rafmacalaba/datause-encoder-data huggingface dataset:
Table 1.1a below outlines the many ‘moving parts’ that make up a system of infrastructure finance for cities. These are the sorts of instruments that may be used to mobilize funds, smooth out capital expenditures in line with revenue generation, and the types of infrastructure spending that need to be undertaken in cities. Table 1.1b enumerates some of the most important environmental factors that affect how any particular structural element in the financing system may operate.

These two tables illustrate a few important points about systems of urban infrastructure finance that will be elaborated further in the paper:

- 1) With so many different structural elements at play in urban infrastructure finance, there is a very wide variety of permutations and combinations available for organizing the responsibilities for the use of funds, and the sources generated to finance them. As we will discuss further in Section 2, there is considerable variation in arrangements across countries. Strong normative conclusions on universal “do’s and don’ts” are hard to come by. IMF (2009) seeks to draw lessons on what sorts of arrangements work best for overall budgetary management in decentralization. The list of recommendations is short and rather general, necessarily leaving considerable scope for tailoring specifics to country context and requiring considerable judgment to assess how much deviations from these principles really matter. (e.g. “The sequencing of decentralization matters, as resources should be made available to subnational governments _pari passu_ with the assignment of spending responsibilities.” P.3)

- 2) Urban infrastructure finance systems are necessarily structurally more complex than for example a national government because of the interdependencies across levels of government. This complexity arises not only from administrative divisions, but also geographical dispersion of cities. Even an autocratic unitary government needs to manage communications and rules of engagement in far flung places<sup>2</sup> . The institutional environment variables in table 1.1b l illustrate some of the most important areas of articulation across the system.

- 3) While the structures of urban finance and accompanying institutional arrangements may vary considerably from one place to another, it is difficult to predict performance of an urban finance system on the basis of  these variables. Well –functioning systems generating solid funding for infrastructure expansion and operations can look very different. Good finance systems can be

> 2 Keay (2008) offers an interesting recount of the Indian Mughal Emperor Akbar’s innovations of administrative arrangements to consolidate his hold over conquered kingdoms. Rather than delegating full control of geographical areas to a loyal ally, he parceled out specific functional responsibilities to his agents in the field.

3
robust information about the role of jobs for individual trajectories out of poverty, only infrequently report social indicators like trust or social and political activities. This has made it difficult to link work-related experiences to individual behaviors and attitudes that are normally associated with higher levels of social cohesion.

In the light of these limitations evidence for this paper primarily comes from research that has explored narrower aspects of the jobs-social cohesion relationship. In sociology and economics there is now a large and increasingly rigorous scholarship that provides estimates of social and economic barriers to social inclusion and mobility in and around labor markets. Other survey-based and ethnographic research has analysed how widely used indicators of social cohesion, such as trust, civic associations and subjective wellbeing, vary along with employment status. While these studies do not typically permit claims that work _causes_ differences in social outcomes, they provide a first indication how experiences and behaviours related to social cohesion differ across relevant groups in the labor market.

For the purpose of this review a useful starting point is a simplified framework of the interaction between social and political institutions and labor market outcomes (Figure 1.) Starting from the the ‘social atomization’ view in sociology I first discuss social processes that influence selection into the labor market. The evidence reviewed here concentrates on the rich interdisciplinary literature on occupational and earnings mobility, as well as on groupand location-specific influences, such as residential and social segregation. These processes relate primarily to the broader inclusion-centered definition of social cohesion used in this paper. However, there are also theoretical reasons to argue that unfair social barriers to work can facilitate conflict and the breakdown of peaceful group relations.

The next section discusses the potential positive consequences of jobs for social cohesion. Drawing on literature on social capital I will argue that, once people are in work, associations and interactions made in and around the work place can contribute to social cohesion by forging ties across otherwise divisive social or ethnic barriers. I discuss these outcomes with the help of survey and ethnographic evidence on social associations and relations of trust shared by members of the work force, as well on the relevance of these experiences for inter-group and –ethnic relations. Other positive effects of jobs reviewed in the article include work-related life satisfaction. Among policy makers subjective wellbeing is increasingly regarded as a central indicator for non-monetary dimensions of economic and social development (OECD 2011:55ff, Dolan and Metcalfe 2012). I discuss differences in 8
_Traditional Channels ofAccountability_ The audit, inspection and control functions should be strengthened, since they tend to be quite weak in transition and developing economies. The auditor-general should be given greater authority and autonomy in exercising his mandate. At the same time, a case can be made for loosening the constraints of the central planning process in developing countries. Central plans lead to a centralization of authority, a reduction in flexibility, innovation and autonomy at the local level and delays in private sector activity. On a more specific point, as fiscal responsibility is decentralized to the state and local levels, it would be beneficial to create the institutional capacity for iocal borrowing so that more reliance is placed on borrowing and less on capital grants to finance capital projects. Examples include loan councils or municipal finance corporations.

# _Oversight of Local Governments: Freedom and Responsibility within Boundaries_

Monitoring and oversight of local governments is an area of concern in both federal and unitary countries alike. For example, the Republic of South Africa Constitution Act 1996 (Section 139(1) (b)) provides for a disbandment of local government in the event of failure to (i) "maintain national standards or meet minimum standards of service"; (ii) "prevent actions prejudicial to the interests of another municipality or the nation as a whole"; and (iii) "maintain economic unity." It further provides for withholding of tax shares and transfers for noncompliance with tax effort (Section 227(2)). The fulfillment of constitutional obligations regarding these provisions require a significant and superior evaluation capacity at provincial and national levels. Evaluative measures that can assist in this oversight include requirement of annual commercial corporate audit of local governments; fiscal capacity measurement using a common yardstick i.e. equalization of municipal assessments; greater emphasis on formula grants over project grants in provincial-local transfers; greater emphasis on public-private-civil society partnership in public provision; opinion polls on service standards and citizen satisfaction; and performance ratings of local governments based upon outputs, outcomes and citizen satisfaction.

Audit of local governments in industrialized countries at the most fundamental level pays attention to three elements, adherenice to the principle of ultra vires, the procedure by-law and fulfillment of funded mandates. The principle of ultra vires states that a local government should do only those things that the statutes say it can do and it may not do anything for which it has n1o 19
combined to create three sectors: farmer, middle and city. The farmer and city categories speak for themselves, while the middle category is a residual category of anyone who is not a farmer and not living in a city. These could be non-agricultural rural dwellers or residents of secondary towns.

The consumption data originate from extensive food and non-food consumption modules in the survey, carefully designed to maintain comparability across survey rounds and controlling for seasonality. The consumption aggregate includes home produced and purchased food and non-food expenditure. The non-food component includes a range of non-food purchases, as well as utilities, expenditure on clothing/personal items, transfers out, and health expenditures. Funeral expenses and health expenses prior to the death of an ill person were excluded. Conservatively, rent is also excluded from the aggregate to avoid large differences in prices for similar quality housing being the driver of any measured urban-rural disparities. The aggregates are temporally and spatially deflated using data from the price questionnaires included in each survey round. As household size may differ between urban and rural households, consumption is expressed in per adult equivalent units rather than per capita. The poverty line is set at 326,474.2 Tanzanian shillings (TSh)<sup>13</sup> , calibrated to yield for our sample of respondents who remained in Kagera the same poverty rate as the 2007 National Household Budget Survey estimate for rural areas (37.6 percent).

Combing the occupational, spatial and consumption changes between 1991/94 and 2010 (Table 2), it emerges that the agricultural share of the sample reduced from 82 percent in 1991/94 to 48 percent in 2010. Tanzania is clearly undergoing a structural transformation.<sup>14</sup> Second, categorizing the households into 6 groups depending on their starting point (farming and middle) and sectoral shifts (farm-farm, farm-middle, farm-city) shows that most people start from farming and remain in farming (1,368 out of 3,301), while the second largest group

> Dar es Salaam (3,118,132, or 2,993,096 excluding more rural areas), Arusha (362,484), Mwanza (596,885, being the sum of Ilemela (331,344) and Nyamagana districts (265,541)), Tanga (298,881). For Uganda UBOS (2012) estimates the total population of Kampala at 1.72 million.

> 13 At the time of the survey one US dollar was worth around TSh 1,450.

> 14 It is important to note that our panel can inform us on how a representative cross-section of the population in Kagera in 1991/94 fared up until 2010. It is, however, not a representative cross-section of Kagera in 2010. Therefore the 2010 figures should not be confused with regionally representative averages..

10
<!-- Start of picture text --> OH =CPre E + =Gay t taoGane ) Gan|= ,T)COre<br>T<br>1 — (———<br>t(r,T) = (7 + 7)<br>1 TTF<br>LMMU = — = —M°® c(r,T)<br>PRE<br>OI = t(r,T)Cposr<br>But we know OH — OI = M°<br>° = t(r,T)Cprg — 107, T)Cposr = T(r, T)(Cpre — Cposr).<br>FKOV = _ _ ¢ Corp PRE —CPOST) _ ° <(r,T(r,T)<br>PRE PRE<br>consumption being equalized over time, consumption in period ¢+1 is a constant fraction or<br>PRE1 =— &Uppeac PRE2 _= &” 7,2 pre0<br>we have (see Annex):<br>LMMU = — = M° oo(r,T) _ pov = — = SexeCBee — CpPost) _ ° otg(r,T)<br>POST PRE POST<br><!-- End of picture text -->
2020. They also provide aggregated tariff rates for the sectors of our model. From 2011 to 2012, the estimates are based on actual tariff rates and trade data in Russia. Russia will implement its tariff commitments under its WTO accession agreement progressively from 2012 to 2020. For 2012 to 2020, Shepotylo and Tarr estimate changes based on the progressive tariff reduction commitments Russia has taken under its WTO accession agreement. Unlike in the case of services, there is no evidence that Russia’s tariff commitments impacted its tariffs prior to 2012. Consequently, we take tariffs in 2011 at the benchmark tariff rates. We take the tariff rates in 2020 (when all tariff commitments are implemented) as the tariff rates in the counterfactual. These tariff are reported in table 12a.

Export tax rates are calculated from the 2001 input-output table of Rosstat and are reported in table 12a. Since we do not change export taxes in the counterfactual simulations, these parameters are not very important to the results.

# **_4.3.5 Input-Output Tables_**

The core input-output model is the 2001 table produced by Rosstat. The official table contained only 22 sectors, and importantly has little service sector disaggregation. In order to disaggregate the table, we used costs and use shares from our 35 sector Russian input-output table for 1995 prepared by expert S. P. Baranov. (For details see <u>www.worldbank.org/trade/russia-wto.) When we broke up a sector such as oil and gas into</u> oil, gas and oil processing, we assumed that the cost shares and use shares of the sector were the same in 2001 as they were in the 1995 table. For example, steel is an input to the oil and gas sector. Suppose in 1995, that oil purchased 55 percent of the steel used in oil and gas. Then we assume that in 2001, oil purchased 55 percent of the steel used in oil and gas.

# **_4.3.6 Regional IO Tables and Trade Flows of the Regions_**

We constructed input-output tables of the 88 regions that are based on data from the regions (described below) and the national input-output table. The input-output tables for our ten regional markets are aggregates of the input-output tables of the regions in their respective regional markets.

We assume that the technology of production is common across regions, so that the input-output coefficients from the national input-output table apply across all regions. As a first step, for each industrial sector, we took the national output from the national input-output table for 2001, and we used the data in Regions of Russia to allocate the shares of that output across the 88 regions. That is, from the Regions of Russia 2001, we have, by region, the value of total industrial output and industry shares of regional industrial output for the year 2000 (table 13.3); thousands of tons of oil recovery, including gas condensate, for the year 2000 19
16 traditional gross or net enrollment ratios. Their most recent version of the data set also takes into account changes in school duration over time within countries.

The second global data set for average years of schooling is that of Cohen and Soto (2001), which covers 95 countries and spans the period 1960 to 2000 on a decade basis. This data set uses 3 main sources of data. They are the OECD database on education, national censuses or surveys published by UNESCO’s Statistical Yearbook and censuses obtained directly from national statistical agencies’ web pages. Based on reports from its members and other non-member countries, the OECD has published detailed information on educational attainment, beginning at the end of the 1980s. This information refers to the population aged 15 to 64 broken up in different age groups and this is the cornerstone of the Cohen-Soto data set for high-income countries. The main advantage of the OECD data set is that the information is presented in a standardized form across countries. Cohen and Soto extend the study performed by the OECD to missing periods and countries.

One key difference between the Cohen-Soto dataset is in the methodology for extrapolating the missing data. Barro and Lee extrapolate missing data for the whole population either backwards or forwards to obtain educational attainment for missing years. As opposed to using the whole population, Cohen and Soto utilize estimates for age-specific groups, which they argue tend to result in more reliable estimates. Cohen and Soto also claim that for some countries, they had more recent census information than that used by Barro and Lee.

In order to obtain the broadest coverage of countries for data on educational attainment, we combined the information from the Barro-Lee and Cohen-Soto data sets to obtain the resultant data set that covers 144 countries for the period 1960 to 2000. If educational attainment data on a specific country is contained in both data sets, then we follow Bosworth and Collins (2003) and take the simple average of two data values to obtain our final measure of the average years of schooling.

Even though average years of schooling or average educational attainment is by far the most commonly used proxy of the human capital stock, there are still deficiencies with the unweighted sum of schooling years as a measure of the economically productive human capital stock. First, average educational attainment assumes that each additional year of schooling increases the human capital stock by the same magnitude, regardless of the amount of education already attained. This implies that a year of primary education tends to increase the human capital stock by the same amount as a year of tertiary education. Second, average educational attainment does not account for quality differences in the different education systems in the world. As such, it assumes that a year of education in a less developed country increases the human capital stock as much as a year of education in a developed country. Therefore, the following section focuses
considering, for example, economic performance of a country. We controlled for two simple measures of economic performance, income level and income growth in the five preceding years. The results suggest that these two factors explain some of the difference between aggregate perception-based indicators and indicators generated from firm-level surveys. (Of course, those who see corruption as a major source of poor performance would expect corrupt countries to be poor and grow more slowly, so this might be considered over-controlling.) Adding further controls about development of a country such as urbanization did not change our finding. Figure 1 below presents the results graphically. The left panel shows the scatter diagram of correlations between CPI and ES rankings. The right panel adjusts the CPI rankings through regressions on income levels and growth (2005-2009). In the graphic the fitted CPI rankings are “compressed” as, for example, the top ranked countries are dragged down by the control for income levels. The results are inconclusive.

What we are left with is a significant variation of the apparent incidence of corruption among sectors, types of permits and other “targets” of corruption. Data on the size and frequency of bribe payments for a contract in the construction industry alone suggests considerable withincountry variation on the reported frequency and scale of bribe payments even within this limited sphere (Kenny, 2006). Indeed, the significant in-country variation between measures of different types of corruption casts some doubt on the utility of one overall measure such as the CPI as a guide to the extent and nature of the corruption problem within an economy.

Comparing individual country rankings, we observe some intriguing differences between the ranking in the CPI and our aggregate firm-based indicator. We consider the respective rankings of 107 developing countries. While most countries are ranked broadly similarly, China and India are clearly not. They rank at 35 and 40 respectively on the CPI. On the aggregate ranking derived from enterprise surveys they rank 69 and 100 – just two countries, but over 35 percent of humanity. Two other intriguing cases are Belarus and Venezuela. The CPI ranks them at 70 and 99 respectively; the enterprise ranking yields place 34 and 13. For countries like Venezuela and Belarus it might be that firms are afraid that their responses might not remain confidential or outside perception is affected by other views about the economy. For China and India we can only speculate about rosy perceptions of foreign respondents captured in the CPI.

5
# **_Annex VIII: Summary of Findings of the Research on Estimating Money Demand for Egypt:_**

||**Data Pro**|**perties**||||**M**|**odeling App**|**roach**||
|---|---|---|---|---|---|---|---|---|---|
||**Sample**<br>**and**|**Freuenc**|||**Te**|||||
|**Author (year)**|<br>**Source**|**qy**|**Variables**|**Integration**|**yp**|**Specification**||**Estimat**|**es**|
|Scobie 1983|1947 -<br>1981|annual|GNP, Money<br>Balances and<br>domestic<br>price levels|NA|Structural<br>model|LM2;<br>LGNP_t,<br>LGNP_t-1,<br>LCPI_t,<br>LCPI_t-1,<br>LM2_t-1,<br>LM2_t-2|LM2 = 0.1<br>– 0.86* LC<br>1 – 0.1 LM|+ 0.38* LGNP_<br>PI_t + 0.2 LCPI<br>2_t-2|t + 0.04 LGNP_t-1<br>_t-1, 0.66* LM2_t-|
|Handy 1998|1973 -<br>1997|annual|Currency<br>(CUR),<br>Domestic|M2X,<br>Inflation,<br>offshore|ADL|three models,<br>CUR, M2X,<br>M2:|CUR|M2X|M2|
||||Liquidity<br>(M2X<br>excluding<br>foreign<br>currency<br>deposits),<br>Total<br>liquidity<br>(M2), RGDP,<br>CPI,<br>Inflation,|interest are<br>all I (1)||Independent<br>variables:||||
||||Interest,<br>Offshore<br>|||RGDP,|1.07*|1.86*|1.69*|
||||interest,<br>Dummy<br>1985-1992|||CPI,|0.55*|0.625*|0.653*|
|||||||Inf,|-0.22*|-0.15*|-0.13*|
|||||||interest rate,|0.004|0.010*|0.019*|
|||||||offshore<br>interest rate|0.012*|-0.0010|0.00394*|

91
- 17 -

<u>Table 5. Regression Results for Bids in Road Procurements</u>

||(1)|(2)|(3)|
|---|---|---|---|
|Entrant bid dummy||3.62|7.17|
|||(4.27)|(4.55)<br>|
|Dummy for entrant facing with incumbent(s)|||-3.90 <sup>*</sup><br>(2.12)|
|Share of incumbent bidders in auction||22.38 <sup>***</sup>|26.84 <sup>***</sup>|
|||(5.16)|(5.92)|
|Number of contracts before 2002||-0.32|-0.64|
|||(1.94)|(1.98)|
|ln(_N_)|-10.37 <sup>***</sup>|-4.77 <sup>*</sup>|-3.19|
||(2.82)|(2.56)|(2.59)|
|Lot length (km)|0.14 <sup>***</sup>|0.17 <sup>***</sup>|0.18 <sup>***</sup>|
||(0.05)|(0.05)|(0.05)|
|Lot length<sup>2</sup>(km) 1/|0.31 <sup>***</sup>|0.30 <sup>**</sup>|0.26 <sup>**</sup>|
||(0.12)|(0.12)|(0.11)|
|Lane|-17.89 <sup>***</sup>|-22.42 <sup>***</sup>|-22.88 <sup>***</sup>|
||(4.37)|(4.71)|(4.59)|
|Lane<sup>2</sup>|4.07 <sup>***</sup>|4.61 <sup>***</sup>|4.63 <sup>***</sup>|
||(0.65)|(0.65)|(0.64)|
|New roads|8.38 <sup>**</sup>|11.74 <sup>***</sup>|12.09 <sup>***</sup>|
||(3.51)|(3.38)|(3.30)|
|Rehabilitation|0.47|4.53 <sup>*</sup>|4.96 <sup>**</sup>|
||(2.83)|(2.49)|(2.47)|
|Contract duration|0.27|0.41 <sup>**</sup>|0.48 <sup>***</sup>|
||(0.17)|(0.16)|(0.16)|
|Donor 1|2.30|-3.18|-2.80|
||(2.33)|(2.22)|(2.26)|
|Constant|34.54 <sup>***</sup>|18.60 <sup>*</sup>|11.18|
||(9.41)|(10.21)|(10.36)|
|Obs.|188|188|188|
|R-squared|0.680|0.727|0.730|
|_F_-statistics|90.15|68.21|65.71|

1/ For presentation purposes, the coefficients are multiplied by 1,000. Note: The dependent variable is the bidding amount. The robust standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% significance levels, respectively. Source: Author’s calculation.