rafmacalaba/data-use-mentions-extended huggingface dataset:
48</sup> and they confirm that the relationship is linear and also find an elasticity of around 0.3.

Access to political power is ranked on a scale from 1 to 7 in the GROW<sup>_up_</sup> dataset. Ethnic groups are "powerful" (monopoly of power or dominant group in power), have access to central power through a formal system of power sharing (as "Senior" or "Junior" partner) or are “excluded” from power (self excluded, powerless or discriminated).

Strong executive constraint is measured as a dummy indicating whether or not we have executive parity or subordination of the executive at the country level, a value 7 for “xconst” variable in Polity IV dataset.

Our main empirical specification to test our hypothesis is as follows : Where _log_ ( _lightpc_ ) _i,j,t_ is the logarithm of per capita night light intensity for ethnic group _j_ in country _i_ at time _t_ . The variable _exconsthigh_ is a dummy for strong executive constraint at the country level. _Power_ Status_ is the power access status (powerful or excluded) of the different ethnic groups. _Ci,t_ is a set of country/year dummies to control for shocks and changes that are common to all ethnic groups within a country/year, as well as for changes in satellites and their sensor settings. We also

> 46The discussion in this section relies on Mueller and Tapsoba (2015).

> 47We divide night light by (interpolated) ethnic group population size to have a per capita variable.

> 48They provide regional GDP per capita for 1,503 regions from 82 countries.

61
# **Figure 1. GDP growth rate (y/y, quarterly data)**

<!-- Start of picture text --> %<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text -->

**Figure 2. Consumer Price Index (CPI; y/y, monthly data) and inflation target**

<!-- Start of picture text --> CPI (y/y)<br>Inflation target of the NBP<br>% Fluctuation band<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: CSO.

Source: CSO, NBP.

**Figure 3. Warsaw Stock Exchange indices (monthly data)**

**Figure 4. Capitalization of stock market**

<!-- Start of picture text --> sWIG80 mWIG40 WIG WIG20 zl billion zloty billion % of GDP (right - hand scale) %<br>1400 600 50<br>45<br>1200 500<br>40<br>1000 35<br>400<br>30<br>800<br>300 25<br>600 20<br>200<br>400 15<br>10<br>100<br>200 5<br>0 0 0<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Notes: January 2002 = 100. Notes: capitalization concerns only domestic companies.
would have been useful in accounting for total mineral production and revenues for an unbiased estimates of mining activities on district inequality in Africa.

To partially address this limitation, we take advantage of the available time-series minerals prices data and use them as numeraires for minerals at the district level across countries over time. We thus use mineral prices data for the United States since 1900 available from USGS.<sup>16</sup> These price data are recorded in constant prices and annual average prices. As detailed in the empirical strategy, we use the constant prices to examine the main impacts of minerals’ constant unit dollar values on district inequality in Africa. We then use annual average prices for robustness checks on our main results. Our analysis is strengthened by the inclusion of mineral price data, since we are better able to account for the effects of mineral prices’ volatility on spatial inequality in Africa.

GLCF data on NDVI<sup>17</sup> were used to construct a measure of district-level vegetation, which has been shown to be a good proxy for agricultural output (see e.g., Labus et al. 2002 Ren et al. 2008).<sup>18</sup> High NDVI suggests a favourable vegetation landscape relative to low NDVI. NDVI is handy as a proxy, because consistent data on agriculture productivity across all districts in Africa are difficult to find. Obviously, district inequality is likely to be correlated with agriculture—a dominant sector in Africa. Andres and Ramlogan-Dobson (2011) and Chong (2004) assert that income distribution is relatively equally distributed across agriculturedependent economies. We therefore control for the district NDVI standard deviation to account for this fact.

Using TAMSAT data, we constructed annual district rainfall averages and standard deviations aggregated from monthly rainfall averages data. We use rainfall data in two ways: first, as a control for climate variability, and second, as a proxy for agricultural productivity variations, similar in spirit to Miguel, Satyanath, and Sergenti (2004)
working paper. 5

- Chiu, C.-W. J., H. Mumtaz, and G. Pint ́er (2017). Forecasting with var models: Fat tails and stochastic volatility. _International Journal of Forecasting 33_ (4), 1124–1143. 8

- Choi, S. and M. K. Shim (2019). Financial vs. policy uncertainty in emerging market economies. _Open Economies Review 30_ (2), 297–318. 7, 15

- Choudhary, M. A., F. Pasha, and M. Waheed (2020). Measuring economic policy uncertainty in pakistan. 5

- Cogley, T. and T. J. Sargent (2005, April). Drift and volatilities: Monetary policies and outcomes in the post wwii u.s. _Review of Economic Dynamics 8_ (2), 262–302. 10

- Dahlhaus, T. and T. Sekhposyan (2018). Monetary policy uncertainty: A tale of two tails. Technical report, Bank of Canada. 5

- Davis, S. J. (2016). An index of global economic policy uncertainty. Technical report, National Bureau of Economic Research. 1, 14, 29, 30

- Davis, S. J., D. Liu, and X. S. Sheng (2019). Economic policy uncertainty in china since 1949: The view from mainland newspapers. _Work Pap_ , 1–35. 5

- Devadas, S. and S. M. Pennings (2018). Assessing the effect of public capital on growth: An extension of the world bank long-term growth model. _World Bank Policy Research Working Paper_ (8604). 17

24
<!-- Start of picture text --> 1,000<br>3<br>S00<br>3<br>2 eo<br>Zs400 400<br>i==<br>,TM<br>0 P<br>CF€e§€e§ CPF§§ ££ § CESFSF VYSSS&SSS&& SF HKSES&&SES&&&&& s<br>HE WorldPop ©ENEN Census<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>2<br>3<br>s<br>&22 40<br>g33<br>i§§ 20<br>5<br>0 .<br>CFgsgs FPF§§ FS §¥ CCSSSS VYFSF&FSF&& SF &SFSF SESeorePyeorePyrePyPy<br>HE WorldPop 4M Census<br><!-- End of picture text --> <!-- Start of picture text --> 1,000 60<br>3 2<br>S00 3<br>3 s<br>2 eo &22 40<br>3Zs400 3g33<br>i== i§§ 20<br>,TM 5<br>0 P 0 .<br>CF€e§€e§ CPF§§ ££ § CESFSF VYSSS&SSS&& SF HKSES&&SES&&&&& s CFgsgs FPF§§ FS §¥ CCSSSS VYFSF&FSF&& SF &SFSF SESeorePyeorePyrePyPy<br>HE WorldPop ©ENEN Census HE WorldPop 4M Census<br>2,000 80<br>§>> 1.500 zgogo<br>zs<br>8<br>z 522<br>g 1000 g 4 4<br>3 2<br>= &<br>R500 R 20<br>0
<br>0.007|-0.002|
|(0.28)|(-0.91)|
|AGOA Non-LDC * Manufacturing<br>0.047**|0.002|
|(2.58)|(0.98)|
|AGOA Non-LDC * Mining<br>-0.287|-0.023|
|(-1.52)|(-1.64)|
|AGOA Non-apparel * Agriculture<br>0.147*|0.011*|
|(2.34)|(2.02)|
|AGOA Non-apparel * Manufacturing<br>0.040|-0.002|
|(0.93)|(-0.52)|
|AGOA apparel<br>0.200***|0.012***|
|(4.78)|(3.63)|
|Treatment group-specific time trends<br>Yes|Yes|
|Country-product fixed effects<br>Yes|Yes|
|Country-year fixed effects<br>Yes|Yes|
|Product-year fixed effects<br>Yes|Yes|
|ntheses, clustered by HS 6-digit product. ***, **, and * in<br>Observations<br>27,420,560|dicate significance at 1%, 5%, and<br>27,420,560|

Notes: Robust t-statistics in parentheses, clustered by HS 6-digit product. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. Estimates of a variant of Eq. (1) allowing for separate coefficients for agriculture, manufacturing, and mining sub-sectors shown.

As an alternative to Eq. (1) we use detailed data on the tariffs that the US applied to all HS 6-digit products and all countries over the period 1997-2017, to estimate the trade effects of tariff
household surveys data set. Within-skill-groups variance is the variance of the residuals from a regression of log hourly wage on a vector of workers’ socio-demographic characteristics, including all possible interactions between education (five categories), experience (five-year bins), and gender. Cells contain the contribution of each component to the level or change in variance (shown in parentheses) and the corresponding share their represent. When a given year was not available for a particular country, the closest year available was used. See supplementary online appendix S1.1 for details on this “circa” concept where _S_ ( _i, t_ ) denotes the sector and occupation of worker _i_ in year _t_ . Following equation (2), this paper decomposes the variance of hourly wages each year into the variance across sector-occupation cells, the variance across education-gender-experience cells, the covariance between these two terms, and a residual. As before, the differences over time are simply the differences of each component. To proceed with the decomposition, the occupations and sector classifications were harmonized into seventeen industries and nine occupations.<sup>13</sup> The results are summarized in table 2, panel a, for Latin America, and table S2.8 in supplementary online appendix S2 for each country individually. In table 2, columns 1, 2, and 3 report the results for each year individually, and columns 4 and 5 decompose the changes in inequality over time. The within-component (the residual) accounts for most of the total wage variance. Specifically, it accounts for 58, 61, and 66 percent of the inequality levels in 1995, 2002, and 2015, respectively. However, changes in the variance across sector-occupation cells play a large role during the expansion of inequality in 1995-2002, explaining 53 percent of the increase. Instead, during 2002-2015, the role of sector-occupations is more muted, accounting for 31 percent of the decline in dispersion. The within component continues to dominate the observed changes during 2002-2015, contributing to 45 percent of the decline
|**-6.330****|
||(-0.695)|(-0.601)|(0.376)|(-3.133)|(-2.602)|(-2.322)|
|Observations|3709|3709|2792|1153|3709|3709|
|**B.**<br>**Pooled sample, Statio**|**n based CV of Rain**||||||
|Flooded Upazilas 1998|**-0.350*****|**-0.272****|**-0.328****|-0.0437|-0.100|-0.250|
||(-3.055)|(-2.470)|(-2.068)|(-0.278)|(-0.652)|(-1.323)|
|CV Rain Station Interpolated|-0.831|-0.123|-4.199|**-5.532****|-2.222|-1.789|
||(-0.448)|(-0.0668)|(-1.472)|(-2.021)|(-0.874)|(-0.609)|

13
index, the number of different points of interest recorded in Open StreetMap, and the year that pixels changed to being an impervious surface are important predictors of female LFP. Municipal area is positively associated with female LFP, which makes sense in the Mexican context because many of the smaller municipalities are located in the poorer south of the country where female LFP tends to be lower. Median nighttime luminosity in the municipality is positively associated with female LFP, consistent with nighttime lights indicating economic activity. Mean nighttime lights in the municipality, which conditional on median is a measure of right-skewness or inequality in luminosity, is negatively associated with LFP. This is consistent with women in more developed areas being able to afford not to work. Areas with a greater concentration of vegetation, as proxied by mean NDVI, have lower female LFP, perhaps also reflecting greater wealth in suburban areas with more vegetation. The correlates of male LFP are more difficult to interpret. The share of bare land in the municipality is positively and significantly correlated with male LFP, as is the number of untagged highway points, and the latter may be positively correlated with economic activity.

<u>Table 2 - Model diagnostics by dependent variable and sample type</u>

||Female<br>LFP|Male<br>LFP|Female<br>Unemp|Male<br>Unemp|
|---|---|---|---|---|
|**State - Simple Random Sample within Selected A**|**GEBs**||||
|Number of (non-state) variables selected|41|22|19|10|
|<br>Marginal R<sup>2</sup>|0.127|0.061|0.025|0.028|

13
in its monthly “market price watch bulletin”, building on market level price information it has been collecting in the country since 2011.

_Table 6: Spatial price deflator based on WFP Standard Food Basket (SFB)_

||Standard food basketprice(SYP)|Spatialprice deflator|
|---|---|---|
|Damascus|342,867|106.2|
|Aleppo|310,336|96.1|
|Rural Damascus|340,465|105.4|
|Homs|318,922|98.8|
|Hama|334,474|103.6|
|Lattakia|355,953|110.2|
|Idleb|322,260|99.8|
|Al-Hasakeh|253,223|78.4|
|Deir-ez-Zor|273,609|84.7|
|Tartous|346,070|107.2|
|Ar-Raqqa|290,455|90.0|
|Dar’a|352,944|109.3|
|As-Sweida|347,134|107.5|
|Quneitra|341,690|105.8|
|National*|322961.6|100.0|

_Notes_ : National average weighted by population _Source_ : World Bank calculation using April 2022 market price watch bulletin and HNAP population estimates 20 The Classification of individual consumption by purpose, abbreviated as COICOP, is a classification developed by the United Nations Statistics Division to classify and analyze individual consumption expenditures incurred by households, non-profit institutions serving households and general government according to their purpose. The classification is generally used by countries in NA estimations and budget surveys, particularly in relation to CPI weights estimation.
TSle 3 shows that, compared to those in wage employment, the self-employed re on average *.z years older, they have completed about ane year less of 0hool, 4 and they earn about the same amount per hour but work eight hours more pec week. Workers in OUAs are about one year older and have completed a year and a half less schooling; they earn 1.25 Intis less per hour and work three hours more per week than workers in Lima.

Table 3 **_AGE,_** S(HXLDM, EARNflM, AND H1AS W)REI _BY T=A.&ON_ AND SIP,

||**Age**in years|Years<br>of schol<br>completed|Hourly<br>earnin8s<br>In<br>June<br>1985 Intis|Hours<br>worked<br>per week|
|---|---|---|---|---|
|Ietropolitan LIM|||||
|1Wge<br>ezployees|33.6 (12.3)|9.2 (3.4)|6.71 (7.8)|48.43 (14.8)|
|Self-etloyed|39.8 (13.3)|8.4 (3.9)|6.37 (6.5)|50.70 (21.4)|
|AUlLimwrkers|35.9 (13.0)|8.9 (3.6)|6.59 (7.4)|49.25 (17.5)|
|Other urban areas|||||
|Wage efployees|33.7 (12.6|) 7.5 (3.5)|4.95 (5.4)|49.51<br>(16.4)|
|Self-eaployed|40.1 (13.3
_Table 6: Double-difference estimates of VHI impact on adult BMI_

|Age in<br>1992/93|Per capita<br>consumption<br>quintile in<br>1992/93|<br>Treatment<br>effects|z-stat|F-test consumption<br>quintile differences|Prob> F|
|---|---|---|---|---|---|
|18+|all|0.303|2.91|F(4, 1358) = 6.18|0.000|
|18+|1|-0.425|-2.02|||
|18+|2|0.049|0.26|||
|18+|3|0.302|2.09|||
|18+|4|0.168|1.31|||
|18+|5|0.617|6.15|||

# VI. **HEALTH SERVICE UTILIZATION**

An obvious channel through which VHI impacts on nutritional status is increased use of health care. Using the VLSS panel to investigate this possibility is not straightforward because the wording of the health utilization questions changed significantly from one wave to the next. We adopt two approaches: in the first, we use the panel and do the best with what is available; in the second, we focus on the 1998 wave and estimate VHI impacts via single-difference matching (i.e. comparing _levels_ of utilization of the insured and matched uninsured, rather than _changes_ in them).

In the 1992/93 VLSS, respondents who reported illness or injury during the previous 4 weeks were then asked whether a contact with _any_ health service provider had occurred during the same period. Examples of different types of provider were read out, but they were simply examples. Respondents were then asked who the _first_ contact was with, but they were not asked whether
Figure 1: Distribution of overall management scores, PISA vs WMS <!-- Start of picture text --> BRA CAN COL<br>-4 -2 0 2 4 -4 -2 0 2 4 -4 -2 0 2 4 6<br>KS test p-value: 0.181 KS test p-value: 0.887 KS test p-value: 0.116<br>GBR GER ITA<br>-4 -2 0 2 4 -2 0 2 4 -4 -2 0 2 4<br>KS test p-value: 0.965 KS test p-value: 0.700 KS test p-value: 0.008<br>MEX SWE USA<br>-4 -2 0 2 4 -4 -2 0 2 4 -2 0 2 4<br>KS test p-value: 0.350 KS test p-value: 0.870 KS test p-value: 0.956<br>PISA 2012 index WMS index<br>.4 .4 .4<br>.2 .2 .2<br>Density Density Density<br>0 0 0<br>.4 .4 .4<br>.2 .2 .2<br>Density Density Density<br>0 0 0<br>.4 .4 .4<br>.2 .2 .2<br>Density Density Density<br>0 0 0<br><!-- End of picture text --> Note: Data for the World Management Survey index for all countries except for Mexico and Colombia can be found at www.worldmanagementsurvey.org. Distribution of overall management indices standardized within countries. Kernel density curves estimated using WMS sampling weights (calculated as the inverse probability of being interview on log of number of students, public status, and population density by state, province, or NUTS 2 region as a measure of location) for the WMS data and _school final weights_ for the PISA data
OECD countries within the same region (Africa; East Asia and Pacific; Europe and Central Asia; Latin America and the Caribbean; Middle East and North Africa; South Asia). Moreover, to ensure consistency with available classifications, input-output tables and the WBES product classification have been aggregated into 15 sectors. To avoid possible endogeneity related to sector reliance potentially driven by conflict exposure, we set the input-output table to reflect data before the beginning of our sample period (year 2000).

13
.359|0.418|0.396|
|1996 Controls|Yes|Yes|Yes|Yes|Yes|
|MunicipalityFE|Yes|Yes|Yes|Yes|Yes|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression using a sample of communities within 30 kilometers of the former homeland borders (on both sides). The dependent variable is the difference in the percentage of population in given age group for 2011-1996. All regressions include 1996 controls and municipality fixed effects. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
.01|
|2006|Science|5|7.07*|0.00|10.83*|0.04|
|2006|Science|9|6.24*|0.00|0.88|0.78|
|2006|Science|11|4.91*|0.01|1.34|0.65|
|2006|Arabic|5|4.45*|0.05|5.62|0.33|
|2006|Arabic|9|6.24|0.00|6.60*|0.00|
|2006|Arabic|11|5.45|0.00|3.86*|0.05|
|2008|Math|5|3.85|0.00|2.96|0.17|
|2008|Math|9|7.65|0.00|6.74*|0.00|
|2008|Math|11|0.23|0.88|-1.20|0.45|
|2008|Science|5|2.77|0.17|-4.25*|0.09|
|2008|Science|9|10.01*|0.00|7.11*|0.00|
|2008|Science|11|1.97|0.40|1.91|0.43|
|2008|Arabic|5|6.88*|0.01|-3.62|0.28|
|2008|Arabic|9|8.20*|0.00|6.00*|0.01|
|2008<br>Source: Authors’ ca|Arabic<br>lculations based o|11<br>n NAfKE 2006 a|1.44<br>nd 2008 surveys|0.36|
output, while specifications that lack household fixed effects tend to show a negative relationship. This is regardless of whether the specification is linear or quadratic. By contrast, in Uganda, quadratic specifications tend to result in negative coefficients on variance while linear specifications tend to result in positive coefficients. This is regardless of whether household fixed effects are included or not.

While with rainfall we could conclude that MERRA-2 and ERA5 were poor choices, with temperature all three products produce roughly similar results. But this appears to have as much to do with the lack of a strong relationship between the temperature metrics and outcomes, as with the products themselves. Where we do find differences, it is likely to be due to cross-country variation and with the specific functional form of the regression. To the extent that these differences may reflect the on-the-ground reality in each country, it suggests that the use of variance, or any temperature metric, requires specific knowledge about the country and application where it will be used.

# **4.3.4 Selecting a Remote Sensing Product**

Summarizing our results, mean and total rainfall are clearly substitutes for each other. While these two metrics perform substantially better than most other rainfall metrics, the choice between them seems to be immaterial. The same is true for the number of and share of rainy days. These two metrics outperform other rainfall metrics but produce results extremely similar to one other. In terms of remote sensing products, MERRA-2 is seemingly inferior to the other five precipitation products. Regardless of which of the four rainfall metrics are used, coefficients on these metrics derived from MERRA-2 data tend to be non-significant while coefficients derived from the other remote sensing products are significant. This suggests that MERRA-2 suffers from classical measurement error, with sufficient noise introduced into the data to result in rainfall metrics being uncorrelated with outcomes. ERA5 is also inferior to CHIRPS, CPC, ARC2, and TAMSAT. As is evinced by the descriptive statistics, ERA5 tends to report much higher volumes of rainfall, which suggests the
column 3). Similarly, men who report that their family members usually use the reserved space are not more likely to respond to the IATs (Table A4, column 4).

### **IAT measures**

To assess commuters’ implicit attitudes toward women riding in the public and reserved spaces, we designed two IAT instruments. These instruments test the strength of association between the reserved space and safety (“Safety”), and between the public space and provocation (“Advances”), respectively. Pictures (stimuli) were taken to distinctly show the car type (women-reserved or public) but to be very similar on other characteristics, such as crowding and lighting. In the “Safety” IAT, respondents must then associate those stimuli with words connoting a greater to lesser sense of safety, such as “afraid” or “worried” versus “relaxed” or “oblivious”. In the “Advances” IAT, participants were asked to sort the stimuli into categories connoting degrees of openness to sexual advances from men, such as “seductive” and “provocative”, in contrast to “serious” and “saintly”. The IAT results are summarized in a D-score, with a positive score suggesting a stereotypical association (reserved space with safety, public space with openness to advances). More details on the IAT methodology and how it was adapted to our study context are provided in Appendix E.<sup>20</sup>

# **4 Descriptives**

## **4.1 Ride Environment**

Trains are densely packed throughout the rush-hour period, which corresponds with the times during which the reserved space is in operation (Figure A2). We observe similar patterns across crowdsourced and administrative data. Half of the SuperVia passengers are women,<sup>21</sup>

> 20The Online Appendix with the full instruments is available here.

> 21This share of women riders is reported in SuperVia’s operational briefs.

14
.<br>Brazil: Expanded discriminatory procedures in 1994.<br>China: Non-transparent, closed, and uncompetitive<br>procedures.|
|30 April<br>1999.|None.|Korea: Construction of Inchon International Airport.<br>Japan: Computer products and services.<br>Japan: Design and consulting and construction<br>services.|
|30 April<br>2000.|None.|Japan: Public works.<br>Taiwan (China): General procurement procedures.<br>Canada: Provincial price preferences.<br>Mexico: Implementation of New Procurement Laws<br>and NAFTA-related tendering periods.<br>Korea: Airport construction.<br>Germany: “Sect Filters” (to ensure that the principles<br>of Scientology are not used or spread in fulfillment of<br>a contract.)|
|30 April<br>2001.|None.|None in addition to prior years.|

_Source_ : Website of the Office of the United States’ Trade Representative.

8