rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1. The Complexity of Land Policy Challenges Changes by Stage of Urbanization**

|Rural-Urban Land<br>Use Conversion<br>(Property Rights and<br>Strong Institutions )|Land Use and<br>Infrastructure<br>Coordination<br>(Rights of way and<br>Planning<br>Infrastructure)|Densification<br>(Zoning, high FSI,<br>TDRs)|Metropolitan Land<br>Use Planning<br>(Coordination and<br>consolidated<br>management)|
|---|---|---|---|
|Early<sup>7</sup><br>✔||||
|Intermediate<br>✔|✔|✔||
|Advanced<br>✔|✔|✔|✔|

In this paper, we review how countries have built the institutions for property rights and valuation to support urban development. In the next section, we present a summary of the underlying institutions and valuation methods that provide a basis for most of the instruments discussed later in the paper. In section 3 we provide a review of international experience on land assembly focusing on the two most widely used alternatives: eminent domain/ land acquisition and land readjustment (LR). Land assembly through compulsory acquisition is typically conflict ridden.<sup>8</sup> LR is presented as an alternative to eminent domain, because it requires the voluntary participation of landowners. In section 4, we highlight international examples of land-based instruments that can be used as part of an overall portfolio of investments needed to finance urban infrastructure and redevelopment. These include user charges, local government debt, as well as land based financing instruments – such as property taxes, exaction and development impact fees, and land sales or leases. Land-based instruments provide cities with an opportunity to enhance urban efficiency by intensifying the use of land and managing densities while sharing the cost of infrastructure provision with private landowners and developers.

# **2. Land Valuation Methods**

The success of tools used for urban expansion and land based financing is based on robust systems for determining land values. Developed countries rely on a number of forms of available data and supporting institutions to assess land values. Among these are market data

> 7 The 2009 World Development Report (World Bank 2008) provides a definition of the stages of urbanization as follows: Early 0-25% urban, Intermediate 25-75% urban; Advanced 75% urban and more.

> 8 Consider China, where government acquisition of rural land for urban expansion has generated much public discontent and protests.  In the United States, urban revitalization projects are blocked by lawsuits undertaken by dissatisfied property owners who are unwilling to sell their landholdings to make way for new development.  The conventional way of dealing with property owners’ resistance is either to buy them out or to force them out using the government’s compulsory purchase power (Hong, 2011).

3
The results illustrate what poverty trends would have looked like had the underlying relationship between consumption and its correlates remained constant from 1996/97 to 2008/09. Coefficients’ time invariance is arguably a significant assumption.<sup>14</sup> However, bearing in mind the evidence that the model based on the IAF 1996/97 is less affected by measurement error, the out-of-sample predictions for 2002/03 and 2008/09 produce revised consumption estimates that are demonstrably less biased than the original figures. Furthermore, using the 1996/97 parameters, variations in consumption figures over time are fully explained by variation in the independent variables (see Appendix).

Figure 3, below, shows median consumption per capita in 2002/03 and 2008/09 using both the official and revised consumption data. Whereas in 2002/03 the difference between the original and revised figures seems to be remarkable only in the South, the 2008/09 model estimate shows both higher median consumption in the Southern provinces and urban areas and much lower consumption in the Northern and Central provinces and rural areas. For example, in the revised estimates for the urban South median consumption is only slightly higher than in the official figures for 2002/03, but the gap widens dramatically in 2008/09. By contrast, in rural Nampula and Niassa-Cabo Delgado the revised estimates for median consumption are significantly lower than the official figures for both periods.

> 14 An alternative could be to re-estimate the inaccurately measured observations using a so-called ‘hybrid model’ (see Appendix). The model is estimated by using a sample of arguably reliable observations, and the estimated _β_ coefficients are then applied to the whole sample; the fitted consumption estimate is assigned only to the biased observations, and errors are estimated by the random assignment of normally-distributed errors from the equation. Both methodologies come with their drawbacks; estimates based on a reduced sample are not regionally representative (due to the regional pattern of dropped observations), while estimates based on 1996/97 coefficients assume no variance over time. As the results for the two methodologies are quite similar, we opted for the latter (the 1996/97-based model), judging that the lack of regional representation posed a much bigger problem than time-inconsistency.

9
values of a near country or the estimated average f.o.b. unit values of (an) appropriate major exporter(s) adjusted by adding an estimate of insurance, freight, and other relevant costs to the importing country. External prices are converted to domestic currencies using the annual average market exchange rate for the same period as the price data. Internal prices are defined in the Modalities as the representative wholesale price ruling in the domestic market or an estimate of that price where adequate data are not available. The initial tariff equivalents have to be adjusted to take account of differences in quality or variety and applied to tariff lines which are frequently defined at 8, 10, orl2 digit levels.

Where a tariff equivalent resulting from these guidelines is negative or lower than the current bound rate, countries were allowed to establish the initial base tariff equivalent at the current bound rate or on the basis of national offers for that product. The levels of tariff equivalents resulting from tariffication using 1986-88 data, supplemented by the rules as stated above, are specified as the base level (i.e. to be applied in 1995) for the implementation of the reduction commitments on market access.

The post-UR nominal border protection rates are derived from the tariffication commitments. In this paper, the specific tariffs in the Schedule, assumed to reflect the difference between internal and border prices as defined in the Modalities, [T = (Pd - Po))], are translated into _ad-valorem_ tariff equivalents simply by dividing the specific tariff by the appropriate border price as defined in the Mddalities. The average border price in 1986-88 used in estimating the pre-UR tariff.equivalents of NTBs are now used to convert the post-UR base specific tariffs in the Schedules into _ad-valorern rates_ in the base period. To translate the final or bound specific tariffs in the schedules (tariffs to apply at the end of the implementation period) into final _ad-valorem rates,_ I use either (a) constant world prices at the level during the base period, 1986-88 or (b) World Bank projections of world prices for the year 2000.

19
# C.1 Empirical Implementation of FABLE

The model baseline extends for a period of 200 years, with an emphasis on the rst century, and the starting point being the world economy in 2004. It is consistent with the IPCC (2000) A1B climate change scenario's storyline that describes a future world of strong economic growth, global population that grows quickly until mid-century and slows thereafter, the rapid introduction of new and more ecient technologies, and balanced energy use across all sources. It also foresees that, as the economy grows, its economic structure changes toward a service economy, including the expansion of ecosystem services sector. The majority of model's baseline parameters are based on the Global Trade Analysis Project (GTAP) v.7 data base (Hertel 1997, Narayanan and Walmsley 2008) and its satellite data for land use and global climate change policy (Hertel et al. 2009). The values of other model parameters come from variety of the international or government agencies (United Nations, FAO, U.S. Energy Information Administration) and economic, agronomic and environmental science literatures. The values of baseline parameters are summarized in Table B.3 and Figure C.1.

## C.1.1 Population

We assume that the population, Π _t,_ follows logistic (Verhulst) model with declining growth rates over time: where Π0 is level of population in 2004 _,_ Π _T_ is the limiting population in 2104 _,_ and _π_ is the population growth rate. Compared to standard exponential growth assumption the logistic model provides a better t to demographic projections, and has been recently adapted in the economic literature (Guerrini 2006, Bucci and Guerrini 2009, Guerrini 2010). Data on population in 2004 are from GTAP v.7 database. The estimate of limiting population is from United Nations Department of Economic and Social Aairs Population Division (2011). The logistic growth rate of population is calibrated to match United Nations Department of Economic and Social Aairs Population Division (2011) demographic projections.

7
<!-- Start of picture text --> Figure 1: Service Value added (% of GDP) and log per capita GDP<br>HKG<br>MAC<br>LUX<br>GRC FRA USA<br>BELUK<br>PRT TWNITAAUSDEUSWEDNKCHEISL<br>AUT JPN<br>ESP SGP<br>HUN FIN<br>FJI<br>IRL<br>South Asia LKA TON CZE KOR<br>NOR<br>PHL<br>BGD India<br>PAK<br>NPL THA<br>KHM<br>MYS<br>IDNBTN China<br>VNM<br>East Asia<br>LAO<br>6 8 10 12<br>Real GDP per capita logs 2005<br>100<br>80<br>60<br>40<br>Services value added (% of GDP) 2005<br>20<br><!-- End of picture text --> _Source_ : World Development Indicators, 2008, World Bank.

How distinctive is this pattern of growth for South Asia? Figure 1 shows the size of service value added in GDP in South Asia, East Asia, and OECD countries, as a function of the level of GDP per capita. All South Asian economies have a much bigger share of services in GDP relative to their level of real GDP per capita. In contrast, service sector is relatively smaller in all East Asian countries considering their much higher income levels. South Asian GDP composition resembles that of developed economies like Ireland and Norway, rather than that of China and Malaysia. Despite being a low income region, South Asian countries have adopted the growth patterns of middle/high income countries i.e. their growth is driven by services and not by agriculture or manufacturing.

8
of analysis is that they are also the smallest administrative unit where the location of a mine can be identified.

In the analysis, we introduce two sets of explanatory variables: variables related to the location and magnitude of mining activity, and control variables. In line with the distribution formula of the _Canon_ , and using plant-level mining data from the Peruvian Ministry of Energy and Mining, we distinguish between three types of districts within mining departments:<sup>5</sup> _producing districts_ , which hosted a mining facility during 2004-2006; _non-producing districts in producing provinces_ , which, despite not hosting any mining activity, receive a larger share of the _Canon_ than districts in provinces where there is no mining production; and _non-producing districts in non-producing provinces_ , which still receive a share, albeit the smallest, of the _Canon_ because they are located in a producing department. Our final sample consists of 87 producing districts, 453 non-producing districts in producing provinces, and 742 non-producing districts in non-producing provinces (Table 2).

For all districts, we use information about the amount of _Canon_ they received during 2002-2006; the amount of other government transfers during 2002-06; and the value of mineral production at the district and province level during the same period.<sup>6</sup> In addition, we also use in the analysis time invariant district characteristics such as altitude, area, and the location of provincial capitals, and district socioeconomic characteristics from the 1993 Census, prior to the mining boom (for more details, see Table 1).

Table 2 presents summary statistics of these three groups of districts. Of interest are the two socioeconomic outcomes for which we have information in both 1993 and 2007: illiteracy and basic necessities uncovered. Overall, in 1993, producing districts seemed to display a slightly higher literacy rate than-non producing districts; however this difference disappears once we control for basic district characteristics (Table 3). And there are no significant differences regarding the index of basic necessities uncovered between producing and non-producing districts. Of the control variables, the only major difference between producing and non-producing districts is regarding geographic area: on average, producing districts have twice the square kilometers than their non-producing counterparts. While at first sight the difference can appear surprising, the selection of larger districts is consistent with random location of mineral abundance across the provincial territory, as larger districts have a higher probability

> 5  We exclude from the analysis the department of Lima and departments where no mining activity has taken place during 2002-06.

> 6 The value of production at the district level is the log of (one plus the) accumulated dollar value of mineral production by all mining facilities within the district, as reported by the Ministry of Energy and Mining (MINEM) for the period 2002-2006. The mineral prices used are the average annual prices per mineral reported by MINEM for the same period.

9
|_log GDP per capita_|0.140|0.136**|-0.097***|0.217|0.179**|-0.104***|
|---|---|---|---|---|---|---|
||(0.151)|(0.069)|(0.035)|(0.157)|(0.072)|(0.036)|
|_log urbanization_|0.800*|0.604***|0.739***|1.289***|0.608***|0.734***|
||(0.454)|(0.206)|(0.126)|(0.471)|(0.225)|(0.132)|
|_Trade openness_|0.001|-0.000|-0.000||||
||(0.001)|(0.001)|(0.000)||||
|_External risk_||||**0.055****|**0.023***|**-0.011***|
|||||**(0.026)**|**(0.012)**|**(0.006)**|
|_EAP_|0.319|-0.652*|-0.991***|0.115|-0.668*|-1.013***|
||(0.617)|(0.341)|(0.353)|(0.616)|(0.346)|(0.358)|
|_SSA_|0.402|-0.209|-0.732*|0.938|-0.171|-0.703*|
||(0.717)|(0.378)|(0.376)|(0.742)|(0.398)|(0.387)|
|_LAC_|0.718|-0.083|-0.633*|0.624|-0.102|-0.668*|
||(0.630)|(0.345)|(0.345)|(0.614)|(0.352)|(0.348)|
|_MENA_|1.079|0.365|-0.082|0.911|0.404|-0.074|
||(0.705)|(0.373)|(0.377)|(0.768)|(0.407)|(0.402)|
|_SA_||-0.021|-0.078||0.158|-0.081|
|||(0.660)|(0.426)||(0.680)|(0.429)|
|_ECA_|0.642|0.083|0.084|0.521|0.062|0.087|
||(0.553)|(0.319)|(0.336)|(0.539)|(0.325)|(0.337)|
|_log of Population_|-0.168**|-0.071**|-0.076***|-0.248***|-0.069**|-0.092***|
||(0.067)|(0.032)|(0.025)|(0.072)|(0.035)|(0.028)|
|_Constant_|-5.536**|-4.631***|-2.365***|-6.976***|-5.142***|-1.978**|
||(2.310)|(1.068)|(0.872)|(2.444)|(1.225)|(0.924)|
|_Observations_|632|948|944|623|925|907|
|_Number of countries_|55|82|100|49|73|90|
|_Standard errors in par_|_entheses_||||||
|_*** p<0.01, ** p<0.05_|_, * p<0.1_||||||

Using 5-year non-overlapping averages, we estimate a panel with random effects model<sup>7</sup> : i 0 1 i 2 i 3 i 4 i 5 i i log(GG ) = ββ + ββ log(SS ) + ββ log(GG ) + ββ GG +ββ EE +ββ XX + εε

> 7 As in Shelton (2007), we use a random effects model over a fixed effects (FE) one, considering the trade-off between measurement error bias and omitted variable bias, and that an FE model would exacerbate the first one.

16
groups have very unequal household incomes. The mean household income of whites is in the order of four to five times larger than that of African households, and the differential on a per capita basis is even greater. Rates of unemployment among Africans are 31.3% for women and 18.9% for men. These compare with rates among whites of 10.2% for women and 3.2% for men. Not surprisingly, the latter work longer hours than the former. The difference is particularly large for men, and reflects the gap between their market hours of work.

These comparisons highlight the importance of taking account of time inputs and the productivity of domestic work in measuring living standards. If households on low incomes cannot use non-market time productively while those on high incomes can produce close substitutes for market output at reasonable implicit prices, aggregate household income or consumption measures that exclude domestic output will yield results that understate the degree of inequality across households. There may also be serious errors in the ranking of households towards the middle and upper end of the distribution if domestic output is omitted in the measure of living standards.

# **5.3 SSA TUS2000 and EMNV98 as inputs to policy analysis**

In presenting a case for collecting information on time use, the World Bank (2000) argues: “... failing to recognize gender divisions of time and task allocations within households can result in policies that don’t achieve their objectives or that produce unintended outcomes. For example, policies that increase demand for female labor may not elicit the expected supply response if women cannot reduce their time on household maintenance or care activities. Or girls may be taken out of school to cover for mothers who enter the labor force (Grootaert and Patrinos 1999; Ilahi 1999b; Lokshin, Glinskaya, and Garcia 2000). Understanding how households allocate time and other resources by gender can thus provide the basis for more effective policies – and policies that generate fewer unintended and undesirable consequences.” (p.153-4) As already emphasized, estimation of these types of behavioral effects of policy changes requires a model formulated within the framework outlined. They cannot be inferred from a comparison of data means for individuals, as in the preceding tables, or derived 50
19. Deolalikar, A. B. and Rao V. (1998), ‘The Demand for Dowries and Bride Characteristics in Marriage: Empirical Estimates for Rural South-Central India’, in Gender, Population and Development, edited by Krishnaraj, M., Sudarshan, R. and Shariff, A. Delhi: Oxford University Press, pp. 122-40.

20. Deshpande, C. R. (1978), Transmission of the Mahabharata Tradition. Vyasa and Vyasids. Simla: Institute of Advanced Study.

21. Dharmalingam, A. and S. P. Morgan (2004), ‘Muslim-Hindu Fertility Differences in India’, Demography, Vol. 41, No. 3, pp. 529-545.

22. Dreze J. and Murthi, M. (2001) ‘Fertility, Education and Development: Evidence from India’, Population and Development Review 27(1), pp. 33-63.

23. Dreze, J. and Sen, A.K. (1996), Economic Development and Social Opportunity, New Delhi: Oxford University Press.

24. Dyson, T. and M. Moore (1983), ‘On Kinship Structure, Female Autonomy, and Demographic Behaviour in India’, Population and Development Review, Vol. 9, pp. 35-60.

25. Edlund, L.E. (1999), ‘Son Preference, Sex Ratios and Marriage Preference’, Journal of Political Economy, vol. 107, pp. 1275-1304.

26. Filmer, D. and L. Pritchett (1999), The Effect of Household Wealth on Educational Attainment: Evidence From 35 Countries, Population and Development Review, Vol. 25(1), pp.85-120 27. Goldschielder, C. and Uhlenberg, P. (1969), ’Minority Group Status and Fertility’, American Journal of Sociology, 74 (January), pp. 261-272.

28. Government of India (2006), Social, Economic and Educational Status of the Muslim Community of India, Prime Minister’s High Level Committee, Cabinet Secretariat, Government of India.

29. International Institute of Population Sciences, Mumbai and ORC Macro International (2000), India 1998-99 National Family Health Survey (NFHS-2). IIPS, Mumbai.

30. International Institute of Population Sciences, Mumbai and ORC Macro International (2007), India 2005-06 National Family Health Survey (NFHS-3). IIPS, Mumbai.

17
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE undersea cables or satellite communications requires large up-front investments that demand a regional approach.

# The state of East Africa’s infrastructure

East Africa performs below the Southern African Development Community (SADC) and the Economic Community of West African States (ECOWAS) on most aggregate **Table 1.1 Benchmarking EAC with other economic communities** infrastructure indicators (table 1.1). In Western Eastern Southern Central areas such as access to improved Paved road density 38 **29** 92 4 sources of water and sanitation and Fixed-line telephone density 28 **6** 80 13 Internet density, however, East Africa Mobile telephone density 72 **46** 133 84 has performance comparable with or Internet density 2 **2** 4 1 superior to the regional leader— Generation capacity 31 **16** 176 47 southern Africa. The density of fixedElectricity coverage 18 **6** 24 21 line telephones is staggeringly low— Improved water 63 **71** 68 53 less than half the density of Central Improved sanitation 35 **42** 46 28 Africa and one-twelfth that of southern <u>Source: AICD.</u> Africa. Power generation capacity and electricity access are also extremely low, even by African standards. Southern Africa generates 11 times more electricity than East Africa; West Africa, twice as much. Household access to power is three times higher in West Africa and four times higher in southern Africa than it is in East Africa. The aggregates for East Africa as a whole inevitably mask substantial country variations within the region.

||Western|Eastern|Southern|Central|
|---|---|---|---|---|
|Paved road density|38|**29**|92|4|
|Fixed-line telephone density|28|**6**|80|13|
|Mobile telephone density|72|**46**|133|84|
|Internet density|2|**2**|4|1|
|Generation capacity|31|**16**|176|47|
|Electricity coverage|18|**6**|24|21|
|Improved water|63|**71**|68|53|
|Improved sanitation|35|**42**|46|28|
|Source: AICD.|||||

The following sections of the report review the main achievements, challenges, and future benefits of regional integration for each type of network infrastructures. Table 1.2 summarizes the main findings of this sectoral review. The final section of the paper explores the overall financial costs and affordability of implementing the regional integration agenda in East Africa.

5