rafmacalaba/data-use-mentions-extended huggingface dataset:
2), 167-88.</mark> <mark>DiMaggio, P. and Bonikowski, B., 2008. Make money surfing the web? The impact of Internet use on the earnings of US workers.</mark> _<mark>American Sociological Review</mark>_ <mark>,</mark> _<mark>73</mark>_ <mark>(2), pp.227-250.</mark> El-Mallakh, N., Maurel, M. and Speciale, B., 2018. Arab spring protests and women's labor market outcomes: Evidence from the Egyptian revolution. _Journal of Comparative Economics_ , _46_ (2), pp.656-682.

Faberman, J. and Kudlyak, M., 2016. What does online job search tell us about the labor market?. _Economic perspectives_ , _40_ (1), pp.1-15.

<mark>Hendy, R., 2015. Women’s participation in the Egyptian labor market: 1998-2012.</mark> _<mark>T</mark> he Egyptian labor market in an era of revolution_ <mark>, 147.</mark> Hjort, J. and Poulsen, J., 2019. The arrival of fast Internet and employment in Africa. _American Economic Review_ , _109_ (3), pp.1032-79.

ITU (2019). World telecommunication/ICT indicators database.

Kroft, K. and Pope, D.G., 2014. Does online search crowd out traditional search and improve matching efficiency? Evidence from Craigslist. _Journal of Labor Economics_ , _32_ (2), pp.259-303.

Kuhn, P., 2003. The Internet and matching in labor markets. _New Economy Handbook, Academic Press, Amsterdam_ .

Kuhn, P. and Skuterud, M., 2004. Internet job search and unemployment durations. _American Economic Review_ , _94_ (1), pp.218-232.

Kuhn, P. and Mansour,
>Concepts Measured in 1997 Survey<br>11 Economic Growth<br>12 Price Stability<br>13 Integration into World Economy<br>14 Currency Stability/lnvestment Climate<br>15 Rule of Law<br>16 Ease of Portfolio Position<br>17 Productivity<br>18 Balance of Payments<br>19 Political Stability<br>20 Corruption<br><!-- End of picture text --> 31
EMDEs. Real consumption and real GDP data are drawn from the World Bank’s World Development Indicators (WDI). Population statistics are from the United Nations’ National Accounts Main Aggregates Database. It is widely known that aggregate output and consumption data are nonstationary, whereas first differences of these variables are stationary. Hence, we follow the standard practice and carry out the analysis using first differenced series.

Total remittances include personal transfers, defined as all current transfers in cash or in kind received by resident households from nonresident households, and compensation of employees, defined as gross earnings of workers residing abroad. Remittance inflows are drawn from the IMF’s

> 6 A few previous studies have investigated the ability of remittances to reduce macroeconomic volatility. Balli and Rana (2015) follow a methodology pioneered by Asdrubali, Sørensen and Yosha (1996) and show that remittances help delink national income from domestic output fluctuations. Our paper studies how remittance inflows affect measures of international risk sharing. <mark>Differences in institutional quality and stages of development between countries that receive remittances and aid flows and those that send these flows can boost the risk sharing potential of remittances (</mark> Callen, Imbs and Mauro 2015; Parsley and Popper 2021).

> 7 Other studies document that faster growing developing countries tend to receive more private capital inflows, but experience even more public capital outflows (Aguiar and Amador 2011; Alfaro, Kalemli-Ozcan and Volosovych 2014; Kim and Zhang 2020).

4
thors have subsequently extended this approach to a number of advanced economies and EMDEs. The popularity and simplicity in implementing this approach has led to similar measures for several other EMDEs.<sup>3</sup> This work was also extended to specific policies such as monetary policy in Husted et al. (2020) for the United States and Arbatli et al. (2017) for Japan. In Hołda et al. (2019), fiscal and monetary policy uncertainty is identified separately. A strand of textual analysis has also focused specifically on text released by central banks (see, for example, Ehrmann and Fratzscher, 2007).

Another method to measure policy uncertainty is to use statistical models with heteroskedastic errors where the second moments of policy variables vary over time. Fern ́andezVillaverde et al. (2015) use this approach to define tax and fiscal spending uncertainty. Mumtaz and Surico (2018) use this approach to identify both fiscal and monetary policy uncertainty in the United States employing a methodology that can solve the problem of the causal link between measures of uncertainty and other macroeconomic variables reported in Baker et al. (2016), Caggiano et al. (2014), and Stock and Watson (2012).

A third approach uses market data such as options and futures. Monetary policy uncertainty can be measured from the implied volatility of option prices on interest rates and realized volatility from futures (see, for example, Chang and Feunou, 2013; Bauer et al., 2012; and Carlson et al., 2005).

Finally, uncertainty measures can be constructed through survey data by asking participants about their perceptions of uncertainty. In the context of policy uncertainty, the Federal Reserve Bank of New York’s Survey of Primary Dealers (which started in 2004) asks primary dealers to provide their forecast of policy rates and their forecast uncertainty. Husted et al. (2020) use these survey responses to compare their news-based measure of monetary policy uncertainty. Dahlhaus and Sekhposyan (2018) use survey-based
.50]|[0.48]|(0.18)|
|Located in Visayas|0.25|0.15|0.095|
||[0.44]|[0.37]|(0.14)|
|Located in Mindanao|0.23|0.20|-0.049|
||[0.42]|[0.40]|(0.15)|
|Located in ARMM|0.036|0.029|0.028|
||[0.19]|[0.17]|(0.022)|
|Municipalities|83|70|153|

_Source:_ Data for the study comes from field reports of the Armed Forces of the Philippines (AFP) and election returns from the Philippines Commission on Elections (COMELEC).

_Note:_ Summary statistics for the sample of 153 municipalities in which incumbents won or lost the 2007 election by a margin of 5 percentage points or less. Following the classification of the 2000 Census of the Philippines, “strong” building materials for walls are defined as concrete, brick, stone, wood, galvanized iron or aluminum, asbestos and glass. “Strong” building materials for roofs are defined as concrete, galvanized iron or aluminum, clay tiles and asbestos. Standard deviations are reported in brackets. Column 3 reports results of RD regressions using the municipality characteristics as outcomes. Standard errors of these regression estimates are reported in parentheses. *, ** and *** denote statistical significance at the 10%, 5% and 1% levels.

17
.014+|-0.015|0.006|0.008|-0.022++|-0.018|-0.010|0.007|-0.008|-0.014|
||(0.009)|(0.013)|(0.008)|(0.013)|(0.009)|(0.012)|(0.008)|(0.011)|(0.008)|(0.012)|
|Change in urban population|0.038|0.033|-0.079|-0.051|-0.184|-0.196|-0.077|-0.101|0.060|0.107|
||(0.182)|(0.223)|(0.075)|(0.089)|(0.114)|(0.152)|(0.116)|(0.149)|(0.146)|(0.167)|
|State fixed effects|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|Observations|250|250|201|201|247|247|250|250|250|250|
|Adjusted R-squared|0.608|0.629|0.304|0.367|0.651|0.683|0.597|0.626|0.500|0.520|

Notes: Estimations consider changes in district spatial mismatch for 1994-2005. The spatial mismatch index compares the observed industry distribution of district employment across urban and rural locations to a counterfactual where plants in the district are allocated to urban and rural locations according to national propensities by industry to be in urban settings (holding constant for the district the total urban and rural activity)
of each country. The ITU’s ICT Regulatory Tracker provides comparable scores for both regulatory and competition frameworks (ITU (2020)). The Tracker traces private vs. public ownership, the degree of competition for broadband, basic and leased line services, and whether the law recognizes the concept of significant market power. These are classified as G1, which cannot be considered a competition model, to G5, which features independent regulators, a pro-competition environment, and working together across agencies and firms to improve the deployment of ICT. The ITU scores each country according to these criteria. Table 1 summarizes the set of scores for Peru as an example of the different measurements.

LAC seems representative of a competitive environment. Figure 5 ranks the competitiveness of ICT from a monopoly (score = 4) to one that is fully competitive (score = 1). Because these scores are based on self-reporting, they do not necessarily relate to outcomes. Despite seeming to be pro-competition, in terms of costs LAC is more discouraging. The 4
on sales and profits. In our qualitative interviews, three out of 10 of the sample we had identified as showing the highest sale growth in the quantitative survey said they believed the program had not increased sales. Moreover, since firms did not increase their use of marketing practices or personal initiative, there could be limited impacts on generating new customers and prolonged sales impacts.

# **4 How might online training be done better?**

Our results demonstrate the feasibility of conducting online training by Zoom, but show the need for improvement in cost-effectiveness and impact. Based on our observations of the training and qualitative interviews with participants, there appear to be several areas where improvement in content and delivery could occur in any future efforts.

In terms of content, we saw that the training did not result in improvements in marketing practices. Nor are firms innovating by digitization or selling new products or services (Table C.1). Hardy and Kagy (2020) have shown how a lack of demand is a key constraint for the growth of many women-owned businesses. Increasing demand through product innovation and better marketing efforts to generate new sales could help the program have greater impact. Revamping the marketing component to provide specific actionable steps suitable for these types of firms could help result in more impact.

In addition to providing less general and strategic content, and more actionable steps, training could also attempt to foster more networking and sharing of knowledge among those participating. There was some interactive participation via audio and chat features in Zoom, and the instructors also set up Whatsapp groups to communicate with the class. But these did not result in much networking or idea sharing between participants (Table C1), instead serving mainly as a means to communicate with the trainer. Incorporating a more structured networking component via the Whatsapp groups, as in Asiedu et al. (2023) could help enhance effectiveness.

Finally, since the main cost is the instructor salary, the main suggestion to improve costeffectiveness would be to increase the size of online classes
the Dictator’s GDP Estimates?” _Mimeo_ , University of Chicago.

Olson, M. 1993. “Dictatorship, democracy, and development.” _American Political Science Review,_ 87 (3): 567–576.

Rodrik, D. 2000. "Institutions for High-Quality Growth: What They Are and How to Acquire Them." NBER WP 7540.

Rothstein, J. 2010. “Teacher Quality in Educational Production: Tracking, Decay and Student Achievement.” _The Quarterly Journal of Economics,_ 125 (1): 175-214 Wooldridge, J. 2001. _Econometric Analysis of Cross Section and Panel Data_ . MIT Press, 1<sup>st</sup> Ed. Wolfers, J. 2007. “Are Voters Rational? Evidence from Gubernatorial Elections.” Working paper [link].

Yao, Y. and M. Zhang. 2015. “Subnational Leaders and Economic Growth: Evidence from Chinese Cities.” _Journal of Economic Growth,_ 20 (4): 405–436.

34
# **B Details on Household Data from the LSMS-ISA**

The World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMSISA) is a household survey program that provides financial and technical assistance to national statistical offices in Sub-Saharan Africa for the design and implementation of national, multi-topic longitudinal household surveys with a focus on agriculture. The LSMS-ISA-supported countries include Burkina Faso, Ethiopia, Malawi, Mali, Niger, Nigeria, Uganda and Tanzania. We use the data sets from Ethiopia, Malawi, Niger, Nigeria, Uganda, and Tanzania in this work.<sup>26</sup> More details on each country are included in the following sub-sections and details on samples are provided in Table 2.

A common feature of the LSMS-ISA-supported surveys is that each sample household receives a multi-topic Household Questionnaire that elicit comprehensive socioeconomic information that also allows for the construction of consumption and income aggregates. Households engaged in agricultural activities additionally receive an Agriculture Questionnaire that elicits comprehensive information on smallholder crop, livestock and fishery activities and that allows for the construction of plot-level indicators of land and labor productivity and input use, among others. Finally, while the key variables that drive each survey’s sampling design is household consumption and income, each survey provides a large sample of agricultural households in each round.

In our analysis, we only include households which did not move. Although the LSMS-ISA surveys follow individuals who “split off” and create new households, we do not include these movers in our analysis.

## **B.1 Ethiopia**

The LSMS-ISA data from Ethiopia includes three waves. Wave 1 (2011/12) includes 4,000 households in rural and small towns across the country (CSA, 2014). This initial sample was followed in 2013/14 and 2015/16 (CSA, 2015; CSA, 2017). Beginning in Wave 2 (2013/14) the survey was also expanded to include 1,500 households in urban areas.

The Wave 1 data is representative at
identifying the impacts of the opening of Jakarta’s MRT system on ride-hailing activity using plausibly exogenous variation, exploiting the very fine spatial and temporal resolution of our data on daily ridehailing activity and MRT travel, provided by _Gojek_ and Jakarta MRT, respectively.

In its research design and the nature of its data, our paper more closely resembles Rao (2016) and GonzalezNavarro _et al_ . (2021). Rao (2016) examines the impact of the opening of London’s “night tube” service in August 2016 on _Uber_ journeys starting within 200 meters of a “night tube” station.<sup>2</sup> He finds a 22 percent increase in _Uber_ journeys on nights when the night tube is operational, with a decrease in pick-ups around central London stations accompanied by a large increase in station pick-ups outside central London. Gonzalez-Navarro _et al._ (2021) consider a similar question to ours. Instead of focusing on a single public transport project, they use data on the opening of new metro stations from 35 different cities<sup>3</sup> and use a “ring-based” empirical design that compares monthly _Uber_ ridership within 100-meter distance bands before and after a transit station opens relative to the next further out distance band up to a maximum distance of 1,100 – 1,200 meters. They estimate that a new metro station opening increases _Uber_ ridership up to 300 meters of the station by 53 percent in the 6 months after its opening. This is accompanied by a 1.26 km decrease in average _Uber_ trip distance to/from the stations.

While similar in spirit, our paper differs from Gonzalez-Navarro _et al._ (2021) in three important ways. _First_ , we use a different research design that identifies the effect of the opening of the MRT system by comparing _Gojek_ activity before and after the opening of the MRT in the immediate neighborhoods of the newly opened stations to that in (different sets of) control locations. As we discuss in section 5
we estimate above. VVhile panel data is not available to allow us to do this in our main estimation, we are able to implement a cross-country regression for a subset of the countries in our panel.

The additional variables we add to the cross-country regressions are from La Porta 1<sup>4</sup> We considered countries that had income in the top quartile to be high-income countries. These countries had real per capita incomes greater than $11,000 in the last period.

14
Similar questions have been asked in other surveys, such as the U.S. Health and Retirement Study, the American Life Panel, and the English Longitudinal Study on Aging, and have been shown to measure both numeracy and financial knowledge (Lusardi and Mitchell, 2009, 2011c; Banks and Obsfeld, 2007; Stango and Zinman, 2009).

On average, in 2008, 41.4% of respondents correctly answered the question on interest compounding; 23.3% correctly answered the monthly interest payment calculation question; 45.6% correctly answered the question on inflation; and 69.5% correctly answered the question on sales discounts. A large number of respondents reported they ―did not know‖ the answer to these questions. On average, in the pooled sample, 30% of individuals replied ―don’t know‖ to the question on interest compounding; 49% to the question on monthly interest payments; 24% to the question on inflation; and 22% to the question on sales discounts. These findings are similar to those reported in other surveys (Van Rooij et al., 2011; Lusardi and Mitchell, 2011b; and Lusardi and Tufano, 2009a,b), and in data in other seven countries (Lusardi and Mitchell, 2011c). Notably, we find that correct responses to all but one question increased during the financial crisis, which might be explained by increased attention to financial issues in the media or a rise in individuals’ interest in understanding their own finances.

# **[Insert Table 3 about here]**

# 4.4.1 _<u>Constructing a Financial Literacy Index</u>_

We construct an index of financial literacy using principal component analysis (PCA) to summarize the information from the four questions detailed in the previous 16
Policy Research Working Paper 9973

# **Abstract**

How has globalization affected the relative taxation of labor and capital, and why? To address this question, this paper builds and analyzes a new database of effective macroeconomic tax rates covering 150 countries since 1965, constructed by combining national accounts data with government revenue statistics. Four main findings are obtained. (1) The effective tax rates on labor and capital have converged globally since the 1960s, due to a 10 percentage-point increase in labor taxation and a 5 percentage-point decline in capital taxation. (2) The decline in capital taxation is concentrated in high-income countries. By contrast, capital taxation has increased in developing countries since the 1990s, albeit from a low base. (3) Consistently across a variety of research designs, the findings show that the rise in capital taxation in developing countries can be explained by a tax capacity effect of international trade: trade openness leads to a concentration of economic activity in formal corporate structures, where capital taxes are easier to impose. (4) At the same time, international economic integration reduces statutory tax rates, due to increased tax competition. In high-income countries, this negative tax competition effect of trade has dominated, while in developing countries, the positive tax-capacity effect of international trade appears to have prevailed.

This paper is a product of the Development Research Group, Development Economics. 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 pbachas@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
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

Econometric studies are best suited to the analysis of well-defined issues and the testing of hypotheses through statistical analysis of reform determinants and performance. The scope of single-country time-series and cross-section analysis is limited by the number of observation years available, and, as the main questions are related to reform determinants and the significance of various steps in the process, scope for the use of such methods is mainly within the framework of cross-country analysis.

Efficiency and productivity analyses are suitable for measuring the effectiveness with which inputs are transformed into outputs, relative to best practice. Efficiency analysis can be based on econometric techniques such as stochastic frontier analysis (SFA) and corrected ordinary least squares (COLS), or it can use non-parametric methods based on linear programming and optimisation techniques, such as data envelopment analysis (DEA) originating from operations research. Comparative efficiency analysis is most suitable where the units of analysis are whole sectors or companies. These methods have proven useful in analysis and incentive-based regulation of electricity distribution utilities, primarily in developed countries. This is largely due to the fact that the basic technical features and structures of electricity distribution systems are rather similar, making direct comparison plausible and practical.

Single or multi-country case studies are suitable when in-depth investigation or qualitative analysis is needed. This approach provides the methodological flexibility required for these types of investigations and is particularly useful for exploring processes. In addition, case studies are the obvious choice where a combination of different tasks, such as in cost-benefit analysis, is required. They may also lead to the development of hypotheses which can later be tested using more formal cross-country quantitative analysis.

In the following, we outline a selected number of important and current reform-related policy issues in the form of general research questions or hypotheses. Although the policy questions outlined here are by no means exhaustive, they do illustrate the practical relevance of the core reform indicators and their use in evaluating and 16
We also control for a number of country level variables to further deal with the potential omitted variable problem. Specifically, since both bank performance and deposit insurance can be affected by economic conditions in a country, we obtain economic development measures from the World Bank’s World Development Indictor (WDI) database. We use the natural logarithm of GDP per capita to measure the economic development of a country, the variance of GDP growth rate to measure economic stability, the natural logarithm of population to measure country size, and imports plus exports of goods and service divided GDP to measure global integration (Karolyi, Lee, and Van Dijk 2011). We also use stock market capitalization divided by GDP and private credit divided by GDP from the Financial Structure Dataset (Beck, Demirguc-Kunt, and Levine (2010)) to control for differences in financial development and structure.

We define a crisis dummy to be equal to 1 for years 2007 to 2009 and 0 for years 2004 to 2006. A full list of variables and definitions are provided in Appendix I.

# **3. Empirical Methodology and Results**

# **3.1. Summary Statistics**

Table 1 provides the summary statistics of variables used in this study. An average bank in the sample has a logzscore of 3.50, stock return volatility of 0.03 and systemic risk of -0.07. In addition, the average bank has log total asset value of 7.66, and leverage ratio of 0.90. These numbers are comparable to those in previous studies such as Anginer, Demirguc-Kunt, and Zhu (2012).

Table 2 presents the sub-sample comparisons of means of our main variables of interest. In Panel A of Table 2, we partition our sample by whether a country offers deposit insurance and whether the country is in a crisis period. The results suggests that in the non-crisis period, banks in countries without deposit insurance have higher z-score, lower return volatility, and slightly lower systemic risk. Entering into crisis