rafmacalaba/datause-encoder-data huggingface dataset:
Holmes, T. (1995), “Localization of Industry and Vertical Disintegration,” Federal Reserve Bank of Minneapolis.

- Hummels, D (1995), “Global Income Clustering and Trade in Intermediate Goods,” Graduate School of Business, University of Chicago.

- Ivanova, Nadezhda (2005), “Estimates of Own and Cross-Price Elasticities of Disaggregated Imported and Domestic Goods in Russia, Graduate Institute of International Studies, Geneva. Available at http://siteresources.worldbank.org/INTRANETTRADE/Resources/Topics/Accession/elasticities_ NIvanova_sep2005_eng.pdf

Jacobs, J. (1969), The Economy of Cities, New York: Random House.

- Jensen, Jesper, Thomas Rutherford and David Tarr (forthcoming), “The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization” _Review of Development Economics_ .

- Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004a), “Estimating the Ad Valorem Equivalent of Barriers to Foreign Direct Investment in the Telecommunications Services Sectors in Russia.” Available at http://www.worldbank.org/trade/russia-wto.

- Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004b), “Estimating the Ad Valorem Equivalent of Barriers to Foreign Direct Investment in the Maritime and Air Transportation Service Sectors in Russia.” Available at http://www.worldbank.org/trade/russia-wto.

- Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004c), “Estimating the Ad Valorem Equivalent of Barriers to Foreign Direct Investment in Financial Services Sectors in Russia.” Available at http://www.worldbank.org/trade/russia-wto.

Markusen, James R. (1989), "Trade in Producer Services and in Other Specialized Intermediate Inputs," American Economic Review, 79:85-95.

- Markusen, James R, Thomas Rutherford and David Tarr (2005), Trade and Direct Investment in Producer Services and the Domestic Market for Expertise,” _Canadian Journal of Economics,_ Vol 38 (3), 758-777.

Marshall, J.N. (1988), Services and Uneven Development, London: Oxford University Press.

- Matusz, Steven J, and David G. Tarr (2001), “Adjusting to Trade Policy Reform,” in A. Krueger (ed.), Economic Policy Reform, Chicago: University of Chicago Press.

- Romer, Paul. (1994),  “New Goods, Old Theory and the Welfare Costs of Trade Restrictions,” _Journal of Development Economics_ ,” 43(1), February, 5-38.

- Rossat (2002; 2003), _Regions of Russia: Social and Economic Indicators_ , Moscow: Federal Service of State Statistics.

- Rutherford, Thomas F. and David Tarr (2002), "Trade Liberalization and Endogenous Growth in a Small Open Economy,"  Journal of International Economics.

26
5 OVERVIEW

- (d) Systems which (like Kumasi's) don't index rents to inflation or which index incompletely tend to get worse over time. The "wedge" between controlled and market rents gets larger. Specific quantitative results reported in this paper are from 1986; numerical estimates of costs and benefits would differ today. The model mentioned above can be used to update the analysis of controls' effects on profitability and affordability, or can be modified to fit other markets.

- (e) Rent control can affect rents in "uncontrolled" markets, but theory and empirical evidence show that the nature of the effect is ambiguous, in large part because the nature of the "uncontrolled" sector varies. In Kumasi, we found "uncontrolled" rents were lowered by controls; in other markets (e.g. Cairo) they were higher. This will be explored further in the forthcoming synthesis paper.

- (f) The largest benefits from controls accrue to tenants who have lived in their units for a long time. This is consistent with studies of many other markets. Lower income tenants do tend to receive larger benefits; this result varies with type of regime in other studies. Many landlords are themselves low income, and some tenants are relatively well off, blunting rent control's redistibutive effect; this is also true in a number of other studies.

- (g) A number of options for decontrol or relaxation of controls exist, and can be studied with the simple model presented in Chapter 6. The specific recommendations we make regarding decontrol are not immutable. The principle that some degree of political consensus is required for any successful scheme of decontrol seems obvious and quite general.

Let us now summarize the findings of this particular case study more completely.

The major _qualitative_ findings of this paper are as follows: The basis for current rent control in Ghana is the Rent Act of 1963, which allows government to set rents for specific types of property from time to time. This has been done every three to six years since 1973; the increases allowed have been kept far below the rate of general inflation. Rent control has kept rents in Kumasi very low. On average, rents are less than 2 percent of total consumption. There can be few households in Kumasi who cannot afford the monthly rent of a room.

While even controlled rents yielded some return on investment in the first years of controls, they have recently lost touch with prices and incomes to such an extent that landlords cannot hope to recoup investment in rental housing. However, there is some evidence that other investments fared poorly as well, especially financial investments. Investment in rental housing may have, at times, remained one way to preserve part of capital. Although there are major cultural incentives to build houses in Kumasi, very few households build only for their own occupation. In the last few years, landlords have begun to demand payment of rent for years in advance, creating considerable hardship for renters who must find many months income in cash to obtain or hold on to a room.
**Table 2: Effect of Grants on Profits Among Damaged Firms**

Dependent Variable: Real Profits

|||Real Profits||Own hours<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Amount|9.90**|8.96**||-0.324|
||(4.25)|(3.89)||(1.84)|
|Cash Amount|||5.27<br>(4.30)||
|Equipment Amount|||12.21**<br>(5.04)||
|Trimming|No|Yes|Yes|Yes|
|Observations|2024|1993|1587|2095|
|Number of firms|200|200|172|200|

Notes: Fixed effects estimation, estimated over 11 waves for firms reporting profits in 3 or more waves.

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 All regressions also include wave effects. Trimmed samples delete the top 1% of percentage and absolute changes in profits from one wave to the next.
15 During March and April 2009 we collected ownership, licensing, programming and other details on all 68 radio stations operating in Benin; we used direct observations by enumerators and interviews with key informants to assess the availability and quality of public goods in 210 villages across the 32 northern communes; and we implemented a survey of the service access, policy knowledge, and political attitudes of 4200 households residing in these villages, randomly selecting 20 households from each village. Radio access was determined in two ways. Village-level key informants listed which radio stations they were able to receive, and the enumerators used their own transistors to verify and expand on this, if they received additional signals. The codes of the radio listed as accessible to the village were then matched with the radio survey data to examine the nature of programming available to the village. Table 1 summarizes the number and types of radio stations covering this study area.

**Table 1:  Access to Radio Stations in North Benin**

||No. of<br>stations<br>accessible to<br>the region|No. of sampled<br>villages covered<br>(Total=210)|Average<br>Signal<br>Strength|No. of<br>education<br>programs<br>broadcast<br>(over the past<br>3 mths)|
|---|---|---|---|---|
|Government-owned Public<br>Radio|3|200|2667 watts|33|
|Private Non-commercial or<br>Community Radio|21|195|231 watts|50.6|
|Private Commercial Radio|10|49|476 watts|21|
|Religious Radio|2|71|.|.|

In addition to the ubiquitous national public radio, this region of Benin is primarily covered by a number of local community radio stations. These stations are non-commercial in part by vocation, but in part because the market for commercial advertising is weak or non-existent. As a
exploit the richness of the data and control for various individual, household and school level (past as well as contemporaneous) correlates of learning (model 3). For this specification, instrumental variable estimate of peer effect returns a coefficient of 0.70 (model 4). The size of the coefficient is slightly reduced to 0.65 when we additionally treat school type (whether an Islamic school) as endogenous (model 5) (for details, see Asadullah et al., 2007b). In all cases, however, our estimate of endogenous social effect remains statistically significant.

**Table 1:** OLS and 2SLS estimates of peer effects, full sample

||(1)|(2)||(3)|(4)||(5)||
|---|---|---|---|---|---|---|---|---|
||OLS|2SLS|1st<br>stage|OLS|2SLS|1st<br>stage|2SLS|1st<br>stage|
|Mean maths score of peers|0.960|0.713||0.957|0.700||0.657||
||(87.01)**|(2.25)*||(82.33)**|(2.26)*||(2.10)*||
|Fraction of peers with arsenic-|||||||||
|affected homes tubewells|||-0.037|||-0.041||-0.040|
||||(3.35)**|||(3.47)**||(3.44)**|
|N|7710|7710|7710|7710|6595|6595|6595|6595|
|Control for family and school<br>attributes ?|No|No|No|Yes|Yes|Yes|Yes|Yes|
|Regional FEs?|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|F-test stat||11.23|||12.05||11.85||
|Overid||---|||---||0.21||
|R-squared|0.48|0.43|0.02|0.50|0.45|0.08|0.44|0.08|

Note: Absolute value of t statistics in parentheses. + significant at 10%; * significant at 5%; ** significant at 1%. Each regression additionally contains dummies indicating tube well non-availability in school and at home, whether tube well at school is arsenic-contaminated and a set of 5 dummies indicating which day of the week the test was taken. All specifications control for regional FE. Specification (5) additionally instruments the school type (whether an Islamic school).

Lastly, a potential concern of our identification strategy is that the incidence of arsenicosis may be localized – even within the same administrative region (i.e. union), some cluster of households could be more affected by arsenic contamination. If so, this would lead to violation of the exclusion restriction. To this end, we estimate the educational production function restricting our sample to children who are not affected by arsenicosis<sup>10</sup> . And because assignment to arsenic-contaminated water well is random, we use the resultant exogenous variation in student achievement to identify peer effects on the test score of

> 10 This stratification strategy is similar to Bobonis and Finan (2007) who use the exogenous variation in school participation (caused by a randomly placed program, namely the Progresa program) to identify peer effects on the school enrolment of ineligible children residing in the same communities.

5
macroeconomic front, AI’s are seen serving the ultimate goal of reducing government spending, which contributes to reducing the budget deficit, inflation and government borrowing (other things equal).

This is sensed through setting up a dedicated institutional setup with the purpose of providing free services at to the society in order to contribute to maximizing welfare. This is efficiently achieved while fully binding to resource constraints as the main operational feature of this setup builds on maintaining the property itself and not consuming it through the process. In addition to helping achieve inter-generational equity, awqaf help also achieve intra generational equity through supplying services to much consecutive generations. This setup needs simple prerequisites to ensure optimal social returns; this includes honesty of caretakers, entitled legal identity and finally efficient administration. This in turn alters Timur Kuran’s imprecise judgment and assessment to awqaf. Kuran thinks the nonprofit setup of awqaf wasn’t intended as it was initially proposed for corporatization<sup>25</sup> . This is not necessarily right as, awqaf; since its inception, were meant to provide non-fee charitable services to contribute to overall welfare of society. Another significant feature is that it is intended by construction to facilitate access to assets and social services and hence is not by any mean perceived as structural stagnation.

# **Section IV. Gaps and Challenges of Enhancing Inclusion through Islamic Finance**

Although the OIC countries have experienced rapid economic growth and financial expansion during the past decade, low level of financial inclusion is still constraining the development of many Islamic economies. The argument is based on our analysis of two recent databases, the Doing Business Report 2011 by the World Bank and the Financial Access Report 2010 by the CGAP. In order to obtain a comprehensive analysis of the financial inclusion status of OIC countries, we calculated and compared the financial access related indicators of OIC countries with those of other groups of countries, i.e., GCC, OPEC, OECD, OIC excluding GCC, MENA region, low Income countries, and developing countries.<sup>26</sup> We use the data from OECD countries as benchmark, because OECD countries have achieved a highly developed financial environment after overcoming many similar issues facing OIC countries currently.

We also separately examined financial inclusion in GCC and non-GCC among the OIC countries to see the dispersion of the financial inclusion status among all OIC countries. GCC are economically more developed and thus more comparable to its counterparties, such as OPEC and MENA region countries, whereas a comparison between non-GCC and low-income countries should provide more meaningful implication regarding the financial inclusion gap of the poorer

> 25 (Kuran 2011)

> 26 The low income countries and developing countries are defined by the country classification by the World Bank published in January 2011.

25
**Table 4: Percentage change in international trade at various levels of carbon tax in Annex I countries from the BAU case in 2030**

|||**Exp**<br>|**orts**<br>**Carb**<br>|**on tax ra**<br>|**te (US$/t**<br>|**Imp**<br>**CO2) **<br>|**orts**<br>||
|---|---|---|---|---|---|---|---|---|
||**10**|**50**|**100**|**250**|**10**|**50**|**100**|**250**|
|**Australia and New Zealand**|-0.4%|-1.5%|-2.6%|-5.1%|-0.1%|-0.5%|-0.9%|-1.7%|
|**Canada**|-0.2%|-1.1%|-2.0%|-4.1%|-0.2%|-1.1%|-2.0%|-4.4%|
|**Germany**|-0.2%|-0.7%|-1.2%|-2.5%|-0.1%|-0.2%|-0.5%|-1.1%|
|**Spain**|-0.3%|-1.4%|-2.4%|-4.8%|0.0%|-0.2%|-0.5%|-1.3%|
|**France**|-0.1%|-0.7%|-1.3%|-2.6%|0.0%|-0.1%|-0.3%|-0.7%|
|**UK**|-0.1%|-0.5%|-0.9%|-2.0%|-0.1%|-0.3%|-0.6%|-1.3%|
|**Italy**|-0.2%|-1.1%|-1.9%|-4.0%|-0.1%|-0.4%|-0.7%|-1.8%|
|**Japan**|-0.4%|-1.5%|-2.7%|-5.3%|0.0%|-0.2%|-0.4%|-1.1%|
|**EFTA Countries & Rest of European Union**|-0.2%|-1.0%|-1.9%|-4.1%|-0.1%|-0.6%|-1.1%|-2.6%|
|**Russia**|-1.7%|-6.6%|-10.4%|-17.1%|-0.5%|-2.1%|-3.5%|-7.1%|
|**United States**|-0.6%|-2.5%|-4.3%|-8.0%|0.0%|-0.1%|-0.2%|-0.6%|
|**Non Annex I Europe & Central Asia**|0.0%|0.0%|0.0%|-0.5%|-0.1%|-0.3%|-0.5%|-1.3%|
|**Argentina**|0.2%|0.9%|1.6%|2.8%|-0.2%|-0.8%|-1.3%|-2.5%|
|**Brazil**|0.1%|0.3%|0.4%|0.6%|-0.2%|-0.7%|-1.3%|-2.7%|
|**Rest of Latin America & Caribbean**|0.2%|1.0%|1.6%|2.9%|-0.2%|-1.0%|-1.8%|-3.3%|
|**China**|0.0%|0.1%|0.2%|0.2%|0.0%|0.0%|-0.1%|-0.4%|
|**Indonesia**|0.1%|0.6%|0.9%|1.6%|0.0%|0.1%|0.1%|0.0%|
|**Rest of East Asia & Pacific**|0.0%|0.1%|0.2%|0.0%|0.0%|0.1%|0.1%|0.0%|
|**Malaysia**|0.1%|0.2%|0.3%|0.4%|0.0%|-0.2%|-0.4%|-0.9%|
|**Thailand**|0.0%|0.1%|0.2%|0.1%|0.1%|0.1%|0.2%|-0.1%|
|**India**|0.0%|0.0%|-0.1%|-0.4%|0.0%|0.1%|0.1%|-0.2%|
|**Rest of South Asia**|0.1%|0.3%|0.4%|0.7%|0.1%|0.2%|0.2%|0.2%|
|**Middle East & North Africa**|0.5%|2.3%|4.0%|7.7%|-0.7%|-3.0%|-5.1%|-9.2%|
|**South Africa**|0.0%|-0.2%|-0.4%|-0.8%|-0.1%|-0.3%|-0.4%|-0.8%|
|**Rest of Sub-Saharan Africa**|0.2%|0.9%|1.5%|2.6%|-0.3%|-1.1%|-2.0%|-3.8%|

18
# Contents

|If*|Int|roduction<br>................<br>...................<br>....................|1|
|---|---|---|---|
|II.<br>Dei|iin<br>........|..............................<br>..........<br>.......<br>......|**3**|
|III.|Imp|ortance of Nonagricultural<br>Activities in Africa's||
||Rur<br>|alo<br>nt.<br>........................................<br>.<br>|4|
||CA.|Emnloymentistribution...............<br>. .............................<br>|4<br>|
||C.|.....**...**.**...**...**.**.**....**<br>I. .. ..b i. . ..<br>....<br>....<br>ee.....|_1_|
|IV.|AB.<br>Cha|SInoe.<br>......<br>....<br>..................................<br>...........<br>rac<br>teristics<br>of Rural Nonfarm Enterprise..............|..6<br>...................<br>6|
||D<br>S<br>Be|<br>izmae<br> <br>Composition ...e.....e......<br>b*oo**ooooee|8<br>7|
||.<br> <br>C.|<br> <br>.........................<br>....<br>............<br>. .....<br>........<br> <br>Employment<br>dest<br>.............................|7|
||D.|<br>Female participation<br>...*,**o........................|..............<br>8|
|V.|Str|uctural TGransformation<br>by the Rural Nonfarm Activity...|<br>9|
||A.|Megatrens......................................................|9|
||B.|<br>Changes by size and locality.................-......|..<br>12|
||C.<br>|<br>Effect of distance from urban centersn.....<br>|13|
||D.|Changes over<br>time|13|
||E.|<br>Correlates of g<br>r<br>ow<br>th........<br>o.....<br>o....<br>....<br>o.........|15|
|VI.|Mag|nitude of the Farm-Nonfarm<br>Linkages.nkae**eOg*Oe<br>s****|O*0<br>18|
||A.|<br>Capitalflo.s<br>....................<br>.<br>|18|
||B.|<br>Laborflos.|19|
||C.|<br>Backward ln<br>k<br>a<br>g<br>es........<br>...<br>0.........<br>......<br>. .....|<br>19|
||D.|<br>Forward li<br>n<br>k<br>a<br>g<br>es.....<br> <br>|20|
||E.<br>|<br>Consumption<br>lns21<br> <br>||
||_Fe_|Ca.t<br>ion|23|
|VII.|Pro<br>|bable Magnitude of the Agricultural Growth<br>||
||Mu|tipliers......<br>......<br>....<br>.........<br>. ....<br>. ................|24|
||A.|Direct cross-regional<br>estimationm.....oti... on......|. 24|
||B.|<br>A model of regional agricultural<br>multiplierseoseeeeo*|* 26|
||C.|Macroeconomic consequences<br>and simulationsations.....|. 30|
|VIII.|Con|clusions..<br>c<br>l<br>u<br>si......**.**..............<br>. .o<br>n<br>s<br>...........<br> <br>|<br>31<br>|
||A.|Implications<br>for policy..............................|31|
||B.|Implications<br>for researche..........................|..<br>34|
||C.|Magnitude of multiplierse..........................|..e<br>35|
Larsen and Rama (2003) present two practical ways of assessing the poverty impact of investment projects. The first, what they call “the project approach,” calculates the ratio between the additional income generated by a project and its cost. Using this rate-of-return estimate, the nation-wide poverty-reducing effects of a project can be inferred, assuming the elasticity of poverty reduction to economic growth is known. The poverty impact is expressed as

|_Poverty_|_=_<br>_Project_|x<br>_Investment_|x|_Elasticity of poverty_|
|---|---|---|---|---|
|_Reduction_|_rate of return_|_rate_||_reduction to growth_|
|_(in percent)_|_(in percent)_|_(in percent_<br>_of GDP)_|||

The second approach, what they call the “statistical approach,” uses statistical information on the local poverty impact associated with local investment. The nation-wide poverty reduction impact is then expressed as

|_Poverty_|_=_|_Statistical_|x<br>_Local poor_|x<br>_Investment rate_|
|---|---|---|---|---|
|_Reduction_||_impact_|_(in percent of_|_(in percent of_|
|_(in percent)_||_(local)_|_total poor)_|_GDP)_|

Both approaches yield concrete estimates for the poverty reducing impact of investment projects, but they also suffer critical shortcomings. In particular, the project approach considers only direct effects of a project, ignoring indirect effects or externalities; the statistical approach considers only local effects ignoring effects transmitted to other communities or network effects. Notwithstanding these shortcomings, they provide useful benchmark figures for investment appraisal. For example, there are ongoing efforts to estimate the poverty impact of investment projects in Vietnam (Larsen and Rama 2003).

**IV. Incidence analysis** _._ Cross-country regressions and CGE models establish, in very broad terms, the links between expenditure components and poverty reduction. Another set of tools help clarify whether expenditure components indeed reach their intended final beneficiaries, whether these are individuals or local providers of services. These studies have been typically used to assess the efficiency and equity of the allocation of resources within sectors and, to a very limited extent, across sectors.

- _Benefit incidence analysis_ . Benefit incidence analysis (BIA) use household survey data and information on public expenditure to assess the distribution of benefits among different groups, such as households at different income levels. BIA has been typically used to identify who benefits from public spending on education and health care (see Demery 2003, for a review of the literature). There are existing summary household survey data on access to water, sanitation, and other infrastructure by income quintiles (World Bank 2003a) that lend themselves to a benefit incidence analysis of infrastructure but studies of benefit incidence of expenditure items other than education and health care are rare.

36
Fourth, in the context of less developed countries, the impact of a weak infrastructure may negate the advantages of economies of scale. For example, economies of scale may be achieved by having one large school instead of several smaller schools scattered throughout the metropolitan area. If the transportation system is inadequate, however, students may not be able to get to that school. Similarly, citizens may have difficulty getting to a central administration building. Even though there may be economies of scale in centralizing administrative functions, it may still be necessary to decentralize these administrative centers so that people have access to them.

# 1.2 Externalities

The provision of some services results in externalities (spillovers) whereby the benefits (or costs) of a specific service in one local government jurisdiction spill over on to residents of another jurisdiction. For example, a road in one municipality can provide benefits to residents of neighboring municipalities who also drive on it. In this case of an external benefit, the local government of the municipality in which the road is located has no incentive to provide services to residents of other jurisdictions and is thus unlikely to take account of the external benefits when deciding how much to invest in the road. The result is an under-supply of the service that generates an external benefit. The problem of externalities is one that is often faced by central cities who have to bear the costs of public services to residents of neighboring communities without receiving the related fiscal revenues (OECD 2006, 158).

One way to remove the resulting inefficiency is to design government jurisdictions large enough so that all of the benefits from a particular public service are enjoyed within the boundaries of that jurisdiction. Such boundary readjustments would “internalize” the externalities (ensuring that those who benefit from the service also pay for it).

As with economies of scale, the optimal sized jurisdiction will be different for different services. Furthermore, the optimal jurisdiction from the point of view of internalizing externalities may conflict with the optimal size required to achieve economies of scale. Other ways to address 10