rafmacalaba/datause-encoder-data huggingface dataset:
|PPMT|Threshold approach|90%|4.2|1|2|2|2|5|
|---|---|---|---|---|---|---|---|---|
|–||80%|4.4|3|3|5|5|13|
|SEVAT||70%|4.5|5|9|9|13|19|
|||60%|4.6|9|13|19|19|29|
|||50%|4.7|13|19|29|29|41|
||Rankingapproach|||32|36|43|51|58|
|PPMT|Threshold approach|90%|4.2|1|1|4|4|7|
|–||80%|4.4|4|7|13|13|22|
|Legacy||70%|4.5|7|13|22|22|42|
|VA||60%|4.6|13|22|30|30|60|
|||50%|4.7|22|30|42|60|60|
||Rankingapproach|||31|36|46|52|57|

Note: Values are in percentage _Table 5: PPMT Performance Rates: at 18% National Cutoff, Extreme Vulnerable Only_

||Part I: Th|reshold|||||Part II: R|anking|
|---|---|---|---|---|---|---|---|---|
||90% c|onfident|70% c|onfident|50% c|onfident|||
||SEVAT|Legacy|SEVAT|Legacy|SEVAT|Legacy|SEVAT|Legacy|
|Referral failure|15|21|12|29|15|50|23|25|
|Referral success|86|79|89|71|85|50|77|75|
|Failure to refer|95|95|81|55|58|27|30|15|

# 4.5 Disaggregated result and unwillingness to be referred

The first issue addressed in the disaggregated analysis is the differences across various groups of interest. Table 6 shows the disaggregated percentage of the Field Test sample that are eligible for both MOLSA and humanitarian case support. The result shows how cross-modal eligibility rates of the full PMT differ across households with different socioeconomic characteristics: female-headed households, households with high disability and chronic illness, households with deficient assets, households with no working members. It also disaggregated households on their displacement status: host, displaced, and returnee households, and household location in different governorates.

The analysis finds that among households eligible for the humanitarian assistance, socioeconomic variables such as households at the lowest quantile of asset accumulation, households with no member has worked within the last 7 days, and households living in the rural areas are more likely to be eligible for the government support. About 56 percent of humanitarian beneficiaries at the lowest quantile would 20
13 juice (FCOJ) -which had an estimated tariff equivalent of 28 peq cent in 1988 -- would divert trade primarily from Brazil. Despite the significant growth of Mexican exports of FCOJ to the US market (32 per cent), US imports from third countries would decline by only 0.5 per cent. The situation changes, however, once one assumes that in addition to the tariff removal the NAFTA (and Mexican domestic reforms) stimulates significant new investments in Mexican citrus. Assuming a doubling of the rate of growth of Mexican FCOJ output, Spreen _er.aI._ (1992) show--using a long-term model of the world citrus industry--that by the year 2000 this scenario implies a reduction of 4.5 per cent in Brazilian production of citrus (a decline of 14.4 per cent in terms of on-tree revenues) _vi-a-vis_ the baseline and a much smaller impact on the Florida citrus sector (a decrease of 0.61 per cent in terms of orange production and of 6.34 per cent with respect to on-tree revenue).

Analyses of agricultural products heavily affected by US quantitative restrictions provide several interesting insights concerning the potential inplications of the NAFTA for third countries. Sugar provides the best example in this context. Borrell and Coleman (1991) show that bilateral negotiations concerning trade in sugar anid com syrup between the United States and Mexico may have significant effects for other countries. If, for instance, under an FTA Mexico harmonizes its pricing policy with the one prevailing in the United States and both countries administer a joint quota scheme _vs-a-vis_ the ROW, this arrangement would impose significant welfare losses upon net exporters (the Caribbean, for instance, would experience a net welfare loss of US$ 128 million; world welfare would fall by US$ 241 million). An even worse scenario can be imagined if the negotiations lead to an exchange of quota rights between Mexican sugar producers (expanding their access to the US market) and US corn syrup
more resilient. More productive firms have longer survival times in the manufacturing of metal products, chemical products, food and beverages, and machinery and computing products.

Finally, we redo the analysis shown in Figures 2 and 3, this time assuming that exporters lose access to their external financing, as such financing is likely related to receipts in foreign currency or is in the form of letters of trade credit. Figure 7 shows that manufacturers of metals and metal products and manufacturers of machinery and computing products are most adversely affected by the collapse of export demand, with survival times reduced from 19 to 14 weeks. Conversely, retailers and the provision of other services are unaffected and remain the two sectors where firms are estimated to run out of working capital the fastest.

<!-- Start of picture text --> Figure 7: Median Survival Time (adjusted for exporters) based on Fixed<br>Costs by Sector<br>Other Services<br>Retail<br>Construction<br>Manufacturing of Chemical, Plastic, and Mineral Products<br>Manufacturing of Textiles, Apparel, and Leather Products<br>Other Manufacturing<br>Manufacturing of Food and Beverages<br>Manufacturing of Metals and Fabricated Metal Products<br>Manufacturing of Machinery, Computing, and Electrical ...<br><!-- End of picture text --> <!-- Start of picture text --> 12<br><!-- End of picture text --> <!-- Start of picture text --> 16<br><!-- End of picture text --> <!-- Start of picture text --> 20<br><!-- End of picture text --> <!-- Start of picture text --> Note : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,395<br><!-- End of picture text --> Firms in Colombia and Peru are the most negatively affected by the hypothetical loss in external financing. Both countries see a reduction of their median survival time by over one month, going from about 4 months (16 and 15 weeks respectively), down to 11 and 12 weeks (Figure 8). Kazakhstan, which has the lowest trade exposure of about 7 percent and has among the highest proportion of working capital financed through retained earnings (85 percent), remains with a median survival time of 7 weeks under this scenario. Italy, Russia, and Jordan see a reduction of their median survival time by about 1 week relative to the baseline scenario in Figure 3.

18
# **BIDDERS’ ENTRY AND AUCTIONEER’S REJECTION:**

# **APPLYING A DOUBLE SELECTION MODEL TO ROAD PROCUREMENT AUCTIONS**

Antonio Estache Université Libre de Bruxelles Atsushi Iimi<sup>§</sup> World Bank (FEU) Key words: Public procurement; auction theory; endogenous bidder entry; infrastructure development.

JEL classification: D44, H54, H57, C21, D21.

§ We express our special thanks to Japan International Cooperation Agency (former Japan Bank for International Cooperation (JBIC) ODA Operations) for their collaboration in data collection and their insightful comments on an earlier version of this paper. We are also grateful to Decio Coviello, Marisela Montoliu Munoz, Tomás Serebrisky and Tina Søreide for their various suggestions and comments. We also thank many seminar participants in the Chaire Bernard Van Ommeslaghe Conference at the Université Libre de Bruxelles.
expenditure cuts are the most efficient way in which government saving can increase the national saving rate.

<!-- Start of picture text --> Figure 5: Accounting for the Behavior of Private Savings<br>95<br>90<br>85<br>o 80<br>75<br>70<br>65<br>601<br>1951 1958 1965 1972 1979 1986 1993<br>Disposable Inconme----- Consurrption<br>Source: National Accounts<br><!-- End of picture text --> <!-- Start of picture text --> Figure 6: Consumption of Durables<br>4.00<br>3.50<br>a.3.00<br>z<br>e 2.50<br>o 2.00<br>1.50-<br>1.00<br>0.50<br>I 0.00- , I I | q I I I I i I I I I I I I I I I i 1 + -<br>| 1965 1972 1979 1986 1993<br>Source: National Accounts and L6pez et.al (1996a).<br><!-- End of picture text -->

As far as disposable income is concerned, it is worth describing the behavior of its components in order to explain its permanent decline. Figure 7 presents subsidies (S). transfers received by the public sector from private agents (TR), transfers received by the private sector from abroad (TR*) and other (non-interest) net factor payments from the foreign to the private sector (ONFP). In general these items represent less than 1% of GNP and its behavior does not contribute significantly to the decline in disposable income. On the contrary, in recent years subsidies to the private sector have been stable after that fall in the early 1970s. In addition, since 1984 transfers from the external to the private sector have shown an increasing trend and transfers from the private to the public sector have declined. An important clue to understand the permanent deterioration 10
# **8. REFERENCES**

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Bikhchandani, S., Hirshleifer, D. & Welch, I., 1992. A theory of fads, fashion, custom, and cultural changes as informational cascades. _Journal of Political Economy,_ 100(5), pp. 992–1026.

Brown, J., Ivkovic, Z., Smith, P. & Weisbenner, S., 2008. Neighbors matter: Causal community effects and stock market participation. _Journal of Finance,_ Volume 63, pp. 1509–1531.

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38
18 R<sup>2</sup> -O.99 DW-l.90 Sample Period:1977.I-1986.IV The equation shows strong sensitivity with respect to the own interest rate iTD t and to expected inflation. The coefficient of the interest paid on demand deposits is insignificant, low and of the wrong sign. Reestimating the equation excluding this variable yields: R<sup>2</sup> -0.99 DW-l.90 Sample Period:1977.I-1986.IV 4 An Empirical Anpilication: Deficits. Financial Sector Reform and Inflation in Turkey.

In Section 4.1 we use the framework presented above to assess the consistency of fiscal deficits with other macroeconomic targets for Turkey. Section 4.2 discusses the consequences of postponing fiscal adjustment and studies the interrelation between domestic debt issue, monetization and current and future inflation We then use the model to assess the fiscal impact of financial reforms in Section 4.3. Finally
technical inspectorates.

The EU announced a new strategy for the region in 2018, which has heightened the need to examine the effects of integration between CEFTA and the EU. The new EU strategy for the Western Balkans countries (i.e. CEFTA countries minus Moldova) provided a target accession date of 2025 for the next batch of EU expansion, but this target date may be delayed given the 2020 COVID-19 pandemic. Given the current accession status, it is likely Serbia and Montenegro will be first two CEFTA countries to join the EU, with North Macedonia possibly being the next country. There are, however, many challenges to the reforms needed in the EU acquis and Grieveson, Grubler and Holzner (2018) concludes that the target date is ambitious and serves more as an incentive for reforms.

The CEFTA countries also recognize that further integration amongst themselves can help their integration with the EU. After the EU, other CEFTA countries represent the next largest market for imports and exports for CEFTA countries. The CEFTA only covers market access for all industrial goods and most agricultural goods, but has recently expanded to include trade facilitation and services liberalization.<sup>12</sup> In 2017, the CEFTA countries endorsed a regional action plan to promote economic integration amongst themselves, with the view to encourage economic convergence with the EU. For example, the trade measures in the action plan adopt trade facilitation measures and data systems that conform to EU standards. Most relevant for our paper is an action to investigate the impacts of harmonizing their MFN tariff regimes with the EU’s common external tariff regimes as a method to encourage more integration between CEFTA and EU.

Despite the importance and need to evaluate the benefits of integration for CEFTA countries, data availability has limited any analysis on the topic. Databases such as the WIOD or GTAP, which are widely used for general equilibrium counterfactual analysis such as the one performed here, do not contain data for the CEFTA countries. The databases generally list the CEFTA countries in regional aggregates.<sup>13</sup> Constructing a general equilibrium model of the CEFTA countries is also

> 12The agreement was originally signed in 1992 but the current members joined between 2006-207 as previous members (Poland, Hungary, Czech Republic, Slovakia, Slovenia, Romania and Bulgaria) left CEFTA once they acceded into the EU. Croatia was still a member in 2006 but left in 2013 when it joined the EU.

> 13The GTAP database only lists Albania as a separate country, while the rest are either in the “Rest of Europe” or “Rest of Eastern Europe” (for Moldova) regional aggregates. The WIOD database lists the CEFTA countries in the “Rest of the World” aggregate.

12
# **ANNEX** - **HISTORICAL BACKGROUND OF THE CONGO**

With an area of 2,344, 885 square km (905,365 square miles), the Democratic Republic of the Congo is one of the largest countries of sub-Saharan Africa. It is often called the "heart" of Africa because of its strategic location in the center of the African continent.

Congo's population is approximately 45 million and, with the exceptions of some regions, population density remains low. Congo is three times bigger than the state of Texas in the United States, and eighty times bigger than the Kingdom of Belgium that owvned it for about a century. The Congo has about 450 tribes and 250 dialects. Before colonization, several of the numerous tribes and ethnic groups were organized in independent kingdoms and state-like societies. Low density and high diversity of populations create significant challenges for state formation.

The natural resources of Congo are potentially immense. The climate is favorable to profitable agriculture, the network of waterways is naturally navigable, there is mineral wealth in copper, tin, silver, uranium, cobalt, manganese, malachite, tungsten, diamonds, iron, bauxite, and gold. Copper and diamonds are the most important mineral sources; copper, for instance, accounts for about six percent of the world's production<sup>28</sup> .

In 1876 the Association Internationale du Congo, headed by the Belgian monarch, King Leopold II, began to establish trading posts along the River Congo. These kingdoms and state-like societies were recognized by other colonial powers from 1884 as the Congo Free State, which became a Belgian colony in 190829 in reaction to the abuses being practiced by Leopold's authorities It is traditional, as Crawford Young<sup>30</sup> indicates, to analyze the colonial structure in the Belgian Congo in terms of a trinity composed of the administration, church, and large enterprises. On the administration side, as early as the 1920s the density of administration in the Congo was unequaled in Africa, with the significant exceptions of Mauritania and Dahomey, the current Benin. By the time of independence, there were 10,000 Belgian civil servants and officers in the administration, magistrature, and army. No Congolese, rural or urban, could have failed to perceive that he or she was being administered.

As far as the churches were concerned, one could not fail to be impressed by the scope of the evangelical effort in the Congo. In 1958, it was estimated that the Congo 28Sandra W. Meditz and Tim Merrill (eds), Area Handbook: Zaire: A Country Study, (Washington, D.C.: Headquarters, Department of the Army, 1994), xxxv-325.

29 Country Profile: Zaire, (London: The Economist Intelligence Unit, 1996-1997), 3.

30 Crawford Young, Politics in the Congo. Decolonization and Independence, (Princeton, N.J.: Princeton University Press, 1965), 64-66.

27
25 This takes us some way toward a truer picture. But we are still missing something. We are overlooking the fact that households are likely to incur at least some medical outlays every year—possibly even quite high ones several years in a row. So, while it is true that a health shock in year _t_ may not cause a major drop in consumption in year _t_ (if any) because the household borrows to finance the cost of medical care, it is also possible that the household may already be paying off a loan that it was forced to take out in year _t_ -2 when it suffered another health shock. This is more likely the more highly correlated health expenditures are at the household level over time. The rank correlation for health expenditures over the five years between the two waves of the Vietnam 1993-98 Living Standards Measurement Study panel is 0.36, lower than the rank correlation for nonmedical consumption (0.66), but still quite high. Over the two years between the two waves of the China panel used by Wagstaff et al.<sup>18</sup> the rank correlation for medical outlays at the household level is 0.31, compared to 0.66 for household income. With correlations of this size, episodes of coping with expenses incurred following health shocks will likely overlap. In the example above of the Indian household, this would mean that one might need to add to the estimated interest payments and forgone returns on assets and savings incurred in respect of the medical bill of Rs. 2760 further interest payments and forgone returns incurred in respect of earlier health expenses. Thus Flores et al.’s 15% figure is likely to be an underestimate of the hardship caused by medical bills, possibly a considerable underestimate.
depending on the type of day (less so during weekends). The reduction in the number of trips was concentrated in the medium and long distances (greater than 2 km), with a fall of more than 30 percent on weekdays in the 5-20 km range (Figure 5). At the same time, there was an increase in the number of trips of less than 2 km and especially of less than 1 km. In AMBA, the number of trips of less than 500 meters fell by 5 percent, compared to 25 percent across all trip distances. In CABA, the number of trips between 2 and 10 km fell by more than 35 percent in the metropolitan area as a whole and by more than 50 percent.

**_Figure 5:_** _Change in the number of trips on a weekday in 2019 vs. 2020, by trip distance (km)_

<!-- Start of picture text --> a. Bogota b. AMBA<br>6,000,000 14,000,000<br>2019 2019<br>2020 2020<br>12,000,000<br>5,000,000<br>+1.4% 10,000,000 -17.5%<br>4,000,000 +9.2% -30.9%<br>-22.4% 8,000,000<br>3,000,000<br>-30.5%<br>6,000,000<br>2,000,000 -32.2%<br>4,000,000<br>-5.2%<br>+5.1%<br>1,000,000 -38.4%<br>-13.8% 2,000,000 -30.4%<br>-35.6% -23.7% +6.6%<br>0 -7.2% 0<br>< 0.5 0.5 to 1 1 to 2 2 to 5 5 to 10 10 to 20 to > 50 < 1 1 to 2 2 to 5 5 to 7.5 7.5 to 10 to 2020 to 50 > 50<br>20 50 10<br>Source: Authors based on CDR data<br><!-- End of picture text --> Changes in the distribution of distances meant a greater concentration of mobility flows on a smaller number of routes in Bogota but an increased dispersion in AMBA. In 2020, the number of OD pairs in which trips were recorded fell by 28 percent in Bogota on weekdays, and the median number of trips traveled per OD pair grew (+13 percent), indicating a greater concentration of mobility flows. In contrast, in AMBA, the pandemic caused a greater spatial dispersion of mobility flows, especially on weekdays. The number of OD pairs in which trips are recorded declined less than the total number of trips, leading to a drop in the median number of trips per OD pair by 8.5 percent on weekdays.

The pandemic changed the hourly profile of weekday travel, with more intense trip reductions at peak times, reaching 32 percent in Bogota and 38 percent in AMBA in the morning peak hours (Figure 6). The reduction in the number of trips during peak hours in AMBA was concentrated in the flows between residential areas in the northern districts of CABA and Commune 1, an attractor of forced mobility due to the large number of offices located in the area. In both metropolitan areas also the decline in public transport use specifically was greatest during peak hours. This is largely explained by the significant decline in in-person employment and, thus, physical travel during the typical commute hours. However, while in AMBA the time profile of trips changed considerably, with a significant reduction in the midday peak (largely corresponding to school pick-ups and student travel) and in the evening peak hours, Bogota kept an hourly profile comparatively more similar to the pre-pandemic. In both metropolitan areas, the hourly profile of mobility changed more on weekdays than on weekends and more within the central cities (Bogota, D.C. and CABA).

11