rafmacalaba/datause-encoder-data huggingface dataset:
- 44 -

than for coal at 50% then gau ceases to be the least cost fuel. A similar analysia was carried out for smaller systems and again showed gas combined cycle to be the least cost option.

The methodology adopted in this study is that of identifying current good practice technologies and their actual operating characterLatics (rather than theoretical optima) and using known cost data to compare the discounted costs per unit of generating a certain amount of energy over the llfe time of the plant. Similar technlcal data can be provlded for the emiLsions of each type of fuel/plant per kwh as is shown in table 3.2 reprinted by Moore and Crousaillat: Table 3.2: Comparative EmiLmions for Gas Combined Cycle and Coal Steam Plant per kwh

||Coal Steam<br>with Scrubber|Gas-fueled<br>Combined Cycle|
|---|---|---|
|C02, grams<br>|830<br>|380<br>|
|CO, mg<br>|75<br>|34<br>|
|S02, mg<br>|600<br>|0<br>|
|N02, orig|600<br>|350<br>|
|UHC,mg|0<br>|18<br>|
|Waste water,<br>grams<br>|15<br>|0<br>|
|Ash, grams|34|0<br>|
|Rejected heat, XJ|4.3|2.6|

Source: Haupt, Joyce and Kuenstle (19903.

The detail of the senaitivity analyLsi and of the potential for a precise evaluation of the pollution characteristics of particular technologies cannot be matched by econometric analysis because of its essentially aggregate nature. The broad implications of studies such as this, which are reflected in other comparative technology studies, give some hope of reachlng sufficiently broad conclusions that national policy can be rested on them.
to poverty-induced vulnerability that indicates the quota of urban Dakar is 84 percent higher than it would have been under the old RNU system. Rural areas are still assigned consistently higher quotas because of their higher poverty rates (56.8 percent of the population). However, the effect of accounting for riskinduced vulnerability in the total quota in rural areas, which represent more than half the country’s population, is marginal compared with the effect in urban areas. These results hold if the analysis is based on communes instead of population averages.

# 6. Conclusions

About 55 percent of the population in Senegal was vulnerable in 2018/19. Of the vulnerable, two-thirds were poor (poverty-induced vulnerability), while the remaining one-third were people with expenditures above the poverty line, but facing a high probability of becoming poor (risk-induced vulnerability). Relying only on the information from the latest household survey provides valuable insights into the characteristics and regional distribution of the poor and vulnerable. However, it cannot provide highly detailed spatial information that is crucial for supporting the implementation of targeted social programs. This paper addresses the related knowledge gap by combining two separate, but linked strands in the pertinent literature: the small area estimation of poverty and the estimation of vulnerability. The combination enables the estimation of highly disaggregated poverty rates and the determination of the share of nonpoor households at risk of falling into poverty.

Poverty- and risk-induced vulnerability underline different economic challenges: deprivation versus variations in welfare. From a public policy perspective, it is crucial to understand how overlapping factors contribute to (or limit) the capacity of households to increase their incomes and improve their resilience to adverse shocks. If structural poverty is the primary concern, the most suitable interventions are likely to involve cash transfer programs or initiatives that improve the delivery of basic services and facilitate investments in physical and human capital. In contrast, if vulnerability is predominantly risk induced, then implementing a program that addresses the risk may be essential (Skoufias, Vinha, and Beyene 2024). For example, if the risk is caused by wide uninsured income fluctuations, then implementing an insurancebased program may be required to reduce vulnerability.

The analysis described in this paper involved the application of this proposed method to the generation of a poverty and vulnerability map for Senegal. Unlike the previous quotas, which relied on commune poverty rates, the new method is based on a recognition of the dynamic nature of poverty. Nonpoor households experience shocks and may therefore be at risk of falling into poverty. This extension of the 20
measures decrease the price in a competitive market but have potentially no impact in the oversupplied regulated market since there is no change in the monthly utilization of truck.

**Figure 4. Trucking Price and Trucking Market Structure**<sup>**22**</sup>

<!-- Start of picture text --> Traction price Savings<br> $/km<br>Deregulation<br>1.93 Facilitation<br>1.26<br>1.04<br>Utilization<br>km/Month<br>3000<br>9000<br>0 2000 4000 6000 8000 10000<br>50% fleet utilization<br><!-- End of picture text --> _Source: Authors’ Calculations._

# **Section 5 Managing the Risk of Supply Chain Unreliability Risk: Delays and Unpredictability and Their Impact on Economic Operators**

Since moving goods through borders takes a longer time than the time warranted by the infrastructure, vehicle, or physical constraints, managing this risk either through increased inventory holdings or alternative modal choices adds to the already substantial logistics costs in developing countries. Anecdotal evidence indicates that in many LLDCs, the formal operators (such as supermarkets) maintain high inventories (three months or more is frequent in landlocked countries) as compared with their peers in advanced countries.<sup>23</sup>

> 22 Formula (2) is used with the following parameters: Potential truck use of 6,000 km and 9,000 km with corridor improvement (b) Fixed costs of 4,000 USD per month and variable costs of 0.6 USD per km. 23 Guasch 2005.

23
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the above-mentioned enterprise survey result is directly applied to the recent aid figure, it means that 100–400 million U.S. dollars might be wasted for informal payments. Furthermore, when dead-weight losses are taken into account, the total economic cost may be 1.2 to 2.9 times higher than the amount of bribes paid (Auriol, 2006).

Second, strengthening competition in public procurement would be conducive to fostering good governance and market-oriented business environment in developing countries. Good governance is essential for economic growth, and many developing countries are wasting abundant resources for a variety of informal business practices, such as bribes and misappropriations.<sup>4</sup> Francisco and Pontara (2007), estimating the bribe propensity of firms in Mauritania, show that medium-sized enterprises remains relatively weak and thus tend to rely on bribery for expanding their business, which is in turn hindering the country’s private sector development. Olken (2005) shows that resource misuse reaches 20–30 percent of total (disbursed) costs in public road projects and that auditing by a central government agency has a positive impact on reducing such misuse.

Auction-based procurement systems are inherently transparent, open, accountable, credible and nondiscriminatory, while mutual transactions involving a small number of players tend to be less transparent and more susceptible to collusion and corruption (Krishna, 2002; Boehm and Olaya, 2006). Transparent and efficient business practices in improved public procurement systems would have positive externalities on local private business behavior, raising competitiveness of the economy as a whole. Not surprisingly, there is a strong correlation between pervasive informal payment practices and side payments for public contracts, as shown in Figure 5. It depicts such a cross-country correlation from enterprise surveys in about 100 countries.

> 4 There is a debate on whether good governance could contribute to economic growth at the aggregate level (Burnside and Dollar, 2000; Easterly, et al., 2004).
- Alder, S. (2017). Chinese roads in India: The effect of transport infrastructure on economic development. Working paper.

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- BenYishay, A., Trichler, R., Runfola, D., and Goodman, S. (2018). Final Report: Evaluation of the Infrastructure Needs Program II, A geospatial impact evaluation of the effect of INP II road improvements on economic development.

- Blankespoor, B., Bougna, T., Garduno-Rivera, R., and Selod, H. (2017). Roads and the geography of economic activities in Mexico. Working paper.

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- Callaway, B. and Sant’Anna, P. H. (2021). Difference-in-differences with multiple time periods. _Journal of Econometrics_ , 225(2):200–230. Themed Issue: Treatment Effect 1.

- Castaing Gachassin, M. (2013). Should I Stay or Should I Go? The Role of Roads in Migration Decisions. _Journal of African Economies_ , 22(5):796–826.

- Christensen, P. and McCord, G. C. (2016). Geographic determinants of China’s urbanization. _Regional Science and Urban Economics_ , 59:90–102.

- Datta, S. (2012). The impact of improved highways on Indian firms. _Journal of Development Economics_ , 99(1):46–57.

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(12)

# Applying Theorem 8.5 of Iakwani (1980) on (12) gives

- where W = lt(l G) is the social welfare before the price change and W' is the social welfare after the price change, nm is the mean expenditure on the jth item and Cj is the conen index of the jth item. Equation (13) immediately gives the price elasticity of the aggregate welfare as

which indicates that if the price of the jth commodity increases by 1 percent, then the aggmegt welfae changes by ej percent. e will always be negative. Thus, the magnitude of E3j can be used to evaluate the efects of price changes on the aggregate welfare.

# 4. Mathodological Problem

This section provides a brief discussion of many methodological problems which arise in the measurement of economic welfare and inequality in Uhmine.

Our study is based on the data obtained from fte Family Budget Surveys (CBS), which had been carried out regularly in the Soviet Union since the 1950s. These surveys are the only source that provides mcome distibution data over time. But they have been subjected to ever criticisms (McAuley 1979, Shenfield 1983 and Ainson and Micklewright 1992); the main among them is that the surveys are not representative of the population. The sample covered the teritory of the former Soviet Union incompletely and unevenly (Cazes and Cacheux 1992). It is not known what proporDi of families were sampled in Utkrine. The families in the sample were mainly selected on the basis of their industrial affiiation of wage earnms. Consequently, social groups (such as students) which did not live in households with wUOing members were excluded from the survey. The old age pensioners (orginaly excluded) have been incluied in the survey since 1977. The prObabi;Ly of a household being selected in the survey increased monotonical1y with the number of wage earners **in** the households. This wVIl clearly bias the results on economic welfare and inequality.

7
Table 2 (continued)

|Comoodity Model|Modeling Purposes|Modeling Structure|Empirical Scope|Model Contribution|
|---|---|---|---|---|
|Sugar(1986)|Explain determinants of Cuba-USS<br>sugar trade arrangements and<br>policies.|R illateral market involving<br>supr export revenue<br>maximization subJect to<br>trade balance requirements.<br>18 equations.|Cuba-USSR<br>Trade.<br>Amual 1960-1983.|nalyzes role of bilateral<br>trade arrangement within<br>wortd trade network.|
|FatsandOils<br>(1981)|Explain and forecast long term<br>changes in the markets for fats<br>and oils and high proteln meals.|Competitive marketkhere<br>inventory adjustments provide<br>a link between the mrkets<br>forfats and oils and for high<br>proteinmats.59 equations<br>(est.).|World. Arnual<br>1961-1977.|Multicdity<br>model with<br>individualcommodity<br>prices<br>simultaneously linked through<br>a general oil price index.|
|Grains and<br>Soybeans (1985)|Explain and forecast medium<br>term changes of prices, trade,<br>production, consuaption and<br>ending stocksforeach comodity<br>by cocutry or region.|Competitive market featuring<br>12 commodity stuodels with<br>related country and regional<br>detail and including cross-<br>linkages betwen commodities.<br>1200 equations.|World. Arnual<br>1960-81.|Large-scale multicmodity<br>model with individual commodity<br>prices simultaneously Linked<br>through export and import<br>equilibrium.|
|Tin (1981)|Simulate buffer stock<br>stabitization schemes.<br>Forecast long term price<br>pricemovements.|Coipetitive market including<br>GSA and ITC storkpi les in<br>inventory adjustment. 23<br>equations.|Industrialized and<br>Developing Couatries.<br>Annual 1955-1975.|Constructed implied stocks<br>*raiabte to capture inventory<br>effects.<br>Supply equations<br>dependonmrket price<br>relations to mining cost<br>responses.|
|Rubber (1981)|Sinulate impact of exchange<br>rate and taxation policies on<br>the supply and prices of natural<br>rIbber. Forecast medium<br>term<br>price movements.|Competitive<br>mrket including<br>synthetic price substftution<br>effects and inventory<br>adjustments. 20 equations.|World. Annuat<br>1957-1977.|Substitution between natural<br>and synthetic rubber linked<br>oil prices. Market share<br>equations subsequentLy<br>developed.|
|Rubber (1984)|Simulate impact of market<br>stabilization along the lines<br>of the International Rubber<br>Agreement. Forecast long<br>term rubber prices.|Competitive market including<br>private inventories and public<br>buffer stock adjustmnts.<br>87 equations.|World. Annual<br>1971-80 and<br>1982-86.|Employed distributed lag schemes<br>in construction of rubber supply<br>as well as demand equations.|
# **3. Data and Empirical Approach**

To investigate potential recall bias in agricultural harvest estimates, we make use of three nationally representative multi-topic household surveys from Sub-Saharan Africa, the 2004/2005 Malawi Integrated Household Survey (IHS), the 2005/2006 Kenya Integrated Household Budget Survey (KIHBS), and the 2001 Rwanda _Enquête Integrale sur les Conditions de Vie des Menages_ (EICV). These surveys were chosen because of their 12-month fieldwork calendar and because interview locations were randomized across regions throughout the data collection.<sup>3</sup> The 2004/2005 Malawi Integrated Household Survey (IHS) was collected over 13 months from March 2004 to March 2005, and covered all districts of Malawi excluding Likoma Island (Malawi NSO, 2005). Fieldwork was conducted concurrently in the three main agricultural regions of the country (north, central and south) to prevent regional averages from being distorted by seasonal bias. The sample was selected using a two-stage stratified sampling design based on a frame from the 1998 census, and was structured to be representative at the district level. The total number of households interviewed was 11,280. Of these households, the analysis was restricted to those households involved in rain-fed agriculture. Households reported agricultural information with respect to the most recently completed agricultural seasons (2002/03 or 2003/04).

The 2005/2006 Kenya Integrated Household Budget Survey (KIHBS) was collected over 12 months from May 2005 to April 2006, covering all eight provinces of the country concurrently (Kenya NBS, 2007). The KIHBS sample was also selected using a two-stage stratified sampling

> estimates of production, as concluded in the review of evidence by Fermont and Benson (2011). The same study notes that farmer reports were closer to actual production and of lower variance than crop cuts.

> 3 In the three surveys studied, households are clustered in enumeration areas (EAs, also referred to as primary sample units), with all households in the EA interviewed over the course of several days. Therefore, the randomization necessary for our study is at the EA level. The survey documentation does not explicitly state that the enumeration areas were randomly allocated (within strata) over time, although it is implicit given the overall survey objectives (poverty and well-being measurement over a 12-month period). Two of the authors worked directly on the Malawi survey and sample design, and are able to confirm this is the case. For all three surveys, we examined the pattern of EAs interviewed within and across geographic areas in each survey to ensure the distributions were even. We also examined whether household characteristics (like landholdings, female headship, and head’s education) vary significantly with interview month and they do not. These results are available upon request.

7
districts located 50-125 km from a highway, districts located 125-200 km from a highway, and districts over 200 km from a highway.

In all of our empirical work, our core focus is on the non-nodal districts of a highway. We measure effects for nodal districts, but the interpretation of these results will always be challenging as the highway projects are intended to improve the connectivity of the nodal districts. For the GQ network, we follow Datta (2011) in defining the nodal districts as Delhi, Mumbai, Chennai, and Kolkata. In addition, Datta (2011) describes several contiguous suburbs (Gurgaon, Faridabad, Ghaziabad, and NOIDA for Delhi; Thane for Mumbai) as being on the GQ network as “a matter of design rather than fortuitousness”. We include these suburbs in the nodal districts. For the NS-EW network, we define Delhi, Chandigarh, NOIDA, Gurgaon, Faridabad, Ghaziabad, Hyderabad, and Bangalore to be the nodal districts using similar criteria as that applied to the GQ network.

Tables 1a and 1b present simple descriptive statistics that portray some of the empirical results that follow. Table 1a starts by providing the count of districts by distance bands to the GQ network and by distance bands to the NS-EW network. As we do not need the panel nature of districts for these descriptive exercises, we retain some of the smaller districts that are not continuously measured to provide as complete a picture as possible (the total district count is 370). For both highway networks, roughly one-third of districts fall within 0-10 or 10-50 km from the network, with roughly two-thirds of districts over 50 km away from the network.

Panel A provides descriptive tabulations from the 1994 and 2000 data that come before the GQ upgrades, and Panel B provides similar tabulations for the 2005 and 2007 data that follow the GQ upgrades. Columns 2-4 provide aggregates of manufacturing activity within each spatial grouping, averaging the two surveys, and Columns 5-7 provide similar figures for young establishments less than four years old. Column 8 provides means of labor productivity across plants in the range. One important observation from these tabulations is that non-nodal districts in close proximity to the highway networks typically account for around 40% of Indian manufacturing activity.

Table 1b provides some simple calculations. Panel A considers the log growth in activity from 1994/2000 to 2005/2007, combining districts within spatial range. Panel B instead tabulates the change in the share of activity accounted for by that spatial band. Share changes in Panel B are calculated separately for distances from the GQ and NS-EW networks such that they sum to zero for each group. Accordingly, we do not present a totals row in Panel B. Share of labor productivity is also not a meaningful concept.

11
- Corral, P., Kastelic Himlein, K., Mcgee, K.R., Molina, I. (2021), A Map of the Poor or a Poor Map?, World Bank Policy Research Working Paper Series No. 9620. Washington DC: The World Bank.

- Dabalen, A., Ferrè, C. (2008), Updating Poverty Maps: A Case Study of Albania. Unpublished working paper. Washington DC: The World Bank,

- Dang, H.A., Lanjouw, P., Serajuddin, U. (2014), Updating Poverty Estimates at Frequent Intervals in the Absence of Consumption Data: Methods and Illustration with Reference to a Middle-Income Country, World Bank Policy Research Paper No. 7043. Washington DC: The World Bank.

- Dang, H.A., Lanjouw, P., Serajuddin, U. (2017), Updating Poverty Estimates at Frequent Intervals in the Absence of Consumption Data: Methods and Illustration with Reference to a Middle-Income Country, _Oxford Economic Papers_ , **69(4)** , pp. 939-962.

- Elbers, C., Lanjouw, J.O., Lanjouw, P. (2002), Micro-level estimation of welfare. World Bank Policy Research Working Paper No 2911. Washington DC: The World Bank.

- Elbers, C., Lanjouw, J.O., Lanjouw, P. (2003), Micro-Level Estimation of Poverty and Inequality. _Econometrica_ , **71(1)** , pp. 355–364.

- Institut National de la Statistique (INS) (2010), Enquête nationale sur le Budget, la Consommation et le Niveau de vie des ménages pour l’année 2010, Tunis.

- Institut National de la Statistique (INS) (2015), Enquête nationale sur le Budget, la Consommation et le Niveau de vie des ménages pour l’année 2015, Tunis.

- Institut National de la Statistique (INS) in collaboration with the World Bank (2020), Carte de la pauvreté en Tunisie, Tunis.

Kish, L. (1965), _Survey Sampling_ . New York: John Wiley & Sons.ì

- Lemieux, T. (2002), Decomposing changes in wage distributions: a unified approach, _Canadian Journal of Economics_ , **35(4)** , pp. 646-688.

- Liu, J., Iannacchione, V., Byron, M. (2002), Decomposing design effects for stratified sampling. _Proceedings of the survey research methods section, American statistical association_ , pp. 2124-2126.

22
<!-- Start of picture text --> Percent<br>15 —Advanced economies —EMDEs<br>10<br>5<br>0<br>2000 2003 2006 2009 2012 2015 2018<br><!-- End of picture text --> <!-- Start of picture text --> Percent =World<br>20 —Advanced economies<br>—EMDEs<br>15<br>"0<br>5<br>0<br>1970 1978 1986 1994 2002 2010 2018<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>2.0 —United States —EuroArea —Japan<br>1.5 ——<br>1.0<br>0.5<br>0.0. ————<br>1.0<br>2019 2020 2021 2022<br><!-- End of picture text --> <!-- Start of picture text --> Percent Advanced economies<br>3.5 —EMDEs<br>3.0 —_—e eS<br>2.5<br>2000<br>1.5<br>1.0 -—W<br>2019 2020 2021 2022<br><!-- End of picture text -->