rafmacalaba/datause-encoder-data huggingface dataset:
Neumayer, Eric. 2005. Do International Human Rights Treaties Improve Respect for Human Rights? _Journal of Conflict Resolution_ 49(6): 925-53.

Pebley, A. R., Goldman, N., & Rodriguez, G. . 1996. Prenatal and Delivery Care and Childhood Immunization in Guatemala: Do Family and Community Matter? _Demography_ 33(2): 231–47.

- Prasad, Eswar S., Raghuram G. Rajan, and Arvind Subramanian. 2007. Foreign Capital and Economic Growth. In _NBER working paper ; no. 13619_ , Text. Cambridge, MA: National Bureau of Economic Research.

- Rosenberg, Gerald N. 1991. _The Hollow Hope: Can Courts Bring About Social Change?_ Chicago: University of Chicago Press.

- Simmons, Beth A. forthcoming 2009. _Mobilizing for Human Rights: International Law in Domestic Politics_ . New York: Cambridge University Press.

- Steele, F., Diamond, I., S. Amin. 1996. Immunization Uptake in Rural Bangladesh: A Multilevel Analysis. _Journal of the Royal Statistical Society_ 159(2): 289-99.

- Streatfield, K., Singarimbun, M., Diamond, I. 1990. Maternal Education and Child Immunization. _Demography_ 27(3): 447-55.

- UNICEF. 1996. _Sustainability of Achievements : Lessons Learned from Universal Child Immunization : Report of a Steering Committee_ . New York: UNICEF.

- Vreeland, James Raymond. 2008. The Effect of Political Regime on Civil War: Unpacking Anocracy. _Journal of Conflict Resolution_ 52(3): 401-25.

World Health Organization. Dept. of Vaccines and Other Biologicals. 1999. _Informal Meeting on Issues of Immunization Data and Monitoring/Management of Adverse Events : Geneva, 17-20 November 1998_ . Geneva: World Health Organization.

26
Galal & Nauriyal 19 Table 6 reports the average growth rates for network expansion as well as labor productivity before and after reform. Thanks to increased investment, the network expanded dramatically in the post reform period in all countries, except the Philippines and Malaysia. This pattern is consistent with our analysis of the extent to which countries resolved the commitment problem. In particular, it is consistent with our conclusion that Chile and Jamaica were the most successful countries in resolving the commitment problem, while the Philippines and Malaysia were the least successful.

**Table 6. Network expansion and labor productivity before and after reform** (average annual growth rates, and lines per worker, respectively)

|Country|Perio<br>Pre-ref.|d<br>Post-ref.|Networ<br>Pre-ref.|k Expansion'<br>Post-ref.|Labor Pro<br>Pre-ref.|ductivity<br>Post-ref.|
|---|---|---|---|---|---|---|
|Argentina|1981-90|1991-92|5.3|9.4|58|96|
|Chile|1981-86|1987-92|7.5|14.3|48|81|
|Jamaica|1981-87|1988-92|6.2|18.8|35|26|
|Malaysia|1981-86|1987-92|17.6|12.3|26|54|
|Mexico|1981-89|1990-92|7.0|12.8|95|122|
|Philippines|1980-85|1986-92|7.2|4.9|35|36|
|Venezuela|1981-90|1991-93|6.5|11.8|68|83|

a. The pre-reform/post-reform periods for which data are reported are: Argentina:1981-90/1991-92; Chile: 1981-86/ 1987-92; Jamaica: 1981-87/1988-92; Malaysia: 1981-86/1987-92; Mexico: 1981-89/1990-92; Philippines: 1980-85/ 1986-92; and Venezuela: 1981-90/1991-93.

Source: International Telecommunications Union, several editions.

Table 6 also shows a marked improvement in labor productivity, especially in Argentina, Chile, Mexico and Venezuela. These are the countries which adopted efficiency inducing pricing regimes (RPI-X or benchmark regulation). In contrast, labor productivity either declined or showed negligible improvement in Jamaica and the Philippines, the only countries in the sample which adopted rate of return regulation.

# **IV.2 Returns to capital and impact on consumers**

Table 7 reports the average (after tax) rates of return on net worth before and after reform in the sample countries. Net worth is used as a denominator rather than revalued assets because reliable data on the latter were not available. The table clearly indicates that all producers did better after reforn. However, there is a large variance around the mean.
**Manually Tagged Tweets: Subjects**

|Date|Count|Distrust<br>Institutio|ns<br>Irrelevant|Lack of<br>informati|on<br>Other|Partisan-<br>ship|Personal<br>economic<br>impact|
|---|---|---|---|---|---|---|---|
|Jan 2011|142|24.2|8.9|12.1|45.2|20.2|10.5|
|Apr 2011|142|24.7|7.7|13.4|27.5|19.0|22.5|
|May 2011|142|21.1|11.3|6.0|44.4|13.5|18.0|
|Aug 2011|91|22.6|29.8|9.5|36.8|6.0|8.3|
|May 2012|142|10.1|42.4|5.0|28.8|14.4|5.0|
|Aug 2012|131|27.1|32.9|15.3|64.7|16.5|10.6|
|Sep 2013|141|10.1|23.9|5.8|58.0|5.8|2.2|
|Total|931|19.3|21.5|9.2|42.6|13.8|11.2|

Table 1: Results of manually categorizing the tweets by the domain experts. Numbers are percentages except the counts column. Rows do not sum to 100%, as tweets can be given more than one category. Source: Author’s calculations.

period we have significant changes. Nevertheless, we make a few observations of trends that conform to the discussion in Section 2.1 of Calvo et al. [2014].

First, we see a general decline in the “personal economic impact” category starting around May 2011. This mirrors the change in opinion regarding the subsidy reform observed in the survey data that was thought to be driven in part by the initial belief that the changes would not benefit everyone. Second, we see a similar decline in the “lack of information” and the “distrust of institutions” categories. However, August 2012 shows an increase in both of these categories to previous levels. We will have to wait until we have labeled all of the tweets to be sure that this is not a sampling aberration. These observations are discussed further in Section 6.

Table 2 contains the results of the second tagging task for identifying sentiment expressed in tweets. The main takeaway from this exercise is that people do not seem to use Twitter to express approval. It is, of course, possible that the design for choosing tweets to classify for sentiment was poor. However, given the reasonably large number of tweets this seems unlikely. Overall, it appears that only around 3% of the labeled tweets contained any positive sentiment. This very large class imbalance makes classification of any further existing positive sentiment tweets difficult. We note some avenues for further research in this direction in Section 7. The paucity of positive sentiment tweets, aside, it does appear as though the number of Negative and Strongly Negative sentiment tweets decline somewhat over the period while those tweets that do not express any sentiment is increasing. This may be construed as increasing approval, though any conclusions should wait until we classify all of the remaining tweets.

10
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE (figure 2a)—among the highest in the region—with the remainder due to poor governance, red tape, and financing constraints. Poor transportation is the infrastructure constraint that weighs most heavily on the productivity of Senegal’s firms, with electricity a close second (figure 2b).

**Figure 2. Infrastructure deficits constrain firms’ productivity**

<!-- Start of picture text --> a. Weight of infrastructure deficits among all factors that sap b. Weight of various sectors in productivity deficit attributable to<br>business productivity (percent) infrastructure (percent)<br>Benin Mali<br>Senegal Senegal<br>Mali Mauritania<br>Mauritania Niger<br>Burkina Faso Burkina Faso<br>Niger Benin<br>0% 50% 100%<br>0% 20% 40% 60% 80% 100%<br>percent of productivity deficit attributable to sectors percent of productivity deficit attributable to sectors<br>Electricity Customs clearance Transportation ICT Water<br>Infrastructure Others<br><!-- End of picture text --> _Source:_ Escribano, Guasch, and Pena 2010.

# The state of Senegal’s infrastructure

Senegal has had a privileged position compared to its neighbors with regard to transit, trade, external markets, and political stability. With an economy heavily focused on international markets—in particular trade and tourism—Senegal is highly competitive compared to other African countries. The costs of exporting and importing are considerably lower than in other coastal countries in the region, but significant progress is still needed to achieve the average international competitiveness of developing countries in general.

Most of Senegal’s population is concentrated in the Dakar area. The remaining part of the country, by contrast, is sparsely populated and characterized by fragmentary infrastructure coverage. Density varies from about 77 inhabitants per square kilometer (inhabitants/km<sup>2</sup> , 199 inhabitants/square miles) in the west-central region to 2 inhabitants/km<sup>2</sup> (5 inhabitants/square miles) in the eastern section (figure 3a).

The rapid pace of urbanization—6 out of 10 people are forecasted to live in urban areas by 2015 (World Bank 2007a)—has made it difficult to balance the provision of infrastructure between urban and rural areas in Senegal. Population growth averaged 2.7 percent annually over the period 2000–08, above the average of other low-income countries (2.3 percent per year).

Despite the good economic performance of the past decade, Senegal still remains a poor country. The highest incidence of poverty remains in rural areas (figure 3b). The poorest regions (Ziguichor and Kolda in the south, and Kaolack and Diourbel in the central region) are also those with the lowest access to 4
Policy Research Working Paper 6019

# **Abstract**

The 2005 Paris Declaration on Aid Effectiveness sets targets for increased use by donors of recipient country systems for managing aid. The target is premised on a view that country systems are strengthened when donors trust recipients to manage aid funds, but undermined when donors manage aid through their own separate parallel systems. This study provides an analytical framework for understanding donors’ decisions to trust or bypass country systems. Empirical tests are conducted using data from three OECD-DAC surveys designed to monitor progress toward Paris Declaration goals.

Tests show that a donor’s use of the recipient country’s systems is positively related to: (1) the donor’s share of aid provided to the recipient (a proxy for the donor’s reputational stake in the country’s development); (2) perceptions of corruption in the recipient country (a proxy for the trustworthiness or quality of the country’s systems); and (3) public support for aid in the donor country (a proxy for the donor’s risk tolerance). Findings are robust to corrections for potential sample selection, omitted variables or endogeneity bias.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at sknack@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **<u>Table A2: Results from first-stage regressions to account for non-response</u>**

|||<br>**Likelihood of non-response**||
|---|---|---|---|
||**EA FE**|**Constituency FE**|**Province FE**|
|Head's age|-1.368***|-1.260***|-1.152***|
||(0.119)|(0.100)|(0.093)|
|Male head|-0.500***|-0.350***|-0.320***|
||(0.120)|(0.104)|(0.096)|
|Married head|0.264**|0.099|0.062|
||(0.122)|(0.105)|(0.097)|
|Head completed high school|0.131|0.051|0.056|
||(0.102)|(0.086)|(0.081)|
|Head completed college or higher|0.150|0.087|0.154|
||(0.160)|(0.137)|(0.128)|
|Head in farming|-0.357***|-0.462***|-0.463***|
||(0.104)|(0.083)|(0.076)|
|Share with high schooleducation|-0.017|0.131|0.092|
||(0.187)|(0.157)|(0.146)|
|Share with salary|-0.312**|-0.708***|-0.792***|
||(0.154)|(0.128)|(0.120)|
|Share self-employed|-0.753***|-0.659***|-0.404***|
||(0.127)|(0.103)|(0.090)|
|Share unemployed|0.397***|0.390***|0.398***|
||(0.131)|(0.110)|(0.103)|
|Dwelling: traditional hut|-0.047|-0.176**|-0.260***|
||(0.098)|(0.079)|(0.070)|
|Dwelling: detached house|-0.331**|-0.477***|-0.536***|
||(0.137)|(0.111)|(0.098)|
|Dwelling: flat|0.060|-0.101|-0.129|
||(0.304)|(0.235)|(0.209)|
|Total no. of rooms|-0.138***|-0.023|-0.018|
||(0.034)|(0.024)|(0.022)|
|Electricity access|-0.368*|-0.357**|-0.284*|
||(0.212)|(0.161)|(0.147)|
|Tap water access|-0.352*|-0.541***|-0.432***|
||(0.212)|(0.144)|(0.128)|
|Pseudo-R|0.0720|0.0611|0.0535|
|Log Lik.|-2065|-3182|-3846|
|No. of enumeration areas|345|||
|No. of constituencies||140||
|No. of provinces|||10|
|No. of observations|5,256|6,756|7,113|

_Notes_ : Fixed effects logit model is used to predict the probability of a sample household reporting owning or using land, excluding the households that are not reporting owning or using land and are renters or obtaining free dwelling from companies. Due to enumeration areas (EAs) containing all observations of reporting owning land or all of non-response, higher levels – of Constituencies and Provinces – fixed effects logit models are conducted to generate predicted probability of responding for each of households that are in the EAs having all reporting owing land. Based on the desirability of parametrizing the model on the association between responses on land use/own and non-responses, the point estimates should not be interpreted as causal relationship.

30
15

**Table 6: Public Capital and Economic Growth**

|(Dependent Variable=<br>|y(90) -y(<br>|70); St<br>|andard E<br>|rrors in pa<br>|renthese<br>|s)<br>|||
|---|---|---|---|---|---|---|---|---|
|_Regression_<br>_independent variables_<br>_____|1|2|_3_|4<br>___|5<br>|6<br>|7<br>|8<br>|
|Constant|0.88<br>(0.57)|1.20<br>(1.42)|1.42<br>(0.56)|1.53<br>(1.41)|1.29<br>(0.43)|1.54<br>(1.07)|1.63<br>(0.42)|1.89<br>(1.04)|
|y(70)|-0.38<br>(0.10)|-0.38<br>(0.10)|-0.36<br>(0.10)|-0.36<br>(0.10)|-0.41<br>(0.09)|-0.41<br>(0.09)|-0.39<br>(0.08)|-0.39<br>(0.08)|
|total capital (ln((sr+sG)/X))|0.67<br>(0.18)|0.68<br>(0.18)|---|---|0.39<br>(0.18)|0.39<br>(0.18)|||
|**private capital (ln(sX/X))**|-||0.31<br>(0.11)|0.31<br>(0.11)|--|---|0.20<br>(0.08)|0.20<br>(0.08)|
|public capital (ln(sJX))|**---**||0.30<br>(0.11)|0.30<br>(0.11)|---|---<br>|0.22<br>(0.10)|0.29<br>(0.11)|
|Human capital (In(sA/X))|0.27<br>(0.07)|0.28<br>(0.07)|0.25<br>(0.07)|0.25<br>(0.07)|0.20<br>_(0.05)_|0.21<br>(0.06)|0.18<br>_(0.05)_|0.19<br>(0.06)|
|Efficiency (e)|**---**|**---**|**---**|**---**|0.28<br>(0.07)|0.28<br>(0.08)|0.26<br>(0.07)|0.26<br>(0.07)|
|debt ratio (DEBTIY)|**---**|**---**|**---**|**---**|-0.28<br>(0.18)|-0.29<br>(0.19)|-0.41<br>(0.18)|-0.42<br>(0.19)|
|**public consumption (ln(CG/Y))**||**---**|**---**||-0.33<br>(0.13)|-0.31<br>(0.13)|-0.29<br>(0.13)|-0.29<br>(0.13)|
|effective depreciation(ln(X))|---|0.13<br>(0.24)|---|0.04<br>(0.53)|-|0.10<br>(0.41)||0.10<br>(0.39)|
|adj.**RA2**|0.43|0.42|0.45|0.44|0.69|0.68|0.71|0.71|
|**Standard Error**(SER)<br>|0.31|0.31|0.31|0.31|0.23|0.24|0.22|0.22|
|Aschauer (1997)|||||||||

- Debt-financed public capital increase -- increases in the public capital stock, financed entirely through the issuing of debt would lead to lower growth. This result is derived by taking the following derivatives of the estimated growth equation under the condition that d(KG /Y)* = d(DEBT/Y) and inserting the Mexico-specific parameters from Table 6: _18_

**18** The following expression states that a 10 percent increase in the steady-state ratio of public capital to GDP (i.e, d(ln((K<sup>0</sup> /Y)*) = dln(s/X) = 0.10) reduces the (20-year) growth rate by -2.0*(sJIX). Using the Mexico-specific parameters in Table A4. 1, which refer to averages over 1970-90, the public investment ratio was around 8 percent - fiDP. This yields a decline in the 20-year growth rate by -2.14 percent. Translated into an annua, growth rate, this represents a decline of roughly 0.1 % per year. Currently, the investment ratio is only around 3.5 percent. Hence, a 10 percent increase in the
**Figure 4: Energy flows and transactions structure in the Philippines, 2016**

<!-- Start of picture text --> Privatized Privatized DU’s Own<br>GENCOs NPC IPPs IPPs Generation<br>WESM<br><!-- End of picture text --> <!-- Start of picture text --> Transmission<br><!-- End of picture text --> <!-- Start of picture text --> System control<br><!-- End of picture text --> <!-- Start of picture text --> Retail supply<br><!-- End of picture text --> <!-- Start of picture text --> Distribution<br>Wires business<br><!-- End of picture text --> <!-- Start of picture text --> Contestable<br>> 1 MW consumers<br><!-- End of picture text --> <!-- Start of picture text --> Captive<br><!-- End of picture text --> _Source_ : “The Energy Report: Philippines” 2013-2014 Edition. KPMG.

22
In 1981, the labor force in China totalled 452 million, out of a population of 990 million. Of the 452 million persons in the labor force, 70 percent (316.4 million) were engaged in agriculture; 9 percent (40.7 million) in heavy industry; 6 percent (27.1 million) in light industry; 10 percent (45.2 million) in services (which includes education, health, public administration, dafense, commerce, housing and miscellaneous services); and 5 percent (22.6 million) in infrastructure (which includes electricity, construction, and transport (World Bank, 1985s 40). As of 1981, the Ministry of Education estimated the status of the total labor force with respect to training, as shown in Table 1:

# Table 1s Education Level of the Chinese Labor Force, 1981

|Education Level|Zof total<br>labor force|
|---|---|
|Higher|0.5|
|Technical Secondary|0.9|
|Senior Secondary|6.7|
|Junior Secondary|20.5|
|Primary|37.8|
|Less than primary|33.7|

Source: World Bank, 1983: 137.

This report is concerned with the 0.9 percent reported on the second line, plus some undefinabile fraction of the 6.7 percent on the third line, who were given vocational education in senior secondary schools. A fair estimate is that we are dealing with technical and vocational education arrangements that have involved about 2.0 to 2.5 percent of a labor force of 500 million, or between 10 and 12.5 million workers. Other workers have received some technical or vocational training on-the-job or in association with their work. It was reported to us that China has some 400,000 firms (of which 6,000 are designated as 'large"), employing 70 million workers. Many of these firms, but especially the large ones, offer in-service training to their employees. No estimate can be given of the number involved in such training not leading to a formal c.;edential.

Second, the study has been restricted to secondary level training and education.

-2-
Figure S2: Prevalence of moderate and/or severe food insecurity food insecurity among adults, by health/financial concerns and consumption quintiles

**(A)** Prevalence of moderate or severe food insecurity in adults is calculated using the FIES and is presented by (i) whether respondent reports being concerned that a family member or the respondent will fall ill with COVID-19 (left panel) or that the household will suffer financially from the pandemic (right panel), and (ii) household annual per capita consumption quintile using pre-COVID-19 data. **(B)** Prevalence of severe food insecurity in adults is calculated using the FIES and is presented (i) by whether respondent reports being concerned that a family member or the respondent will fall ill with COVID-19 (left panel) or that the household will suffer financially from the pandemic (right panel), and (ii) by household annual per capita consumption quintile using pre-COVID-19 data.

<!-- Start of picture text --> A<br>Concerned that family/self will fall ill with COVID−19 Concerned about the financial threat of COVID−19<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>No Yes No Yes No Yes No Yes No Yes No Yes<br>Ethiopia Malawi Uganda Ethiopia Malawi Uganda<br>First Quintile Second Quintile Third Quintile<br>Fourth Quintile Fifth Quintile<br>B<br>Concerned that family/self will fall ill with COVID−19 Concerned about the financial threat of COVID−19<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>No Yes No Yes No Yes No Yes No Yes No Yes<br>Ethiopia Malawi Uganda Ethiopia Malawi Uganda<br>First Quintile Second Quintile Third Quintile<br>Fourth Quintile Fifth Quintile<br>Prevalence of moderate or severe food insecurity Prevalence of moderate or severe food insecurity<br>Prevalence of severe food insecurity Prevalence of severe food insecurity<br><!-- End of picture text -->
Croke et al. (2016) meta-analyses are extremely common in many areas of health research (Turner, Bird and Higgins, 2013), and it is important to examine when this is the case. In the case of deworming, the cost of MDA for STH is estimated at just $0.30 per treatment (Givewell, 2016). Since the majority of those treated in MDA programs will either be uninfected or have only light intensity infections rather than the moderate to severe infections thought to account for the bulk of STH morbidity (De Silva et al. 2015, Montresor et al. 2015), statistical power to pick up population-wide effects is typically limited (Bundy, Walson and Watkins, 2013). TMSDG include studies in environments with prevalence below the 20% threshold at which the WHO recommends deworming, further weakening statistical power by including estimates from settings where there are few worm infections to begin with.

In this paper, we first assess statistical power in TMSDG, and conclude that power is inadequate to rule out weight gain effects that would make MDA cost effective relative to school feeding programs aimed at similar populations. It is also underpowered to reject the hypothesis that the effect of MDA is different from the effect that might expected, given deworming’s effects on those known to be infected, and given STH prevalence in the populations where the MDA trials took place. We then update the work of TMSDG to create a more comprehensive, and thus better-powered, meta-analysis. The update includes 22 estimates from 20 studies examining the impact of multiple-dose MDA on child weight at longest follow-up, twice as many as TMSDG. It includes four studies not identified in TMSDG and additional data from six studies discussed by TMSDG but not included in their meta-analysis for the child weight outcome, either using procedures in the _Cochrane Handbook for Systematic Reviews of Interventions_ (Higgins and Green, 2011) to extract 4