance. The views expressed do not reflect the views of the World Bank. All errors are our own.

> †Development Impact Evaluation, World Bank
**<u>Table 1: National Food-Based Dietary Guidelines by Food Group, and Decisions for Regional Food-Based Dietary Guidelines</u>**

|**Food Group**|**Bang**|**ladesh**|**Indi**|**a**|**Sri L**|**anka**|**Ave**|**rage**|**Decision for Regional FBDG**|
|---|---|---|---|---|---|---|---|---|---|
||**Min**|**Max**|**Min**|**Max**|**Min**|**Max**|**Min**|**Max**||
|**Starchy**<br>**staples**|270g<br>(dry)|450g<br>(dry)|270g (dry)|600g (dry)|300g (dry)|550g (dry)|280g|533g|Use India's guideline amounts<br>(widest range); require 2 unique<br>starches<br>_Serving size = 30g_<br>_Min servings = 9_<br>_Max servings= 20_|
|**Vegetables**|300g (at<br>least<br>100g<br>DGLV|600g|300g (at least<br>100g DGLV)|300g (at<br>least 100g<br>DGLV)|3 cups (raw)<br>= 90–390g|5 cups (raw)<br>= 150–650g|300g|433g|<br>Use Bangladesh's guideline<br>amounts (widest range); require<br>3 unique vegetables, including<br>one DGLV<br>_Serving size = 100g_<br>_Min servings = 3_<br>_Max servings= 6_|
|**Fruits**|100g<br>(one<br>citrus,<br>one vit<br>A-rich)|300g<br>(one<br>citrus, one<br>vit<br>A-<br>rich)|100g|100g|1 whole<br>(120g)|2 whole<br>(240g)|107g|213g|<br>Use rounded average, 100–200g;<br>require 2 unique fruits<br>_Serving size = 100g_<br>_Min = 1_<br>_Max= 2_|
|**Pulses/Fish/**<br>**Meat/Eggs**|1/3–1/2<br>c (60–|1⁄2 c<br>pulses|60g pulses|120g<br>pulses|3 servings<br>fish, pulses,|4 servings<br>fish, pulses,|70g pulses|107g pulses|Use Sri Lanka's guideline<br>amounts (clearest and most|
||100g)<br>pulses +<br>1 piece<br>fish or<br>meat<br>(50g?)|(100g) +<br>4 piece<br>fish or<br>meat<br>(200g?)|DG says aim<br>for 100–200g/<br>week fish, but<br>no flesh food<br>required<br>(50g/serving)|DG says<br>don’t<br>exceed 3<br>eggs/week|meat or<br>eggs (90g<br>total)|meat or<br>eggs (120g<br>total)|or 50–90g<br>ASF|or 120–200g<br>ASF|comprehensive across protein<br>sources). Require 2 unique<br>protein sources and do not<br>require meat.<br>_Serving size = 30g for pulses,_<br>_50g for meat/fish/egg_<br>_Min = 3_<br>_Max= 4_|
|**Dairy**|100–<br>150ml|200ml|300ml|300ml|200ml|400ml|200ml (or<br>60g milk<br>powder)|300ml (or<br>90g milk<br>powder)|Use mean, 200–300ml; require 1<br>dairy source (e.g., powdered<br>milk, fresh milk, or yogurt)<br>_Serving size = 100ml (or 30g_<br>_milk powder)_<br>_Min = 2_<br>_Max= 3_|
|**Fats and Oils**|30g|60g|20g|40g|2 servings<br>nuts or<br>oilseeds<br>(30g total)|4 servings<br>nuts or<br>oilseeds<br>(60g total)|30g|60g|Use mode, 30–60g<br>_Serving size = 15g_<br>_Min = 2_<br>_Max= 4_|

_Source_ : Compiled by authors from _Dietary Guidelines for Bangladesh_ 2013; _Dietary Guidelines for Indians – A Manual_ 2011; _Food Based Dietary Guidelines for Sri Lankans_ 2011. _Note:_ Bangladesh and Sri Lanka use the same image for their food pyramids, although recommended servings differ slightly.
Accounting for the respondent’s primary source of information results in a significant increase in the age effect for three out of six questions. The age effect almost doubles for the question of whether climate change is real and whether it is manmade and increases the share of respondents who believe that it will affect their children by about 36 percent. These large and statistically significant changes do not alter the relationships uncovered by our baseline estimations, however; they do not eliminate the positive age gradient in climate change awareness and risk perceptions.

Respondents who plan to move away from their current place of residence, either within their country or abroad, do not exhibit significantly different levels of skepticism regarding the reality of climate change or its anthropogenic character, but they are more likely to think that climate change will affect both them and children. These correlations do not affect the age gradient observed in our baseline specification.

Risk aversion, beliefs that success is meritocratic, trust in government, and a preference for the market economy are all associated with climate awareness. Beliefs in meritocratic success and preference for the market economy are associated with greater perceived risks of climate impacts. Respondents with high future discount rates appear less convinced that climate change is real, and that variable has no significant effect on other climate beliefs. Including these confounding factors does not affect the age gradient.

If all confounding variables described above are added to the regression, the age-climate gradient becomes similar in magnitude to the regression that controls for the use of social media, which appears to account for the bulk of the overall increase in the marginal effect of age on climate beliefs.

For all countries in our sample except nine countries in the Middle East, North Africa, and Western Europe, we have sub-national administrative data on a number of environmental risks as well as critical infrastructure that can contribute to climate resilience. We capture the following dimensions of environmental risks: (i) _flood frequency_ – the number of flood incidents between 1985 and 2022 from the Dartmouth Flood Observatory; (ii) _drought risk_ – frequency of severe drought when more than 30 percent of cropland is affected over 1984−2022 from the Food and Agriculture Organization; (iii) _heat risk_ – extreme heat hazard based on daily maximum 100 year return period, from the Global Facility for Disaster Reduction and Recovery; (iv) _landslide risk_ – 17
Table 4: Correlations across countries and methods

|||Pea|rson|||Spea|rman||
|---|---|---|---|---|---|---|---|---|
||EBP|Cubist|XGB|BRF|EBP|Cubist|XGB|BRF|
|**Panel A: Assets**|||||||||
|Burkina Faso|0.743|0.734|0.850|0.694|0.715|0.698|0.812|0.683|
|Madagascar|0.875|0.907|0.910|0.906|0.795|0.847|0.851|0.849|
|Malawi|0.664|0.768|0.844|0.664|0.658|0.794|0.848|0.741|
|Mozambique|0.897|0.921|0.917|0.922|0.781|0.812|0.803|0.827|
|Sri Lanka|0.919|0.937|0.935|0.916|0.884|0.911|0.911|0.908|
|Tanzania|0.868|0.911|0.899|0.895|0.804|0.862|0.842|0.838|
|Vietnam|0.868|0.890|0.904|0.866|0.873|0.887|0.902|0.863|
|Average|0.833|0.867|0.894|0.838|0.787|0.830|0.853|0.816|
|**Panel B: Poverty**|||||||||
|Malawi|0.786|0.865|0.851|0.737|0.786|0.841|0.839|0.710|
|Tanzania|0.836|0.874|0.858|0.794|0.852|0.889|0.872|0.832|
|Average|0.811|0.870|0.854|0.766|0.819|0.865|0.855|0.771|

Note: The first four columns present the simple mean of pearson correlation across simulations for each country. The last four columns present the simple mean of spearman correlation across simulations for each country. The results are based on 100 simulations for each country and method.
**39**

Lipton, Michael (1977): _Why Poor People Stay Poor: Urban Bias and World Development_ , Temple Smith, London, and Harvard University Press.

Loayza, Norman and Claudio Raddatz. (2005): ‘The Composition of Growth Matters for Poverty Alleviation’ World Bank (mimeo, September).

Martin, Will, and Devashish Mitra (2001) “Productivity Growth and Convergence in Agriculture versus Manufacturing”, _Economic Development and Cultural Change_ 49(2), pp.402-422.

Maxwell, S. (2004) Launching the DFID consultation “New Directions for Agriculture in Reducing Poverty”, Department for International Development. Available at http://dfid-agricultureconsultation.nri.org/launchpapers/simonmaxwell.html Mellor, John (1976): The New Economics of Growth: A Strategy for India and the Developing World; Ithaca, NY, USA; Cornell University Press.

Mitchell, T.D., T.R. Carter, P.D. Jones, M. Hulme, and M. New (2003): “A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (1901-2000) and 16 scenarios (2001-2100)”; _Journal of Climate_ : submitted.

North, Douglas (1959): “Agriculture in Regional Economic Growth”, _Journal of Farm Economics_ 41(5), pp.943-951.

Perry, G., D. de Ferranti, D. Lederman, W. Foster and A. Valdes (2005): _Beyond the City: the Rural Contribution to Development—Complete Report_ ; World Bank, Washington D.C.

Ravallion, Martin (1997) ‘Can High Inequality Countries Escape Poverty?’ _Economic Letters 56(1), pp.51-57._ Ravallion, M. (2001). “Growth, Inequality and Poverty: Looking Beyond Averages” _World Development_ 29(11), pp.1803-1815.
26 45. If these figures are accurate, they depict a massive underuse of agricultural land by larger farm enterprlses. In view of the possibility that Idle land may be expropriated by the state, andowners would surely tend to understate the proportion of tholr holdig not currently In production. Therefore, the census data probably underestkuate the amount of land loft Idle. On the other hand, If the data ar accurate, they still reflect a hlgh level of underexpoitatlon; also, If ndownr did own up to the amount of land left out of production, thth would suggest that they do not treat very seriusly the threat of expropriation. However, In all probabillty, the data are not accurate (see Appendix A).

_46._ Since the vast majority of ejidos are IndIvIdual rather than collective, there Is some justification for treating the parcel rather than the total land endowment of the community as the basic unit of enterprise. There Is a large gap between the area formed by summing all the parcels In Individual ejidos (equal to 17 percent of land In holdings) and the area covered by the whol ejido sector (ust over half the land In holdings) (Table 8). This gap corresponds prknarlly to the communa **l** ands of Individual ejidos; the collective ejidos contribute to a lesser degree.

47. The considerable amount of land tied up In communal areas raises kmportant policy Issues: If'this land were poorly managed It would greatly reduce the overall productivity of the ejido sector In relation to private farms. From a crop output perspective poor management Is probably not a slgniftcant constraint since much communa **l** and Is steeply sioping with poor soils and and lknited crop potential: even If management were significantly kmproved the kmpact on crop output would probably be lklited.

48. However, the returns resulting from hmproved management of communal forest and rough grazing land would probably be considerable. At the moment,
**Table 6: Determinants of household consumption**

||_OLS _|_(I)_<br>|_OLS_|_(II)_<br>|_MLE(_|_III)_<br>|
|---|---|---|---|---|---|---|
|_Independent_<br>_Variables_|_Coefficients_|_Standard_<br>_Errors_|_Coefficients_|_Standard_<br>_Errors_|_Coefficients_|_Standard_<br>_Errors_|
|Uses destructive fishing gear|||0.028|0.033|0.074**|0.031|
|Household size|-0.084***|0.013|-0.085***|0.013|-0.086***|0.013|
|Dependent ratio|-0.275***|0.093|-0.272***|0.093|-0.276***|0.093|
|Female head of household|-0.215**|0.090|-0.221**|0.090|-0.229***|0.092|
|Household head’s age|-0.022**|0.009|-0.023**|0.008|-0.022***|0.008|
|Head’s age squared|0.000**|0.000|0.000**|0.000|0.000**|0.000|
|Household head literate|0.105**|0.049|0.102**|0.044|0.099**|0.042|
|Mean male education|0.001|0.006|0.001|0.006|0.001|0.006|
|Mean female education|-0.004|0.005|-0.004|0.005|-0.005|0.005|
|Farmer|-0.148**|0.059|-0.150**|0.059|-0.152***|0.059|
|Fisher|-0.055|0.062|-0.064|0.064|-0.079|0.063|
|Employed|0.050|0.053|0.047|0.054|0.042|0.055|
|Number of income sources|0.040***|0.014|0.039***|0.014|0.040***|0.014|
|Access to credit|-0.111|0.075|-0.114|0.074|-0.116|0.073|
|Own land|0.068|0.062|0.068|0.063|0.069|0.063|
|Value of physical assets|8.8e<sup>-8</sup>***|3.3e<sup>-8</sup>|8.8e<sup>-8</sup>***|3.3e<sup>-8</sup>|8.9e<sup>-8</sup>***|3.3e<sup>-8</sup>|
|Participates in village meetings|0.026*|0.015|0.024*|0.015|0.023|0.015|
|Participates in AIGA|0.018|0.072|0.020|0.072|0.022|0.073|
|MPA village|-0.125***|0.029|-0.121***|0.031|-0.114***|0.031|
|Observations|648||648||648||
|RSquare/Loglikelihood|34.75||34.79||-6411||

Notes:

*** , **, * indicate significance at the 99, 95, and 90 percent confidence interval, respectively Robust standard errors reported, adjusted for clustering effect <u>Coefficient for dummy variables for each of the MPAs and constant not reported</u>

36
havior ignore the health externality of risky sexual behavior, such a transfer program can be justified by the negative externalities generated by a higher number of HIV positive individuals within a society. That is, the (expected) transfer can be viewed as a Pigouvian subsidy aimed at correcting the externality.

The use of lotteries and thus an uncertain return contingent on behavioral change makes lottery incentives different from traditional CCT programs. The expected utility of a lottery with cash prizes depends on individuals’ attitude towards monetary risk. If individuals exhibiting risk-loving preferences in monetary gambles are also more risk-loving in other domains, including sexual behavior, lottery incentives may be particularly effective in targeting individuals with the highest risk of HIV/AIDS. Moreover, if people tend to overestimate small percentages, as growing evidence from prospect theory suggests (Kahneman, 2011; Barberis, 2013), the perceived return from participating in a lottery may also be higher than the return from an incentive program that pays the expected return with certainty, or likewise lotteries may provide stronger incentives for behavioral change compared to a traditional CCT holding the budget constant.<sup>2</sup>

# **2.3 Trial design**

The was a randomized trial. It had three arms – a con- study parallel group separate trol arm with an allocation ratio of 40% and two intervention arms (low-value lottery and high-value lottery) with an allocation ratio of 30% each. In the low-value lottery arm individuals were eligible to win lottery prizes worth 500 malotis or approximately $50 every four months. In the high-value lottery arm individuals were eligible to win lottery prizes of twice that amount. Lotteries were organized every fourth month and an individual in the intervention arms was awarded a lottery ticket if he/she tested negative for two curable STIs (syphilis and _trichonomiasis vaginalis_ ) in the week before the lottery draw. In expected terms, and conditional on being STI negative, the lottery paid $3.3 every four months in the low-value lottery group, $6.6 in the high-value lottery group and $4.9 in the pooled intervention group. In the pooled intervention group, and over the two year trial period, participants were thus eligible to receive approximately $30 in expectation.

Both lottery prizes represent a meaningful proportion of household income in a country where GDP per capita was $2,433 in 2012. The study population were primarily shepherds and self-employed and data from the Lesotho Labor Force Survey (2008) show that monthly earnings in the informal sector were 235 maloti (approximately $23.5) for men 2Furthermore, a large, albeit uncertain, payment may be preferable to a lower but certain payment flow if consumers face saving constraints and cannot purchase an indivisible good out of current income.

5
61

# TABLE 15 EMPLOYMENT OF EXPATRIATES, BY CURRENT SIZE OF FIRM AND ETHNIC GROUP BY ENTREPRENEUR

|||||Current Size<br>|of Firm (Numb<br>|er of Workers<br>|)<br>||
|---|---|---|---|---|---|---|---|---|
|||c50<br>|51-100<br>||_101-250<br>|251-500<br>|501-999<br>|>1000<br>|All<br>|
|wortionof<br>,iloying<br>.bltiates|T<br>pb<br>NP<sup>0 </sup>|0/3<br>0/3<br> -<br>0/0|2/5<br>2/4<br>01|_419_<br>2/5<br>1/3|4/6<br>2/3<br>2/3|5/9<br>1/2<br>4/5|1/1<br>0/0<br>1/1|16/33<br>5/14<br>10/16|
|aof|T|0.00|1.40|0.36|0.31|0.24|0.54|0.34l|
|atriates|P|0.00|0.00|026|0.23|0.08|0.00|0.17|
|100|NP|0.00|1.81||<br>0.55|0.40|0.41|0.54|0.50|

**I** oilW uribumi runl-pribumi
at 50 percent to one where tariffs are eliminated in six sectors where the US and other countries have indicated an interest from an exporting perspective – chemicals, electronics and electric equipment, forest products, jewelry, industrial machinery and sport equipment. These sectors are identified in WTO (2008). Under the baseline scenario, US exports are projected to increase 2.2 percent (export-weighted). Under a zero-for-zero deal for the six sectors, the overall change in US exports is 4.2 percent. Free trade in these sectors would therefore increase US exports by an additional 2 percentage points.<sup>20</sup> The marginal impact of zero-for-zero deals in the six sectors would roughly double the increase in US exports compared to the Doha baseline used for comparison purposes. Adler et al. (2009) come to a very similar conclusion: sectoral deals on chemicals, electronic and electrical goods, and environmental goods would double the trade and real income gains for their sample of 22 large countries.

These simulations suggest that sectoral agreements can be important in increasing the incentives of the relevant export industries to support multilateral trade negotiations. Zero-forzero and sectoral agreements were a feature of the Uruguay Round. Examples of such agreements – under which subsets of (mostly) OECD countries agreed to eliminate tariffs, either immediately or following a transition path – included deals on agricultural, construction and medical equipment; beer; furniture; paper; pharmaceuticals; and toys (Mann and Liu, 2009). The most prominent example of a zero-for-zero deal that was incorporated into the WTO is the Ministerial Declaration on Trade in Information Technology Products, generally called the Information Technology Agreement (ITA), concluded in 1997 by 39 countries accounting for 90 percent of world trade in the products covered (Fliess and Sauvé, 1998). Signatories agreed to eliminate tariffs over a three-year period on a MFN basis.

A well-known feature of multilateral trade negotiations is that participants seek to limit free-riding by other countries. For example, internalization, defined by Finger (1974, 1979) as the sum of all imports originating in countries with whom a country exchanges concessions as a percentage of total imports of goods on which concessions are made, was about 90 percent for the US in the Dillon (1960–1) and Kennedy (1964–7) Rounds. A sectoral agreement that is applied on a MFN basis is basically a mechanism to formalize a high level of “internalization” among the largest traders in a given product. What is needed is a “critical mass” of countries – what Schelling (1978) has called a “ _k_ -group”, the minimum number of countries (‘ _K_ ”) out of a larger

> 20 This is an upper bound, as it assumed that tariffs are removed on all 2,138 tariffs lines that are mapped to these six sectors, rather than the much more limited subset of tariff lines that are listed for each of the six sectors in WTO (2008). These calculations were done at the 6-digit level of the Harmonized System by Marcelo Olarreaga, based on the model and database described in Hoekman and Olarreaga (2007).

23