rafmacalaba/datause-encoder-data huggingface dataset:
penalties for failure to meet projections, without comparable rewards for exceeding projected performance.7/ An overall cost comparison between the BOT approach and a sovereign debt financing would have to take into account the potential effect of these adjustments. It should also put some monetary value on the "insurance" aspect provided by having equity investors or subordinated lenders in a BOT project, which would not be present in a sovereign debt financed project.

Table 2

# REPORTED EQUITY RETURNS IN BOT PROJECTS

|Proiect|Information<br>Source|Projected<br>Return<br>on Equity|
|---|---|---|
|Pakistani Power<br>Projects|Presentation of Mohammad<br>Akram Khan, Advisor, Minis-<br>try of Water and Power, Gov-<br>ernment of Pakistan, London<br>BOT Conference, June 1989|18%|
|Gazi Power Plant,<br>Turkey|Stevenson, "The Turkish BOT<br>Power Project Experience,"<br>May 1989|16%|
|Labuan Water Supply<br>Project, Malaysia|Remarks of Mohamad Hanafiah<br>Omar, London BOT Conference,<br>June 1989|18%-20%|
|Bangkok Second Stage<br>Expressway|BECL, Presentation to Inves-<br>tors, September 1988|3%-21%8/|

7/ See Stevenson, "The Turkish BOT Power Project Experience," May, 1989.

8/ These are figures for the base case assumptions. The 3% return is for the first 10 years; it becomes 21% over the 31 year life of the concession.

37 -

-
for a subset of the Indian population. Eligibility for anthropometric measurement within sampled households varies by year – in 1998, children under 3 and ever-married women aged 15-49 were measured, while in 2005 and 2015 all children under 5, all women aged 15-49, and random subsample of men aged 15-54 were measured. The results of applying the (Henry 2005) equation are reported in the first panel of Table 1.<sup>4</sup> As discussed in Appendix A, data generated by the National Nutritional Monitoring Bureau (reported in (Indian Council of Medical Research 2009)) provides additional measurement of height and weight by age for rural areas of 16 Indian states in 2000-2002 to extrapolate outside of the age range in the NFHS. The NFHS estimates shown in Table 1 highlight three facts that will be important later: (1) adult men have higher REE than adult women who have higher REE than children, (2) REE is higher in urban than rural areas as urban individuals are slightly taller and substantially heavier for a given age/sex, and (3) REE has been increasing over time for a given age and sex, mostly due to increasing weight (see Appendix Table S.1 in the supplementary appendix).

# **2.2 Activity Levels (AL)**

Activity levels are estimated by aggregating up from the most detailed activity data available. With data on the share of time allocated across _J_ different activities, an estimate of individual _i_ ’s activity level (AL) is as follows: where _Shareij_ is the fraction of individual _i_ ’s time spent on activity _j_ . _AFj_ is an activity factor for activity _j_ that measures the intensity of activity _j_ relative to rest (equal to one). Individual _AL_ are thus weighted averages of activity factors where the weights vary across individuals based on their time-use.

The most detailed activity data for India comes from the Time-Use Survey (TUS) conducted in 1998-1999 by the National Sample Survey Organization for six states (Gujarat, Haryana, Madhya Pradesh, Meghalaya, Orissa, and

> 4Validation studies by (Shetty et al. 1986) and (Ferro-Luzzi et al. 1997) find that these types of prediction equations, which are typically estimated on European descended populations, have a reasonably good fit for subjects in India.

6
_We know of no analysis of the effects of the current crisis on urban and rural workers._ During the East Asian crisis, the initial impact of the crisis was felt particularly hard in the urban manufacturing and construction sectors.<sup>9</sup> In Indonesia, both overall and salaried employment declines were larger in urban areas, and urban women suffered substantially larger wage declines than rural women.

_To sum up, existing empirical evidence from past and present crises suggests four hypotheses:_

- Men and women experienced roughly equal falls in income and employment, although in some settings, the added worker effect may lead to smaller falls among women.

- Young workers experienced greater increases in unemployment and informal employment than adult workers.

- Impacts on less educated workers were more severe than better educated workers, but the results depend on the country context and the nature of the shock.

- The shock may have reduced employment and earnings more for urban workers than rural workers.

_Evidence on the vulnerability of different types of workers to this shock remains quite limited._ Past crises in Argentina and Indonesia may not be relevant to the current great recession, since the macroeconomic causes and consequences are very different, and most initial evidence on the current crisis has focused on unemployment and is often limited to OECD or European countries.<sup>10</sup>

# **3 Data and Methodology**

## **3.1 Data**

_This analysis draws on repeated cross sectional household data from 17 countries_ (Table 1) _._<sup>11</sup> These include five Latin American and Caribbean countries (Argentina, Brazil, Chile, Costa Rica and Mexico), seven countries in the Europe and Central Asia region (Bulgaria, Latvia, Lithuania, Macedonia, Poland, Romania and Turkey), two counties in East Asia (Indonesia and the Philippines), two countries in Middle East and North Africa countries (Egypt and Jordan) and South Africa. Countries differ in terms of the indicators collected, span and frequency of data, and coverage of rural areas: ECA countries do not report hours and earnings, and include only the means of key variables by demographic cell rather than individual workers’ outcomes; three LAC countries (Argentina, Brazil, and Chile) cover only urban areas; and earnings are often reported for salaried workers (Chile, and Indonesia). The size of the shock is measured as a change in GDP growth rates, and varies widely, from 1.7 percentage points in Indonesia

> 9 See Fallon and Lucas (2002).

> 10 In particular, exchange rates have changed relatively little during this crisis, while both Argentina and Indonesia experienced extraordinary currency devaluations during their crises.

> 11 For European countries, we use Eurostat Survey data instead of individual country’s Labor Force Survey, which were not available. Eurostat provides the mean of key labor market indicators by demographic cells. For European countries, age, education, and gender estimates rely on data from the 8 cells defined by the three characteristics, while urban/rural estimates rely on a separate set of 8 cells defined by residence, age, and gender.

7
Fambon Samuel, A. Ajab Amin, F. Menjo Baye, I. Noumba, I. Tamba, and R. Tawah (2001). “Pauvreté et Répartition des Revenus au Cameroun Durant les Années 1990.” Cahier de Recherche No. 01-06 du CREFA, Département d’économie, Université Laval, Canada.

Fannin Matthew (2000). “Construction of a Social Accounting Matrix for County Fermanagh, Northern Ireland. Department of Agricultural Economics, University of Missouri-Columbia.

Fofack Hippolyte, Celestin Monga and Hasan Tuluy (2001). “Household Welfare and Poverty Dynamics in Burkina Faso: Empirical Evidences from Household Surveys.” World Bank Policy Research Working Paper No. 2590.

Fofack Hippolyte (2002). “The Nature and Dynamics of Poverty Determinants in Burkina Faso in the 1990s.” World Bank Policy Research Working Paper No. 2847.

Glewwe Paul (1991). “Investigating the Determinants of Household Welfare in Cote d’Ivoire.” _Journal of Development Economics_ , Vol. 35(2): 307-337.

Hillman Arye (2002). “The World Bank and the Persistence of Poverty in Poor Countries.” _European Journal of Political Economy_ , Vol. 18 (2002) 783-795.

Horton Susan, R. Kanbur, and D. Mazumdar (1994). “Labor Markets in an Era of Adjustment”. EDI Development Studies, The World Bank.

International Monetary Fund (2002). “IMF World Economic Outlook” The International Monetary Fund. Washington DC.

Iqbal Zubair and Ravi Kanbur (1997). External Finance for Low-Income Countries. International Monetary Fund. Washington DC.

Iyoha Milton A. (2000). “An Econometric Analysis of External Debt and Economic Growth in Sub-Saharan African Countries,” in External Debt and Capital Flight in Sub-Saharan Africa, ed. By S. Ibi Ajayi and Mohsin Khan, International Monetary Fund, Washington DC.

Klugman Jeni (2002). Overview in “A Source Book for Poverty Reduction Strategies.” Volume I: Core Techniques and Cross-Cutting Issues. The World Bank, Washington D.C.

Krieckhaus Jonathan (2002). “Reconceptualizing the Development State: Public Saving and Economic Growth.” World Development Vol. 30, No. 10 pp. 1697-1712.

Ndulu Benno (1991). “Growth and Adjustment in Sub-Saharan Africa. In _Economic Reform in Sub-Saharan Africa: A Symposium_ , ed. A. Chhibber and S. Fischer. The World Bank.

Njinkeu Dominique and Ernest Bamou (2000). “Trade and Exchange Rate Policy Options for the CFA Countries: Simulations with a CGE Model for Cameroon”. African Economic Research Consortium Working Paper No. 96. AERC Nairobi.

Pattillo Catherine, Hélène Poirson, and Luca Ricci (2002). “External Debt and Growth”. Working Paper No. 02/69, International Monetary Fund.

Pyatt Graham, and Jeffery I. Round (1990). “Social Accounting Matrices. A Basis for Planning.” A World Bank Symposium, The World Bank, Washington, D.C.

Rasmussen P. (1957). _Studies in Intersectoral Relations_ . Amsterdam: North-Holland.

Ravallion Martin (2001). “Growth, Inequality and Poverty: Looking Beyond Averages.” World Development, Vol. 27, No. 11, pp. 1803-1815.

Roland-Holst David and Ferran Sancho. (1992). “Relative Income Determination in the USA: A Social Accounting Perspective”. Review of Income and Wealth. 38 (2), 311-327.

32
**<u>Table A.3: Estimates Controlling for the Colonial Land Revenue System</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||ln(Landgini, 2012)|ln(Landless, 2012)|ln(Tech use, 2012)|ln(Land Sale, 2012)|
|**ln(MA1996)**|**0.278*****|**0.386**|**0.285****|**-0.403**|
||**(0.06)**|**(0.24)**|**(0.14)**|**(2.31)**|
|British control (yes=1)|-0.061|0.075|-0.020|2.926*|
||(0.04)|(0.13)|(0.08)|(1.60)|
|No landlord (yes=1)|-0.084|-0.019|0.074|3.497|
||(0.05)|(0.15)|(0.10)|(2.15)|
|ln(Slope)|-0.030|-0.078|-0.084*|-1.226|
||(0.03)|(0.09)|(0.05)|(1.21)|
|Elevation|-0.242|0.169|0.283|5.813|
||(0.16)|(0.42)|(0.25)|(5.52)|
|Crop Suitability|-0.003**|-0.005|-0.001|-0.001|
||(0.00)|(0.00)|(0.00)|(0.05)|
|ln(Rain)|0.039|0.146|0.028|-0.383|
||(0.06)|(0.15)|(0.09)|(2.21)|
|ln(Temperature)|-0.542|0.554|0.816|6.563|
||(0.43)|(1.04)|(0.67)|(15.37)|
|ln(Rain CV)|0.129|0.340|0.340|10.346*|
||(0.15)|(0.38)|(0.23)|(5.61)|
|ln(Temp. seasonality)|-0.042|-0.045|-0.090|-3.010|
||(0.08)|(0.21)|(0.12)|(3.38)|
|Pop. density, 1961|0.011|0.038|-0.006|0.098|
||(0.01)|(0.03)|(0.02)|(0.35)|
|Constant|-1.657|-11.039**|-10.019***|-46.566|
||(2.54)|(5.51)|(3.74)|(92.78)|
|First Stage<br>ln(dist to GQ) x ln(Slope)|-0.129***|-0.128***|-0.128***|-0.128***|
|.|(0.04)|(0.03)|(0.03)|(0.03)|
|ln(dist. to GQ) x Elevation|0.217|0.161|0.161|0.161|
||(0.18)|(0.17)|(0.17)|(0.17)|
|ln(dist. to GQ) x ln(Slope)|0.002***|0.002***|0.002***|0.002***|
||(0.00)|(0.00)|(0.00)|(0.00)|
|Weak identification|8.05|10.96|10.96|10.96|
||0.0000|0.0000|0.0000|0.0000|
|Observations|200|212|212|212|
|R-squared|0.1756|0.3458|0.5119|0.1446|

Note: All columns are estimated by Lasso and include state fixed effects. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
participation in non-farm activity. However, the land surface does not play a significant role in self-employment activity participation.

The three distance variables, “distance from the village center to the site of the township government”, “distance from the village center to the nearest railway station”, and “distance from the village center to the nearest bus station”, which measure the geographic position of the village, all play significantly roles in non-farm activity participation. In general, the site of the township government is the nearest fair for farm households. The railway station often situates in the hubs of transport/communication, or in urban centers. The distance to railway stations can reflect the cost of a long-distance migration. The results suggest that, households live in villages far from markets and/or hubs of transports are less likely to participate in non-farm activity. The long distance from the market not only increases the transport/commute costs of non-farm activity participation, but also lowers the profitability for the local non-farm business. In particular, if we consider that railway stations often locate in cities where there are more wage-paying job opportunities, the significant negative impacts of the distance between the village center and the nearest railway station on the wage-paying activity participation suggests that the geographic remoteness of a village (which is often aggravated by the insufficiency of the transport infrastructures) is one major barrier for the surplus rural labor to participate in wage-paying activity elsewhere. If we consider that bus stations often locate in local fairs / markets where there are better opportunities for small business, the significant negative impacts of the distance between the village center and the nearest bus station on self-employment activity participation suggests that the segmentation from markets lowers the potential for households to participate in self-employment activity.<sup>29</sup> Improving rural infrastructures and reducing transaction costs are therefore critical for the development of rural non-farm activity.

> 29 The survey data suggests that households living near bus stations are more likely to participate in self-employment activities, such as running stores or restaurants.

17
- 10 -

municipalities, noting new construction and evidence of impending subdivisions and sales.

# General Revaluations

2.20 Over time, the occasional failures of these maintenance procedures result in the accumulation of a significant number of omissions. All municipalities are therefore forced to eventually undertake general resurveys. In most industrial countries, general resurveys are carried out at periodic intervals. These resurveys are normally comprehensive: an effort is made to physically inspect each property and verify the information contained on its corresponding property record.

2.21 In Brazil, general resurveys are neither periodic nor comprehensive. No municipality attempts to follow a predetermined schedule for resurveying properties. The scope of such resurveys, when they occur, is often highly selective. Some municipalities survey only the zone of urban expansion, making no effort to verify ownership and property characteristics in neighborhoods already on the tax rolls. Resurveys may be further limited to higher-income neighborhoods or to classes of property on which major increases in revenue are expected.

# D. Inflation Adjustments

2.22 Municipalities have two means of adjusting the level of the property tax in their jurisdictions. They may either adjust the nominal tax rate, or they may make an across-the-board adjustment in property assessments. The latter is far more widely used, and has been crucial to the viability of the property tax.

2.23 Brazil has experienced high rates of inflation during most of the twentieth century. In the 25 years between 1962 and 1987, annual inflation averaged 65 percent, a cumulative total of 286,000 percent. In the first seven years of the 1980s, annual inflation has averaged 160 percent. To maintain the buoyancy of tax revenues, Brazilian municipalities index valuations.

2.24 Federal law gives the municipalities considerable latitude in this regard. Municipalities are permitted to make annual increases in valuations, at a uniform percentage rate, without physical inspection of individual properties, provided the adjustment is approved by the municipal council as a whole. Mayors may make these adjustments unilaterally, provided the percentage increase does not exceed the official inflation index for treasury bonds.

2.25 These adjustments apply both to existing valuations and to the unit costs used to value new construction. As a result, new construction and existing construction are valued at consistent prices. Because the adjustments are not based upon actual price changes in the real estate market, the assessed values and actual market values tend to diverge. Particularly during periods of hyperinflation, adjustments lag actual inflation in market prices, resulting in a gradual decline in assessed values in real terms.
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ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 4.3  GSM roaming in ECOWAS**

|To><br>From⬇|Benin|Burkina Faso|Cape Verde|Côte d'Ivoire|Gambia|Ghana|Guinea|Guinea-Bissau|Liberia|Mali|Niger|Nigeria|Senegal|Sierra Leone|Togo|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Benin||*|||*|||||*|*||*|*|*|
|Burkina Faso|*||*|*|*|||*|*|*|||*||*|
|Cape Verde|PO|PO||PO|PO|PO||||PO|PO|PO|PO||PO|
|Côte d'Ivoire||||||||||||||||
|Gambia|PO|PO|PO|PO||PO|||PO|PO|PO|PO|PO|PO|PO|
|Ghana|||||PP|||||PP|||PP||PP|
|Guinea||*|||*|||||||||*||
|Guinea-Bissau|||*||*|||||||||||
|Liberia|||||*|||||||||||
|Mali|PP|PP|||PP|PP||||||PO||PO|PP|
|Niger|*||||*|||||||||||
|Nigeria|||||PO|||||PO|||PO||PO|
|Senegal|PP|PP|PO||PP|PO|||PO|||PP||PO|PP|
|Sierra Leone|*||*|*|*||*||*|*||*|*||*|
|Togo|PO|PO||PO||PO||||PO||PO|PO|||
|ONE (Zain)||||||||||||||||
|Orange Zone||||||||||||||||
|One World (MTN)||||||||||||||||

Note: * Not specified if postpaid only. PO=Postpaid only. PP=Prepaid and postpaid. Source: Derived from Ampah and others 2009.

Two factors explain the relatively advanced state of regional integration with regard to mobile roaming arrangements. One is the existence of a proactive regional regulatory association for ICT. The other is the existence of a number of large mobile operators with presence across multiple ECOWAS countries.

ECOWAS and its regional telecom regulator have been particularly active in promoting regulatory harmonization of the ICT sector. Through a commissioner for infrastructure and a Department of Transport and Telecommunications, ECOWAS promotes key objectives in the telecommunications sector, such as establishing a single liberalized market, harmonizing laws and regulations, coordinating and integrating regional infrastructure projects, and enhancing GSM in the region. The national members of the West Africa Telecommunications Regulators Association (WATRA) communicate regularly to keep abreast of telecom issues in the region and share information. The existence of this relatively developed institutional structure has helped to facilitate the roaming arrangements that are observed in the region.

59
# **5.2 Discussion of the counterfactual analysis simulating the effects of trade integration with the EU**

Our second set of counterfactuals involves simulating the integration process between the Western Balkans and the European Union. Since accession to the European Union involves benefiting from freedom of movement for goods, services, people, and capital, we can safely assume that EU accession will lead to a decrease in the costs of trade and migration for Western Balkan economies. Specifically, we reduce the iceberg trade costs between the Western Balkan regions and EU countries by 4%, and the iceberg migration costs by 3%.<sup>4</sup> The assumptions on the size of the reduction in trade and migration costs are based on the work of Caliendo et al. (2021) who study the enlargement of the European Union in the 2000s using a dynamic trade model in which factors of production, goods, and services can move faster within and across national borders. We conduct three counterfactual simulations. The first two assume a reduction of migration costs and trade costs independently from one another. In the third simulation, both trade and migration costs are reduced together. This exercise allows quantifying the gains from coordinating trade and labor mobility policies during market integration.

The main results of this simulation are shown in Figure 6. They indicate that the reduction in trade costs associated with EU accession generates relatively even gains across and within economies in the Western Balkans region. Table 7 in the Annex shows that all Western Balkan economies experience gains, ranging from around 2.8% to 5.04%. These gains from EU accession are largely attributable to reductions in the price index and increased access to new goods. This effect is consistent and evenly distributed not only across regions but also across income groups. By contrast, we observe more substantial heterogeneity in the gains from the migration channel, with Albania and Montenegro experiencing higher gains compared to Bosnia & Herzegovina and Serbia. Specifically, the gains of the former economies are larger than 1.5%, while in the case of the latter, the effects are close to 0%. When we consider the synergies of combining the two cost reductions -trade and migrationour findings suggest that implementing the policies simultaneously may be feasible, but the additional gains from the interaction of the policies are relatively small compared to implementing the policies independently since the gains only increase, on average, by 1% when the two policies are combined. Finally, in terms of the distributional effects across groups, we find very similar effects between the different groups in our analysis, suggesting that accession to the EU has the important advantage of spreading the gains equitably.

> 4Given that tariffs between the European Union and the Balkans are already at zero, we modeled the impact of EU accession as a reduction in the iceberg trade cost.

17