rafmacalaba/datause-encoder-data huggingface dataset:
significant impact on transaction costs, which in turn affect trade. To examine bilateral trade, the authors use an augmented gravity model that, in addition to distance and the GDP of each country, includes dummy variables for common border, common language, common trade area, and common religion and a set of variables representing institutional quality (voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption). The analysis controls for multicollinearity and includes a composite institutional quality index. Results show that increasing the overall quality of institutions above its mean would significantly increase bilateral trade.

This paper extends the analyses of de Groot et al. (2004) and Hummels (2001) in several respects. First, it uses bilateral trade data from 2003, thus updating the data by five years.<sup>2</sup> Second, it uses a new data set, the first of its kind, which contains quantitative, country-level data on important aspects of trade logistics, including inland transport and trade-related transactions, using concrete metrics of cost and time. Thus it extends Hummels’s model by expanding the transport time measure to include time for document processing, customs clearance, and inland transport, and to include timeliness (or variability in time) rather than average time only.

# **Data**

The data set, compiled by the World Bank in 2005, contains detailed country-level data on the time and cost of moving a typical 20-foot FCL container from the port of entry to a firm in the most populous or commercially active city in the country—or to the port of exit from a firm in that city.<sup>3</sup> The use of a disaggregated supply chain framework makes it possible to measure time and cost for such activities as trade document processing, approvals needed for import or export transactions, customs clearance, technical clearances, inland transport, terminal handling, and container security measures. In addition, the data illuminate underlying policy and institutional issues that affect time and cost along the supply chain, such as the percentage of containers inspected, the number of agencies with the power to inspect goods, and whether risk-based criteria are used for inspections.

> 2 The data set includes either 2003 data or the most recent data available as of August 2005. Of the 80 economies, 70 had 2003 data available, 6 had 2002 data, 2 had 2001 data, 1 had 2000 data, and 1 (Angola) had 1999 data. 3 The survey excluded ocean freight time and cost, since that would have involved an extremely large number of bilateral trade partners for each country. It included distance, however, used as a surrogate for shipping cost.

6
deficit, extend the tax base, and improve revenue collection. This stabilization program was then extended to the provincial level. However, it was not until 2000 that a new system of local government was implemented was implemented to replace the diverse and racially segregated structures that operated in the apartheid era. The pace of implementation and reform at the local level has been slow for several reasons:

- Under the new local government system, well-off former ‘white’ areas, generally well served by physical infrastructure, were merged with impoverished and underdeveloped black townships.  This created additional strain, both financially and physically, on the capacity of local councils to deliver services and businesses, including the supply of electricity.

- In the 1980s, a culture of non-payment of local rates and user charges developed in many black areas.  The local government deficits were written off by central government.  Despite the political legitimacy of the new authorities, the nonpayment culture persisted post-1994 and, in some instances, spread beyond the black areas, thereby perpetuating the problem of indebtedness.

- The national shortage of skills and experience in running representative and accountable governmental structures is particularly evident at local level, and has inhibited the implementation of reforms.<sup>76</sup>

Prior to 1996, the government provided an operating subsidy to the municipalities. This subsidy was not adjusted to reflect inflation and in some cases it actually declined even in nominal terms. Thus, despite the consolidation many of the newly created municipalities could not cope financially or technically in many areas of service provision, including electricity distribution.<sup>77</sup> In the early years of the transformation, the EDI as a whole seemed to be financially solvent. For example, in 1996 total revenues exceeded total costs by nearly R 1.2 billion, net of the more than R 1.3 billion in electrification costs incurred that year. Still, because of the manner in which these revenues were distributed, many of the smaller municipalities were revenue inadequate. In 1994, four municipalities earned 50 percent of the surplus, 22 municipalities earned 75 percent and the top 100 earned 99 percent of the total, respectively. This left the remaining 300 municipalities collectively earning less than 1 percent of the total surplus for that year. The 12 largest municipalities accounted for approximately 75 percent of all electricity sales in the municipal sector.

> 76 Oxford Analytica. 2004. “SOUTH AFRICA: Local government faces financing problem.” January 30 2004.

> 77 Galen, P. 1998.  “Grappling with Change: The South African Electricity Supply Industry.” National Renewable Energy Laboratory.  Golden, Colorado.

47
paying taxes when obligated funds fail to arrive and the fairness of caste-based affirmative action as a principle of resource allocation.

In this paper we highlight a limited selection of public discourse culled from a vast array that includes discussions on diverse themes. We focus mainly on discussions about distributive justice, including caste-based affirmative action and criteria for BPL selection. In our view, this is one area where the voice of the poor is most crucial.

# **6. DISCUSSION**

This section is broadly divided into two segments. In the first segment we show how the contextual realities of poverty and social inequality shape the culture of deliberation in the GS, i.e., the themes introduced and the discursive styles observed. In the second segment we explore how deliberation acts as a vehicle for questioning governmental definitions of poverty and creating a shared, intersubjective<sup>10</sup> understanding of what it means to be poor. In both segments, our focus is on how poverty influences the concerns and discursive styles of the poor and the implications of this participatory exercise for cultivating their voice.

# **How Poverty Shapes Deliberation and the Discursive Styles of the Poor**

Let us begin with an excerpt from a GS meeting in Dharmapuri (TN), which reveals the patterns of participation and the identity of the usual interlocutors in a typical

> 10 This term is used primarily in phenomenological sociology to refer to the mutual constitution of social relationships and reality. It suggests that people can reach an agreement about their understanding of what they have experienced in their life-world and create a shared world based on their subjective understandings. There is no objective reality that exists outside of this mutually shared subjective understanding of phenomenon.  As the term has been used here, it means a mutually shared and constructed meaning of poverty.

19
WPS3784 the quality of exported crops will have a much greater impact and return if constraints to farmers moving out of subsistence and into commercial crops are also addressed.

To do this analysis, one cannot just compare the average observed income of coffee farmers with that deduced for subsistence farmers since these two groups of farmers have different characteristics, some of which are correlated with the probability of belonging to one or other category. It is important to take into account the propensity of farmers to move out of subsistence and into coffee production. Here this is based on the characteristics of those already producing coffee.

The propensity score matching (PSM) methods, pioneered by Rubin (1977), Rosembaun and Rubin (1983), and Heckman, Ichimura and Todd (1997), allow one to build a counterfactual and estimate the average effects of participating in coffee markets on monetary household incomes. In other words, they help determine how much extra income is associated with switching from subsistence to coffee, assuming a binding land constraint. The method consists of first estimating the probability of producing coffee (probit model) that defines a propensity score p( **x** ), for a given vector of observable characteristics **x** . In a second stage, one can use either traditional (one-to-one) or more recent matching methods to pair participants (coffee farmers) and non-participants (subsistence farmers). Traditional PSM methods (Rubin 1977, Rosembaun and Rubin 1983) pair each participant with a single non-participant, the matching being based on the degree of similarity in the estimated probabilities of being a participant. More recently developed non-parametric matching estimators use a weighted average over multiple observations to construct the matches (see Heckman et al, 1997). We apply both methods in this analysis. Finally, the outcome differential, here the per capita income gain, is estimated using kernel methods.

More formally, denote<sup>_Y_</sup> 1<sup>the outcome (income) conditional on participation in coffee</sup> farming and _Y_ 0 the outcome conditional on non-participation. The impact of participation is 12
Cicowiez, Martín, and Hans Lofgren. 2016. “GEM‐Trade – A General Equilibrium Model for Trade Policy Analysis with an Application to Armenia and the Eurasian Economic Union.” Unpublished.

Cicowiez, Martín, and Hans Lofgren. 2017a. “Building Macro SAMs from Cross‐Country Databases: Method and Matrices for 133 countries.” Unpublished.

Cicowiez, Martín, and Hans Lofgren. 2017b. “GEM‐Core User Guide.” Unpublished.

Decaluwé, Bernard, André Lemelin, Véronique Robichaud, and Hélène Maisonnave. 2013. PEP‐1‐1: The PEP Standard Computable General Equilibrium Single‐Country, Static CGE Model, Version 2.1. Partnership for Economic Policy.

Dessus, Sébastien, Kiichiro Fukasaku, and Raed Safadi. 1999. Multilateral Tariff Liberalisation and the Developing Countries. Policy Brief No. 18. OECD Development Centre.

Dessus, Sébastien, and Rémy Herrera. 2000. “Public Capital and Growth Revisited: A Panel Data Assessment.” _Economic Development and Cultural Change_ , 48 (2): 407‐418.

Foster, Vivien, and Cecilia Briceño‐Garmendia. 2010. _Africa’s Infrastructure: A Time for Transformation_ . Agence Française de Développement and World Bank.

IMF. 2017. World Economic Outlook. April 2017. International Monetary Fund.

Lofgren, Hans, Martín Cicowiez, and Carolina Diaz‐Bonilla. 2013. “MAMS – A Computable General Equilibrium Model for Developing Country Strategy Analysis.” _In Handbook of Computable General Equilibrium Modeling. Volume 1A_ , edited by Peter B. Dixon and Dale W. Jorgenson. North Holland, Elsevier B.V.

Lofgren, Hans and Martín Cicowiez. 2014. “A Note on TFP, Government Capital Stocks, and Related Data Needs in MAMS.” Development Prospects Group, World Bank.

Lofgren, Hans, Rebecca Lee Harris, and Sherman Robinson, with assistance from Moataz El‐ Said and Marcelle Thomas. 2002. “A Standard Computable General Equilibrium (CGE) Model in GAMS.” Microcomputers in Policy Research Vol. 5. Washington, D.C.: IFPRI.

McDonald, Scott. 2015. “A Static Applied General Equilibrium Model: Technical Documentation: STAGE Version 2.” Unpublished.

World Bank. 2017. World Development Indicators. September 18, 2017.

‐29‐
# **Table A3 (continued) Descriptive statistics by country**

|**Countries**|Mean|Std.<br>Deviation<br>CIF|Coef. of<br>Variation|Mean|Std.<br>Deviation<br>COD|Coef. of<br>Variation|Correlation<br>CIF & COD|
|---|---|---|---|---|---|---|---|
|**Developing (continued)**||||||||
|Rwanda|2.66|6.94|2.61|1.07|2.59|2.41|-0.13|
|Sierra Leone|7.28|10.11|1.39|0.77|2.78|3.63|-0.41|
|Sri Lanka|5.16|2.58|0.50|1.10|2.37|2.15|0.33|
|Sudan|5.13|3.16|0.62|1.03|1.49|1.45|0.41|
|Swaziland|4.88|3.78|0.78|4.03|8.01|1.99|0.07|
|Tanzania|4.80|4.98|1.04|0.87|1.36|1.56|-0.06|
|Trinidad and Tobago|-1.18|8.44|-7.14|3.09|7.95|2.57|-0.28|
|Tunisia|6.34|2.21|0.35|2.59|2.16|0.83|0.30|
|Uganda|3.54|5.12|1.45|0.99|1.76|1.78|0.18|

41
# **B.2 Data**

<!-- Start of picture text --> 0.100<br>Year<br>1999<br>2000<br>0.075 2001<br>2002<br>2003<br>2004<br>2005<br>0.050 2006<br>2007<br>2008<br>2009<br>2010<br>0.025<br>2011<br>2012<br>2013<br>2014<br>0.000<br>0 20 40 60<br>Mean PM 2.5 value<br>0.03<br>Year<br>2000<br>2001<br>2002<br>2003<br>0.02 2004<br>2005<br>2006<br>2007<br>2008<br>2009<br>2010<br>0.01<br>2011<br>2012<br>2013<br>2014<br>0.00<br>-100 0 100 200<br>Annual % change in PM 2.5<br>Density plot by year<br>Density plot by year<br><!-- End of picture text --> Figure 11: Densities of pollution levels (left) and changes in pollution (right) for 219 areas with an urban patch of over 5,000 people and densities of 300 per square kilometer or higher.

xii
published by the Bangladesh Agricultural Development Corporation. To estimate current water abstraction for _boro_ production, estimates of evaporation and percolation losses were combined with annual data maintained by the Department of Agricultural Extension on area of irrigated _boro_ . In order to assess the potential change in the future, Key Informant Interviews were conducted by the Institute of Water Management in Dhaka. In addition, several experts from the Bangladesh Agricultural Development Corporation and the International Rice Research Institute were consulted.

The expected change in surface water abstraction for Khulna City was estimated by combining projections of future population (IWM 2010) with the basic water requirement of 50 liters per person per day for domestic use and a water requirement of 15 liters per person per day for other uses, and an allowance of 20 percent loss in evaporation and leakage.

# _Simulations_

We set up the Rainfall-Runoff model and estimated the runoffs from various catchments for the IPCC: A2, A1b, and B1 emission scenarios with each of the three GCMs for changing climate (temperature and rainfall) in 2050. We then used the nine sets<sup>31</sup> of runoff estimates from the Rainfall-Runoff model, projections of water abstraction for 2050, the observed flow in the Ganges and Gorai Rivers over the past 16 years, and the dry season water flow of the Brahmaputra in 2012 as proxies for 2050 dry season water flow from major rivers upstream in the hydrodynamic module of the MIKE 11 model. We estimated various alternative time series of water flow and flow velocity along 230 river branches in the southwest region of Bangladesh. The water flow estimates from the hydrodynamic module of the MIKE 11 model were used as inputs for the upstream boundary of the BoB model. The Global Tide model was set up in parallel and was simulated to estimate the tides and the water level along the coast (the downstream boundary of the BoB model) for alternative scenarios of SLR and land subsidence in the Ganges Delta, in particular for 35cm, 40cm, 47cm, 52cm, 62cm, and 67cm RMSLR by 2050.<sup>32</sup>

> 31 Three emission scenarios x three GCMs = nine scenarios.

> 32 The 35cm RMSLR is estimated from 27cm global SLR by 2050 + 2mm per year subsidence up to 2050; 40cm RMSLR is estimated from 32cm global SLR by 2050 + 2mm per year subsidence up to 2050; 47cm RMSLR is estimated from 27cm global SLR by 2050 + 5mm per year subsidence up to 2050; 52cm RMSLR is estimated from 32cm global SLR by 2050 + 5mm per year subsidence up to 2050; 62cm RMSLR is estimated from 27cm global SLR by 2050 + 9mm per year subsidence up to 2050; and 67cm RMSLR is estimated from 32cm global SLR by 2050 + 9mm per year subsidence up to 2050.

21
Policy Research Working Paper 7902

# **Abstract**

Using a partially constructed panel database of 48 Sub-Saharan African countries from 2000 to 2013, this paper analyzes the structure of housing finance in Africa, its determinants, and its impact on inclusive growth. The findings show that market capitalization and urbanization are key - positive determinants of housing finance, and the post-con flict environment is conductive to greater housing finance development. This result suggests that housing finance is driven by standard market forces of demand and supply. In addition, the analysis finds that housing finance development in Africa is not yet an effective tool for reducing economic inequality, at its current, very early stage. However, the paper shows that above a given threshold, housing finance could be efficient at reducing inequality. Finally, there is a slightly positive relationship between housing finance and greater economic development in Africa. All these findings suggest that policies to boost housing finance development in Africa would be fruitful in the medium to long terms.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group and the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at nguena@aaye.org, ftchanatchana@worldbank. org, and azeufack@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- 31 -

Figure 2. Entities having initiated renegotiation in transport concessions in Latin America <!-- Start of picture text --> Contractor Government Both Not renegotiated<br>Total 25.6 28.0 4.9 41.5<br>Road 13.0 42.6 7.4 37.0<br>Rail 50.0 0.0 50.0<br>0 20 40 60 80 100<br>Share of total transactions (%)<br>Source: Estache et al. (2008).<br><!-- End of picture text --> Figure 3. Major reasons for renegotiation in transport concessions in Latin America <!-- Start of picture text --> Tariff 51.4<br>Demand 44.0<br>Investment requirement 35.8<br>Cost overrun 13.8<br>Service quality 9.2<br>Technological specification 2.8<br>Government subsidy 0.9<br>0 20 40 60<br>Share in total renegotiated contracts (%)<br><!-- End of picture text --> Note: The reasons for renegotiation could be multifold; thus percentages may add over 100 percent.

Source: Estache _et al._ (2008).

Even under the assumption that contracts are complete, public ownership may be preferred because of the significant cost of contracting. Levin and Tadelis (2005) shows that if some contingencies are left out of the contract due to costly contracting, an optimal contract for governments would be either to pay the agent for meeting a minimal time requirement or for meeting a contracted performance requirement. But it should not be both. This is because that time and effort are assumed substitutes for the purpose of improving quality. If both time and quality constraints are specified in the same contract, the agent can achieve a given level of
Finally, we test the sensitivity of our results for the alternative classification of households by migration destination, whereby migrants to India are categorized as domestic migrants. The concern here is that characteristics of migrants to India could be similar to the characteristics of domestic migrants. Such migrants predominantly come from the rural Terai region of Nepal and are usually involved in agricultural or manual labor for low wages. At the same time, Nepalese working in Arab countries in the Gulf and the Far East are educated, employed in the better paying jobs, and can send more money home. Hence, combining households with migrants from India with those from other countries potentially underestimates the impact of international migration.

The comparison of simulations based on this alternative categorization with those based on the preferred specification results in relatively small differences in the simulated poverty rates. The increase in the poverty rate under the scenario of no migration is smaller (2.8 percentage points) compared with the increase in poverty simulated with the preferred specification (3.6 percentage points). The scenario using 1995/96 levels of migration resulted in a 1.8 percentage point increase in poverty in the specification reclassifying Indian migrants versus a 1.9 percentage point increase using the specification classifying migrants to India as international migrants.

There are several qualifications to and possible caveats on our results. First, our results are obtained using the 2004 cross-sectional data. We have no instruments to control for possible household- or community-level endogeneity. In this sense, our estimations of the impact of work-related migration are valid only to the extent that unobserved family and community characteristics are captured by the variables included in our empirical specification.

Second, our analysis focuses only on the direct impact of migration and remittances on households with a migrant. Migration and remittances improve the welfare of households in the sending communities by stimulating local economic development. Migrants channel remittances into productive investment at home. Even when some households spend most of the remittances on current consumption, the resulting demand for goods and services can be met by other working adults in the community, thus generating strong positive externalities. We argue that our estimates provide lower bounds on the actual impact of migration for work and remittances on 25