rafmacalaba/data-use-mentions-extended huggingface dataset:
America by market capitalization, and the fourteenth largest among developing countries. Relative to developed countries, the Colombian stock market is small – its size relative to the total value of U.S. listed companies at the end of our sample is 0.4%. However, MSCI indices typically include the largest companies and the most actively traded stocks in each country. The average market capitalization of our sample stocks is six times larger than the average size of other listed companies in Colombia. Similarly, the yearly traded value of our sample stocks is seven times larger than that of the other listed companies (Table OA2, Panel B). Moreover, in terms of market capitalization, the stocks in our analysis are similar the largest ten stocks in the Russell 2000 index – a group that has been extensively used to analyze many key question in finance, including the price impact of index recompositions, the role of institutional ownership on price informativeness, and the effect of passive investors on corporate decisions, among many others (e.g., Chang et al., 2014; Boone and White, 2015; Appel et al., 2016).

# **3 Empirical Analysis and Results**

We analyze three main issues related to equity index rebalancings. First, we measure the price impact of these episodes. Second, we analyze the behavior of investors in response to these episodes. Finally, we combine the information on prices and traded quantities to estimate the demand elasticity for stocks.

## **3.1 The price effect of index additions and deletions**

We measure the price impact of index additions and deletions by looking at the price dynamics of Colombian stocks included into or removed from MSCI international equity indexes. We calculate abnormal returns as the difference between the stock returns and the implied returns from a single factor model, _Rst − β_<sup>ˆ</sup> _sRt_<sup>_D/s_</sup> . The stock’s beta, _β_<sup>
Policy Research Working Paper 8919

# **Abstract**

Using data from the International Benchmarking Network for Water and Sanitation Utilities, this paper on the water sector in pourashavas (municipalities) in Bangladesh provides an analysis of the trends in the water sector development over 2010–16. The main purpose of the paper is to examine the average performance of the water sector providers in the pourashavas to encourage conversation on identifying and addressing deficiencies in service performance in comparison with that in the rest of Bangladesh and the world. This analysis finds that although pourashavas perform on the lower end of the spectrum compared with the rest of Bangladesh on many indicators, the top 20 percent of the pourashavas are globally competitive on indicators of staff productivity, cost coverage, and daily per capita consumption of water.

This paper is a product of the Water Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at gjoseph@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
migration to South African mines and the economic consequences for neighboring laborsupplying countries. For each economy, equations are estimated for migration to the mines, domestic crop production, cattle accumulation, and estate labor. It is found that, in the sending economies, domestic crop production (i.e. production in rural areas) is negatively affected by the withdrawal of labor (in the _short run_ ), but positively affected by investments remitted by migrants (in the _long run_ ).

This conclusion is confirmed by the work of Rozelle, Taylor and DeBrauw (1999) on a sample of farm households in the northeast of China in the Hebei and Liaoning provinces. They find that migration involves labor lost _initially_ which depresses yields, but that access to capital through migration-induced remittances increases yields _eventually_ . The short run versus long-run distinction is important for analyzing the impact of migration on production in the place of origin.

# **Consequences for human capital and growth at the place of origin**

Migration can have a negative effect on the local stock of human capital by removing individuals with particular skills, or it can have a positive affect by stimulating investment in the acquisition of human capital by potential migrants (Stark and Wang, 2002). These two effects can act in opposite directions so that the overall effect is a matter of empirical investigation. The idea of a ‘beneficial brain drain’ in which migration prospects induce investment in human capital has been studied in the case of international migration by Beine, Docquier and Rapoport (2001) using aggregate crosssection data for a set of developing countries. In order to control for the endogeneity of migration, the authors simultaneously estimate country-specific migration rates, investments in human capital and the growth rate of the national economy, using the expost migration rate as a proxy for the ex-ante probability to migrate. The results indicate that migration prospects play an important role in education decisions, but given the use of aggregate cross-section data the results should be considered only suggestive at this stage.

41
.05|1.18|0.64|0.76|86,642|186,889|1.21|1.50|0.91|0.85|

_Note_ : MAPE refers to mean absolute percentage error; l_MAPE refers to the mean percentage error based on predictions accuracy of apartments sized between 50 and 60 square meters; local ratio refers to the ratio between observed and predicted values for apartments sized between 50 and 60 square meters. Exchange rates are based on 2019 conversions.

The estimations show that among the five countries, at the national level and in US$ terms, Pakistan has the cheapest _BHP_ (US$ 21,849), followed by Morocco (US$ 53,282), South Africa (US$ 63,745), Albania (US$ 71,205), and Costa Rica (US$ 113,064). In PPP$, however, the price levels for a basic apartment between the five countries is more equal. Visualization of the raw data in Figures 1 and 2 offer some 29
Policy Research Working Paper 5975

# **Abstract**

This paper evaluates how microfinance performed in providing business financing in 27 Sub-Saharan African countries. It uses data from the 2009 and 2010 Gallup World Poll, a nationally-representative survey of at least 1,000 individuals per country, conducted in up to 157 countries per year. The data, supported by rigorous statistical evidence in related literature on the use of microcredit around the world, demonstrate that economic gains from microcredit have been more modest than what was once believed. On the other hand, the analysis suggests that the poor save in order to start new businesses and that the introduction of formal products for small savings can be a key financial innovation. The authors also analyze the challenges the poor face in setting money aside to save, and discuss what policymakers can do to promote savings.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. 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 author may be contacted at lklapper@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
# **7. DATA APPENDIX**

The Household Budget Survey (HBS, 2009-2012) Variables used form HBS:

- **Access to Banking** (Bank_Access): Accessibility of Banking services according to the location of the dwelling.

- Original variable name in dataset: ZOR_BANKA Dummy variable equals 1 if accessibility is rated “easy” or “very easy”, equals 0 if accessibility is rated “difficult” or “very difficult”

The Survey on Income and Living Conditions (SILC, 2006-2012)

- **Mortgage and Other Payments** (Mortgage): Possession of a mortgage, loan repayment or rent payment in last 12 months preceding the survey. Original variable name in dataset: HE010 Dummy variable equals 1 if respondent possesses the payment, equals 0 if he/she does not have any such payment.

- **Arrears on Mortgage and Other Payments** (Arrear_Mortgage): Possession of an arrear on a mortgage, loan repayment or rent payment in last 12 months preceding the survey. Original variable name in dataset: HE010 Dummy variable equals 1 if respondent has an arrear on the payment, equals 0 if he/she does not have any such arrear.

- **Credit Card and Other Payments** (Credit_Card_Payments): Possession of a hire purchase installment, credit card or other loan payment in last 12 months preceding the survey. Original variable name in dataset: HE030 Dummy variable equals 1 if respondent possesses the payment, equals 0 if he/she does not have any such payment.

- **Arrears on Mortgage and other Payments** (Arrear_ Credit_Card): Possession of an arrear on a hire purchase installment, credit card or other loan payment in last 12 months preceding the survey. Original variable name in dataset: HE030 Dummy variable equals 1 if respondent has an arrear on the payment, equals 0 if he/she does not have any such arrear.

The Global Financial Inclusion Database, (FINDEX, 2011, 2014)

- **Bank Account
1] Female is a dummy variable where 1 = female-led bu|0.43<br>el, *** significant at 1<br>siness and 0 if male|0.33<br>% level.<br>-led business. School C|0.34<br>losure due to COVID-19|0.08<br>is a dummy vari|0.17<br>able where 1 = schools|0.17<br>closed in country at ti|0.09<br>me of survey wave.|

<mark>Notes: * significant at 10% level, ** significant at 5% level, *** significant at 1% level. [1] Female is a dummy variable where 1 = female-led business and 0 if male-led business. School Closure due to COVID-19 is a dummy variable where 1 = schools closed in country at time of survey wave.</mark> [2] OLS pooled regressions include Region of the world Fixed Effects and Time Fixed Effects and full set of controls including Firm Controls (Sector + Size, Age and Role in Business); Mobility Controls (Movement around retail & recreation/workplaces); GDP Per Capita; and Demographic Controls (Married and Types of Dependents).

<mark>[3] Outcomes refer to the specific activities that had an impact on the entrepreneur's ability to focus on their work in the past 30days.</mark> 41
To capture the political view of property rights, we use variables from the Polity IV dataset, averaged over the period 1995-99 and Beck et al. (1999)’s Database of Political Institutions (DPI). The variables, **Democracy** and **Autocracy** , scored from 0-10 (0 = low; 10 = high) reflect the general openness (closed-ness) of the political institutions in the country respectively and are from the Polity IV Dataset<sup>28</sup> . **Checks** from DPI, measures the number of influential veto players in legislative and executive initiatives. The political view predicts that greater competition and more checks and balances will limit the ability of the elite to dictate policy and institutional development.<sup>29</sup> Rajan and Zingales (2003) argue that trade openness proxies for the extent to which certain established interests can restrict entry into their country’s markets. According to this view, one would expect property rights to be better protected in countries that are more open to international trade. As a measure of openness of the country, we use **Trade** which is the extent of trade as a percentage of GDP of the country. The variable is taken from the World Development Indicators and averaged over the period 1995-99. Trade as a percentage of GDP is a potentially endogenous variable since a country’s actual openness depends on investor rights. An alternative to this variable is the **Frankel and Romer** (1999) measure of natural openness that is based only on geographic characteristics. We present results with both Trade/GDP and the Frankel and Romer (1999) measure though the latter is available for only 63 of the 80

> 28 According to the Polity IV Dataset, the structure of the state is determined by broadly three interdependent elements - presence of institutions and procedures through which citizens can express effective preferences about alternative policies and leaders, the existence of institutionalized constraints on the exercise of power
in Section 6 it describes the proposed computational approaches for calculation of the extended EB estimators and for estimation of their MSEs. In Section 7, several simulation exercises, imitating those performed by MR (2010) but extending them to more realistic scenarios, are conducted to compare the different methods. Finally, conclusions are presented in Section 8.

# **2 Traditional ELL approach**

The Elbers, Lanjouw and Lanjouw (2002, 2003) methodology is the de facto small area estimation approach utilized by the World Bank. The methodology has been widely applied across the globe to produce poverty maps conducted by the institution.<sup>3</sup> The popularity of the methodology can be attributed, to some degree, to the availability of the PovMap software (Zhao, 2006),<sup>4</sup> which was programmed in C and offers users a simple point and click interface. The PovMap software is also incredibly computationally efficient and fast, allowing users, even with limited computing power, to work with census data without facing memory limitations. In 2018, a Stata version of the PovMap software was released (Nguyen et al., 2018). The Stata command “sae” replicates most of the procedures and methods of the original PovMap software, and has become popular because it allows for expansion of the methods available to users.<sup>5</sup> The ELL method assumes that the welfare _ych_ for each household _h_ within each location _c_ in the population is linearly related to a 1 _× K_ vector of characteristics (or correlates) _xch_ for that household, according to the nested error model: where _ηc_ and _ech_ are respectively location and household-specific idiosyncratic errors, assumed to be

> 2A factor that likely contributes to these being well known is the availability of software, that can be easily used to apply these approaches; PovMap (Zhao, 2006) for ELL and R’s sae package (Molina and Marhuenda, 2015)

> 3Poverty mapping is the common
/19, an LSMS-ISA survey which also collected data on certain taxes including business taxes, land use fees, and agricultural income taxes. We also integrate these survey data with administrative data obtained from various ministries and their subsidiary agencies.

The study finds that the fiscal system is progressive, poverty-reducing and equalizing. However, those results vary when the available information is further disaggregated. An analysis by tax type shows that direct taxes are progressive while indirect taxes are regressive. Moreover, indirect taxes account for more than two-thirds of taxes relevant to children. On the transfer side, direct and indirect in-kind transfers are progressive. Transfers are predominantly indirect in-kind transfers, with education spending being by far the largest in-kind transfer. Primary education spending is progressive, while secondary education spending is regressive across levels of child deprivation. Public spending on health is progressive as well. The study does not find significant differences in incidence by gender, however in rural areas, primary education and health spending are neutral, and do not show the progressivity seen in urban areas or overall.

3
1.

**Actual and forecasted GDP growth** : We further use yearly information on actual and recently forecasted GDP growth from the World Economic Outlook (WEO) of the International Monetary Fund (IMF). WEO is usually published twice a year (in April and September/October). It presents IMF staff economists’ analyses of global economic developments during the near and medium term. Every April and October, the WEO provides year-ahead and current-year GDP growth forecasts. We refer to the year for which the forecast is being made as the target year. Forecasts made in the Fall WEO before the target year are called year-ahead forecasts and those made during the Spring target year are called current-year forecasts. During our sample period, forecast data are available for 195 countries. As shown by An, Jalles, and Loungani (2018), IMF growth forecasts are virtually identical to other growth forecasts from the private sector, and can therefore be regarded as consensus forecasts from a wide variety of market analysts from both the official and private sectors. After merging these IMF data with ECI we were left with 174 destinations, which account for 99.7% of all exports in 2006. Table A2 reports the export shares to the main destinations in 2006, both in the full ECI data and in

6
The strong growth of the EV market is in contrast to the recent decline of the overall passenger vehicle market. Nevertheless, the EV diffusion has been highly uneven across countries. Norway leads the world in EV adoption, where the EV share tripled from 18% in 2015 to 67% in 2020. Sweden and Netherlands also saw remarkable growth in the EV market share in the new vehicle market, reaching over 20% in 2020. For countries such as China, Spain, Canada and the United States, the EV share is smaller (3%-5% in 2020), but still represents significant growth over the years (Figure 3). Appendix figure S1 shows the map of the EV penetration rate in 2020 for all countries around the globe. On average, EVs have a stronger presence in richer countries: Northern Europe has generally higher EV shares while African and Asian countries have relatively low EV penetration. But it is not clear what explains the large differences among countries with similar socio-economic characteristics.

**Cross-country variation in EV choices** There is also considerable variation in the types of EVs purchased across regions. Figure 4 presents the average battery capacity, post incentive price, and vehicle size for battery EVs sold during 2013-2020 in four main regions in the estimation sample: China, Japan, Europe,<sup>1</sup> and North America.<sup>2</sup> We present the sample and sales-weighted averages in dark and light shades, respectively. Sales-weighted average battery capacity is generally higher than the sample average in all regions except Japan, indicating battery capacity is a key attribute that consumers select on. In particular, consumers in Europe and North America disproportionately prefer EVs with higher driving range. The sales-weighted average post-incentive price is lower than the sample average post incentive price in all regions, as expensive models tend to have lower market shares. Finally, in North America, the sales-weighted average size is slightly larger than the sample average, which may suggest that consumers prefer larger
& Schargrodsky, 2010; Zegarra et al., 2008). In addition, those studies that have found empirical support for this relationship frequently qualify their findings in several ways. In particular, these evaluations of land titling programs highlight impact heterogeneity and the importance of the implementation approach (Mushinski, 1999; Dower & Potamites, 2005). Taken together, the findings of these studies suggest that credit markets thrive within a plethora of enabling factors, of which land titling, and thus the ability to use real estate as collateral, is an important, but not the sole driver to access to credit. The fact that we find no evidence that titling increases borrowing is consistent with the lessons of this literature.

The most basic economic argument linking land tenure security, investment and agricultural productivity is the possibility that the land, and any investments sunk into it, may be expropriated. This possibility acts like a tax on investment and reduces the incentive to invest. Ali, Deininger & Goldstein (2014), for example, find that a pilot regularization land program had large impacts on investment and maintenance of soil conservation measures in Rwanda. Jacoby et al. (2002); Hornbeck (2010) and Galiani and Schargrodsky (2010) provide other important examples of this mechanism. In contrast, we show that there is no evidence that land registration in Ghana and the associated increase 3
actively discourage their use. Finally, administrative data showed that the program also increased the grade passing rate, i.e., led fewer students to have to repeat a school year.

We now study the effects of this high-school financial education program on students’ financial and labor market outcomes, up to nine years after the program ended (and students graduated from high school). Nine years after graduating high school, most students would have entered the labor market, even if they had gone to university. In Brazil, students tend to start university one or two years after graduating from high school (because students frequently use this time to study for entrance exams) and take between four and eight years to graduate.

Based on students’ name and age, we obtained the taxpayer identification number (CPF) for nearly 16,000 students in the original sample. The percentage of students for whom we found a CPF is 44.27 percent in the treatment group and 46.02 percent in the control group. The small difference in percentage of CPFs found across the treatment and control group is statistically significant at the 10 percent level. Nevertheless, the baseline characteristics from the 2010 survey are balanced across the treatment and control groups in the sample of 16,000 students with a CPF. We then use the CPF to consult administrative data housed at the Central Bank of Brazil (BCB). These data include bank account ownership (but not account balances), use of various credit products, as well as information on formal employment status and formal microenterprise ownership. We follow students until February 2020, just before the COVID-19 pandemic hit Brazil.<sup>2</sup> To the best of our knowledge, ours is the first study to present long-term evidence on the effectiveness of financial education via a large, randomized controlled trial, and to jointly analyze a diverse set of administrative records on long-term outcomes.

Relying on the comparison of the randomized treatment and control groups, we find no effect of the high-school