rafmacalaba/datause-encoder-data huggingface dataset:
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 9. Zimbabwe’s road indicators benchmarked against Africa’s low- and middle-income countries, as of 2008**

|Indicator|Unit|Low-<br>income,<br>fragile<br>countries|Resource-<br>rich<br>countries|Zimbabwe|Middle-<br>income<br>countries|
|---|---|---|---|---|---|
|Classified road network density|km/1,000 km<sup>2</sup>of land area|96|98|51|278|
|Total road network density [a]|km/1000 km<sup>2</sup>of land area|145|128|100|318|
|GIS rural accessibility|% of rural population within 2 km of all-season<br>road|32|20|46|31|
|Main road network condition [b]|% in good or fair condition|55|68|63|86|
|Rural road network condition<br>[c]|% in good or fair condition|56|61|54|65|
|<br>Classified paved road traffic|AADT|843|1,408|861|2,451|
|Classified unpaved road traffic|AADT|55|54|19|107|
|Primary network<br>overengineering|% of primary network paved with 300 AADT or<br>less|47|15|16|18|
|Perceived transport quality [d]|% firms identifying transport as major business<br>constraint|32|27|n.a.|18|
|_Source:_AICD Road Sector Datab<br>a. Total network includes the clas<br>b. Main network for most countrie|ase on 40 Sub-Saharan African countries.<br>sified and estimates of unclassified and urban netw<br>s is defined as result of adding the primary and sec|orks.<br>ondary networks.||||

c. Rural network is generally defined as the tertiary network and does not include the unclassified roads d. _Source:_ World Bank—IFC Enterprise Surveys on 32 Sub-Saharan African countries. AADT = average annual daily traffic; GIS = geographic information system. n.a. = Not applicable.

**Table 10. Quality of classified roads in Zimbabwe, 2010**

||Paved/s|ealed|Gra|vel|E|arth|To|tal|
|---|---|---|---|---|---|---|---|---|
||km|%|km|%|km|%|km|%|
|Very good|705|7|39|0|6|1|750|4|
|Good|1,512|15|298|4|90|12|1,900|10|
|Fair|2,720|28|706|9|56|7|3,482|19|
|Poor|4,857|50|6,670|86|612|80|12,141|66|
|Total|9,793|100|7,713|100|764|100|18,273|100|

_Source:_ 2010 Zimbabwe Road Condition Survey.

Based on the physical configuration and condition of Zimbabwe‘s infrastructure network, it is possible to estimate the resources that would be needed to clear the current rehabilitation backlog and maintain the network in good condition. Comparing recent spending against these norms indicates that Zimbabwe has not succeeded in securing adequate resources for road-network preservation and maintenance (figure 12). There is a shortfall of about 50 percent between recent spending on road maintenance and rehabilitation and the requisite amounts. As the benchmarking shows, this situation is quite commonplace in Sub-Saharan Africa.

31
|Herrera-Sosa, Valerio,<br>Monroy-Taborda, &|Georgia|STEP (Skills<br>Measurement|OLS|Log hourly<br>earnings;|0.0540*** for<br>years of|0.0021***<br>for reading|0.0843** (emotional<br>stability), 0.0900**||informal<br>employment,<br>full-time<br>employment<br>Potential<br>experience|
|---|---|---|---|---|---|---|---|---|---|
|Chen (2015)||Survey)||gender as|schooling|proficiency|(hostile bias||and|
|||2012; 663<br>men and<br>women, aged<br>15-65||dummy<br>variable|||attribution)||experience-<br>squared,<br>mother's<br>edcuation,<br>and indicator<br>variables for<br>wage<br>earners,<br>occupations<br>and<br>economic|
||||||||||sector|
|Liang & Chen (2014)|Yunnan<br>Province,<br>China|STEP (Skills<br>Measurement<br>Survey) for<br>Urban<br>Yunnan,<br>2012; 1,007<br>men and<br>women|OLS|Log hourly<br>earnings;<br>gender as<br>dummy<br>variable|0.0802*** for<br>years of<br>schooling<br>without<br>cognitive or<br>noncognitive<br>measures|0.124* if<br>reading at<br>work is 11-25<br>pages, 0.173*<br>if writing at<br>work is >25<br>pages,<br>0.199** if<br>thinking<br>required at<br>work every|0.0666* - 0.174***<br>for openness for<br>specifications<br>without and with<br>schooling and<br>cognitive measures;<br>no other significant<br>noncognitive skills|Included but<br>not reported|<br>Potential<br>experience,<br>experience-<br>squared;<br>gender;<br>sector of<br>employment;<br>occupation|
|||||||day||||
|Linz & Semykina (2010)|Transition<br>countries<br>(Armenia,<br>Kazakhstan,|Employee<br>surveys in<br>five formerly<br>socialist|OLS|Log<br>earnings|0.046*** for<br>years of<br>schooling<br>without|<br>None|Armenia: 0.091***<br>(locus of control),<br>0.054***<br>(Challenge-|-0.145***<br>for female<br>dummy<br>(pooled|Tenure and<br>tenure-<br>squared;<br>dummy|
||Kyrgyzstan,<br>Russian<br>Federation|economies;<br>7,860<br>observations|||noncognitive<br>skills (pooled<br>country||affiliation); Russian<br>Federation:<br>0.075*** (LOC);|country<br>samples)|variables for<br>type of<br>occupation|
||,<br>Serbia)|,<br>men aged 16-<br>70 and|||<br>samples)||<br>Serbia: 0.027***<br>(LOC), 0.016** (C-||<br>and for<br>sector of|
|||women aged<br>|||||<br>A); not significant<br>||employment|
|||16-65|||||for other countries|||
|Valerio, Herrera-Sosa,|Armenia|STEP (Skills|OLS|Log hourly|0.0178** -|-0.0001 - -|-0.0756** - -|-0.3307***|Potential|
|Monroy-Taborda, &||Measurement||earnings|0.0246***|0.0002 for|0.0872** for|for female|experience|
|<br>Chen (2015)||Survey)|||||agreeableness,|dummy|<br>and|

11
Annex IV: Estimated probability of being poor across Socioeconomic Groups

||_Urba_|_n Areas_|_Rural_|_Areas_|
|---|---|---|---|---|
|_Socioeconomic Groups_|_1994_|_1998_|_1994_|_1998_|
|Salaries Public|.003|.027|.021|.054|
|Salaries Prive.|.023|.101|.198|.242|
|Artisans/Commer5ants|.0432|.085|.224|.225|
|Autres Actifs|.069|.165|.309|.635|
|Agriculteurs Rente|.134|.106|.489|.529|
|Agriculteurs Vivriers|.252|.419|.525|.619|
|Inactive|.079|.131|.559|.473|

# Annex V-A: Distribution of poverty status (poor and non poor) by education level of household head

|||1994||1998|
|---|---|---|---|---|
|Education level|Poor|Non poor|Poor|Non Poor|
||(%)|(%)|(%)|(%)|
|No education|93.14|74.51|93.75|71.33|
|Primary not completed|4.21|7.03|2.85|10.44|
|Primary completed no secondary|2.29|6.70|2.06|6.13|
|Secondary not completed|.33|7.89|1.30|5.82|
|Secondary completed and higher|.03|3.86|.04|5.78|
|All|100|100|100|100|

25
- 36 -

discourage improved performance, and building national commitment to institutional development are the crucial factors.

There are two strategy components at the institutional level. One is made up of general strategies: a) simplification of objectives and activities at the institutional level; and b) increased professionalization of "people-oriented" work through socialization. At this level, for example, monitoring and evaluation should be restricted to the minimum amount of quantitative and written information; and effective oral communication between the levels of the system improved.

The second element consists of additional management action to simulate specificity and competition. For specificity, these include inter alia improved employee selection and recruitment; staff incentives that include both pecuniary and normative components; staff evaluation systems that permit tracing of individual staff actions; stronger middle management support; more complex organizational structures with more levels and stronger "staff" structures. Means for increasing competition include giving greater voice to clients, beneficiaries and suppliers; improved performance in political and regulatory agencies; and enhancing internal competition by focusing management on results. Internal monitoring and evaluation plays a crucial role in internal competition, "... but its use should be selective, more economical, and driven by the interest of managers." (1987, p. 175,.

# The U.S. Experience Interpreted

Incentives have played an important part in establishing M&E capacity in the U.S. At a national level, by tying funding and requirements for evaluation to prestigious programs of educational improvement, the federal government created not only a broad context that associated M&E with educational change, but also provided the means through which institutional capacity could be established. This is seen most directly in funding for the creation of evaluation capacity in state education agencies.

Federal funding also supported the "professionalization" of the practice of evaluation. From this profession came the methodological modifications that enabled evaluation practice to move more closely to the needs of decision-makers, and away from large scale national research.

The administrative structure for M&E that has emerged is complex, with federal standards and guidelines, coordination and implementation from state agencies, and implementation at local levels. Staff support for the function has expanded at all levels. Vertical coordination has been establis1hed by mandated reporting requirements.

These requirements, as well as the minimum standards for student achievement established at the state level and criteria for accreditation have increased the specificity of the work of educational evaluators, district super<sup>4</sup> nt^ndents, headmasters and teachers.

At the same time, it is important to note the failure of the Vocational Education Data System in the first half of the 1980s. An overly
# **Table S7b: Reduced forms for Table S7a**

|||Using total<br>Variations i|Weighting<br>n city-industr|Using<br>ytrend term||Variations in|citycross-se|ctional term|Using 1990|
|---|---|---|---|---|---|---|---|---|---|
||Base<br>estimation<br>(Column 2,<br>Table 3)<br>|<br>technology<br>trend with<br>all ethnic<br>groups<br>|<br>patents by<br>backwards<br>Anglo-<br>Saxon cites<br>|<br>industry<br>groupings<br>based on<br>mfg roles<br>|Using first-<br>generation<br>immigrants<br>|Using later-<br>generation<br>immigrants<br>|Using<br>bachelor's<br>educated<br>workers<br>|Using non-<br>bachelor's<br>educated<br>workers<br>|<br>ancestry<br>distribution<br>for city<br>weights<br>|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||The<br>P|dependent va<br>anel A: Wei|riable is Δ log<br>ghting bilatera|bilateral exp<br>l routes by th|orts on the int<br>e interaction o|ensive margin<br>f exporter and|by exporter-<br>importer tra|importer-ind<br>de in industr|ustry<br>y|
|Δ Log estimator for tech|0.938***|0.999***|0.795***|0.248**|1.257***|0.570*|1.224***|0.893***|1.126***|
|flows from the United States|(0.298)|(0.311)|(0.158)|(0.111)|(0.387)|(0.269)|(0.323)|(0.300)|(0.314)|
|Observations|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|||||Panel B: E|xcluding samp|le weights||||
|Δ Log estimator for tech|0.648***|0.789***|0.374***|0.124***|0.875***|0.386***|0.637***|0.650***|0.766***|
|flows from the United States|(0.112)|(0.107)|(0.063)|(0.044)|(0.144)|(0.102)|(0.118)|(0.105)|(0.120)|
|Observations|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 3.
data from 2-3 repeated cross-sections for each country. In total we use 43 surveys from between 1996 and 2011, and only include people in rural locations. Our sample is restricted to those DHS that record cluster location, whether a respondent worked in the last year or not, and if so in what occupation. Work need not be paid. Summary statistics are in Annex 4. Sample size is 100,788 men and 312,769 women aged 15-49.<sup>25</sup> While the majority of males and females do report working (paid or unpaid), the percentages are only 82% and 67% respectively for our sample. We do not think of this as the usual selection problem of whether to work or not and, if so, what occupation to choose based on wage differentials. Working is closely tied to the farm and the decision for many may be more whether to work on the farm or to carry out other household responsibilities not considered work. We thus model a multinomial choice between not working, working in agriculture, and working in a non-agricultural occupation. Thus, an increase in agricultural work may both draw people into the workforce and draw people out of nonagricultural work activities. We note that a comprehensive study of intra-household dynamics and choices is beyond the scope of this paper. Instead, we are estimating the reduced form effects of rainfall on occupation as stated in the surveys.<sup>26</sup> For both men and women, the dominant activity is working in agriculture but this is especially true of men, both in terms of the choice among the 3 activities (58 vs. 44%) and conditional on working (71 vs. 66%). The average age of respondents is between 28 and 29 for both men and women. Men generally have more education with about 66% reporting at least primary school versus 53% for women.

Since all DHS used in our study are georeferenced at the cluster level, matching to the Willmott and Matsuura (2012) climate data is straightforward. However, different rounds of the DHS do not survey the exact same clusters, and the number of clusters typically increases over time. We created “superclusters” by matching each cluster to the geographically closest cluster in the first survey in its country.

We estimate the multinomial choice of not-working, work in agriculture, and work in nonagricultural occupation. Agricultural work is the reference occupation, so covariate effects on it are a residual (since marginal effects must sum to zero across the three choices). The general specification is

> 25 Reducing the sample to the 25-49 age group to include only respondents who have completed all possible education does not change results.

> 26 Furthermore, we are aware that people in different places may conceptualize work in different ways. Thus while we cannot be sure that we are capturing precisely the same margin in all contexts, we are identifying local changes over time in the way people answer the same question of whether they are working, and if so in what occupation.

24
Policy Research Working Paper 7157

# **Abstract**

Does research add value to aid? Specifically, does impact evaluation research help or hinder the delivery of development projects? This paper analyzes the question by constructing a new data set of 100 impact evaluations and 1,135 projects approved by the World Bank between 2005 and 2011. The analysis finds that the delivery of projects with impact evaluation is significantly timelier: common delays are avoided and the gap between planned and actual disbursements is reduced by half. Evidence-based mid-course corrections, a clearer implementation road map, strengthened capacity on the ground, and observer effects are possible channels to explain the results. Hopefully, this analysis will stimulate discussion over the optimal balance between project financing and the impact evaluation research needed to deliver development outcomes.

This paper is a product of the Impact Evaluation 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 authors may be contacted at alegovini@worldbank.org, vdimaro@worldbank.org and ctpiza@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
32 ductivity of workers in the labor market it will be encouraged. If it promises only to raise **the** productivity of workers **engaged** in undocumented home production it may be overlooked. Public policies have probably shifted the gains arising from technical change toward workers in the market labor force rather than nonsarket workers, and toward mon rather than women (Boserup, 1970; Blumberg, 1984; Ngwira, 1987). **This** oversight and distortion are corrected only when **the** quantitative importance of nonsarket and in-kind income is estimated, and its bearing on public sector priorities explicitly evaluated. Economic-demographLc household surveys in low income countries that account for the productive use of time in both market and nonmarket spheres are beginning to change how we view vomen **as** a productive and innovative agent in the development process (e.g. Binswanger at l.. 1980; Birdsall and McGreevy, 1983; Evenson, 1983; Gertler and Newman. 1988). Much work remains to be undertaken if the assessment of policies is to be neutral in its encouragement of nonmarket and market productivity.

Time Allocation and Labor Productivity Hicroeconomic analysis of family behavior assumes that people allocate physical resources and time in response to the family's nonhuman endowments, the opportunity value of the time of- each family member, and the relative prices of the family's potential market inputs and outputs. The market place sets **the wage** rate offered for various types of labor and thereby<sup>determines</sup> the marginal value of time for those working in the market or hiring<sup>in simi-</sup> lar labor. This framework **is** readily extended to account for differences in fertility **and human** capital investmnts that **add** to the productive capabilities of family members. First the implications of the neoclassical unified family allocation model are r^stated, and then relaxed to allow for
4 commercial sources, some countries did not want to put up with the conditionality of official creditors, even if it could get them loans on slightly more favorable terms. In some countries, the private sector did most of the external borrowing, without explicit government guarantees. From 1982 to 1986, total debt to commercial lenders (from the RALl 70) remained essentially constant in real terms. (Table 1 shcws debt deflated with manufacturing unit value index of developed countries.) After 1982 the share of commercial credit to the HICs fell, but this was partially offset by an increase in their share of the credit from official sources. The low-income countries, which had not borrowed heavily from commercial creditors, suffered a decline in their shares of both official and commercial credit.

Commercial credit includes public and publicly guaranteed (PPG) longterm commercial credit, and also short-term and non-guaranteed long-term credit, which was mostly from commercial creditors to private borrowers. Some of the debt that started out as short-term or non-guaranteed (PNG) long-term debt had by 1986 been reclassified as long- term PPG debt in the reschedulings. The oanks found that, because they had not made sure that short-term loans were put to self-liquidating uses, they had to recognize them as long-term obligations, at best. The developing countries found that, when foreign-exchange controls prevented private debtors from servicing nonguaranteed debt, the foreign creditors effectively pressured the debtor governments to assume the debt. In many cases this was the price for keeping open the nation's lines of trade credit. Since a substantial part of the long-term PPG debt in 1986 was originally lent as short-term and private nonguaranteed debt, it seems best to lump all three categories together from the start.
<!-- Start of picture text --> 140<br>120 —— Euro Stoxx Broad |<br> IAA” --- Standard& Poor's 500 |<br>100 ERR — Nikkei 225 )<br>80 nN wi |<br>60 aN Aa |<br>rn “En<br>40<br>: :<br>20 |<br>|<br>;<br>PEL$ S IFESEFSds SSEPEa S H 8S<br><!-- End of picture text -->
# **3.1 Carbon Emission by Source**

Most carbon emissions in Ethiopia emanate from the agriculture sector, land-use change and forestry, as shown in Figure 3.1. Most of the emissions from the agriculture sector come from livestock. This includes enteric fermentation, manure left on pasture, and manure applied on soil. Emissions from land-use change and forestry include emission due to forest clearance and land-use conversion for smallholder agricultural expansion, promotion of large-scale agriculture, human settlement and authorized and unauthorized logging. Emissions from unsustainable extraction of fuelwood also are typically included within forestry; however, in the subsequent analysis, fuelwood will be considered separately.

_Figure 3.1: The sources of carbon emission in Ethiopia_ <!-- Start of picture text --> Carbon Emission by Source Carbon Emission by Source<br>(in millions of tones) (in %)<br>6.11 2.51 4.45 1.83<br>27.50 20.03<br>1.00 25.30 0.73 18.43<br>74.86 54.53<br>Fuels Kerosene Firewood Fuels Kerosene Firewood<br>Livestock fertilizer forestry Livestock fertilizer forestry<br><!-- End of picture text --> _Source: The livestock and fertilizer data are sourced from FAO_ _<u>(</u>_ <u>www.fao.org/faostat/en/#data/GT</u> _<u>, 2017a. The forestry and</u> firewood data are from the Environment, Forests and Climate Change Commission (MEFCC) (_ <u>Mefcc.gov.et/Ethiopia-forest-sectorghg-emissions, 2017)</u> _<u>.</u> The data on fuels and kerosene are from the International Energy Agency (IEA)_ _<u>(https://www.iea.org/statistics/statisticssearch/report/?country=Ethiopia&product=oil, 2015). Emission factors are from FAO (http://www.fao.org/faostat/en/#data, 2017b), and Intergovernmental Panel on Climate Change (IPCC)</u>_ _<u><mark>(http://www.ipccnggip.iges.or.jp/public/2006gl/vol4.html,</mark></u> 2017)._ Figure 3.1 shows that livestock accounts for 74.86 million tones (54.53 percent of total emissions), and forestry and firewood together emit 52.8 tons of carbon (38.46 percent of total carbon emissions), respectively. In contrast, the use of petroleum (fuel and kerosene) accounts for only 6.28 percent (i.e., 8.62 million tons of carbon) of overall emissions. Emissions from fertilizer use in the agriculture sector are based on 650,000 tons of fertilizer used in 2012 (Rashid et al, 2013).

Each of the carbon emission sources mentioned above results from either direct consumption by households or use of intermediate inputs in the production of other goods and services. Using statistics on total outputs and inputs of different sectors in the Ethiopian economy, we can compute the direct and indirect carbon intensities and emissions shown in Table 3.1 that result from the different sources of carbon emissions.<sup>1</sup> Table 3.1 shows that firewood, coal, and livestock have respectively the highest carbon

> 1 The mathematical formula used to arrive at the figures presented in Table 3.1 is given in Appendix C.

7