rafmacalaba/data-use-mentions-extended huggingface dataset:
stock market accounting for just over half of the total value traded in both periods. Corporations, pension funds, mutual funds, and brokerage firms account for 98 percent of this volume over the entire sample, with banks, insurance companies and other institutions representing a small fraction of the trading activity of domestic institutions. Consistent with international trends, there is a growing presence of foreign investors over time. The number of foreign investors nearly doubled from 1,440 to 2,830,<sup>6</sup> and the share of total traded value by these group increased from 3.8% in the first half of the sample to 19.1% in the second half. Figures 1 and 2 plot the time series of the share of traded value and share of market capitalization respectively by investor type. These figures also highlight the increasing importance of foreign investors in recent years.

According to Table 1-Panel B, the average number of trades by domestic institutions and foreign investors are 317 and 316 respectively. The distribution of trades amongst different institutions, however, is quite heterogeneous, with brokerage firms and mutual funds performing the largest number of trades on average. We restrict our analysis by considering only those investors who traded at least a 100 times over the 10 year period.<sup>7</sup> We collect stock and market data from Bloomberg, Datastream and the _Superintendencia Financiera de Colombia_ (Colombian Financial Superintendency, SFC). We obtain stocks returns, share price, trading volume, and bid-ask spread from Bloomberg and book value of equity, shares outstanding, and aggregate institutional ownership from Datastream and the SFC. The number of traded stocks fluctuated between 62 and 71 during our sample period.<sup>8</sup> In Table 2, we present basic descriptive statistics relating to the stock market during our sample period. According to the table, the performance of the Colombian market closely mimicked that of the Latin American region. The table is also indicative of increased participation by foreign investors
it is collinear with the country-year and city fixed effects and cannot be estimated separately. However, a distance-specific transport cost elasticity can be calculated. Column 7 reports the coefficient of interest when _pt ∗ xi_ is replaced with _ln_ ( _pt_ ) _∗ xi_ . It is again negative and significant as expected. This can be translated into a distancespecific elasticity using three additional parameters: A simple regression of _ln_ ( _Pdiesel_ ) on _ln_ ( _Poil_ ) using the (Deutsche Gesellschaft f ̈ur Technische Zusammenarbeit, 2009) data for the fifteen sample countries provides an estimate of _εPdiesel,Poil_ = 0 _._ 6. Treating the Teravaninthorn and Raballand (2009) average fuel share as the marginal fuel share implies _ετ,Pdiesel_ = 0 _._ 35. Combining these estimates implies _εGDP,τ_ = _−_ 0 _._ 2 at the median distance from the primate, 439 km, and -0.4 one standard deviation (465 km) farther away. This calculation is meant to be illustrative, as it may suffer from several potential biases, including upward (toward zero) bias from substitutability of oil in the production of transport and downward (away from zero) bias from substitutability of transport in the production of city activity.

Country size varies dramatically within the estimation sample. For example, the farthest city in Sierra Leone is only 310 kilometers away from the primate, whereas in Mozambique, the farthest is over 2000 kilometers away. In order to ensure that not all variation is coming from the largest countries, column 8 shows a specification in which _ptxi_ is replaced by _ln_ ( _pt_ ) _ln_ ( _xi_ ).<sup>39</sup> The coefficient of interest is negative and significant.

The prices of commodities other than oil were rising in parallel with the oil price in this time period. It could be the case that country governments spent these commodity windfalls disproportionately near the primate city, either because it is easier for government officials to travel to project sites in cities near the
development groups and randomly selected from within this group for survey of rural households. From each district, 7–9 villages were selected, depending on the district’s size and listing of India’s rural population.<sup>4</sup>

# **4.2** **<u>Survey Features</u>**

The IHDS is ideal for estimating the impact of electricity access and consumption on welfare indicators owing to its exhaustive coverage of survey topics and sample representativeness. It is India’s first survey for measuring detailed income, as well as consumption and ownership of consumer goods. Income-related questions cover a variety of sources (e.g., wages and salaries, net farm, net family business, property, and pension). The survey also contains education-related questions, including educational outcomes, study hours, school enrollment, and school completion rate, along with household characteristics. Compared to the Demographic and Health Surveys and Living Standards Measurement Surveys, the IHDS covers energy more extensively. It includes elaborate questions on fuel use, cash expenditures for fuels, time spent collecting biomass fuels, and types of stoves and electric appliances used in the household. It also asks detailed questions related to electricity use, including reliability of power supply and source of household electricity (i.e., whether serviced by the local state electricity board or a neighbor). Such detailed energy questions allow us to investigate the drivers of household demand for electricity and analyze the welfare impacts of household access more comprehensively.

In addition to household-level data, the survey covers key features of the villages where the surveyed households are located (e.g., availability of social development programs in the area or distance to village facilities). Such area characteristics can directly affect outcomes of interest, such as employment, income and poverty indicators, and the probability of electricity presence in the village. Because this survey covers villages with and without electricity, it allows for identifying an

> 4 The 2005 IHDS
# 4 RESEARCH DESIGN

## _4.1 Data_

DatafortheprojectcomesfromasurveyofruralhouseholdsinColombiafieldedbetweenMarch and June of 2017. The survey was commissioned as part of the performance evaluation<sup>14</sup> of USAID’s Land and Rural Development Program (LRDP), a program designed to improve institutional capacity in relation to land formalization, land restitution, and broader rural development outcomes. The survey was fielded at the halfway point of the LRDP’s lifespan, part of a broader effort to measure the program’s strengths and weaknesses up to that point in time.

The survey captured almost 1,500 responses across fifty rural and conflict-affected municipalities,25ofwhichwereselectedbecauseoftheirinvolvementinorrelevancetotheLRDPand an additional 25 which were selected through matching on observables to serve as comparable municipalities that did not experience any LRDP programming (Figure A.1). Survey responses were collected on tablets and in-person, in or near the respondent’s home. More detailed information on data collection, sampling, implementation, and ethical considerations of sampling in violent contexts are available in the Appendix.

This survey data has a number of strengths for the study of displacement. First, the sampling frame explicitly over-sampled areas and communities that have experienced a lot of displacement and as a result, produced a sample where approximately half of respondents have been displaced (sampling characteristics can be seen in Figure A.2). Second, and relatedly, the survey contains long modules both on displacement experiences as well as on a variety of postdisplacement outcomes that can be used to explore the effects of displacement. Finally, the sample is otherwise relatively homogeneous, capturing rural areas with a relatively high degree of exposure to conflict. This means that – following statistical adjustment to control for potential confounds in who is displaced – there is good coverage for comparing respondents whohaveexperienceddisplacementagainstthosewhohavenotexperienceddisplacement. An important limitation, however, is that the sample is not representative of the universe of IDPs in Colombia.<sup>15</sup> In terms of which outcomes to focus on with respect to displacement effects, we consider a broad range,
# **References**

Assy, Angela Elzir, Tiago Ribeiro, David Robalino, Furio Rosati, Maria Laura Sanchez Puerta and Michael Weber (2018). “The Jobs that Youth Want and the Support They Need to Get Them: Evidence from a Discrete Choice Experiment in Kenya.” Working Paper. 2018 Jobs and Development Conference.

Bangladesh Bureau of Statistics (2015). Labour Force Survey: Bangladesh 2013.

Bonhomme, Stephane, and Gregory Jolivet (2009). “The Pervasive Absence of Compensating Differentials.” _Journal of Applied Econometrics_ 24(5): 763-795.

Boxall, Peter C. and Wiktor L. Adamowicz (2002). “Understanding Heterogeneous Preferences in Random Utility Models: A Latent Class Approach.” _Environmental and Resource Economics_ 23: 421-446.

Brown, Charles (1980). “Equalizing Differences in the Labor Market.” _Quarterly Journal of Economics_ 94(1): 113-134.

Dey, Matthew, Christopher Flinn (2005). “An equilibrium model of health insurance provision and wage determination.” _Econometrica_ 73: 571–627.

Dey, Matthew, Christopher Flinn (2008). “Household search and health insurance coverage.” _Journal of Econometrics_ 145: 43–63.

Diamond, Peter A., and Jerry A. Hausman (1994). “Contingent Valuation: Is Some Number Better than No Number?” _Journal of Economic Perspectives_ , 8 (4): 45-64.

Duncan, Greg J. and Bertil Holmlund (1983). “Was Adam Smith Right After All? Another Test of the Theory of Compensating Wage Differentials.” _Journal of Labor Economics_ 1(4): 366-379.

33
five then formed the maintenance group for the plot assigned to the threshold group payment treatment.<sup>5</sup> Upon completion of the recruitment and assignment process, all five members of each maintenance group were assembled on the reforestation plot assigned to them. They received training on tree maintenance including how to water the newly planted trees (and at what frequency), how to remove dead leaves and other flammable materials in saplings’ vicinity, how to set up firebreaks, and how to protect the newly planted trees from being eaten by wildlife or livestock. The members of each group were also informed of the mechanism via which they would be remunerated at endline – either the linear group payment scheme, or the threshold one. We ensured that any payment earned by the group would be shared equally between all members of that group, independent of how much effort they put in. We did so by transferring one-fifth of the group payment to each of the five group members via mobile money bank accounts, and we announced this payment procedure beforehand.

Trees had been planted in two reforestation plots in each of the 33 forest blocks, and 325 of our 330 participants participated in our baseline survey.<sup>6</sup> Unfortunately, we were not able to visit two of our 33 blocks at endline. Etouayou of the Nosebou forest in the western part of Burkina Faso was not accessible at endline because of a flood, and Matiacoali of the Tapoaboopo forest in the east could not be visited at endline because of armed unrest. We thus have endline tree survival information on 31 blocks (consisting of 62 reforestation plots); we have baseline survey information for 305 of the 310 participants in these blocks, and endline information for 290.

> 5Whether threshold group payments are able to outperform linear group payments obviously depends on the severity of the free-rider problem within maintenance groups, which, in turn, depends on both group size and on group composition. In laboratory experiments with random group assignment, four subjects in
>**Change**|
|**Dar es Salaam**|5.35|-0.56|17.08|
|**Other Urban Areas**|12.6|-0.34|10.49|
|**Rural Areas**|82.06|-1.82|55.41|
|**Total Intra-sector Change**||-2.72|82.98|
|**Population-Shift Effect**||-0.38|11.60|
|**Interaction Effect**||-0.18|5.41|
|**Change in Poverty**||-3.28|100|

The sectoral decomposition was calculated using both the 1991/92 and 2000/01 HBS data. For this analysis, in order to ensure that the decomposition properly accounted for changes in population shares, the 2000/01 data was reweighted to reflect the regional population distribution reported in the 2002 national census. As a result, the decline in the headcount rate shown here, 3.28, is slightly greater than the decline of 3.0 shown in Table 1 (from 38.6 to 35.6), based on calculations using the original weights.

Altogether, 55 percent of poverty reduction nationally was attributable to poverty declines in rural areas, and 39 percent was due to population shifts and declines in urban areas, with the remainder accounted for by the interaction effect. What does this suggest about the potential sources of poverty reduction for Tanzania in the future? Because both urban poverty reduction and urban-rural migration were already rapid during the 1990s, it is very unlikely that either can be an _increased_ source of poverty reduction in the near future. This is especially the case because the headcount rate in Dar is now low enough, at 17.6%, that the potential for reducing it further is limited. Consequently, accelerating the rate of decline of poverty
53

# Table 11: Topical distribution of the most-cited 50 publications of the top four countries

||Canada|Netherlands|United Kingdom|United States|
|---|---|---|---|---|
|Health and its value|7|5|6|4|
|Efficiency and equity|2|16|9|3|
|Determinants of health and ill-health|5|3|4|12|
|Public health|2|||4|
|Health and the economy|6|4|2|7|
|Health statistics and econometrics|2|2|3|7|
|Demand for health and health care|4||1|3|
|Medical insurance|2|3|1|4|
|Supply of health services|2|3|3|1|
|Human resources|2|1||2|
|Markets in health care|2|2|1|1|
|Economic evaluation|10|10|17|1|
|_Total_|_46_|_49_|_47_|_49_|
|Normalized Herfindahl index|0.100|0.163|0.185|0.113|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed institution was given full credit for a publication in the case of a co-authored publication, but only the first institution was credited where an author listed multiple institutions. Institutional affiliations below the institution level were aggregated up to the level of the institution, so that for example publications originating from the Harvard School of Public Health were allocated to Harvard University along with publications originating from the Department of Economics at Harvard University. As explained in the text, only addresses with five or more articles to their name were retained for cleaning and aggregating. Institutions were
# **1. Introduction**

Around the world, the public sector is producing more and more data. Since 2005, the number of countries without a population and housing census conducted over the preceding ten years has fallen by nearly 80 percent (from 36 to 8), the number of countries without a labor force survey conducted over the same period has fallen by 50 percent (from 98 to 49), and the number of countries without nationally representative administrative data on learning assessments within a five-year range has fallen by 83 percent since 2008 (from 36 to 6) (World Bank 2021a).

As highlighted by the World Development Report (WDR) 2021: Data for Better Lives, the abundance of data produced by the public sector can have a transformational role in development and efforts to reduce poverty. Among many fruitful uses, the data can be used to increase access to government services, prepare for and respond to emergencies, target resources and foster the inclusion of marginalized groups, save money and resources in policy implementation and service delivery, monitor progress and track performance, increase accountability, and empower individuals (World Bank 2021b).

Yet are we really seeing the returns from the data that one could hope for given their abundance? This paper identifies the conditions under which data produced by the public sector are valuable for development, and expands on the assertions and examples highlighted by the WDR 2021. The clear identification of these conditions can help governments understand how to improve the supply of and demand for data. To this end, we first develop a theoretical framework detailing 12 conditions needed for data to generate value. Next, we use a collection of case studies to provide support for these conditions and showcase how they matter in practice. Our examples and analysis distill that for data to maximize value, the data should have adequate coverage (be complete, frequent, and timely), be of high quality (be accurate, comparable, and granular), be easy to use (be accessible, understandable,
/sup><br>(0.414)|
|Patient is Pregnant|-0.348<sup>_∗_</sup>|
||(0.205)|
|Ethnic group: Bambara|0.124|
||(0.0854)|
|Respondent Speaks French|-0.223<br>(0.138)|
|Respondent is Literate in French|-0.510<sup>_∗∗∗_</sup><br>(0.144)|
|Respondent Has Primary Education or Less|-0.140<br>(0.120)|
|Patient Answered Clinic Survey|-0.367<sup>_∗∗_</sup><br>(0.174)|
|Pseudo R-Squared|0.141|
|N|1093|

_Notes_ : Standard errors clustered at the clinic level in parentheses. Respondent refers to individual who answered clinic survey. ***, **, and * indicate significance at the 1, 5, and 10 percent significance levels respectively.

A3
# **Appendix C. Data sources and definitions**

There is no official definition of “city” or “agglomeration” in Brazil. The lowest administrative level consists of more than 5000 municípios. However, these vary greatly in size and many functional economic and population agglomerations consist of a number of municípios. In this paper, we therefore follow the example of a study of Brazilian urban dynamics by IPEA, IBGE and UNICAMP (2002). It defined agglomerations based on their place in the urban hierarchy from “World Cities” (São Paulo and Rio de Janeiro) to subregional centers. For each agglomeration, this study identified the municípios that were a functional part of the urban area. The municípios belonging to each agglomeration were then further classified into eight categories according to how tightly they are integrated in the agglomeration, from “maximum” to “very weak”. The main criteria used in these classifications were centrality, function as a center of decision making, degree of urbanization, complexity and diversification of the urban areas, and diversification of services. These were measured by a range of census and other variables such as employed population in urban activities, urbanization rate, and population density. We modified this classification slightly by also including smaller municípios to existing agglomerations if their population exceeded 75,000 population and more than 75 percent of its residents lived in urban areas in 1991, or if they were completely enclosed by an agglomeration.

The agglomeration definitions developed by IPEA, IBGE and UNICAMP (2002) are based on municípios boundaries valid at the time of the Brazilian Population Census of 1991 and the Population Count of 1996, while our study captures dynamics from 1970 to 2000. During this time, many new municípios were created by splitting or re-arranging existing ones. In fact, the number of municípios increased from 3951 to 5501 during these three decades. To create a consistent panel of agglomerations for the 1970 to 2000 period, we therefore used the Minimum Comparable Area (MCA) concept as implemented by IPEA researchers.
third restriction eliminates the need to model fathers’ labor supply, further reducing dimensionality with limited loss of empirical generality because most fathers with small children work full-time. Our estimation sample includes households in which, on average, parents are more educated than in the full dataset and earn higher wages. Appendix C provides further comparison between our estimation sample and the full dataset.

## **3.2 Descriptive Statistics**

Our estimation sample consists of 600 two-parent households and 150 single-mother households that meet the criteria described above (sample sizes are rounded to the nearest 50 to comply with ECLS-B confidentiality rules.) In Table 1 we define the main variables used in estimation and report descriptive statistics. About 27 percent of two-parent households and 41 percent of single-mother households use a child care center as the main form of care and pay on average $4.50 and $3.88 per hour of full-time care, respectively (all monetary values are expressed in dollars of 2003). The average quality of child care centers, as measured by the ITERS score, is on average satisfactory, as values of 4 and 5 denote satisfactory and good quality, respectively. This average is in line with previous work for the US population (e.g., Blau and Mocan (2002)) and is the same as the mean value of ITERs for two-year olds in the full ECLS sample. About 5 percent of households pay non-relatives for home-based care and 20 percent use relatives as the main form of care. Single mothers rely more on relative care than two-parent households. The average child cognitive score is approximately equal to 129 and 124 in two-parent and single-mother households, respectively.

We observe the usual gender differentials in parental labor market outcomes, as women participate substantially less than men (only 50 percent of them work) and earn lower wages on average. The average gender wage gap in two-parent households is 15 percent, smaller than in the full dataset, and the correlation between father’s and mother’s wage is 0
the frequency curve of the nearest point. If necessary, the design water level has been interpolated or extrapolated from the frequency curve.

Apart from design water levels, wave information is also necessary to define required crest heights. The MARD’s dike design guidelines also provide detailed information about waves. However, design wave heights at the dike toe are not available and have to be calculated from offshore wave heights using wave transformation modelling on the most updated bed topography. The wave information used in this assessment has therefore been estimated in a simplified way, as described in the next two paragraphs.

During storms, wave conditions near the dike toe will be severe due to strong winds, and the depth will most likely limit certain wave characteristics such as the maximum wave height that can exist during these conditions. Therefore, a constant wave height (Hs)/water depth (d) ratio has been applied, herein: Hs/d = 0.6. This ratio is considered to be a good first-order estimate of the maximum wave height at a certain water depth, based on coastal engineering practice. This ratio has also been verified and confirmed by analyzing the detailed wave computation results in the current dike standards.

The toe of the embankments is set at -0.25 meters with respect to the reference level in Vietnam. This number has been estimated based on wave and water depth information near the toe from the current sea dike standards. Using the toe and design water levels, a design water depth has 7