rafmacalaba/datause-encoder-data huggingface dataset:
|Health SystemQualityand EfficiencyImprovement|Huihui Wang|
|---|---|
|Health SystemQualityand EfficiencyImprovement|Huihui Wang|
|Health SystemQualityand EfficiencyImprovement|Huihui Wang|
|Health SystemQualityand EfficiencyImprovement|Huihui Wang|
|KYRGYZ REP IMPLEMENTATION OF NSDS|Saida Ismailakhunova|
|KYRGYZ REP IMPLEMENTATION OF NSDS|Saida Ismailakhunova|
|KYRGYZ REP IMPLEMENTATION OF NSDS|Saida Ismailakhunova|
|KYRGYZ REP IMPLEMENTATION OF NSDS|Saida Ismailakhunova|
|KYRGYZ REP IMPLEMENTATION OF NSDS|Saida Ismailakhunova|
|EnhancingDemand-Side Governance of the Road Administration|Ifeta Smajic|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|Moldova Health Transformation Project|Volkan Cetinkaya|
|ServingPeople,ImprovingHealth Project|Olena Doroshenko|
|ServingPeople,ImprovingHealth Project|Olena Doroshenko|
|ServingPeople,ImprovingHealth Project|Olena Doroshenko|
|ServingPeople,ImprovingHealth Project|Olena Doroshenko|
|ServingPeople,ImprovingHealth Project|Olena Doroshenko|
|ServingPeople,ImprovingHealth Project|Olena Doroshenko|
|ServingPeople,ImprovingHealth Project|Olena Doroshenko|
|ServingPeople,ImprovingHealth Project|Olena Doroshenko|
|ServingPeople,ImprovingHealth Project|Olena Doroshenko|
|ServingPeople,ImprovingHealth Project|Olena Doroshenko|
|ServingPeople,ImprovingHealth Project|Olena Doroshenko|
|Fightingagainst wildlifepoachingand illegal trade in Africa|Claudia Sobrevila|
|Strengtheningthe Capacityof South Sudan Audit Chamber|Eric Brintet|
|Strengtheningthe Capacityof South Sudan Audit Chamber|Eric Brintet|
|Tunisia - Communications forpolicyreforms|Antonio Nucifora|
|Second Private Sector Competitiveness and Economic Diversificati|o Ganesh Rasagam|
|Second Private Sector Competitiveness and Economic Diversificati|o Ganesh Rasagam|
|Second Private Sector Competitiveness and Economic Diversificati|o Ganesh Rasagam|
|Second Private Sector Competitiveness and Economic Diversificati|o Ganesh Rasagam|
|Second Private Sector Competitiveness and Economic Diversificati|o Ganesh Rasagam|
|Second Private Sector Competitiveness and Economic Diversificati|o Ganesh Rasagam|
|Second Private Sector Competitiveness and Economic Diversificati|o Ganesh Rasagam|
|Second Private Sector Competitiveness and Economic Diversificati|o Ganesh Rasagam|
# **Figure 1: Added variable plot from Model (1) in Table 8.**

This figure shows the line of best fit from Model (1) in Table 8, namely, the OLS regression between the rates of formal account ownership from household surveys and the logarithm of deposit accounts per thousand adults. The estimation only takes into accounts surveys taken after 2003.

<!-- Start of picture text --> NLD BEL<br>AUT<br>SVN<br>ESP<br>ITA CZE EST<br>GRC<br>HUN<br>POL LTU<br>LVA<br>IDN<br>BWA INDZAF<br>SWZ PAN<br>NAM<br>UGA<br>MWI LSO<br>RWA<br>PAK<br>5 6 7 8<br>Ln(Deposits/1000 Adults)<br>100<br>80<br>60<br>40<br>% Households w Savings<br>20<br>0<br><!-- End of picture text --> 60
ture extraction be known. In all cases, depreciation should b)C costed at its replacemnernt value.

- ii. Fi nancin Costs. Holding a stock of cap)ital of any kind( for a perio(i of tirne involves fiinanicinig costs, either payments such as interest that muist **be** made to creditors, or compensation for the use of one's own capitai. The latter is the cost of equity capital and is the rate of return that is just re(lllire(l to cornpensaLe the owner for ulsinlg his funi(is in this firin instead of placing theml elsewhere. Thus, it is an opportunity cost which partly takes the form of a forgone return. The cost of equity finarncinig for a give(I firil will consist of two **components** the market rate of return that could have beei earned elsewhere plus the risk premliumxi associated with this firm. The latter is (lifficult to measure. The financing cost shoul(d be based on the full replacernent value of capital of all forms held by the firm. This includes the **net** value of accounts payable (i.e., accounts payable less accounts receivable). Furthermnore, the cost of finance should be the real cost rather than the norninal cost. For example, the nominal interest rate will include a component which compensates creditors for the fall in the value of their asset due to inflation. As such, it represents a chaiige in the principal rather than an interest cost. The nominal interest should be reduced by the rate of inflation, unless, of course, the asset is indexed for inflation.

- iii. Capital LossL.C. Finally. if the relative price of a capital good falls over the period, that shouild also be treated as a cost of holdirng the capital. Of course, this terin couild either be positive or negative. If the price of a non-renewable resource in the gr(ouln(d rises, this reduces the cost of holding it, and vice versa. In(leed, in the theory of resource extraction, expected changes in price are a hey deterIninant of the decision as to how mullch to extract.

Capital costs should include each of the three itemls as apl)rop)riate for all forms of capital whether del)reciable capital, land(. inventories, non-renewable resource stocks or renewable resouirces. There shoiil(d be no other deduictionis for these items. In particular, costs of acquiring the capital, incluiding leases and property rights to resources shouild not be deducted. To do so would involve double-colunting.

_Present_ Value and _Discounting._ The above discussionl concerns rents in the current period. Firins will typically operate for several perio(ds and will take decisions from a long-term perspective. At a givcn point of tirne, what will be re]evant is the present value of future rents rather than just current perio(d rents. This shotuld b)c what a profit-inaximizing firmn is interested in imaxirmizing. There are several **iSStles** involved in measuring the present value of future rents. One concerns the tinme horizoni its(, lf. The typical practice is to take the time horizon as being the 20
percent have only one or two workers, and only 6.2 percent have five to nine workers.<sup>1</sup> Likewise, among manufacturing firms in Tanzania, only 8.9 percent of firms with less than 10 workers have 5 to 9 workers, whereas 60.8 percent have 1 or 2 workers. Based on Table 1, these numbers imply populations of around 770 to 2150 manufacturing firms with 5 to 9 workers. This is about the same size as the number of manufacturing firms with 10 or more workers, which is in the 1000 to 2000 firm range for most countries in the table. Once one starts getting towards large firms, with 100 or more workers, the population sizes in most African countries get quite small – there are 80 of such firms in Tanzania, 114 in Uganda, and 251 in Ghana according to this data. Some policies are targeted just at exporting firms. Mauritius is the only one of the countries in Table 1 for which data on the number of export-oriented firms is available, reporting 386 such firms in 2010.<sup>2</sup> Second, note that there is considerable heterogeneity in what firms produce within the broad category of manufacturing. For example, of the 2,203 Ethiopian manufacturing firms with 10 or more employees, 562 were involved in the manufacture of food products (itself a heterogeneous category), 127 in paper and paper products, 363 in furniture, 47 in textiles, 87 in rubber and rubber products, 75 in chemical and chemical products, and the remainder in a variety of other products. As a consequence, the number of medium and large firms in most African countries in any given manufacturing sector is likely to be relatively small.

# **2.2 Fact 2: There is considerable heterogeneity in firm performance among these firms.**

There is also tremendous heterogeneity in firm outcomes. Table 2 uses the World Bank Enterprise Surveys to illustrate the variation among firms in three key outcomes that policy interventions often try to affect: employment, firm sales, and whether or not firms export. I break the data down into three size categories: fewer than 10 workers, 10-100 workers, and more than 100 workers.<sup>3</sup> Few of the Enterprise Surveys contain data on firms with less than 5 workers, so the first size category should be considered as encompassing small firms with 5 to 9 workers.

> 1 <u>http://www.gov.mu/portal/goc/cso/ei717/toc.htm</u>

> 2 <u>http://www.gov.mu/portal/goc/cso/ei869/toc.htm. Export-oriented firms are those with an export processing zone</u> certificate and those manufacturing goods for export.

> 3 All numbers reported are for the unweighted data, to illustrate the variation among the sample of firms.  This illustrates the variation in the actual data one might have in using the data that is currently regularly collected on African firms; using the survey weights also gives large heterogeneity.

4
and child mortality, as well as increases in coverage of maternal and child health services including antenatal cares, contraceptive prevalence and skilled birth attendance (CSA, 2014).<sup>3</sup> Now with the MDG period coming to an end and new targets set in the Sustainable Development Goals, studies are investigating not only if the countries progressed towards achieving the targets, but also if the achievements were inclusive (Wagstaff et al., 2014). Indeed, health inequalities matter for several economic and noneconomic reasons, as supported by vast empirical literature in this area. However, the available evidence on health inequality studies is mixed and context specific; there have been appreciable and widening inequalities in some countries and improvements in others (e.g., Barros et al. 2012; Wagstaff et al., 2014). These studies also point out important heterogeneities in the results arising from the type of health service or the way it is delivered. For example, Barros et al. (2012) find that those services that are traditionally delivered at health facilities, such as skilled birth attendant and antenatal care visits, have more socioeconomic inequalities than those delivered by outreach or mass campaigns. This would point to the role of various constraints that disproportionately affect health care utilization by the poor. Similarly, a recent multicountry study of progress in health MDGs by Wagstaff et al. (2014) finds that the poor are more likely to face health risks, including child undernutrition and mortality,

> 3 According to the 2014 mini DHS Survey, the reductions in undernutrition and child mortality levels from 2000 and 2005 are considerable. Similarly, during the same period, there has been considerable expansion in maternal health service.  However, trend should not be confused with the overall health status.

3
carried out when refugees are settled. Data are updated on a continuous basis and they are principally used to identify beneficiaries of the various protection and cash programs that the UNHCR and other organizations administer to refugees. It is, in short, the refugee “census” and contains individual and household (case) socio-economic information but does not contain information on welfare. The second data set is the Home Visits database (HV), which has been administered in Jordan in successive rounds starting from 2013 for the purpose of targeting the cash assistance program. The HV questionnaire results in almost 200 variables that can be used for analysis and includes questions on income and expenditure, which we use to construct our welfare aggregate. For the purpose of this paper, we use the second round implemented between November 1, 2013 and September 30, 2014.

The unit of observation used by the study is the “case”. The UNHCR defines a case as: _“A processing unit similar to a family headed by a Principal Applicant. It comprises (biological and non-biological sons and daughters up to the age 18 (or 21) years, but also includes first degree family members emotionally and/or economically dependent and for whom a living on their own and whose ability to function independently in society/in the community and/or to pursue an occupation is not granted, and/or who require assistance from a caregiver.”_<sup>9</sup> The case identifier is present in both PG and HV data and for this reason it was possible to merge the PG and HV data into one dataset. The final sample used for this paper included over 43,000 observations, about a third of all cases registered in Jordan in 2014. This sample is not a random sample of the refugee population but Verme et al. (2016) showed that it is the closest approximation to a random sample that one could have with existing data.

The welfare aggregate was constructed using two questions on expenditure present in the HV data. The HV data contain questions on income and expenditure. The income question was clearly underreported and, as customary when measuring welfare in poor and middle-income countries, expenditure or consumption is preferred as a measure of welfare. The two expenditure questions backed each other up and by combining them into one indicator it was possible to correct for possible underreporting. The resulting welfare aggregate showed common properties to consumption aggregates in terms of distribution. We had also knowledge of the households (cases) that received assistance via the UNHCR cash program. For these households, the value of the programs was subtracted from the aggregate to obtain expenditure net of social assistance. Most households were also receiving food vouchers but interviewers and respondents to the home visits questionnaire were instructed to exclude these

> 9 Information provided by the UNHCR Jordan office.

6
is in Panel D of Table 2.<sup>12</sup> Clubs allow for peer to peer extension support as members visit each other’s fields and sometimes jointly grade their tobacco. In addition, club members are expected to assist colleagues with farming activities and other social support in the event of sickness. This is to ensure sufficient tobacco production at the end of the season to offset any input loans—which must be covered by the club anyway. As a result, the clubs create an environment characterized by both learning by doing and learning from others (Munshi, 2004). We find that almost all clubs interviewed by the TCS (98 percent) acknowledge having enhanced their farming ability due to fellow advices. Also, around 65 percent of club members provide labor assistance to fellow members in farming activities such as baling, grading, harvesting or shade building. In contrast, only 44 percent of respondents claim to have actually received labor assistance from fellow club members. Almost half of the club members get advice in diverse issues such as personal matters, HIV, or other tips on how to spend the extra income generated by tobacco. In fact, tobacco clubs represent an alternative to the farmers’ informal network of family and friends, and diversify the farmer’s social network. In the end, these supporting networks provide the needed motivation and identity to withstand the hard work required to grow tobacco. Overall, 96 percent of respondents feel more motivated to work harder after membership in a tobacco clubs.

# **2.3 Tobacco Clubs: Outcomes**

The services provided by tobacco clubs together with the incentives entitled by club regulations are likely to have significant impacts on farm outcomes. Participation in the clubs allows for market access (for inputs and outputs), higher output prices, lower credit prices, better quality inputs, higher net prices, more effort, more stable and consistent labor supply, and higher tobacco quality. A priori, these factors could have large impacts on various tobacco farming outcomes, such as yields, sales, land allocation, and prices.

We begin with a cursory inspection of the data from the Malawi Integrated Household Survey and compare club members and non-members across major household characteristics

> 12No information on this theme is available in the IHS.

14
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# **Revisiting the Effect of Food Aid on Conflict:**

# **A Methodological Caution**

_By_ PAUL CHRISTIAN* AND CHRISTOPHER B. BARRETT<sup>†</sup> JEL C36, D74, F35, H36, O19, O57, Q18 Keywords: Aid Delivery, Humanitarian Assistance, Agricultural Policies, Conflict and Violence

* Development Economics Research Group, World Bank (email: pchristian@worldbank.org). †Charles H. Dyson School of Applied Economics and Management and SC Johnson College of Business, Cornell University (email: cbb2@cornell.edu). Thank you to Jenny Aker, Marc Bellemare, Brian Dillon, Teevrat Garg, Eeshani Kandpal, Erin Lentz, Stephanie Mercier, Nathan Nunn, Marc Rockmore, Steven Ryan, and seminar audiences at Cornell, Minnesota, Tufts, UCDavis and the World Bank for helpful comments, and to Utsav Manjeer for excellent research assistance. 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.