rafmacalaba/datause-encoder-data huggingface dataset:
||Model 6|Model 7|Model 8|Model 9|
|---|---|---|---|---|
|Intercept|**0**_._**54**|**0**_._**50**|**0**_._**49**|**0**_._**54**|
||[0_._42; 0_._65]|[0_._40; 0_._58]|[0_._39; 0_._57]|[0_._44; 0_._62]|
|EA: Mean War Experience|_−_0_._03|_−_0_._02|_−_0_._02|_−_0_._01|
||[_−_0_._09; 0_._04]|[_−_0_._08; 0_._05]|[_−_0_._09; 0_._05]|[_−_0_._08; 0_._06]|
|EA: Distance to Chiefdom HQ|0_._00|0_._00|0_._00|0_._00|
||[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|
|EA: Distance to District HQ|0_._00|0_._00|0_._00|0_._00|
||[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|
|EA: Distance to Freetown|0_._00|0_._00|0_._00|0_._00|
||[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|
|EA: 2008 % of Gov. Health Care Use|0_._02<br>|0_._02<br>|0_._02<br>|0_._02<br>|
||[_−_0_._02; 0_._06]|[_−_0_._03; 0_._06]|[_−_0_._03; 0_._06]|[_−_0_._02; 0_._06]|
|EA: 2008 % of Gov. School Use|**0**_._**28**|**0**_._**28**|**0**_._**28**<sup>_⇤_</sup>|**0**_._**29**|
||[0_._21; 0_._34]|[0_._22; 0_._34]|[0_._21; 0_._34]|[0_._22; 0_._35]|
|EA: 2008 ELF score|_−_0_._04|_−_0_._05|_−_0_._05|_−_0_._03|
||[_−_0_._12; 0_._03]|[_−_0_._12; 0_._03]|[_−_0_._12; 0_._03]|[_−_0_._11; 0_._04]|
|EA: 2008 Mean Poverty Score|_−_0_._03|_−_0_._03|_−_0_._03|_−_0_._04|
||[_−_0_._10; 0_._03]|[_−_0_._11; 0_._03]|[_−_0_._11; 0_._03]|[_−_0_._11; 0_._03]|
|EA: 2008 Dist. to Passable Road|0_._00|0_._00|0_._00|0_._00|
||[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|[0_._00; 0_._00]|
|EA: 2008 Mean Agricultural Work|**0**_._**17**|**0**_._**19**|**0**_._**19**|**0**_._**15**|
||[0_._10; 0_._27]|[0_._13; 0_._28]|[0_._13; 0_._28]|[0_._08; 0_._23]|
|EA: 2007 Malaria Treatment|0_._03|0_._03|0_._03|0_._02|
||[_−_0_._05; 0_._10]|[_−_0_._05; 0_._09]|[_−_0_._05; 0_._09]|[_−_0_._06; 0_._08]|
|EA: 2007 PC Visit|_−_0_._03|_−_0_._03|_−_0_._03|_−_0_._03|
||[_−_0_._06; 0_._00]|[_−_0_._06; 0_._00]|[_−_0_._06; 0_._01]|[_−_0_._06; 0_._00]|
|EA: 2007 LC Visit|0_._02|0_._02|0_._02|0_._02|
||[_−_0_._01; 0_._05]|[_−_0_._01; 0_._05]|[_−_0_._01; 0_._05]|[_−_0_._01; 0_._05]|
|EA: 2007 Well in Community|0_._01|0_._01|0_._01|0_._01|
||[_−_0_._02; 0_._04]|[_−_0_._02; 0_._04]|[_−_0_._02; 0_._04]|[_−_0_._02; 0_._04]|
|LC: % Female|_−_0_._24<br>[_−_0_._53; 0_._07]||||
|LC: % CDF Participation||_−_0_._11<br>[_−_0_._37; 0_._16]|||
|LC: % SLPP|||0_._00||
||||[0_._00; 0_._00]||
|LC: Competition||||_−_**0**_._**06**|
|||||[_−_0_._10; _−_0_._02]|
|BIC|-224.78|-223.03|-212.91|-228.88|
|Num. obs.|607|607|607|607|
|Num. groups: Local Council|19|19|19|19|
|Variance: Local Council (Intercept)|0.00|0.00|0.00|0.00|

Coefficients with _p <_ 0 _._ 05 in **bold** Table 4: DV: 2011 EA-level percentage of households that report using government health facilities. Multi-level linear models with random intercepts at the council level level.
(1) where Yij is the employment or income in nonfarm activity j in location i, Xia is the agricultural employment or income in location i, and Zij is a vector of variables that are relevant for explaining nonfarm employment or income, and uij is an error term. For instance, Hazell and Haggblade (1990) estimated equation (1) using state and district level data on farm and nonfarm income from India where the vector of variables Zij included interaction of agricultural income with road density, population density and irrigation. The theoretical model presented in Foster and Rosenzweig (2004a), however, demonstrated the need for distinguishing between di ff erent types of nonfarm activities.<sup>9</sup> The reduced form in Foster and Rosenzweig (2004a & b) relates closely to equation (1) where nonfarm activities include traded manufacturing products and non-traded services. Equation (1) is estimated using both village (focusing on employment) and household (income) level panel data from India. Foster and Rosenzweig (2004a) include village and household level fi xed e ff ects in the vector of explanatory variables Zij to account for unobserved heterogeneity. They instrument for yield growth in agriculture (proxy for Xia) to remedy potential endogeneity problems. In this formulation, the urban in fl uence on location of nonfarm activities is subsumed in the fi xed e ff ect and is eliminated when equation (1) is estimated in its rst di erenced form.

In contrast with the literature on farm-nonfarm linkages, the economic geography literature maintains that economic activity need not spread evenly across geographical space even if land is undi ff erentiated across locations. Locations of economic activity are characterized by distinct regional patterns which result from the interplay of two opposing forces. Dispersion of economic

> 9 The heterogeneity of the nonfarm activities has been discussed widely in the literature on nonfarm activites, for example, see surveys done by Lanjouw and Lanjouw, 2001; Lanjouw and Feder, 2001; Haggblade, Hazell and Reardon, 2006).

7
I

# Cents and Sociability: Household Income and Social Capital in Rural Tanzania'

Beyond apparently now old fashioned 'physical" capital, human capital, natural capital, institutional capital and social capital all clamor for attention. With capitalism all the rage, perhaps the proliferating rechristenings as 'capital" of otherwise perfectly serviceable concepts is understandable. But fashion aside, the popularity of 'capital'isms is due in part to the robust usefulness of the underlying metaphor: stuff that augments incomes but is not totally consumed in use. The attraction of investigating the incomes of households by examining their ownership of the various "capitals" is obvious. However, while obvious, an exclusive focus on households is seriously incomplete. Factors both at the national level of policies and institutions (Olson, 1996) and at the community level affect the fortunes of households and are potentially as important as the household's own capitals.

In this paper we show that associational relationships and social norms of villages in rural Tanzania are both _capital_ and _social._ After outlining the various concepts of social capital in the introduction we tell how (and why) we created data on social capital using a large The Social Capital and Poverty Survey was conducted as part of a Participatory Poverty Assessment led by Deepa Narayan as a joint activity of the government of Tanzania, the University of Dar Es Salaam, and the World Bank, funded by the British Overseas Development Agency. The Human Resource Development Survey data used in this paper come from a nationally representative survey of 5,000 households in Tanzania which was a joint effort undertaken by the Department of Economics of the University of Dar es Salaam, the Government of Tanzania, and the World Bank, and was funded by the World Bank, the government of Japan, and the British Overseas Development Agency. We would like to thank Jonathan Isham, and Sushenjit Bandopadhyay for collaboration in the early stages of the research, Christiaan Grootaert, Dean Joliffe, Michael Kremer, Peter Lanjouw, and Jonathan Morduch for useful comments, and Deon Filmer for help and insights on the econometrics.
- details of the total project costs and financing plan

- justifications for the cash-flow estimates

- management and operation plans for the facility

- justifications for expenditures plans and revenues estimates

- contents for implementing supplementary projects

- contents for subsidy request if any.

Bids are evaluated according to a set of criteria spelled out in article 13 of the decree and summarized in Table 3.

Table 3: Categories for the evaluation of bids

||Content|Score|
|---|---|---|
|Category 1|Composition of the concessionaire, such as organization types,<br>relationship betweensponsors|50|
|Category 2|Feasibility of the project proposals, such as project costs,<br>constructionperiod, site, content ofconstruction|150|
|Category 3|Financing plan, suchas equity and debtfinancing capacity|300|
|Category 4|Economic feasibility of the project, such as user fees,<br>concession period, rate of return, scale of supplementary<br>projects|200|
|Category 5|Facility management capability, such as the appropriateness of<br>the maintenance plan, the operation and management plan, land<br>acquisition|200|
|Category 6|Contribution to social benefits, such as the provision of services<br>to thefacility users|100|
|Totalscore||1000|

Source: PICKO (2004).

Bids obtaining a total score below 600/1000 points or bids which do not score above 60/100 point on category 3 are disqualified. The competent authority then selects at least two preferred bidders with which competitive negotiations are organized. Negotiations can involve up to 30 preparatory meetings which are followed by 5 to 7 negotiation rounds. The best available offer results from these negotiations which can take up to 52 weeks. Overall, the procurement of PPI projects takes on average 4 years to complete whereas international experience shows an average timeline on or around 88 weeks.

# _2. A legacy of civil works procurement_

Rules and procedures applied to the procurement of PPI projects are influenced by rules and procedures originally developed for the procurement of civil works. As shown in Table 3, the evaluation of bids relies heavily on the means (technical and financing) to be 23
**Figure 9. Characteristics of Urban Informal Firms Owners, %**

<!-- Start of picture text --> Female Age groups Education<br>8.3 9.4 3.9 2.9 4.9 0.0 7.0<br>15.8<br>10.7<br>35.3<br>52.3<br>48.4<br>44.0<br>81.0 77.7 90.6<br>60.7 81.2<br>50.5 51.8 34.0 64.7<br>44.6 31.5<br>28.6<br>12.2 13.7 9.7<br>10.7 12.8 0.0<br>3.0 5.5 Total LE-SE HE-SE Emplo<br>Total LE-SE HE-SE Emplo yer<br>yer No educati on Primary Secondary<br>Total LE-SE HE-SE Emplo 15-24 25-54 55+ Vocational University<br>yer<br>60 100 100<br>80 80<br>40 60<br>60<br>40<br>40<br>20<br>20<br>20<br>0<br>0<br>0<br><!-- End of picture text --> Source: Own elaboration based on IES. Notes: Weighted average including Mozambique, Zambia and Zimbabwe.

# **5.2 Distribution of Job-Related Characteristics by Employment Type**

Urban informal and formal workers are employed in similar sectors, with some variation across informal employment types. The sector of employment can be roughly categorized into four groups: agriculture, industry, services, and other (not classified).<sup>53</sup> Figure 9 shows that 44.3 percent of urban informal jobs are in the services sector, only six percentage points less than the share of urban formal wage jobs in the service sector. Looking within the informal sector, LE-SE and HE-SE are more active in the services sector (54.4 and 58.4 percent, respectively) than are informal wage employees (38 percent), perhaps reflecting crowding into low value-added self-employment activities. That said, the conditional analysis shows that, while industry and service sectors are more likely than the agricultural sector to employ informal wage workers, unpaid workers are mainly employed in agriculture, even in this urban sample (45.3 percent).<sup>54</sup> This is not surprising since agriculture is often a subsistence activity to directly provide food for household consumption. For all three types of informal employment, workers in the lower welfare group have higher shares of employment in agriculture and lower shares in other activities, especially in services, than workers in the higher welfare group (Figure 11).

> 53 “Other sector” includes Professional, Scientific and technical activities; Administrative and support service activities; Education; Health; Art, entertainment and recreation; Activities of households as employers; and Activities of extraterritorial organizations and bodies.

> 54 When looking at the employment distribution by sector at the country level, we note a few deviations from the regional pattern. For instance, services is the most important sector for unpaid workers in Cameroon, Eswatini and Zimbabwe while industry dominates in Guinea, Senegal and Togo. Agriculture is the main sector of employment for LE-SE in São Tomé and Príncipe, being a small island state with only 70,000 people living in its urban capital, and with an economy still predominantly driven by agriculture (and tourism).

24
Table 5. Multinomial Logit Model: Labor Market Transitions: 2002-2004 (Standard errors in parentheses)

||Employme|nt Transitions|Unemploym|ent Transitions|Inactivity|Transitions|
|---|---|---|---|---|---|---|
||to Inactivity|to Unemployment|to Inactivity|to Employment|to Unemployment|to Employment|
|**_Men_**|-0.925***|0.285|-1.926***|-0.252|1.012***|1.166***|
||(0.159)|(0.207)|(0.440)|(0.476)|(0.163)|(0.167)|
|**_Age (years)_**|||||||
|25-44|-0.484|-0.814***|0.341|0.065|0.645***|0.458|
||(0.300)|(0.283)|(0.398)|(0.371)|(0.239)|(0.280)|
|44-54|0.476|-1.130***|0.934|0.535|-0.538*|-0.267|
||(0.331)|(0.397)|(0.568)|(0.634)|(0.294)|(0.289)|
|55+|1.475***|-3.524***|24.235***|22.287|-3.163***|-1.459***|
||(0.386)|(1.090)|(0.887)||(0.531)|(0.352)|
|**_Single_**|0.130|-0.118|-0.046|-0.553|0.748***|0.362|
||(0.245)|(0.231)|(0.335)|(0.379)|(0.209)|(0.283)|
|**_Education_**|||||||
|Vocational|-0.602***|-0.380|-0.030|1.190**|0.428**|0.818***|
||(0.189)|(0.237)|(0.373)|(0.457)|(0.168)|(0.143)|
|High School|-1.141***|-0.782**|0.428|0.761|0.113|0.478**|
||(0.250)|(0.317)|(0.464)|(0.516)|(0.208)|(0.184)|
|College|-0.944***|-0.328|-1.455|1.715*|2.277***|0.943|
||(0.349)|(0.395)|(1.485)|(0.996)|(0.783)|(0.614)|
|University|-2.339***|-1.833***|-33.714***|0.766|0.487|0.780*|
||(0.484)|(0.532)|(1.308)|(1.134)|(0.939)|(0.418)|
|**Constant**|-0.869**|-0.783**|1.162|0.821|-1.926***|-1.746***|
||(0.432)|(0.388)|(0.905)|(0.781)|(0.341)|(0.342)|
|Number of Obs||2107||421||2306|

_Note_ : The following are base groups: Female, 15-24 years of age, elementary or unfinished elementary school education, Banja Luka municipality. Standard errors are corrected for stratification and intra-cluster correlation.

The results also indicate measurable labor market disadvantages among women. For example, men are less likely to leave employment for inactivity. Men are also more likely to transition out of inactivity into employment. And, at least between 2001 and 2002, men were more likely to move out of unemployment back into employment, holding other things constant.

The results with respect to age are more mixed. Young workers (less than 25 years of age) are more likely to go into inactivity and unemployment. Between 2001 and 2002, they were also less likely to move out of inactivity. However, between 2002 and 2004, they seemed more likely to move out of inactivity.

# **IV. Labor Market Outcomes and Welfare**

How do labor market dynamics documented in the previous section relate to the welfare of workers, its evolution and its distribution? A first step would be a conceptual understanding of how economic growth translates into welfare outcomes and specific gains for the poor. In principle, there are a number of channels linking growth opportunities to welfare outcomes for the poor or workers at various points of the welfare distribution. This is illustrated using a simple framework depicted in Figure 5. The labor market is a principal channel through which economic growth translates into poverty reduction. The working poor gain from higher wages while the unemployed poor gain 13
# Introduction

The contribution of cities to economic growth is now well accepted both in the economics and public policy literature. Estimates suggest that they contribute as much as 80 percent to the global GDP. By providing a spatial environment where firms and people meet, cities facilitate trade and interactions, help connect different stages of production, and take advantage of economies of scale. The agglomeration processes that cities bring are crucial for development, pushing countries to grow faster (Duranton, 2008, Glaeser, 2014, Duranton, 2015). Cities that succeed at attracting new firms, workers, and investment are often identified as competitive cities. They often perform above their national average, generate jobs, and increase income levels to their citizens (World Bank, 2016).

Fast growing cities are often subject to congestion effects that can hinder their ability to become competitive. Congestion effects of growing cities are well documented and include things such as traffic congestion, crime, and pollution. These are what Ed Glaeser has called the “demons of density” (Glaeser, 2012). While a lot has been said about the links between productivity and traffic congestion (Ciccone & Hall, 1993; Graham, 2007), the links between pollution and city competitiveness have been less explored (Tolley 1974, Glaeser 1998).

Large population numbers pose increased pressures on local environments and when not well managed can lead to pollution that may affect livability and in turn, productivity in a city. Among the different types of environmental challenges that can stem as a result of the increased densities found in cities, air pollution poses serious challenges, given the difficulties in regulating emissions and the fact that this externality transcends the geographic limits of the city, making it very difficult to assign costs (and benefits) related to it. Consequently, many growing cities, particularly in developing countries, pay little or no attention to how to manage air pollution, ignoring the impacts that this may have not only on the health and livability of its citizens, but also on the productivity of firms and workers, and ultimately, the competitiveness of their city.

In a recent report, the World Bank has laid out a clear definition of what is a competitive city<sup>2</sup> : a city that is growing fast, generating jobs, and increasing the level of income for its inhabitants. This work provides a framework for thinking about building competitiveness in a city, through strong institutions, efforts to provide good infrastructure and well‐functioning land markets, foster skill and innovation, and providing the needed enterprise support and finance for the local economy to thrive. This framework, however,

> 2 World Bank, 2016.

2
Table 3a – Mexico Cluster analysis overview

||Type III|Type II|Type I|None|
|---|---|---|---|---|
|Percent of total sample|33.1|25.1|20.2|21.6|
|Percent of 12-14 year olds|14.1|33.1|24.3|28.5|
|Percent of 15-17 year olds|38.9|11.9|27.5|21.7|
|Percent of 18-24 year olds|43.4|26.8|13.1|16.7|
|Percent of males|31.9|23.2|22.2|22.7|
|Percent of females|34.2|26.7|18.4|20.7|
|Average Parental<br>Education (poverty proxy)|Primary graduate,<br>some secondary|Primary graduate,<br>some secondary|Secondary<br>graduate|Secondary<br>graduate|
|Percent of rural|42.9|24.4|18.1|14.6|
|Percent of urban|29.9|25.3|20.8|23.9|

Table 3b – Chile Cluster analysis overview

||Type III|Type II|Type I|None|
|---|---|---|---|---|
|Percent of total sample|16.8|28.0|8.7|46.5|
|Percent of 15-17 year olds|8.3|20.4|15.9|55.5|
|Percent of 18-24 year olds|22.2|32.8|4.2|40.8|
|Percent of males|23.7|19.1|13.2|44.1|
|Percent of females|10.8|4.7|35.9|10.8|
|Average Parental|Primary Graduate|Some Secondary|Some Secondary|Secondary|
|Education (poverty proxy)||Education|Education|Graduate|
|Percent of rural|27.2|29.5|7.0|36.3|
|Percent of urban|15.3|27.7|9.0|48.0|
|Percent of indigenous|21.7|26.3|8.7|43.3|
|Percent of non-indigenous|16.3|28.2|8.7|46.9|

27
Shares of sub-groups of foreign banks are also reported in Table 1. Foreign global banks are more dominant in Latin America, both in terms of numbers and asset share, while foreign regional banks are relatively more common in Eastern Europe. In terms of parent origin, European banks dominate in Latin America and Eastern Europe.

From Bankscope, we collect each bank’s financial information on gross loans, asset size, capitalization, liquidity, and funding structure.<sup>8</sup> We also collect financial information for the parents of foreign banks from Bankscope based on the ownership information constructed in our data set.<sup>9</sup> Similarly to affiliate characteristics, the parent variables we gather data on include: size, equity ratio, liquidity ratio, and deposit funding ratio.

Figure 1 shows that the growth of credit fell across all bank ownership types and regions during 2008 and, in particular, during 2009, at the height of the global financial crisis. However, it appears from this figure and from Figure 2 that the drop in credit was most significant for foreign relative to domestic banks. These graphs do not control for any other bank characteristics that might be driving these results. Hence, in the next section, we explain the estimations we undertake to dissect the behavior of foreign banks relative to other banks during the crisis.

# **IIIEmpirical Methodology**

Our baseline specification to analyze bank lending behavior follows equation (1) below: _∆L_ i,j,t = μi + α jt + _Crisis_2008_ t × _Foreign_ i,j + _Crisis_2009_ t × _Foreign_ i,j + _Crisis_2008_ t × _X_ i,j, + _Crisis_2009_ t × _X_ i,j+ + ui,j,t (1)

> 8 Variables with nominal values are converted into real terms using the consumer price index for the US. Gross loans and total assets in million US dollars are divided by the US consumer price index (2005=100) from the IMF’s International Financial Statistics.

> 9 Since financial information for non-financial institutions is not available in Bankscope, parents’ financial data is missing for those foreign banks with non-financial institutions as their foreign parents.

8
# **IV. CONCLUSION**

Competition in procurement auctions for infrastructure projects has been largely limited. The water supply and sanitation sector is not exceptional. It is partly because of high technical complexity and partly because of auction design flaws. How to divide a project into lot contracts is an important policy choice for auctioneers. The paper examines the tradeoff relationship between competition in auctions and size of contracts. Larger works could benefit from economies of scale and scope, which are exhibited by large infrastructure projects in general. On the other hand, large contracts could mitigate competition if potential bidders are constrained by technical skills and other resources.

With the data on public procurement auctions for water and sewage projects in developing countries, it is shown that the bidder entry is perhaps endogenous, especially influenced by the bundling and unbundling strategy of potentially different types of contracts, such as water treatment plant and distribution network works. If these two activities are bundled in a single lot package, competition would likely be curbed significantly because there may be only a few firms that can implement both works together. This adverse entry effect would in turn raise the equilibrium bid, thus pushing up public procurement costs of infrastructure. There is no evidence of positive scope economies in the underlying bidder cost structure. Therefore, there is no rationale for bundling the water treatment plant and network components.