rafmacalaba/datause-encoder-data huggingface dataset:
time to haul water as a function of collection time as measured in 2008, separately for rural and urban communities.

The estimated impact also varies significantly by region (Table 6). The median effect of a halving of collection time on school attendance is small (one percentage point or less) in the following regions: Western, Central, Greater Accra, Volta, Eastern, Ashanti and Brong Ahafo. In these regions hauling times per round trip are low, less than 17 minutes on average. In the north of the country (Northern, Upper East, and Upper West regions), where hauling times per round trip are longer, 25 to 30 minutes on average, the estimated impact on girls’ school attendance is in the range 5-6 percentage points.

The magnitude of the effect may seem strong in the Northern regions, in particular when compared to what was found by Koolwal and van de Walle (2013). By their estimation, a onehour reduction in time to the water source would lead to a 10 percentage point increase in school enrollment in Yemen and Pakistan. In our case, we find that a 50% reduction in hauling time (corresponding to roughly 15 minutes reduction in time to the water source in the Northern, Upper East and Upper West regions of Ghana) would increase median school attendance among girls in these regions by 5-6 percentage points. Both methodological approach and study area differ between the two studies, though. Importantly also, our measure of hauling time is a per-trip measure as our data contain no information on households’ numbers of daily trips to their water sources.

# _6.4. The case of boys_

Water fetching is usually described as an activity undertaken primarily by women and girls. However we test if there is any impact of water infrastructure also on boys’ school attendance. We consider boys aged 5 to 15, and use hauling time as the main variable of interest, using as instruments the one-period lagged hauling time, the dummy variable indicating whether the household head is a widow(er) or is divorced, and households’ place of residence (rural or urban community). Model (8) is estimated on a sample of 1,174 observations. Estimation results are shown in Table 7. Interestingly the effects of hauling time on school attendance are almost exactly the same for boys as for girls, a result which is in line with findings described in Koolwal and van de Walle (2013). However the impact of household size and composition on school attendance is very different for boys and girls. Girls living in larger households are less likely to attend school in general, whereas household size does not affect boys’ overall school attendance. We however find that the higher the 16
# Microfinance and Moneylenders: Long-run Effects of MFIs on Informal Credit Market in Bangladesh

**Claudia Berg**<sup>1</sup>

George Washington University **M. Shahe Emran** IPD, Columbia University **Forhad Shilpi** World Bank

**Key Words** : Microfinance, Moneylenders, Microcredit, Interest Rates, Informal Borrowing, Long-run Effects, Bangladesh, Identification through Heteroskedasticity **JEL Codes:** O17, O12, C31

> 1 We would like to thank Dilip Mookherjee, Will Martin, Daniel Westbrook,Wahiduddin Mahmud, Pallab Mozumder, and participants in Canadian Economic Association annual conference 2013, IGC South Asia conference in Dhaka in July 2012, and Development workshop at GWU for helpful comments.  We owe a special debt to Frank Vella for extensive discussions and guidance on heteroskedasticity based identification.  For access to data, we are grateful to Mahabub Hossain (household Panel data) and Wahiduddin Mahmud and Baqui Khalily (village cross-section data).  We benefited from discussions with Zabid Iqbal on the InM-PKSF cross-section survey.  Emails for correspondence: cberg@gwmail.gwu.edu, shahe.emran@gmail.com, <u>fshilpi@worldbank.org.</u> The views expressed in the paper are those of the authors and should not be attributed to World Bank or its affiliates. The standard disclaimers apply.
WPS3723

# **Determinants of City Growth in Brazil**

Daniel da Mata<sup>*</sup> , Uwe Deichmann<sup>†</sup> , J. Vernon Henderson<sup>‡</sup> , Somik V. Lall<sup>†</sup> , and Hyoung Gun Wang<sup>‡</sup>

* DIRUR, Instituto de Pesquisa Econômica Aplicada (IPEA), Brasilia † Development Research Group, The World Bank, Washington  DC

‡ Department of Economics, Brown University, Providence, RI

# **Abstract**

In this paper, we examine the determinants of Brazilian city growth between 1970 and 2000. We consider a model of a city, which combines aspects of standard urban economics and the new economic geography literatures. For the empirical analysis, we constructed a dataset of 123 Brazilian agglomerations, and estimate aspects of the demand and supply side as well as a reduced form specification that describes city sizes and their growth. Our main findings are that increases in rural population supply, improvements in inter-regional transport connectivity and education attainment of the labor force have strong impacts on city growth. We also find that local crime and violence, measured by homicide rates, impinge on growth. In contrast, a higher share of private sector industrial capital in the local economy stimulates growth. Using the residuals from the growth estimation, we also find that cities that better administer local land use and zoning laws have higher growth. Finally, our policy simulations show that diverting transport investments from large cities toward secondary cities does not provide significant gains in terms of national urban performance.

World Bank Policy Research Working Paper 3723, September 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

## <u>Acknowledgements</u>

This paper is a product of a joint research program between the World Bank and the Instituto de Pesquisa Econômica Aplicada (IPEA), Brasilia. This research has been partly funded a World Bank research grant and by the Urban Cluster of the World Bank’s Latin America and Caribbean Region, and is also an input to the World Bank’s urban strategy for Brazil. We have benefited from discussions with Carlos Azzoni, Pedro Cavalcanti Ferreira, Ken Chomitz, Dean Cira, Marianne Fay, Mila Freire, João Carlos Magalhães, Maria da Piedade Morais, Marcelo Piancastelli, Zmarak Shalizi, Christopher Timmins and Alexandre Ywata de Carvalho. All errors are the authors’. A preliminary version of the paper was presented at the World Bank/ IPEA Urban Research Symposium in Brasilia (April 2005).

1
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assumptions on government operations and using growth rates from the model, the government debt to GDP path can be obtained by employing equation (13).

The simulations in section 3.1 outline the various growth scenarios for the economy. Under the Baseline Scenario, the annual GDP growth rate for the next 15 years varies in the range of 4 – 5 percent whereas under the most optimistic High Growth Scenario II the annual GDP growth rate varies in the range of 6.5 – 8.5 range. An interesting question in this regard is that whether such huge variation in growth rates spell remarkably different implications with regards to government debt position or not. To make quantitative progress on this front, the time path of government operations need to be fed into equations (13) and (14). In spirit of previous sections, the 7<sup>th</sup> Five Year Plan projections for government operations are used till 2020 after which the variables are assumed to remain at the level targeted in 2020. The assumptions on government operations are summarized below:<sup>10</sup>

- Starting from 9.3 percent in 2015, tax revenue reaches 14.1 percent of GDP by 2020

- Starting from 1.5 percent in 2015, non‐tax revenue reaches 2.0 percent of GDP by 2020

- Starting from 10.5 percent in 2015, current expenditures reaches 13.9 percent of GDP by 2020

- Starting from 2.0 percent in 2015, interest payment reaches 2.5 percent of GDP by 2020

- Starting from 6.9 percent in 2015, government capital expenditure reaches 7.8 percent of GDP by 2020<sup>11</sup>

The analysis of fiscal policy is carried out for the scenarios listed in section 3.1. Note that in all the scenarios aggregate investment remained the same, while the effective investment differed depending on the efficiency of public capital. The following figure plots the time path of government debt to GDP ratio for the different scenarios. Government debt to GDP ratio rises steadily over time as growth rate of GDP outpaces growth rate of government debt. The <!-- Start of picture text --> Government Debt to GDP (%)<br>130<br>110<br>90<br>70<br>50<br>30<br>Baseline E + P HG I HG II<br>2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text --> 10 Intermediate year values for all variables correspond to what has been targeted in the Seventh Five Year Plan. 11 This is consistent with the inputs provided in section 3.1.

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find no evidence of this using data for Argentina, Chile, and Mexico during the 1980s and 1990s.<sup>5</sup> The remainder of this paper is organized as follows. Section 2 discusses the data, and in particular our measures of bank internationalization. Section 3 presents the results on the impact of bank internationalization on the interest market discipline by depositors through banks’ funding costs and deposit growth rates. Section 4 concludes.

# **2. The data**

In this paper, we examine an international sample of 898 banks located in 83 countries for the period 1999-2009. Income statement and balance sheet information on individual banks is taken from the Bankscope database.<sup>6</sup> In addition, Bankscope provides a bank’s significant shareholdings in other banks, if any. Using this information, we can match owning banks with directly owned banks. To ensure data quality, we only include owning banks that report some ownership data in a given year and are publicly listed. In addition, we require the owning bank to have a consolidated balance sheet in Bankscope so that we are able to measure the size of owned banks relative to the size of the consolidated owning bank.

In this paper, we are interested in seeing whether international banks can rely relatively less on the financial safety net in their countries of operation. For this purpose, we construct

> 5 Several studies examine whether a bank’s size reduces market discipline, as a large bank may be deemed too big to fail. Kane (2000) considers US bank mergers, finding that stockholders of large-bank acquirers have gained value when a deposit institution target is large and even more value when a deposit institution target was previously headquartered in the same state. Benston, Hunter and Wall (1995) similarly find that bank mergers and acquisitions are in part motivated by enhancing the deposit insurance put option. Penas and Unal (2004) consider the returns to bond holders around US bank mergers, finding that adjusted returns on merging banks’ bonds are positive across pre-merger and announcement months

> 6 We compile time series information on ownership relationships using Bankscope’s yearly CDs from 1999 to 2008, while ownership data for 2009 are gathered from Bankscope as available through the Wharton Research Data Services (WRDS).

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countries increased from 33 to 36 percent from 2004 to 2007. Expanding use of gas in Greece, the Netherlands, the United Kingdom, Portugal, Spain, and Sweden offset declines in Finland and Denmark and the flat trends in the other countries. The empirical model below may shed some light on the role of relative fuel and carbon permit prices in these fuel share adjustments. Nevertheless, the shift to less carbon intensive natural gas, the increasing generation of renewable energy (see Figure 3), and the declining carbon intensity in several countries suggests that carbon dioxide emission abatement may have occurred even during the trial period for the EU ETS.

During the first phase of the EU ETS, exchanges emerged to trade spot and futures contracts derived from Phase I and Phase II. Because the Phase I allowances could not be carried over into Phase II, future contracts based either on Phase I or Phase II allowances were independently priced. For a variety of reasons, evidence suggests that allowances were over-issued during Phase I. As a result, toward the end of the trial period in 2007, emission allowance prices fell to zero (see Figure 4). However, when allocations for the second phase were determined, additional oversight was given to the European Commission and this appears to have resulted in a binding Phase II cap.

In the short-run, fuel substitution decisions are likely linked to the relative costs of obtaining or selling marginal allowances and for this reason we use spot prices for carbon permits as reported by PointCarbon in our model. This is a reasonable proposition and can be tested in the analysis below by determining whether carbon prices are statistically significant in the input demand functions.

In interpreting our results, we should point out that the pricing incentives for short-run substitution, which our model measures, may be different that the incentives for new investment in capacity. Said somewhat differently, fuel-switching and related actions are short-term tactics meant to minimize costs, while investments are made with an expected flow of profits in mind. Ellerman (2008), for one, argues that the investment decisions were guided by market valuations of traded Phase II allowances, as given by prices for the December 2008 futures contract also shown in Figure 4. And while there is no clear reason to expect that such investments were brought forward into 12
We begin by describing the data used to construct this round of the governance indicators in Section 2. As discussed in more detail below, we have added five new data sources in this round, including a new and very large cross-country household survey, the Gallup World Poll, as well as new expert assessments from Global Integrity, the International Budget Project, the International Fund for Agricultural Development, and the OECD Development Center. We have also made numerous minor revisions to the past data from several of our underlying sources in order to make them more comparable over time, and we have also introduced data from some of our new sources into the aggregate indicators for previous years. These revisions have resulted in minor changes to our previous estimates for 1996-2005, and so the entire new dataset described here supersedes previous releases. In Section 3 we briefly describe crosscountry differences and changes over time in governance as measured by our aggregate indicators. Section 4 concludes.

# **2.  Methodology and Data Sources for 2006**

In this section we briefly describe the update of our governance indicators for 2006, as well as some minor backwards revisions to our past data spanning the period 1996-2005. Our methodology for constructing aggregate governance indicators has not changed from past years, and a detailed discussion can be found in Kaufmann, Kraay, and Mastruzzi (2004). The six dimensions of governance we measure are:

1. _Voice and Accountability (VA)_ – measuring the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media.

2. _Political Stability and Absence of Violence (PV)_ – measuring perceptions of the likelihoood that the government will be destabilized or overthrown by unconstitutional or violent means, including domestic violence and terrorism 3. _Government Effectiveness (GE)_ – measuring the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies 3
power generators, Endesa, Chilgener and Colbun, decided to sign transportation contracts with Gas Andes in order to feed natural gas to combined cycle generation plants that each one decided to build. Interestingly, Endesa signed a contract with this pipeline even though its major shareholder was Enersis, the leader of the Transgas project. Open access acted as a safeguard for a power company that was involved in the project that lost the competitive race.

The Gas Andes project started to be built at the beginning of 1996 once the environmental impact assessment study was approved and began to transport gas to a power plant and the grid of Metrogas in Santiago in August 1997. An extension to Valparaiso was built one year later by the power companies Endesa and Colbun allowing them to operate their own power plants and to supply gas to two newly formed gas utilities, Gas Valpo and Energas, which are still competing to sign contracts with industrial and residential users in that zone.

As a result of the fierce competitive race between Transgas and Gas Andes a new clean source of energy was introduced to Central Chile. There was an immediate pro-competitive effect in the Central Interconnected System (SIC) which accounts for almost 80% of the Chilean market. In fact the wholesale price of electricity in Santiago has declined over 35% in real terms since 1994. Natural gas has become an effective competitor to hydroelectricity and coal, the two traditional sources of power in the central system (see Figure 10).

Natural gas also had a strong impact on the manufacturing sector. Just one year after it was introduced in Santiago it accounted for around 40% of total consumption of energy (excluding electricity) by industrial users in that city. It has been estimated that by 2005 that share will reach up to 70% (see Figure 11). Tighter environmental norms for atmospheric emissions have been a very strong incentive for gas penetration. In fact, natural gas will allow large reductions in industrial emissions of the main sources of air pollution: around 62% for particles, 33% for NOx and 79% both for S02 and CO.

10
- 26 -

**Chart** 6

**Turkey: Consolidated Budget Deficits**

<!-- Start of picture text --> 8<br>4 ,4<br>0<br>-2<br>4<br>1990 1991 1992 1993 1994 1995 1996 1997<br>F WPrimary Balance _Interest Payments -- Overall Balance<br><!-- End of picture text -->

**Chart** 7

**Financing of the Budget Deficit**

<!-- Start of picture text --> 10<br>8<br>6<br>2<br>1990 1991 1992 1993 1994 1995 1996 1997<br>= foreign borrowing domestic borrowing central bank advances<br>_other At cash deficit<br><!-- End of picture text --> It is clear from the charts above that the fiscal stance until the 1994 crisis was on a worsening path. After 1991, the primary stance of the government worsened, which contributed to the risk premium in real interest rates, and the budgetary burden of interest payments increased considerably. Both in 1992 and 1993 the primary budget balance was negative and PSBR started to increase rapidly, coming close to 12 percent of GNP. Rather than reducing expenditures, in mid-1993, the government started to resort to heavier Central Bank financing of the deficits, as interest payments were identified as a potential area for savings. Despite the deterioration in fundamentals, the government refused to pay higher interest rates in the debt auctions in late 1993, and canceled several auctions altogether.