rafmacalaba/data-use-mentions-extended huggingface dataset:
br>|**Doing Business, The World Bank**<br>see Botero, Djankov, La Porta, Lopez-de-Silanes, and<br>Shleifer, "The Regulation of Labor", Working Paper 9756,<br>National Bureau of Economic Research, June 2003<br>|
|Conditions-of-<br>employment index<br>(db_cond_empl)|0 to 100, higher values<br>indicating more rigid<br>regulation|Working time requirements, including mandatory minimum<br>daily rest, maximum number of hours in a normal workweek,<br>premium for overtime work, restrictions on weekly holiday,<br>mandatory payment for nonworking days, (which includes<br>days of annual leave with pay and paid time off for<br>holidays), and minimum wage legislation. The constitutional<br>principles dealing with the minimum conditions of<br>employment are also coded.|to 2003|Based on: NATLEX database (International Labour<br>Organization); Constitutions, available online on the U.S.<br>Law<br>Library<br>of<br>Congress<br>website;<br>International<br>Encyclopaedia for Labour Law and Industrial Relations,<br>and Social Security Programs Throughout the World.<br>Legal advice from leading local law firms was solicited to<br>confirm accuracy in all cases. Following the OECD Job<br>Study and the International Encyclopaedia for Labour Law|

42
- 6 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

There are two different nighttime light products from DMSP-OLS that can be used to delineate urban areas: the stable or ordinary product (NTL), and the radiance-calibrated (RC) product. For this work we use the latter, given that such data include a correction for the saturation issue likely to be an important issue for most large cities in the region. Further, previous work has suggested that the RC information provides a better proxy for socioeconomic variables than the stable products (Hsu et al., 2015; Ma et al., 2014). Radiance-calibrated annual composites for 1996, 2000 and 2010 obtained from NOAA National Centers for Environmental Information<sup>1</sup> were used to delineate urban extents. These composites have a spatial resolution of 30 arc-seconds (about 1 km at the Equator).

Another known issue of the DMSP-OLS products is the “overglow” effect: dim lighting detected from light in surrounding areas of cities because of the scattering of lights in the atmosphere (Wu et al., 2014). A novel deblurring process was applied to address the issue of over glow in the radiancecalibrated products. This process involves the use of two sequential filters, a standard deconvolution and the frequency of illumination maxima, to withdraw the light from the surroundings back and restacking it vertically on its source pixels at city centers (Abrahams et al., 2016).

# _3.2.1 Calibration and interannual correction of deblurred RC data_

Deblurred DSMP-OLS RC annual composites for the years 1996, 2000 and 2010 were previously inter-calibrated and corrected for a multi-temporal analysis of urban form and city productivity in Latin America (Duque et al., 2017a). In that work, the three nighttime images were used to delineate urban extents in each year for most of the Latin American and Caribbean cities that had more than 50,000 people
# **Appendix C. Calibration**

# _Appendix C.1. Other data sets used for the calibration_

We use the housing sales registry of the City of Cape Town. This data set includes records of housing transactions, including sales year, area, type, price and location of properties. We only consider transactions that took place in 2011 and aggregate the sales information at the Sub-Place level. We calculate a median price (denoted _Ps_ ) per square meter of land for Sub-Places with more than 20 transactions recorded. We also use a data set of average formal dwelling sizes provided by the Municipality of Cape Town, at the Transport Zone level. We aggregate the values at the Sub-Place level (denoted _qs_ ).

# _Appendix C.2. Estimation of construction function parameters_

We estimate the relation between the estimated stock of formal private housing and housing prices in cross-section to identify the parameters of the construction function _{_ κ _, a}_ . Combining (6) and the equilibrium condition (iv), we have: 4 where ∑ _Ni_<sup>_FP_</sup> ( **x** ) = _N_<sup>_FP_</sup> ( **x** ) is the number of household living in formal dwellings. We regress _i_ =1 the log of the previous equation at the sub-place level, denoted _s_ : where _Ns_<sup>_FP_</sup> is the number of households in formal housing at the Sub-Place level, _Ps_ is the median price per unit of land, _Ls_<sup>_FP_</sup> is the amount of available land for formal housing and _qS_ is the dwelling size. From equation (C.1), we expect coefficients γ3 to be close to 1 and γ4 to be close to _−_ 1. Note that equation (C.1) theoretically only applies to formal private housing. However, because housing in low-income neighborhoods is a mix of formal private, formal subsidized housing and
*

<!-- Start of picture text --> a.Direct transfers b. In‐kind transfers<br>300 300<br>250 250<br>200 39.2 200<br>150 47.3 150 31.4<br>100 100<br>115.9<br>129.0<br>50 50<br>21.1<br>0 0<br>1 2 3 4 5 1 2 3 4 5<br>Quintiles of per capita MIPP Quintiles of per capita MIPP<br>Rural Pension BPC Education Primary Education Pre School<br>Other Salario Familiar<br>Abono Salarial Bolsa Familia Education Young Adult Education Upper Secondary<br>Unemployment Benefits Health Benefits Education Tertiary<br>Share of market income plus pensions (% ) Share of market income plus pensions (% )<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal.

Looking at how the benefits of transfers are distributed across the population, we find that Bolsa Familia, rural pensions, and BPC are highly favorable to the first quintile (Figure 10 panel a). More than half of Bolsa Família transfers and rural pensions are concentrated in the first decile, while close to 46% of the 36 While high, the Brazilian incidence ratio of direct transfers among first quintile households is still below that of South Africa (500%).

26
and 20 percent in 1999, and to 24 and 15 percent by the year 2000 (Bautista, Power and Associates 1979; Manasan and Pineda 1999; Habito 2002).

Between 1994 and 2000, the overall weighted nominal tariff declined by 66.9 percent, the declines ranging from 88.9% in mining through 64.0% for non-food manufacturing, 57.6 for livestock, 45.6 for crops to 19.9 for “other agriculture.” In 2000, the average sectoral tariff rate was highest in food manufacturing (16.6 percent), and lowest in “other agriculture” (0.2 percent).

Revenue from import tariffs remains one of the major sources of government funds. In 1990, the share of revenue from import duties and taxes in total revenue was 26.4 percent. It increased marginally to 27.7 percent in 1995, but then dropped sharply to 19.3 percent in 2000, largely due to the tariff reduction program. The reduction in tariff revenue was largely replaced by an increase in income and profit taxes from 27.3 percent in 1990 to 30.7 percent in 1995 and 38.6 percent in 2000, with the excise and sales tax share fluctuating between 23 and 28 percent.

# **1.4  Poverty Profile in the Philippines**

Figure 1 presents the evolution of the poverty headcount index and the Gini coefficient from 1985 to 2000. The poverty headcount index dropped continuously from 49.2 percent in 1985 to 36.9 percent in 1997, but then rebounded to 39.5 percent in 2000 as a result of the 1998 El Nino and the Asian Crisis. El Nino resulted in a 30 percent contraction in agriculture, the greatest drop in more than 30 years. On the other hand, income inequality has steadily increased over this period, as the Gini coefficient climbed from 0.42 in 1985 to 0.51 in 2000.

In 1994, the base year the household survey underlying our analysis, about
<!-- Start of picture text --> 55<br>Chart 4c: Coefficients component of the gender gap in Science<br>score: 2003 vs. 2007<br>45<br>35<br>G<br>e<br>n 25<br>Sience-2007<br>d<br>e 15 Science-2003<br>r<br>5<br>G<br>a<br>-5 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>p<br>Percentile<br><!-- End of picture text -->

# <u>Location gap</u>

In the absence of information on urban vs. rural school location, information on the size of community was used. In the data, size of community is grouped in four categories: under 3,000, 3,000-15,000, 15,000 to 500,000 and over 500,000. A dichotomous variable was created whereby large communities are defined as those with population over 15,000 and small communities those with population under 15,000. Approximately 46% of schools were located in large communities in 2003 and 42% in 2007. Over time, mean mathematics scores increased by 12% in schools in large communities, vs. 14% in small communities; similarly, science scores increased by 17% in large communities and 22% in small communities.

Estimates of the overtime change in the location gap over the entire distribution of scores are a lot more revealing. They suggest that while in 2003 the location gap was of similar magnitude across the distribution of scores (that is for both low and high performing students) at about 35 points for mathematics and between 30 and 50 points for science, in 2007 the location gap in both mathematics and science increased monotonically as one goes to higher points in the distribution of scores; for worse performing students it
After estimating the effect of sentiment shocks on the total equity inflow (or outflow) into (or out of) the country, we then 7 Its coverage has increased significantly over time, reaching currently a wide industry and geographic coverage. As of 2013, the EPFR global was collecting information from more than 29,000 equity funds and 18,000 fixedincome funds representing US$20 trillion of assets invested in over 80 advanced economies and EMs.

8 The EPFR dataset has been found to be a reliable data source. Comparing TNAs (Total Net Assets) and monthly returns of a subsample of EPFR funds to CRSP mutual fund data, Jotikasthira et al. (2012) found only minor differences between EPFR and CRSP datasets.

9 Most funds followed by the EPFR global dataset (i) are located in advanced economies and (ii) account for a significant share of the external funding received by EMs. As a result, country flows dataset has proved to be a good (high frequency) proxy of total gross inflows in (or out) of emerging countries. For instance, Miao and Pant (2012) showed that EPFR fund flows correlate well with BOP recorded capital flows into EMs.

10 We focus on EMs for two reasons. First the EPFR data coverage is generally much higher for EMs than for AEs, so the correlation between EPFR equity flows and equity flows measured by the IMF Balance of Payments is higher for EMs. Using the fund’s domicile in the EPFR database to distinguish foreign vs. local funds is also more accurate when focusing on EMs. A high number of funds investing in AEs are domiciled in regional tax heavens (e.g. Luxembourg for European funds) which makes them foreign from the point of you of many AEs, even though they are local funds. This problem is much less present for EMs.
# _Births to older mothers_

As there is no explicit age restriction to enrolling into secondary education in Brazil, there is no age cut-off at which we can say _treatment_ is received or not. However, as the majority of secondary school students enroll between the ages of 15 and 17, we expect the secondary school expansion to have less an effect on the fertility of older women compared to younger women. Finding a qualitatively similar effect on births at ages 30 and older may indicate that unobservable factors are influencing municipal childbearing more generally. To test this, we regress the municipal births for women age 15 to 40 ( i , ) on aa the contemporaneous school introductions. Regressions include age-specific male and female sex-bb ff= 15, ... ,40 ratios, but otherwise contain the same control variables as in Equation (3). The conditional correlations between contemporaneous school density and the percent change in age-specific births are reported in Figure 5. Estimates for all groups over 23 years of age are statistically insignificant and small in magnitude. We interpret this as further evidence that there are no unobserved municipal changes that affect both childbearing and the introduction of secondary schools. _Selective migration_ _Selective migration_ Migration might lead us to estimate a negative value for if the introduction of a secondary school induces an outflow of sexually active teens, creating a spurious negative relationship between αα school introductions and teenage childbearing. In Appendix S3 we provide a detailed analysis of migration patterns in Brazil, using information from the 2010 Brazilian population census available publically through IPUMS International. These data allow us to analyze migration patterns for a subset of the Brazilian municipalities (831 municipalities). We summarize the key findings here.

20
Cohodes, S. R., & Goodman, J. S. (2014). Merit Aid, College Quality, and College Completion: Massachusetts’ Adams Scholarship as an In‐Kind Subsidy. _American Economic Journal: Applied Economics_ , _6_ (4), 251–285.

- Constantine, J. M., Seftor, N. S., Martin, E. S., Silva, T., & Myers, D. (2006). _Study of the Effect of the Talent Search Program on Secondary and Postsecondary Outcomes in Florida, Indiana and Texas. Final Report from Phase II of the National Evaluation_ . ED Pubs.

- Cunha, J. M., Miller, T., & Weisburst, E. (2018). Information and College Decisions: Evidence From the Texas GO Center Project. _Educational Evaluation and Policy Analysis_ , _40_ (1), 151–170.

- Dearden, L., Fitzsimons, E., & Wyness, G. (2014). Money for nothing: Estimating the impact of student aid on participation in higher education. _Economics of Education Review_ , _43_ , 66–78.

- Deming, D., & Dynarski, S. (2009). _Into College, Out of Poverty? Policies to Increase the Postsecondary Attainment of the Poor_ (Working Paper No. 15387). National Bureau of Economic Research.

- Denning, J. T. (2017). College on the Cheap: Consequences of Community College Tuition Reductions. _American Economic Journal: Economic Policy_ , _9_ (2), 155–188.

- Denning, J. T. (2018). Born Under a Lucky Star: Financial Aid, College Completion, Labor Supply, and Credit Constraints. _Journal of Human Resources_ .

- Denning, J. T., Marx, B. M., & Turner, L. J. (2017). _ProPelled: The Effects of Grants on Graduation, Earnings, and Welfare_ (Working Paper No
source of the indicators of vulnerability used here and survey data is unavailable for some countries on each indicator. Coverage sometimes declines when an additional indicator is added (World Bank 2018). It is thus important to ascertain that adding a new indicator adds value in that it allows a good number of people to be identified as vulnerable that would not have been identified without its inclusion. In other words, an indicator adds value when its correlation with other indicators is low.

In the absence of a model, an aggregation of indicators into a single index requires deciding how the different indicators are to be weighted<sup>4</sup> , whether to take into account the depth of deprivation on a given indicator, and on how many indicators a household has to be deprived to be considered vulnerable (see World Bank 2018 for a full discussion of considerations). We follow the approach of the World Bank’s multidimensional poverty measure (World Bank 2018) and present the number of people vulnerable according to each separate indicator and counting the number of indicators that individuals are deprived on. As with the World Bank’s multidimensional measure of poverty, the index is thus a simple expression of an approach whereby the number of deprivations for one person are counted (Atkinson 2003).

People at risk are people that are exposed and vulnerable. Figure 3 summarizes the measure, highlighting that someone is at risk if they are exposed, and vulnerable. While we propose this core structure of the measure of at risk, this still leaves much to be decided such as the selection of events, probabilities, indicators and number of events and indicators on which someone is exposed or vulnerable. We propose that someone is exposed if they are exposed to any one natural hazard and vulnerable if they are

> 4 Applying no weights is also a choice on weighting as it assumes all dimensions are equally important.

8
6%|-0.7%1,|<br>-2.7%|
|**Fiscal Balance**|(% of GDP - cy basis)|-4.7%|-4.2%|5.9%|-3.9%|-3.9%|-5.1%|-3.8%|**-1.5%,**|1.3%|
|**M2**|**(%**change)|22.4%|-4.2%|24.1%|23.3%|20.2%|10.3%|13.2%|20.40/,|18.2%|
|Domestic Credit|**(%**change)|18.1%|-4.2%|21.5%|26.3%|17.8%|8.4%|6.0%|17.8%|_15.6%_|
|REER|(1980=100)|100.0|102.8|105.9|108.7|107.3|95.3|85.0|79.9|77.4|
|Inflation|**(%**change in CPI)|19.7%|12.7%|5.3%|3.7%|0.9%|2.4%|1.8%|2.5%i|3.8%|

Source: World Bank Data Base

## **a. The Stabilization Program of 1984-1987**

In response, Thailand implemented a macro stabilization program during the period 1984-87. The program combined a large devaluation of the nominal exchange rate in late 1984 with tighter financial policies. Its main features were as follows:
Policy Research Working Paper 5831

# **Abstract**

A pricing reform in Turkey increased the residential electricity tariff by more than 50 percent in 2008. The reform, aimed at encouraging energy efficiency and private investment, sparked considerable policy debate about its potential impact on household welfare. This paper estimates a short-run residential electricity demand function for evaluating the distributional consequences of the tariff reform. The model allows heterogeneity in household price sensitivities and is estimated using a national sample of 18,671 Turkish households. The model also addresses the common problem of missing data in survey research. The study reveals a highly skewed distribution of price elasticities in the population, with rich households three times more responsive in adjusting consumption to price changes than the poor. This is most likely because the poor are close to their minimum electricity consumption levels and have fewer coping options. In addition, the welfare loss of the poorest quintile—measured by the consumer surplus change as a percentage of income—is 2.9 times of that of the wealthiest.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at fzhang1@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
Data on conflict intensity may also have systematic bias in some or all countries. Specific aspects of conflict and particularly its economic, social, and demographic consequences may drive our results, but there is not a data source that captures all these aspects of conflict locally and over time. Further research is needed on whether particular sub-groups may be particularly affected by conflict (e.g. those in poverty). Additionally, it is important to note that this paper does not look at all types of situations where child marriage might be affected, including during natural disasters and other forms of humanitarian crisis. As noted by the UN 2017 statement (United Nations General Assembly, 2017), child marriage may be affected by any kind of humanitarian crisis, not only conflict. The DHS does not specifically sample displaced individuals – the very populations that might face some of the largest pressures from conflict (Lu, Siddiqui, & Bharadwaj, 2021). Finally, we may see varying results as different push and pull factors for child marriage interact. Unfortunately, as noted above, the lack of available data on drivers of child marriage from the DHS or on the details of conflict makes it difficult to understand the mechanisms at play.

Future research could help address some of these challenges. Leveraging longitudinal data where it is possible to track risk factors prior to marriage would be highly informative. This would allow scholars to better understand which pathways are at play and their relative contribution to risk of child marriage, including which risk factors may be most important for increasing vulnerability when conflict breaks out. Better data on marriage customs, such as marriage costs, is needed to improve our understanding of country-specific mechanisms behind early marriage. Additional qualitative work that builds on the previous literature could provide further insight on the dynamics at play in different contexts and conflicts.

19
possibility of differential levels of sunk investments in Abidjan and non-Abidjan routes. If the CIV war led to under-investment public goods in the Abidjan route, one would expect that firms and people would find it costly to relocate back to the Abidjan areas after the end of the war in 2007. The sunk investments can create “quasi rents” a la Milgrom and Roberts (1992), and slow down the process of the readjustment and reversion back to the original equilibrium. We consider two potential sources of such sunk investments: built-up density and public non-transport infrastructure provision such as electricity. As the trading and general economic activity suffered in the Abidjan route because of the CIV civil war, it is likely that government reallocated public investment from the Abidjan areas to the non-Abidjan areas. We analyze the effects of the CIV shock on night-lights density as an indicator of public investment in electricity.

# **Evidence on Built-up Density**

It is difficult to find data on indicators of sunk investments, but the recently available global settlement data provide an excellent source especially in the context of developing countries. We take advantage of the global settlement data to explore the effects of the CIV shock on built-up density as a measure of sunk investment. The estimates of the effects of the CIV shock on the proportion of area built-up density are reported in Table 3. The effect of the CIV shock on the integrated areas of the non-Abidjan route in this case is estimated as a quadruple difference, please see the coefficient of the ( _DT_<sup>0</sup><sup>_._45</sup> _× DNA × D_ 2014) in row 1 of Table 3. Column 1, row 1 of Table 3 gives us the average effect on built-up density over the whole integrated segment along the non-Abidjan route; the estimate suggests that the proportion of area built-up experienced a 2.6 percentage points increase as a result of the CIV shock compared to that in the integrated segment of the Abidjan route. This supports the idea that