rafmacalaba/datause-encoder-data huggingface dataset:
**Table A.2: Farm enterprise variable construction notes** (for households involved in crop production)

||ETHIOPIA<br>2011-12|MALAWI<br>2010-11|TANZANIA<br>2010-11|UGANDA<br>2010-11|
|---|---|---|---|---|
|_Seasons_<br>_included_|Only main (_meher_) season<br>|Rainy and dry seasons|Rainy and dry seasons|First and second seasons|
|_Farm tasks_<br>_included_|Post planting (land prep,<br>planting, ridging, weeding,<br>fertilizer application); harvest|Land prep / planting,<br>pre-harvest; harvest|Land prep; weeding;<br>post planting; harvest|All tasks lumped together|
|_Participation_<br>_(indiv)_|Up to 6 hh members<br>identifed per task per<br>plot/task combo|Up to 4 hh members per<br>plot/task combo|Up to 6 individuals per<br>plot/task combo|Up to 3 individuals per<br>subplot|
|_Time use_<br>_aggregate_<br>_(indiv)_|For each indiv/plot/task:<br>hours / day, days / week,<br>weeks / season|For each indiv/plot/task:<br>hours / day, days / week,<br>weeks / season|For each<br>indiv/plot/task: days<br>per season (assume 7 hrs<br>/ day)|For each sub-plot: days per<br>season (assume 7 hrs / day,<br>assume equal labor division<br>b/w individuals listed)|
|_Cleaning_<br>_(time)_|Truncate unreasonably large|values (e.g., an individual wor<br>hours per d<br>|ks more days than the lengt<br>ay.)<br>|h of the season, or _>_16|
|_Hired labor_|For each plot/task: male,<br>female and child hired<br>laborer person-days|For each plot: male, female<br>and child hired laborer<br>person-days for pre-harvest<br>and harvest periods|For each plot/task:<br>male, female and child<br>hired laborer<br>person-days|For each plot: male, female<br>and child hired laborer<br>person-days|
|_Exchange_<br>_labor_|For each plot/task: male,<br>female and child exchange<br>laborer person-days|For each plot: male, female<br>and child exchange laborer<br>person-days|Not in survey|Not in survey|
|_Farm labor_<br>_inputs (hours)_|Own farm labor + hired and<br>exchange labor (convert to<br>hours)<br>In all countries,|Own farm labor + hired<br>and exchange labor<br>(convert to hours)<br> hours supplied by children (a|Own farm labor (convert<br>to hours) + hired labor<br>(convert to hours)<br>ge 5-14) are assigned a wei|Own farm labor (convert<br>to hours) + hired labor<br>(convert to hours)<br>ght of 0.5|
|_Farm labor_<br>_inputs (people)_|Number of family member<br>avai|s with positive hours working <br>lable). Children (age 5-14) are|on farm (no count of hired <br> assigned a weight of 0.5|or exchange workers|
|_Annual_<br>_returns_|Net farm returns are taken fro<br>estimated using the|m RIGA (see section 2 of pap<br> consumption module of the s|er). Quantity of auto-consu<br>urvey. Net returns are trun|med farm production is<br>cated at zero.|

A-2
**Table 1. WTO Trade Disputes 1995-2006, by Income Group**

|**WTO Member**|**Disputes as**<br>**Respondents**|**Disputes as**<br>**Complainants**|
|---|---|---|
|Total low-income economies|20|21|
|Bangladesh|0|1|
|India|18|17|
|Pakistan|2|3|
|Total lower-middle-income economies|46|65|
|Brazil|13|22|
|China|4|1|
|Colombia|2|4|
|Dominican Republic|3|0|
|Ecuador|3|3|
|Egypt|4|0|
|Guatemala|2|6|
|Honduras|0|6|
|Indonesia|4|3|
|Nicaragua|2|1|
|Peru|4|2|
|Philippines|4|4|
|Sri Lanka|0|1|
|Thailand|1|12|
|Total upper-middle-income economies|69|59|
|Total high-income economies|217|235|
|Total|352|380|

Notes: WTO trade dispute from <u>www.wto.org. The income group categories are taken from the World</u> Bank’s classification based on 2005 GNI per capita calculated using the World Bank Atlas method. The groups are: low income, $875 or less; lower middle income, $876 - $3,465; upper middle income, $3,466 - $10,725; and high income, $10,726 or more.

27
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# **I. INTRODUCTION**

Asymmetric auctions are among the most rapidly growing areas in the auction literature. Although the traditional symmetric framework has been attractive for analyzing the general bidding behavior in a tractable manner, it is not always applicable in practice because bidders are usually heterogeneous in various dimensions. The existence of weak bidders—also referred to as fringe or entrant bidders—is particularly important from the competition policy point of view. They are expected to augment the competition effect and break potential collusive arrangements among strong bidders—referred to as incumbent bidders.

The current paper attempts to apply the basic asymmetric auction theory to actual procurement data from official development assistance (ODA) projects of two aid donors. In the context of infrastructure development assistance, the question of how fringe bidders behave under competitive circumstances is of particular interest for several reasons. First, there is always a concern about the limited competition especially in large-scale infrastructure procurements (Estache and Iimi, 2008a). Our data reveal that the average number of firms participating in an auction is about six in road and water-related procurement auctions. In the electricity sector, many contracts attracted only two or three bidders. Hence, it is an even more fundamental question whether or not fringe bidders exist in these procurement markets.

Closely related to this, second, high market concentration in some segments of infrastructure has been a cause for considerable policy concern (e.g., NOA, 2007). In the repeated game circumstances, incumbent bidders may desire to build a reputation of being an aggressive bidder, which helps deter fringe bidders from entering the market. It is a challenging task for auctioneers to invite new entrants to the competitive bidding, while maintaining fair competition environment between incumbent and fringe bidders (e.g., Klemperer, 2002). But is there any strong or incumbent bidder in the infrastructure project markets?
Western cultural elements to suit Asian domestic markets; e.g., making things "smaller and cheaper" or "neater and cleaner".<sup>50</sup> Table 1.6 below shows Sugihara's estimates of the geographical distribution of Asian trade (including India, Southeast Asia, China and Japan) between 1883 and 1928.

Table 1.6 <!-- Start of picture text --> GeographIcal OlslHbuton of Aslds Trade (E nIi)i__n)<br>1is3 1896 1913 1928<br>_lbsWet "a Toti the iest sIa Total t West Asia Toti theVW Asia TOtaW<br>Inda 6~c 44.4 5 17.02 65.85 1o0 4271 63% 2a93 131s 6.15 100% 9674 63%1 41.70 127 156 136.41 64.38 6ss 2s 286 10DS<br>Irm3 4.569 5.1213 406971ID 33.51175 6.37 14 44.52 10 91.80 75 26,56 2 122.251IW 117.39 56 64.33 38200.381<br>SEAdla _ 14.98 670 25.62 1ODs 15.19 399 14.70 39.25 100% 54.17 42.93 41 104.70 10DS 139.69 53 9677 2c1.46 1<br>Im i3.54 6335 522.66 100 1595151% 14.17 4M31.05 100 46.56 5 32.44 39 2.98 1 44.15 55% 58604 34170.50 1<br>Cim E_ 17.78 76s 3.96 1 23.25 15 47 60% 885 34% 25.80 10% 29.99 30.42 496 61.687 10D 61.79 43S 70.71 s 14256 1<br>kn 6.55 47 9.29 514 161021ID 13.46561 1251 47 26.62 10% 46.59 5 36,5342 8614100 6&87.87 9616 171.57 1<br>JBPm EK 5.23 1.2D 6,53 10 615451 6104 48 16.9D100 34.21 47% 36.56 5 72.64 1 97.11 4 12D.38532674<br>Im 3.70 71 1.52 29 23 Io 10 14.69 2x.53 4g 28636 199' 35.00 445 42.0D 7682 1 104.59 425 138635 2S0.18 1<br>Tota _ 5244 2 s 2688 2 121.25 1/ 81.91 5' 552 35' 1s5010 102 214.11 551' 151.61 391 391.90 106' 00 35D44 41 8682 1o<br>In 60.48 6 2428 28 87.88 10oo 77.61 591 4658 361 130.55 1006/ 221.9516o 137.55 3713 370.19 1001' 385. 00 386" 448 80.3 1O<br>NcAs Mad d aCtasspors Ktig reeW to x hii ad mcst d her -n-a-s fan IKg Koig aiothy cam Sun dtwur<br>Souro Su9wa (199D Table 1 130); se aiW defacs fcr s dex.<br><!-- End of picture text --> According to these estimates, the listed countries' exports to and imports from the West between 1883 and 1928 increased by 4.3 percent and 5.4 percent, respectively. Intra-Asian trade between those years, however, experienced a greater rise of 11.2 percent (for exports) and 13.7 percent (for imports). This resulted in a jump in share of intra-Asian exports from 24 to 41 percent and imports from 28 to 44 percent, a significant rise over the 45-year period. Among the countries listed, the greatest increases in share of intra-Asian exports were for China (33 %) and Japan (35 %). For imports, the largest increases were for India (19 %) and Japan (24 pp). Southeast Asia's intra-Asian export share increased by 11 percentage points while its import share dropped by 1 percentage point.

Sugihara (1985) points to the development of the modern cotton industry as key to the growth of intra-Asian trade because it promoted the cotton trade on many levels eventually leading to the emergence of an "Asian inter-regional division of labour with Japan and India as exporters of manufactured goods and importers of primary products on the one hand, and China and Southeast Asia as exporters of primary products and importers of manufactured goods on the other."51 This pattern is shown in the breakdown of Japan's trade statistics in Table 1.7 below.

11
WPS3913

# **Will Consolidation Improve Sub-National Governments ?**

William F. Fox and Tami Gurley<sup>*</sup> World Bank Policy Research Working Paper 3913, May 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* William B. stokely Distinguished Professor of Business, The University of Tennessee, and Economist, U.S. Governmental Accountability Office, respectively.  The paper presents a revised version from that presented at a World Bank Decentralization and Subnational Economics thematic group seminar held on October 18, 2005.  Special thanks goes to Lily Liu and Serdar Yilmaz for serving as discussants.  All views and remaining errors are those of the authors, and do not necessarily represented those of the World Bank. Support for this paper was also provided through a Japan PHRD grant to the Government of Rwanda to assist in ongoing territorial consolidation reforms.
-21-

**Table 8: Sources of Income for Low Income Households (% of Total)**

|**Sources of Income**|**National**<br>||**Low Income"**||
|---|---|---|---|---|
||**Average**|**Average**|**Male-Headed**|**Female-Headed**|
|**Earned**Income|42.2|28.3|31.5|20.4|
|**Social**Insurancev/|23.9|53.8|49.9|63.6|
|(o/w Pensions)|(20.8)|(47.7)|(43.1)|(58.9)|
|Social Benefits**cl**|0.3|0.6|0.6|0.7|
|In-kind Incomed/|29.7|16.2|16.8|14.8|
|Other Sources|3.9|1.0|1.2|_0.5_|

> v Defined as lowest quintile.

v/ Includes unemployment compensation, pensions, children's allowances, etc. e Includes family benefits, disabilities and illness benefits, scholarship, etc.

d/ Includes insurance, gifts, lottery and home-made goods.

Source: World Bank staff estimates from 1992 Individual Budget of Households. NSI.

an unemployment benefit system has been created and social assistance programs were reformed and upgraded. The number of people regularly receiving social assistance stagnated between 1989 and 1990, and then jumped 30 times higher between 1990 and 1992 (UNICEF, 1993, p.<sup>1</sup> 1); in parallel with the large drop in GDP and household incomes by almost 30 percent<sup>6</sup> .

Policymakers face a dilemma. The social safety net is a crucial source of income for low income households. Yet, expenditures on the social safety net are rising and threatening macroeconomic stability. Expenditures on social insurance and benefits reached 6/ The 1992 census indicates a total population of 8,487,317, of whom 2,495,000 were pensioners, 759,420 were children below the age of 6, 1,392,975 were students, and 100,000 were military personnel (estimate), leaving a potential labor force of 3,739,922.

l
- 89 -

compared with the Singapore ALS, the cost per transaion is only slightly higher but within the same order of magnitude. This is to be expectd since half of aU lanes were reserved for seasonal pass holders and were left unmaned. If welfare maximiaton is pursued in lieu of revenue maximiaonper _se,_ then it is desirable to: a) iZuce the number of opeating hours, from 6 a.m. to 10 p.m. to perhaps daylight hours 6 a.m. to _5_ p.m. (as is curently done in Trondheim) or to the morning and afternoon peak periods only (as in the revised Singapore ALS post-1989 period) in order to impact the modal split of tmly congestion-causing traffic; and b) raise entry prices higher to effectuate a reduction in rush hour taffic. The side effects of this twin policy would most lily be an increase in revenues, both because of the relatively unronsive (i.e., inelastic) natre of peak demands as well as the savings in toll opeors' salaries. Alas, no benefit-cost studies have been performed on the Bergen Toil Ring to crosscheck my intuition.

# D. Lessons for Implemendng Cordon Pricing via Tollgtes and Reserved Lanes

98. Some lessons to be drawn from the Bergen Toll Ring of charging for congestion are as follows: a) For cities with limited entries (and detours) in developing countries such as Lagos, Bombay and Jakarta, manual charging of congestion via tollgates is potentially feasible. b) If the fraction of regular commuters is high, the use of reserved lanes for holders of seasonal passes can be justfied. The use of reserved lanes would both reduce the delays that are the major drawback of cordon prcing via tollgates, and lower the capital and operating costs of the toll facility itself. c) Toll collection is a well-established technology for the constrction of toll roads, tunnes and bridges, with almost universal acceptance in terms of administatio. d) The use of toll revenues to finance road constrction and/or public trasport seems to appeal to road users as being 'fair' and appears to be a political prerequisite to implementation.
even more so with regard to net retained premiums (97 percent). However, insurance equity may be overstated by a failure to maintain adequate technical reserves. As discussed further below, several companies specializing in motor and medical insurance report relatively low levels of reserves. This could well imply inadequate reserving policies and thus by extension an overstatement of equity.

The large exposure to bank deposits and company shares is a characteristic of insurance sectors dominated by non-life business. It is explained, on the one hand, by the need for liquidity to settle immediate claims and, on the other, the need for durable assets to cover long-tail claims. Equities and fixed assets provide a better hedge against inflation than either government or corporate bonds. General insurance companies do not have long-term liabilities that are fixed in nominal terms and do not therefore need to hold long-term fixed-interest bonds.

The one company that specializes in life insurance invests in bonds and bank deposits with very little exposure to equities. This company accounts for 86 percent of government bonds and 62 percent of other bonds held by all insurance companies. The main types of life policies sold in Jordan are traditional protection policies with long-term values that are fixed in nominal terms. Demand for participating and unit-linked policies that would favor investments in equities is generally weak. The large holdings of bank deposits is explained by the limited supply of bonds. Because of this, life insurance companies effectively engage in reverse maturity transformation, investing long-term liabilities in short-term assets. The same problem is also faced by the Social Security Corporation.

The growth of holdings of non-government bonds is worth noting, since it reflects the success of mortgage bonds issued in the local market. Even though the total volume of business is still small, it underscores the strong demand by life insurance companies (and the Social Security Corporation) for long-duration assets. In fact, institutional investors complain that commercial banks discriminate against them, offering them lower rates of interest than are available on retail deposits and effectively treating them like captive investors.

**Table 4: Institutional Structure and Asset Composition, 2002**

||% of|% in|% in|% in|% in|%|
|---|---|---|---|---|---|---|
||Total<br>Assets|Liquid<br>Assets|Secu-<br>rities|Real<br>Estate|Other<br>Assets|Own<br>Funds|
|7 Large|54.5|27.4|29.5|15.0|28.1|34.5|
|10 Medium|29.5|40.4|23.5|10.2|25.9|43.1|
|9 Small|16.0|41.3|14.8|10.2|33.7|40.7|

Large: Over 10 M JD; Medium: Between 6 and 10 M JD; Small: Below 6 M JD Source: Insurance Commission Large companies (i.e. those with more than 10 million JD in total assets) invest less in liquid assets and more in securities and real estate (Table 4). Small companies tend to invest more in liquid assets and in other assets, which mainly consist of accounts 9
Activities, and Human Capital Investment in Nigeria," Policy Research Working Paper 8685, World Bank Group, Washington, DC.

<u>http://documents.worldbank.org/curated/en/647701545257537283/The-Relationship-betweenConflicts-Economic-Shocks-and-Death-with-Depression-Economic-Activities-and-HumanCapital-Investment-in-Nigeria.</u>

- Justino, Patricia. 2012. “War and Poverty.” In _The Oxford Handbook of the Economics of Peace and Conflict_ , edited by M. R. Garfinkel and S. Skaperdas. New York: Oxford University Press.

- Marc, A., N. Verjee, and S. Mogaka. 2015. _The Challenge of Stability and Security in West Africa_ . Washington, DC: World Bank; Paris: Agence Française de Développement.

<u>https://openknowledge.worldbank.org/handle/10986/22033.</u>

- Martínez-Cruz, A.L., and C. Rodríguez-Castelán. 2016. “Crime and Persistent Punishment: A LongRun Perspective on the Links between Violence and Chronic Poverty in Mexico.” Policy Research Working Paper 7706, World Bank, Washington, DC.

- Martin-Shields, C.P., and W. Stojetz. 2019. “Food Security and Conflict: Empirical Challenges and Future Opportunities for Research and Policy Making on Food Security and Conflict.” _World Development_ 119 (July): 250–64. <u>https://doi.org/10.1016/j.worlddev.2018.07.011.</u>

- Márquez-Padilla, F., F. Pérez-Arce, and C. Rodríguez-Castelán. 2015. “The (Non-) Effect of Violence on Education, Evidence from the ‘War on Drugs’ in Mexico.” Policy Research Working Paper 7230, World Bank, Washington, DC.

<u>http://documents.worldbank.org/curated/en/226781467993518525/The-non-effect-of-violence-</u> <u>on-education-evidence-from-the-war-on-drugs-in-Mexico.</u>

- Maxwell, D., and R. Caldwell. 2008. _The Coping Strategies Index: Field Methods Manual_ . Geneva: Cooperative for Assistance and Relief Everywhere.

- Mercier, M., R.L. Ngenzebuke, and P. Verwimp. 2016. “Violence Exposure and Welfare over Time: Evidence from the Burundi Civil War.” Working Paper 198, Households in Conflict Network, Brighton, UK.

- Minoiu, C., and O. Shemyakina. 2012. “Child Health and Conflict in Cote d’Ivoire.” _American Economic Review Papers and Proceedings_ 102 (3): 294–99.

- Minoiu, C., and O.N. Shemyakina. 2014. “Armed Conflict, Household Victimization, and Child Health in Côte d'Ivoire.” _Journal of Development Economics_ 108 (May): 237–55. <u>https://doi.org/10.1016/j.jdeveco.2014.03.003.</u>

- Monteiro, J., and R. Rocha. 2017. “Drug Battles and School Achievement: Evidence from Rio de Janeiro's Favelas.” _Review of Economics and Statistics_ 99 (2): 213–28. <u>https://doi.org/10.1162/REST_a_00628.</u>

- NBS (National Bureau of Statistics). 2014. _LSMS-Integrated Surveys on Agriculture: General Household Survey, Panel 2012/2013_ . Abuja: NBS and Federal Ministry of Agriculture and Rural Development; Washington, DC: World Bank.

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_Figure 5: Trade costs in terms of tariff within RECs_ <!-- Start of picture text --> CENSAD ECOWAS<br>ECOWAS<br>SADC<br>SADC<br>IGAD<br>AMU<br>ECCAS<br>COMESA<br>AMU<br>ECCAS<br>COMESA<br>IGAD<br>EAC<br>EAC<br>0 2 4 6 0 20 40 60 80 100 12<br>Share of fully liberalized tariff lines (%)<br>Intra-REC tariff rate on imports (%)<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on United Nations Economic Commission for Africa (UNECA) (2016).

In addition to tariffs, there is heterogeneity in the levels of trade costs associated with non-tariff barriers. AMU and EAC have the lowest intra-regional non-tariff costs<sup>1</sup> , while COMESA, CENSAD and SADC have the highest trade costs among all the RECs. Table 3 shows that, , trading goods within African RECs involves, on average for all tradable goods, additional non-tariff costs amounting to approximately 131%-262% of the value of goods - as compared to when each country trades these goods within its borders. AMU stands out as the REC with the lowest nontariff cost, followed by EAC. While COMESA and CENSAD show the highest levels of non-tariff cost in Africa. The extent of tariff liberalization does not serve as a clear predictor of intraregional trade in the region given that the two RECs with the highest and lowest tariff liberalization have the highest share of intra-regional trade compared to their counterparts.

_Table 3: Comprehensive Trade Costs Excluding Tariff by REC in 2017_

|**Rank**|**REC**|**Nontariff Trade Costs**|
|---|---|---|
|1|AMU|131.40|
|2|EAC|142.70|
|3|IGAD|169.05|
|4|ECCAS|174.73|
|5|ECOWAS|198.39|
|6|SADC|211.58|
|7|CENSAD|228.46|
|8|COMESA|261.97|

Note: Comprehensive trade costs excluding tariff (i.e., nontariff trade costs) includes all additional costs other than tariff costs involved in trading goods bilaterally rather than domestically. _Source: ESCAP-World Bank Trade Cost Database_ 10
Table 10: Heterogeneity by Son and Assets (Ethiopia DOT)

||Nu|mber of child|ren|
|---|---|---|---|
||Household<br>asse<br>above median|ts<br>H<br>be|ousehold<br>assets<br>low median|
|Treatment follow-up 1|0.107||0.361**|
||(0.087)||(0.158)|
|Treatment follow-up 2|0.161||0.308***|
||(0.115)||(0.113)|
|Women have at least one son X|-0.091||-0.183|
|Treatment follow-up 1|(0.119)||(0.201)|
|Women have at least one son X|-0.189||-0.163|
|Treatment follow-up 2|(0.143)||(0.160)|
|Observations|775||605|
|Interaction term 1|-0.091||-0.183|
|p-val|0.446||0.363|
|Interaction term 2|-0.189||-0.163|
|p-val|0.188||0.311|
|Treatment + Treatment X son—|0.016||0.178|
|follow-up 1||||
|p-val|0.859||0.137|
|Treatment + Treatment X son—|-0.027||0.146|
|follow-up 2||||
|p-val|0.779||0.250|
|Difference test p-val:<br>Treatment||0154||
|follow-up 1||.||
|Difference test p-val:<br>Treatment<br>follow-up 2||0.356||
|Difference test p-val:<br>Interaction||||
|follow-up 1||0.690||
|Difference test p-val:<br>Interaction||0902||
|follow-up 2||.||
|Control group mean|2.855||1.728|

Notes: This table displays OLS regressions of the number of children in the household on treatment status including an interaction term equal to one if the woman has no son and is treated. The left-hand column displays the coefficients of the regressions run on the subsample of women above the household asset median and the right-hand column below the median. Standard errors in parentheses are clustered at the village level. Controls include: number of children at baseline, marital status, highest level of education completed, age, age square, a dummy equal to one if the woman is household head and household size.

25