rafmacalaba/datause-encoder-data huggingface dataset:
specificity based on the typology inferred from Drieschova, Giordano, and Fischhendler (2008).<sup>3</sup> While these mechanisms may not be substitutable (as different contexts may warrant different mechanisms), we expect those countries governed by treaty mechanisms that display both flexibility and a more specific, or direct, allocation regime to exhibit more cooperation in their hydro-relations.

# <u>4.1.2. Institutional mechanisms</u>

As elaborated in the previous section, treaties contain a range of institutional arrangements that are expected to facilitate cooperation, by reducing uncertainty, providing monitoring and enforcement, introducing conflict resolution mechanisms, allowing side-payments, etc. In this study we document the absence or presence of a number of such institutions, including whether or not the treaty covering the particular dyad contains an mechanism or enforcement, a selfenforcement mechanism, provision for monitoring, a conflict resolution procedure or mechanism, a river commission or some sort of standing body to oversee and manage riparian relations, and some arrangement for adaptive management.

We assess both the total number of such institutions as well as the presence or absence of each individual type. In all cases, theory predicts that, ceteris paribus, the more such institutions the greater the level of cooperation (and lower level of conflict). We include both a count variable (number of institutional mechanisms), as well as a binary variable indicating the presence or absence of each type of institutional arrangement, in order to isolate the impact of each one.

> 3 For example, the “fixed quantities” allocation mechanism is not flexible yet specific. On the other hand, the “percentage” allocation mechanism is both flexible and specific. While not as flexible as the “percentage” allocation mechanism, the “fixed quantities which vary according to water variability” allocaton mechanism does evince a level of flexibility (as the allocation is based on available water) as well as specificity. The “prior approval” allocation mechanism, like the “consultation” and “prioritization of uses” allocation mechanisms, is flexible but relatively ambiguous and open ended in specifying actual allocations since the agreement itself stipulates no direct allocation amounts.

11
# **Table A6: Heterogeneity in Effects: Quantile Regression By Absolute Error**

## **Dependent Variable: Z-score of errors across policy domains in columns Quantile regression estimates Standard Errors: Clustered at the district government-sector level**

||**(1) Percentile =**|**(2) Percentile =**|**(3) Percentile =**|**(4) Percentile =**|**(5) Percentile =**|**(6) Percentile =**|**(7) Percentile =**|**(8) Percentile =**|**(9) Percentile =**|
|---|---|---|---|---|---|---|---|---|---|
||**0.1**|**0.2**|**0.3**|**0.4**|**0.5**|**0.6**|**0.7**|**0.8**|**0.9**|
|**Federal Government [Yes=1]**|0.0095|0.045|0.057|0.055|0.071|0.014|-0.011|-0.021|0.066|
||[0.034]|[0.038]|[0.053]|[0.055]|[0.072]|[0.078]|[0.092]|[0.10]|[0.13]|
|**District Government [Yes=1]**|-0.08|-0.15|-0.21|-0.21|-0.20|-0.21|-0.27|-0.40|-0.26|
||[0.039]|[0.053]|[0.080]|[0.078]|[0.076]|[0.088]|[0.10]|[0.11]|[0.14]|
|**Manager [Yes=1]**|0.019|0.019|0.031|0.038|0.0074|-0.014|0.0035|0.023|0.083|
||[0.029]|[0.031]|[0.031]|[0.032]|[0.051]|[0.066]|[0.075]|[0.090]|[0.10]|
|**Sector fixed effects**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**District government fixed effects**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Indicator fixed effects**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Percentile fixed effects**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Individual controls**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|||||1455[92]|||||

**Notes:** Standard errors are in parentheses.  Standard errors clustered at the district-government-sector interacted level.  Columns report quantile regression estimates for different quantiles of the distribution of the z-score of absolute errors.  The unit of analysis in all columns is the claim.   The sample used for the analysis is the set of respondents that make claims about the same districts.  The dependent variable is the z-score of the absolute error.  Sector fixed effects are indicators for whether the claim is within an agriculture, education, health, revenue, or trade organization.  Percentile fixed effects are indicators for the 75th, 50th or 25th percentile for the respective indicator for the respective region.    Indicator fixed effects control for the service delivery indicator within which the claim is made. Manager is equal to one if the claim is from a manager.  Individual characteristics included in the specification:  tenure in the civil service of the respondent making the claim;  tenure in the organization;  an indicator for whether the respondent is female; an indicator for whether the respondent holds an undergraduate degree; an indicator for whether the respondent holds a masters degree. Figures are rounded to two decimal places or two significant figures.
|**Table A23: Merchant Internation**<br>**Data Provider**<br>Merchant International Group|**al Gro**|**up G**|**ray**|**Area**|**Dyn**|**amics**|**(MI**|**G)**||
|---|---|---|---|---|---|---|---|---|---|
|**Description**<br>Commercial risk rating agency|headquar|tered in|Lond|on, Un|ited K|ingdom||||
|**Website**<br>www.merchantinternational.co|m|||||||||
|**Data Source**<br>Gray Area Dynamics.||||||||||
|**Type**<br>Expert assessment||||||||||
|**Respondents**<br>Merchant International Group s|taff|||||||||
|**Frequency**<br>Quarterly since 1994||||||||||
|**Coverage**<br>Global sample of countries||||||||||
|**Public Access**<br>Full dataset is commercially av<br>this spreadsheet.|ailable.|Averag|es of|sub-ind|icators|are pu|blicly|availa|ble in|
|**Description**<br>Provides assessments of risks t<br>point scale.|o foreign|investo|r pos|ed by 1|0 risk|factors|asses|sed on|a 10-|
||2007|2006|2005|2004|2003|2002|2000|1998|1996|
|**Voice and Accountability**||||||||||
|NA|..|..|..|..|..|..|..|..|..|
|**Political Stability and Absence of Violence**||||||||||
|Extremism|X|X|X|X|X|X|..|..|..|
|**Government Effectiveness**||||||||||
|Bureaucracy|X|X|X|X|X|X|..|..|..|
|**Regulatory Quality**||||||||||
|Unfair Trade|X|X|X|X|X|X|..|..|..|
|Unfair Competition|X|X|X|X|X|X|..|..|..|
|**Rule of Law**||||||||||
|Legal Safeguards|X|X|X|X|X|X|..|..|..|
|Organized Crime|X|X|X|X|X|X|..|..|..|
|**Control of Corruption**||||||||||
|Corruption|X|X|X|X|X|X|..|..|..|
|_Country coverage_|**156**|**156**|**155**|**155**|**155**|**118**|**..**|**..**|**..**|
|_Year of Publication_|**2007**|**2006**|**2005**|**2004**|**2003**|**2002**|**..**|**..**|**..**|

61
|Estimation of equation (5), incl. 1st-step controls,|x time X,|x t, and X,, t, t|?, union FE*|||||
|---|---|---|---|---|---|---|---|
|Level of cell non-food expenditures (in log)|Clothing|Personal care|Furniture|Phone|Education|Transport|Other|
|per commodity type|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Part 1: Total level of cell non-food expenditu<br>|res<br>|||||||
|Estimated risk ofpolygamy,FE|2.386<br>|-7.895<br>|-20.814***<br>|2.215<br>|12.056"<br>|-8.152<br>|-12.605**<br>|
||(5.529)|(5.537)|(7.711)|(7.371)|(6.659)|(8.486)|— (4.971)|
|Number ofobservations|1482|1478|1482|1478|1482|1482|1482|
|Number of unions|741|739|TAL|739|7Al|TAL|7Al|
|Within R2|0.076|0.081|0.47|0.13|0.14|0.17|0.085|
|Unconditional mean (wave 1, in FCFA)|46723.4|25307.3|2289.2|9376.8|9483.7|11055.5|= 11055.5|
|Part 2: Level of expenditures funded by the|wife|||||||
|Estimated risk of polygamy, F;|-9.387<br>|3.358<br>|-2.395<br>|2.698<br>|-6.635*<br>|-2.327<br>|-0.852<br>|
||(7.152)|(8.529)|(3.260)|(6.648)|(3.991)|(6.924)|—(2.616)|
|Number of observations|1478|1478|1478|1478|1478|1478|1482|
|Number of unions|739|739|739|739|739|739|7Al|
|Within R2|0.100|0.092|0.044|0.096|0.044|0.11|0.051|
|Unconditional mean (wave 1, in FCFA)|7202.7|13167.5|792.1|2818.6|1200.9|3613.3|3608.4|
|Part3:Levelofexpendituresfundedbythe|husband|||||||
|<br>Estimatedriskofpolygamy,F;|<br>18.598**|-7.901|-11.289°*|12.962**|=18.926°**|-1.576|=-5.797+|
||(8.628)|(7.953)|<br>(4.839)|(6.281)|<br>(6.845)|(8.090)|<br>(3.547)|
|Number of observations|1478|1478|1478|1478|1478|1478|1482|
|Number of unions|739|739|739|739|739|739|7Al|
|Within R2|0.058|0.089|0.073|0.080|0.062|0.073|0.082|
|Unconditionalmean(wave1,inFCFA)|33916.8|11893.9|1004.0|5578.1|7360.0|6381.1|6372.4|

Panel union fixed effect model estimates. Standard errors in parentheses are bootstrapped (300 replications); * p<0.10, ** p<0.05,*** p<0.01 Dependent variables: Level of cell non-food per capita expenditures per type of commodities

# Sample: monogamous wives in the two waves

F;: predicted failure probability estimated by a Cox survival model stratified by the polygamy status of the husband's father (see Table 1) All regressions include union fixed-effects

* Controls not shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents, all step-1 controls (see Table 1) interacted with time

In all columns, we control for the level of non-food consumption (in log) Data source: PSF Survey, waves 1 and 2 (2006-2007 and 2010-2012)
include village dummies (in order to account for<sup>_v_</sup> _j_<sup>) and compute standard-errors robust</sup> to an arbitrary variance structure within villages. In addition, to assess the robustness of our results, we will also estimate equation (1) on the subsample of farmers who did not own a mobile phone in 2003. \

# _4.2. Selection on Unobservables_

The decision to purchase a mobile phone might be correlated with unobservable household characteristics ( _wij_ ) which would lead to biased estimates. We deal with this problem by using instrumental variables (IV). As usual, we need instruments that are correlated with the decision to purchase a mobile phone but uncorrelated with _wij_ (other than through their impact on the decision to purchase a mobile phone). We will combine information on mobile phone availability at the village-level and the highest education levels achieved among children in school in 2003 in the household arguing that mobile phone adoption is driven mostly by the educated younger generation within households.

Using data from the GSM coverage dataset described above, we create a variable capturing the supply of mobile phone service at the village-level. This variable is equal to the number of years (in 2006) since mobile phone service became available in the village. This variable, which is correlated with the decision to purchase a mobile phone, is unlikely to be correlated with _wij_ . However, this variable is constant within villages and we need an instrument with household-level variation. We create such a variable by interacting it with the highest education levels achieved among children in school in 2003 in the household. The rationale is that more educated individuals are more likely to hear about and to adopt new technologies such as mobile phones. Moreover, young people within the household, whom educational attainment will have plausibly little influence on household consumption, are likely to be instrumental in terms of convincing their elders to purchase a mobile phone. We exclude older individuals in the household as their 8
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percent. In the water and sewage sector, the average treatment capacity, if the work is to construct water and waste water plants, is 0.08 million m<sup>3</sup> per day. A pipe installation work involves about 33 km of iron pipes on average.<sup>10</sup> Half the contracts are related to the water supply sector. Note that a single contract sometimes covers both water supply and sewage works. While one-third are related to treatment plant construction, two-thirds aim at developing water distribution and collection networks. The electricity sector is most complicated, covering many different elements. The average installed capacity is 118 MW in our sample. If a contract includes transmission line installation, the “average” description may be 51 km of 43 kV lines.

<u>Table 3. Summary Statistics</u>

||Obs|Mean<br>|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|Roads||||||
|Bid amount (million US$)|394|29.40|28.57|0.28|161.22|
|Number of bidders|394|8.02|5.27|2|21|
|Total road length (km)|394|48.97|65.85|2.24|448.00|
|Number of lanes|394|2.87|1.78|1|8|
|_D_(New roads)|394|0.35|0.48|0|1|
|_D_<br>(Rehabilitation work)|394|0.29|0.45|0|1|
|Cost estimate (million US$)|394|34.21|38.36|0.28|145.99|
|Contract duration (month)|394|31.61|11.75|5.00|50.00|
|Water and sewage||||||
|Bid amount (million US$)|329|15.00|23.13|0.33|276.66|
|Number of bidders|329|6.67|3.62|2|16|
|_D_<br>(Water)|329|0.44|0.50|0|1|
|_D_(Treatment plant)|329|0.35|0.48|0|1|
|_D_(Network)|329|0.64|0.48|0|1|
|Treatment capacity (million m3)|329|0.08|0.16|0.00|0.60|
|Total concrete tunnel length (km)|329|0.97|3.78|0.00|24.85|
|Total iron pipe length (km)|329|33.21|59.62|0.00|375.58|
|Cost estimate (million US$)|329|13.74|16.85|0.33|154.26|
|Contract duration (month)|329|28.23|16.66|3.00|72.00|
|Electricity||||||
|Bid amount (million US$)|139|49.06|71.96|0.22|435.49|
|Number of bidders|139|5.32|3.62|2|18|
|_D_(Dam)|139|0.09|0.29|0|1|
|_D_(Generator)|139|0.30|0.46|0|1|
|_D_<br>(Trans. lines)|139|0.26|0.44|0|1|
|_D_(Substation)|139|0.26|0.44|0|1|
|_D_(Civil work)|139|0.65|0.48|0|1|
|Installed capacity (MW)|139|118.74|277.71|0.00|1200.00|
|Number of generators|139|1.37|2.78|0.00|12.00|
|Transmission line capacity (kV)|139|43.54|119.96|0.00|500.00|
|Total transmission line length (km)|139|50.58|149.22|0.00|765.00|
|Cost estimate (million US$)|139|44.27|66.69|0.25|406.61|
|Contract duration (month)|139|28.42|13.52|3.00|66.00|

Source: Author’s calculation.

10 These figures are underestimated, because both treatment plant and network contracts are pooled in our model. If a contract involves either component, the other capacity measurement is set at zero.
values. Average labor productivity is lowest in Paraguay, although the median value is lower in Bolivia (where the average is inflated by outliers). In contrast to the figures for labor productivity, mean total factor productivity is highest in Paraguay, although it is third highest here when measured at the median. Average total factor productivity is lowest in Argentina, although the lowest median value is recorded in Bolivia.

_Table 2: Productivity by country in 2017_

|**Argentina**|**Bolivia**|**Colombia**|**Ecuador**|**Paraguay**|**Peru**|**Uruguay**|
|---|---|---|---|---|---|---|
|**Labor productivity (sales per worker i**<br>|**n 2009 US$) ‐‐ a**|**ll establishm**|**ents:**||||
|**Mean**<br>99,548.50|108,376.60|66,436.85|98,319.01|53,152.62|84,202.32|135,458.40|
|**Median**<br>54,734.67|14,071.38|31,169.38|49,863.20|29,677.77|41,789.34|75,701.70|
|**N**<br>845|287|929|357|312|832|274|
|**Total factor productivity ‐‐ manufactu**|**ring establishm**|**ents:**|||||
|**Mean**<br>2.02|2.34|2.45|2.84|2.91|2.12|2.49|
|**Median**<br>2.08|1.82|2.37|2.57|2.46|2.06|2.86|
|**N**<br>181|53|409|100|39|279|42|

Note: Survey weights are applied in the computation of the data presented above The cumulative distributions of labor productivity (expressed in logs) and total factor productivity are displayed in Figure 1 below. The graph illustrates that there is no clear stochastic dominance among the countries, although the left panel of Figure 1 does show that establishments in Bolivia generally perform poorly in terms of labor productivity, whereas establishments in Uruguay perform well. The right panel of Figure 1 corroborates what was displayed in Table 2—while establishments in Argentina have relatively high labor productivity, their relative performance is less strong according to total factor productivity.

10
**Figure 7: Changes in income after outbreak**

# **7B: Household farming, livestock, or fishing income**

<!-- Start of picture text --> 7A: Share of households with a loss of income after outbreak<br>(as % of HHs with this source of income in the last 12 months)<br>100.0<br> 100.0<br>80.0<br> 80.0 44.0<br>59.1<br>67.6 71.6<br>60.0 60.0<br> 40.0<br>40.0 79.0 78.9 82.9<br>54.1<br>55.3 20.0 27.4 19.2 35.5<br>20.0<br> - 1.9 5.0 9.3 5.4<br>Ethiopia (April/May Malawi (May/June Nigeria (April/May Uganda<br>0.0 2020) 2020) 2020) (June 2020)<br>Ethiopia Malawi (May/June Nigeria (April/May Uganda<br>(April/May 2020) 2020) 2020) (June 2020) Increased No change Decreased<br>7C: Non-farm family business income 7D: Wage employment income<br>(as % of HHs with this source of income in the last 12 months) (as % of HHs with this source of income in the last 12 months)<br> 100.0<br> 100.0<br> 80.0 30.4<br> 80.0<br>55.4 52.4<br>64.9<br> 60.0 83.4 83.5 60.0<br>90.1 93.2<br> 40.0 40.0<br>69.0<br>40.0 43.0<br> 20.0 30.4<br> 20.0<br>8.6 11.3 10.5 3.9 - 0.6 4.6 4.6 4.7<br> - Ethiopia (April/May Malawi (May/June Nigeria (April/May Uganda<br>Ethiopia (April/May Malawi (May/June Nigeria (April/May Uganda<br>2020) 2020) 2020) (June 2020)<br>2020) 2020) 2020) (June 2020)<br>Increased No change Decreased<br>Increased No change Decreased<br><!-- End of picture text --> _Source_ : HFPS Round 1.

11
- 25 -

(3.12') loge, - 7.37 - 0.30 log _Px,_ - 0.52 log _Pm,_ (5.94) (-3-21) (-3.58) ( 0.64 log (1 **_-tx)_** _-_ ( 0.62 _Log_ (1 _+t),_ (-1.44) (-1.90) _ 0.57 _Log (DC),_ (-2.99) Y R' = 0.96, R<sup>2</sup> =<sup>0.94, DW = 1.39</sup>

> <sup>=0.83, DW = 1.76, Chow (2,8) = 0.48,</sup> t = 1970 - 87 + 2<sup>Forecasts (1988-89), R2</sup> Forecast X<sup>2</sup> (2)/2 =<sup>1.68</sup>

t-statistics are in<sup>parentheses. Both equations have high degree of explanation and the error</sup> correction equation<sup>appears to be adequate for the data generating process with the DW equal to</sup> U.5. The specification<sup>is also shown to be stable according to the stability tests as indicated by the</sup>
and oligopsonistic arrangements, making the pricing of cotton by‐products less transparent.

This paper uses the analytical framework developed in the World Bank’s multi‐country cotton study. It finds that though the SSA countries are unlikely to be able to produce biodiesel profitably from cotton oil at foreseeable levels of global energy prices, both cotton oil and meal production should feature explicit‐ ly in these countries’ cotton sector policymaking. These by‐products have in‐ creased their contribution to the total value of cotton and seem likely to maintain their enhanced value. The paper also finds that large cottonseed processing op‐ erations using advanced technology, while efficient from a technological pers‐ pective, tend not to be economically profitable in the SSA context. Smaller, less technologically efficient, labor‐intensive operations perhaps constitute the future of the industry in SSA.

The paper is structured as follows. Section 1 examines the value and uses of cotton by‐products, and recent trends in their contribution, versus that of lint, to the total value of cotton. Section 2 looks at the cotton oil market in the context of the global vegetable oil market, while Section 3 discusses issues regarding the use of cotton oil as a biofuel feedstock. Section 4 summarizes experience with the use of cotton by‐products in the five countries. Section 5 offers policy conclusions and recommendations with the goal of enhancing the contribution of cotton by‐ products to the welfare of African cotton growers.

# **1. The Value and Uses of Cotton By­Products**

Though cotton is often taken to be synonymous with cotton lint, the farm prod‐ uct, seed cotton, also contains the by‐product cottonseed.<sup>**2**</sup> Of lesser value than lint, cottonseed is typically crushed to separate oil from meal. Cotton oil, which competes with other vegetable oils, is typically used for human consumption and sometimes for soap manufacturing and other industrial uses such as plastics and pharmaceuticals. Cotton meal, which competes with other meals, is typically used as animal feed, while hulls or husks (the outer part of the seed) are either used to generate energy for the processing facility or are blended with meal for animal feed; less often hulls and husks are used as fertilizer. Linters—which are

> **2** For the rest of this paper, seed cotton will refer to the farm product. Cottonseed and lint are the two co‐products emerging from the ginning process and, depending on the context, output can be measured in terms of tons of lint or tons of cottonseed. Lint goes to textile manufacturing while cottonseed is crushed to produce cotton oil and cotton meal. To make the discussion easier, in this paper meal will refer to both meal and cake even though the two are different.

**_Page 2_**
A large number of full BIA studies have been undertaken in the developing world."<sup>1</sup> Typically these studies have been undertaken on a piecemeal basis by different authors with little regard for comparability of assumptions with other studies. Comparing the results of different studies is thus somewhat heroic and runs the risk of showing spurious differences due to differences in assumptions. Nonetheless, it is tempting and-one hopes-potentially instructive to do so.

**Figure** 16 shows the concentration indices for government subsidies to the health sector for various developing countries, two industrialized countries (Denmark and the United Kingdom), and various states within India. In the countries with positive concentration indices, the better-off get a disproportionate share of the subsidy, while in the countries with negative indices health sector subsidies are targeted toward the poor. A few developing countries apparently manage to achieve propoor distributions of public spending on health care-Costa Rica and Malaysia stand out, but several Latin American countries (though not Brazil and Peru) manage to target their health sector subsidies on the poor. Both Denmark and the United Kingdom do as well, but only one state in India-Kerala-does.1<sup>2</sup> For many developing countries, however, and the majority of states within India, the picture is one of health sector subsidies-presumably intended primarily for the poor-being expropriated largely by the better-off. In Guinea, for example, the poorest quintile received only 4 percent of public spending on health, while the richest quintile received a staggering 48 percent (Castro-Leal and others 1999). This is failure on a massive scale.

**Figure 16. Inequalities in receipt of government subsidies to health sector**

<!-- Start of picture text --> 0.50 -<br>0.40 4<br>0.30 -<br>*~0.10<br>C<br>0.00 U. .<br>0<br>-0.10<br>-0.20<br>A F3~~=<br>E 0 V<br><!-- End of picture text --> _Source:_ Argentina, Brazil, Bulgaria, Chile, Colombia, Ghana, Guyana, Indonesia, Kenya, Madagascar, Malaysia, Mongolia, S. Africa, Uruguay, Vietnam (Filmer, Hammer, and Pritchett 1998); China author's calculations; Costa Rica World Bank Poverty Assessment; Denmark (Christiansen 1993), Guinea (Castro-Leal and others 2000): Honduras (World Bank 1997); India (World Bank 2001); Peru (World Bank 1999); UK (Propper and Upward 1993).

"See (Castro-Leal and others 2000, Sahn and Younger 2000, Yaqub 1999) for recent multicountry BIA studies or survers of the health sector.

Kerala is, in fact, the only state in the whole country-not just the states shown in the chart-to do so.

_34_