rafmacalaba/datause-encoder-data huggingface dataset:
rate). Study availability, however, was highest for studies having separate Annexes (33%), and 26 of the 27 such completed studies were available. Low completion rates may be explained by the large number of such studies included in more recently initiated prujects. This suggests that the more time that is spent preparing the study, the easier it is for Bank staff to obtain the results.

34. Size of research component. The absolute size of the research component (in monetary terms) is more important in determining success than the proportion of total project expenditures to support research. Studies embedded in research conponents of more than $0.5 million per project had a completion rate of nearly 50%. comnpared to a completion rate of 38% for studies embedded in research componenits of iess than $0.5 million per project. This difference was statistically significant X<sup>2</sup> (1, N =403) - 5.37, P < .05/5. This finding is similar to that for agricultural research _16,_ and suggests that there is some critical investment level above which research will be successfully implemented.

_Table 13: Distribution of studies and completion rates by size of comnmirments to research components, 1982-1989_

||Number<br>Projects|of|Planned<br>studies|As Perce<br>of all<br>studies|nt<br>Completed<br>studies|Completion<br>rate|
|---|---|---|---|---|---|---|
|Less than<br>$0.5 million|64||205|47.0%|78|38.0%|
|More than<br>$0.5 million|42||198|45.4%|98|49.5%|
|Not known/<br>not stated|10||33|7.6%|8|24.2%|
|Total|116||436|100.0%|184|42.2_2%_|

35. Support from Bank Research Committee. The Bank Research Committee has funded 34 studies within the education sector since 1970, many of which have been based on independent research. In only 4 (12%) instances has the research utilized data from project financed studies (Edurural in Brazil; Living Standards MNeasurement Surveys in Peru and Cote d'Ivoire; diversified schools in Colombia). Moreover, those

**IS** Studies within projects with unidentifiable **commitments** to research were not included. **T'he** Yate's .orrection **was** applied since

16 Pritchard, **Arihony** 1. (1990). Lending **by** the World **Bank** for Agricultural Research: A Review of the Years 1981 through 1987.World Bank Technical Paper No 118. Washington. D.C.: World Bank.

15
**Table 2: Physical Violence with Both Spouses Reporting**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Agree;Husband|0.048***|0.037***|0.042***|0.039***|0.044***|
||(0.007)|(0.007)|(0.007)|(0.007)|(0.008)|
|Wife says Husb;Husb says Joint|0.056***|0.049***|0.050***|0.048***|0.051***|
||(0.007)|(0.007)|(0.008)|(0.008)|(0.008)|
|Wife says Husb;Husb says Wife|0.037***|0.032**|0.031**|0.028**|0.036***|
||(0.010)|(0.010)|(0.010)|(0.010)|(0.011)|
|Wife says Joint;Husb says Husb|0.027***|0.022**|0.024**|0.022**|0.022**|
||(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|Wife says Joint;Husb says Wife|0.049***|0.046***|0.039***|0.038***|0.036***|
||(0.010)|(0.010)|(0.010)|(0.010)|(0.010)|
|Wife says Wife;Husb says Husb|0.131***|0.121***|0.123***|0.120***|0.118***|
||(0.015)|(0.015)|(0.016)|(0.016)|(0.016)|
|Wife says Wife;Husb says Joint|0.098***|0.094***|0.095***|0.094***|0.094***|
||(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|
|Agree;Wife|0.135***|0.128***|0.125***|0.124***|0.123***|
||(0.020)|(0.020)|(0.021)|(0.021)|(0.021)|
|Obsv|31229|31229|27890|27890|27657|
|R-squared|0.034|0.043|0.045|0.046|0.051|
|Mean of sample|.16|.16|.16|.16|.16|
|Country FE|Yes|Yes|Yes|Yes|Yes|
|HH controls|Yes|Yes|Yes|Yes|Yes|
|Attitudes|No|Yes|Yes|Yes|Yes|
|Preferences|No|No|Yes|Yes|Yes|
|Marriage|No|No|No|Yes|Yes|
|Bargaining|No|No|No|No|Yes|

(1) “Agree;Joint” is the reference category for the above table.

(2) Attitudes include attitude of husband and wife towards violence; Preferences include knowledge and alignment of preferences of husband and wife for children; Marriage includes years of marriage, total number of children the couple has, whether the couple is in a polygamous marriage; Bargaining includes wife’s education, age when she was married, difference in wife and husband’s education and age, whether husband and wife work, whether she earns more and relative ownership of assets.

(3) HH controls include whether household is in rural area, it has electricity and wealth quintile to which the household belongs.

(4) Robust standard errors in parentheses; * p<0.05, ** p<0.01, *** p<0.001 21
As it is clear from the left panel in Figure 2, the vast majority of the interviewed people did not leave <!-- Start of picture text --> Figure 2: Household members left behind<br>All sample Somebody left behind<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Percentage hh Percentage hh<br>Source: LDPS 2014-15<br>80 25<br>20<br>60<br>15<br>40<br>Percent Percent<br>10<br>20<br>5<br>0 0<br><!-- End of picture text --> any household member behind. However, there were some individuals who decided or were forced to migrate without other members of the household. Indeed, the right panel in Figure 2 shows that some households were split in two and that some migrants left without anybody else.

We can deepen our analysis by breaking apart the previous data by migrant status. Indeed, almost everybody (86%) among the refugees did not leave anybody behind, while the same figure for IDPs and returnees was around 57%. Furthermore, the household head or the spouse remained in the North during the crisis more frequently for returnees, while the same was very uncommon among refugees.

These migration patterns have caused deep changes in the composition of households. While 184 of the surveyed individuals were household heads before the conflict who managed to maintain their role during the crisis, 47 individuals completely changed status and effectively became ‘visitors’ (Table D1). This happened also to some other individuals, e.g. spouses or offspring, suggesting that some forcibly displaced individuals merged into recipient families, losing their original family role in the process.

**Table D1: Role inside the household before and after the crisis**

||||**Son/**|**Role Aft**<br>**Father/**|**er**<br>**Brother/**|||
|---|---|---|---|---|---|---|---|
|**Role before**|**Head**|**Spouse**|**Daughter**|**Mother**|**Sister**|**Other**|**Total**|
|**Head**|184|2|8|5|19|47|265|
|**Spouse**|2|56|1|4|21|27|111|
|**Son/Daughter**|2|0|15|0|3|29|49|
|**Father/Mother**|1|1|1|24|3|10|40|
|**Brother/Sister**|6|0|0|0|13|5|24|
|**Other**|1|1|0|0|0|10|12|
|**Total**|196|60|25|33|59|128|501|

7
**Table 4. TFP Gains from Equalizing TFPR within Industries**

|Country|Morocco|US|China|India|Mean of 52|
|---|---|---|---|---|---|
||||||MIC and LIC|
|Data Year|2013|1997|2005|1994|2002-11|
|TFP gain in %|83.8 (55.6*)|42.9|86.6|127.5|113.4|

_Note_ : Entries are 100 _∗_ ( _Yefficient/Y −_ 1). Numbers for Morocco are authors calculations. Numbers for the mean of 52 MIC and LIC are also authors calculations using results from Inklaar, Lashitew, and Timmer (2015). Values for the U.S., China, and India come from Hsieh and Klenow (2009), values for France are from Bellone and Mallen-Pisano (2013), and numbers for South Korea come from Oh (2016). *TFP gain (in %) from removing market distortions to the level of the less three distorted economies studied in this paper (US, France, and South Korea).

observed in China, despite differing considerably in economic size and structure. Finally, we observe that Morocco’s potential TFP gain are below the mean of 52 middle and low income countries (MIC and LIC). This does not mean necessarily that the level of distortions in Morocco is lower than the average of these 52 economies and than in India. Indeed, India for instance, has less market distortions than Morocco and still has higher potential TFP gain. This is due to the key role that the level of _physical productivity_ plays in computing efficiency gains in this framework.

A full-fledged liberalization would imply radical policy changes. However, it remains purely theoretical. From a practical point of view, it would be more suitable to compute the efficiency gains obtained if resource misallocation in Moroccan manufacturing reaches the level of less distorted economies. To perform this simple counter-factual exercise we choose the three countries in Table 4 with the lowest dispersion in TFPR (US, France, and South Korea). Subsequently we replace the distortion level of Morocco with the average of these three countries and we proceed to compute again the TFP gains associated to this new level of resource misallocation. We obtain a TFP gain in the Moroccan manufacturing sector of 55.6 percent from removing distortions to the level of countries with the best allocation of resources.

An ambitious liberalization program has then the potential to have a substantial impact in aggregate productivity. A full liberalization would imply that the manufacturing sector would almost double in size after the reform process. Furthermore, the strong increase in efficiency in this sector would lead potentially to a transfer of resources from other sectors of the economy (agricultural and services) to manufacturing. However, this broader effect would be damped by the extent to which distortions in the other sectors would be reduced as well causing an additional resource reallocation across sectors.

Finally, in this empirical exercise the effect of the informal sector on market distortions in manufacturing is captured only indirectly. Indeed, lower labor cost in the informal sector would lead to less than optimal labor demand from for16
INFRASTRUCTURE AND GROWTH IN AFRICA

**Figure 9. Potential growth per capita benefits in Africa due to higher infrastructure development**

Scenario 2: Reaching the infrastructure level of the median in the EAP7, using the [IK1] index of aggregate infrastructure and 2001–5 averages

# a. Africa

<!-- Start of picture text --> 8.0<br>7.0<br>6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>North Africa West Africa East Africa Southern Africa Central Africa AFRICA<br>Main Telephone Lines and mobiles Electricity Generating Capacity Length of Road Network<br>Quality of Telecoms Quality of Electricity Road Quality<br>b. North Africa<br>4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Algeria Egypt Libya Morocco Tunisia<br>Main Telephone Lines and mobiles Electricity Generating Capacity Length of Road Network<br>Quality of Telecoms Quality of Electricity Road Quality<br>c. West Africa (ECOWAS)<br>11.0<br>9.0<br>7.0<br>5.0<br>3.0<br>1.0<br>-1.0<br>Benin Burkina Faso Cote d'Ivoire Ghana Guinea Gambia, The Guinea Mauritania Niger Nigeria Senegal Sierra Leone Togo<br>Bissau<br>Main Telephone Lines and mobiles Electricity Generating Capacity Length of Road Network Quality of Telecoms Quality of Electricity Road Quality<br><!-- End of picture text -->

# b. North Africa

# c. West Africa (ECOWAS)

40
21). Other intermediation costs that are levied against the producers’ sales proceeds include Auction Holdings (AHL) cess (2.5 percent), Agricultural Research and Extension Trust (ARET) cess (1 percent), Tobacco Association of Malawi (TAMA) cess (0.6 percent), TAMA handling charges (0.5 percent) and Tobacco Control Commission re-classification and commission charges at 0.3 and 0.1 percent, respectively. Together these charges knock-off about 5 percent from the farmers’ proceeds. At processing level, the key costs are overheads and investment (7 percent), labor and management (4 percent), repairs and maintenance (4 percent) and energy and machine maintenance (8 percent). At the final delivery of the tipped and threshed (T&T) leaf, the main costs comprise transport to the final delivery point (9 percent), administration and overheads (9 percent), interest charges (3 percent) and the cost of boxes that are used to pack the processed leaf.

81. In addition to these costs, there are other hidden costs that are not easy to quantify, but nonetheless reduce the net profit for the producers. Such charges include storage charges at the rural depots and the costs related to the long waiting time to off-load the tobacco at the floors. On average trucks have to wait on long queues for about 2-3 weeks at the floors to off-load. Although there is a booking-in system which is meant to reduce waiting time, the system is marred by irregularities and inefficiencies that undermine the principal of first in first out. Thirdly, the producers also have to wait for weeks before receiving their sales proceeds.

**Figure 21: Burley, composition of assembly costs (%)**

<!-- Start of picture text --> Burley FAM-low<br>Composition of Assembly costs (%)<br>ARET cess TAMA cess TAMA TCC<br>1.0% 0.6% handling classification<br>AHL cess 0.5% 0.3%<br>TCC<br>2.4%<br>commission<br>0.1%<br>Transport to<br>floor<br>8.8%<br>Purchase from Farm<br>grower production<br>24.4% 62.1%<br><!-- End of picture text --> 82. Figure 22 shows the build- up of the financial costs along the entire value chain from production to the international export market for processed leaf. This is a complex chain with costs at many levels and with very few obvious entry points for possible cost reductions. The costs related to cesses, levies and taxes were already substantially reduced following the Tobacco sector reforms undertaken in 2005 (Jaffe 2003; World Bank 2008). There is therefore little scope to further reduce these costs. The overhead costs at the processing and exportation levels are commensurate with the services provided, especially given the high cost of machine maintenance, fuel and electricity. This study has not done a detailed de-bundling of the overheads to be able to objectively suggest whether there may be scope in reducing these. The only cost element in which significant cost reduction may be possible is in the tobacco transport system. As seen from the 38
Cattaneo, Cristina, Carlo V. Fiorio and Giovanni Peri. 2015. “What Happens to the Careers of European Workers When Immigrants “Take Their Jobs”?,” _Journal of Human Resources_ , 50 (3) 655-693.

Chiswick, Barry R. 1980. “An Analysis of the Economic Progress and Impact of Immigrants,” National Technical Information Service, no. PB80-200454. Washington, DC: U.S. Dept. Labor.

Chiswick, Barry R., Yew Liang Lee and Paul W. Miller. 2005. “A Longitudinal Analysts of Immigrant Occupational Mobility: A Test of the Immigrant Assimilation Hypothesis,” _International Migration Review_ , 39(2), 332–353.

Chiswick, Barry R. and Paul W. Miller. 2009. “The international transferability of immigrants’ human capital skills,” _Economics of Education Review_ , 28(2), 162-169.

Congressional Budget Office. 2006. “Potential influenza pandemic: Possible macroeconomic effects and policy issues”. <u>https://www.cbo.gov/sites/default/files/109th-congress-20052006/reports/12-08-birdflu.pdf</u> Constant, Amelie F. 2014. “Do migrants take the jobs of native workers?” _IZA World of Labor_ : 10 doi: 10.15185/izawol.10.

<mark>Corvalan, Carlos F., Timothy R. Driscoll and James E. Harrison. 1994. “Role of migrant factors in work-related fatalities in Australia.”</mark> _<mark>Scandinavian Journal of Work, Environment & Health</mark>_ <mark>20(5): 364-70.</mark> Cortes, Matias and Eliza Forsythe. 2020. “Impacts of the covid-19 pandemic and the cares act on earnings and inequality”, _IZA_ Discussion Paper No. 13643.

Damm, Anna Piil. 2009. “Ethnic enclaves and immigrant labor market outcomes: quasiExperimental evidence,” _Journal of Labor Economics_ , 27 (2): 81-314.

______ 2014. “Neighbourhood quality and labour market outcomes: evidence from quasi-random neighbourhood assignment of immigrants,” _Journal of Urban Economics_ , 79: 139-166.

D'Amuri, Francesco and Giovanni Peri. 2014. “Immigration, Jobs, and Employment Protection: Evidence from Before and During the Great Recession,” _Journal of the European Economic Association_ , 12 (2): 432-464.

Dávila, Alberto, Marie T. Mora, and Rebecca González. 2011. “English-Language Proficiency and Occupational Risk Among Hispanic Immigrant Men in the United States.” _Industrial Relations_ , 50 (April): 263-296.

Diaz, Esperanza, Svenn-Eirik Mamelund, Jarle Eid, Henriette S. Aasen, Oddvar Martin Kaarbøe, Rebecca J. C. Brokstad, Siri Gloppen, Anders Beyer, and Bernadette N. Kumar. 2021. “Learning from the COVID-19 pandemic among migrants: An innovative, system-level, interdisciplinary approach is needed to improve public health.” _Scandinavian Journal of Public Health_ 14034948211019795.

28
corruption is 0.88. While culture undoubtedly matters for the interpretation of survey responses across countries, we do not think that this is a first-order difficulty with crosscountry comparability in survey-based data on governance.<sup>16</sup> In short, as we saw when comparing measures of rules and measures of outcomes, in the case of expert assessments versus survey respondents, both types of data have their own unique strengths and weaknesses. Since neither type of respondent is clearly superior for all purposes, we think it important to continue to rely on a diversity of data sources in both dimensions of our taxonomy of governance indicators.

# **5.  Aggregate or Individual Indicators?**

Our discussion so far has focused on the strengths and weaknesses of alternative types of individual governance indicators. In this part of the paper we turn to the question of whether and when it makes sense to combine various such individual indicators of governance into aggregate or composite indicators combining information from multiple sources. In Table 1 we provide three examples of such aggregate indicators, the Worldwide Governance Indicators (WGI) that we have produced in other work, the well-known Corruption Perceptions Index (CPI) of Transparency International, and the very recently-released Ibrahim Index of African Governance. The WGI consist of six aggregate indicators of governance covering over 200 countries, combining crosscountry data on governance provided by 30 different organizations. The CPI measures only corruption, using a smaller set of data drawn from nine different organizations. The WGI Control of Corruption indicator uses these nine data sources used by the CPI, as well as 13 others not used in the CPI. The Ibrahim Index is an extremely broad collection of a variety of types of indicators, including a number of subjective indicators such as those used in the WGI, and the CPI itself; as well as a number of very broad development outcomes, including per capita income, growth, inequality, and poverty.

> 16 Another way to assess the importance of such biases is to contrast perceptions-based measures of governance with more objective proxies.  In general this is difficult because purely objective proxies are often hard to come by.  One interesting recent example can be found in Fisman and Wei (2007) who study the discrepancy between recorded imports of objects of art into the United States, and the exports reported by partner countries, interpreting the discrepancy as evidence of art smuggling.  They find that this purely objective proxy for illegal activity is highly correlated with the WGI measure of corruption.  However, the correlation is also far from perfect, and as we discuss in the next section this implies non-trivial margins of error in both measures.  It is also interesting to note that this is an objective measure of a governance _outcome_ (art smuggling), in contrast with most of the so called ‘objective’ measures we have discussed that focus on _rules_ regarding governance.

23
Table 17 OLS Forest Level Models of Total Carbon per Hectare and Total Number of Trees per Hectare Dependent <u>Variables in Logs, Robust t Statistics in Parentheses</u>

||Carbon/ha.|Trees/ha.|CrownCover%|Seedlings/ha.|
|---|---|---|---|---|
|CForProposed|0.193|0.344|0.080|-0.041|
||(0.72)|(2.58)**|(0.83)|(0.26)|
|_R_<sup>2</sup>|0.27|0.36|0.28|0.38|
|_N_|130|129|129|122|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01

# Table 18

Random Effects Models <u>of Forest Quality (Panel by Plot)</u><sup>19</sup>

||Carbon/ha.|Trees/ha|CrownCo|ver%|Seedlings/ha.||
|---|---|---|---|---|---|---|
|CForProposed|25,930.635|128.460|-1.7|77|9,915.939||
||(1.95)*|(2.06)**|(0.4|6)|(2.20)**||
|N|620|620|620||620||
|# Groups|130|130|130||130||
|R<sup>2</sup>withingroup|0.03|0.05|0.0|2|0.04||
|R<sup>2</sup>betweengroups|0.35|0.24|0.2|0|0.29||
|R<sup>2</sup>overall|0.23|0.18|0.1|3|0.21||
|Prob > Χ<sup>2</sup>|0.000|0.000|0.0|00|0.000||
|*_p_<0.1; **_p_<0.05; ***<br>Table 19<br>Forest-Level Averag<br>(CForProposed) as th<br>Forest QualityMetric|_p_<0.01; Robust Sta<br>e Nearest Neig<br>eTreatment<br>ATTa|ndard Errors i<br>hbor Prope<br>tForestLev|n Parenthese<br>nsity Scor<br>el|s<br>e Matc|hing for CFs or Prop<br>ATTatPlotLevel|osed CFs<br>|
||Treated/Contro|l<br>ATT|t-stat|Treate|d/Control<br>ATT|t-stat|
|Carbon/Hectare (kg)|88/28|31125.5|1.890<sup>*</sup>|433 /1|87<br>49635.3|5.279<sup>***</sup>|
|Trees/Hectare|88/28|77.9|0.824|433 /1|87<br>96.06|2.255<sup>**</sup>|
|Canopy Cover(%)|88/28|1.56|0.257|433 /1|87<br>1.939|0.668|
|Seedlings/Hectare|88/28|11536.7|2.163<sup>**</sup>|433 /1|87<br>10947.5|3.671<sup>***</sup>|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01

Tables 17 to 19 suggest that the Nepal Community Forestry Programme has effects that may go beyond forests with the legal CF designation, perhaps helping to turn open access NCFs into NCFs that operate like CFs. The effects on forests are estimated to be quite positive, with the most precise estimates again coming from the plot-level models. We estimate that proposed and existing CFs have about 26 tons more carbon and 128 more trees per hectare (34% in the

> 19 Covariates are the same as in previous models.  Results are available from the authors and are similar to previous.

**36 |** P a g e
Gibson J. and S. Rozelle. (2003). “Poverty and Access to Roads in Papua New Guinea”, _Economic Development and Cultural Change,_ Vol. 52 (1), pp. 159-185.

Hutton, G., and M. Varughese. (2016). “The Costs of Meeting the 2030 Sustainable Development Goal Targets on Drinking Water, Sanitation, and Hygiene.” Water and Sanitation Program Technical Paper. Washington, D.C., World Bank.

IMF. (2015). “Making Public Investment More Efficient.” International Monetary Fund, Washington DC.

Lakner, C., and B. Milanovic. (2015). “Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession”, _World Bank Economic Review_ , Advance Access published September 26, 2015.

Lee, K., Miguel, T., and C. Wolfram. (2016). “Experimental Evidence on the Demand for and Costs of Rural Electrification.” Unpublished working paper.

Leigland, J., Trémolet, S. and J. Ikeda. (2016). “Achieving Universal Access to Water and Sanitation by 2030. The Role of Blended Finance.” World Bank Water Global Practice Discussion Paper.

Milanovic, B. (2015). “Global inequality of opportunity: how much of our income is determined by where we live”, _Review of Economics and Statistics_ .

Pritchett, L. (2000). “The Tyranny of Concepts: CUDIE (Cumulated, Depreciated, Investment Effort) Is Not Capital.” _Journal of Economic Growth_ , Vol. 5, Issue 4, pp 361–384.

Straub, S. (2008). Infrastructure and growth in developing countries: Recent advances and research challenges. Policy Research working paper 4460. World Bank.

Straub, S. (2011). Infrastructure and Development: A Critical Appraisal of the Macro-level Literature. _Journal of Development Studies_ . 47, 683–708.

Straub, S. (2015). “Policy Lessons from the Recent Literature on Transport Infrastructure Development,” in _The Economics of Infrastructure Provisioning: The (Changing) Role of the State_ , in Picot, Florio, Grove, and Kranz eds., MIT Press.

World Bank. (Forthcoming, 2017). _Rethinking Infrastructure in Latin America and the Caribbean –Spending better to achieve more_ . Washington D.C.

21