rafmacalaba/data-use-mentions-extended huggingface dataset:
|period|Period for which mortality rate is calculated (takes on the values 1975-79, 1980-84<br>...2000-04)|
|---|---|
|svycountry|Name of country for DHS countries|
|ccode3|Country code|
|u5mr|Under-5 mortality (from World Development Indicators)|
|cname|Country name|
|gdppc|GDP per capita (constant 2000 US$) (from World Development Indicators)|
|gdppcppp|GDP per capita PPP (constant 2005 intl $) (from World Development Indicators)|
|pop|Population (from World Development Indicators)|
|hivprev2001|HIVprevalence in 2001(fromUNAIDS 2010)|
|region|Region|

32
|
||(3.54)|
|Share from 25 to 44|3.12<br>(1.99)|
|Share above 65|4.79***<br>(1.89)|
|Share of unmarried|5.03***<br>(1.45)|
|Share of single households|-2.72***<br>(0.83)|
|Share of non-western immigrants|-1.70**<br>(0.70)|
|Share with long-term illness|1.14<br>(1.04)|
|Mean PM2_._5|6.39***<br>2.42|
|_λ_|-0.11<br>0.16|
|_ρ_|0.71<br>0.08|
|R2|0.56|
|AICc|3736.24|

Standard Errors in parenthesis, significance levels as:<sup>***</sup> _p <_ 0 _._ 01,<sup>**</sup> _p <_ 0 _._ 05,<sup>*</sup> _p <_ 0 _._ 1 20
the calibration of speeds using trips from Google maps. The calibration uses 4,000 random trips. The information was downloaded with the command _gmapsdistance_ in R that uses the Distance Matrix Api from Google. I computed these times between 8 am - 11 am and 5 pm - 8 pm under different traffic scenarios.

To calibrate speeds for each mode and each type of road, I use random trips from Google Maps.<sup>56</sup> I downloaded 4000 random trips between 8 am-11 am, and 5 pm-8 pm using the command _gmaps_ distance in R that uses the Google Maps Distance Matrix Api. I use as an origin and destination, the closest vertex of each type of road or metro line. This tool has the feature that you can calculate times for different modes under several traffic scenarios: pessimistic, optimistic, or none and modes such as: walking, car, or the public transit network. Using this information, I calibrate speeds for each road and each line using the average time spent to

> 56I did not calculate times across census tracts using Google Maps because the network analysis toolkit is much faster, and the command gmaps distance takes a lot of time.

13
2008 rankings from the 2008 Doing Business report and the 2009 rankings from the 2009 Doing Business report. Important to note is between 2006 and 2009, an extra 6 countries were added to the rankings. These 6 countries were not included in the preceding analysis with the remaining countries re-ranked under the assumption that only the 175 countries, available in 2006, existed. This approach does not materially change any results. Some regressions, that _do not_ include the Doing Business Rankings, will be undertaken using data from 2004 to 2009.

Recall the Doing Business Rankings are determined by various components. These, determinants were sourced directly from the World Bank’s Ease of Doing Business website. The following table summarizes the components of the Ease of Doing Business and the corresponding indicators used in this paper.

||<br>**Table 1 - World Bank's Ease of Doing Business**||
|---|---|---|
|**Selected Components**|**Selected Indicators**|**Acronym**|
|Starting a Business|Time (days)<br>Cost(% of incomeper capita)|SBT<br>SBC|
|Registering Property|Time (days)<br>Cost(% of incomeper capita)|RPT<br>RPC|
|ProtectingInvestors|Strength of investorprotection index(0-10)|IPS|
|PayingTaxes|Payments(numberperyear)|TPN|
|Trading Across Borders|Time to export (days)<br>Cost to export (US$ per container)<br>Time to import (days)<br>Cost to import(US$ per container)|XT<br>XC<br>IT<br>IC|
|Enforcing Contracts|Time (days)<br>Cost(% of claim)|ECT<br>ECC|

Other indicators were not included due to limited perceived relevance (eg: closing a business) and due to potential multicollinearity
by the sub-sample of household with 25 or more members. This is not only a rare occurrence in their data but also overall in Northern Uganda. The 2002 Uganda National Census shows that less 1 percent of households in Northern Uganda, rural or urban, have 15 or more members; in rural areas, this represents roughly 0.5 percent of the sample. 21. Unfortunately, it is not possible to directly test whether attacks were random within the data used (or within almost any other dataset). Post-attack data are potentially endogenous to the violence. Additionally, since certain households or individuals may disappear from the sample (through death, abduction or migration), the post-attack population is not representative of the pre-attack population. Consequently, any analysis requires both a pre-attack representative sample and pre-attack characteristics. As a result, the Blattman and Annan study is particularly unique as it is essentially representative of the pre-abduction population.

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# **3. Patent Data**

Our primary research objective is to estimate how the formation of IPAs affects bilateral flows of patent applications, from origin country to destination country across all potential patent-country pairs. In this context, we distinguish among the effects on within-IPA members, the impacts on patenting from member countries to nations outside IPAs, and the effects on flows from origin countries that do not join specific IPAs to countries within them. Our patent data come from the PATSTAT database organized by the European Patent Office (EPO). The database lists the origin nation, from which the patent is originally filed, and the destination nation, which is the patent authority in which registration is desired, as well as the filing date for each application. Because our interest lies in assessing where patents are filed, we include each destination listed in a patent family as a separate application. Similarly, for patents filed through the Patent Cooperation Treaty (PCT) or the EPO, both of which list countries in which protection may be sought during a grace period, we include only those that reached an ultimate national authority. Many applications list only a filing authority, which indicates that the origin is the same country, implying an application that does not cross an international border. Though our focus lies primarily on measuring international patenting effects, we include domestic applications in our empirical setting in order to account for within-country patenting as a reference category in the gravity-model analysis that follows.<sup>6</sup> Our analysis extends over the period 1995 to 2015, during which multiple IPAs were established. We cut off the end date in order to avoid truncation of patents in recent years, which exists due to lags in application filings. Our data covers patent applications originating from 187 origin countries that are ultimately filed in 82 different destination countries. The fact that there are fewer destination countries than origins in our data is a result of the set of patent offices covered in the PATSTAT data set.<sup>7</
|Afghanistan|SAR|Low income|Living Conditions Survey|2011/12,<br>2013/14,<br>2016/17|No|
|---|---|---|---|---|---|
|Armenia|ECA|Upper middle<br>income|Integrated Living Conditions<br>Survey|2010, 2011,<br>2012, 2013,<br>2014, 2015,<br>2016, 2017,<br>2018|No|
|Benin|AFR|Low income|Integrated Survey on Household<br>LivingConditions(EMICOV)|2011/12|No|
|Bhutan|SAR|Lower middle<br>income|Living Standards Survey|2012|No|
|Burundi|AFR|Low income|Household Living Conditions<br>Survey|2013/14|No|
|Cameroon|AFR|Lower middle<br>income|Cameroonian Household Survey<br>(ECAM-IV)|2014|No|
|Colombia|LAC|Upper middle<br>income|Integrated Household Survey<br>(GEIH)|2010, 2011,<br>2012, 2013,<br>2014, 2015,<br>2016,2017|No|
|Gambia|AFR|Low income|Integrated Household Survey|2010/11,<br>2015/16|No|
|<br>Ghana|AFR|Lower middle<br>income|Living Standards Survey|2012/13,<br>2016/17|Yes|
|Other<br>multi-<br> <br>Kenya|AFR|Lower middle<br>income|Integrated Household Budget<br>Survey|2015/16|Yes|
|topic<br>surveys<br>(LSMS-<br>d<br>Kyrgyz<br>Republic|ECA|Lower middle<br>income|Integrated Household Survey
mothers, but private preschools have a small and statistically significant effect if we control for district trends. The presence of any preschool-aged child still negatively affects maternal employment, albeit with smaller magnitudes: -0.034 compared to -0.051 for public preschools in DDD and -0.025 to -0.042 for private preschools. This suggests that some of the reduction in the likelihood of working when taking care of a young child is attributable to individual career and familial preferences. It seems puzzling that eligible mothers with access to an additional public preschool per 1,000 children would have worked more than non-eligible mothers. This may suggest that, controlling for individual preferences, mothers with a young child have greater financial needs and would have been more motivated to work had they been afforded alternative childcare services.

# **_5.2 Alternative specifications_**

Our preschool data were obtained from PODES, which is only available in 9 years of 28 years in our constructed panel. The most conservative approach is to restrict the analysis to PODES years only, as in Table 2. Our second approach is to infer preschool data in-between PODES years using the closest upper year available. Lastly, we can fit a linear projection between non-missing PODES years. Table 3 reports the estimates of the three empirical strategies with the three treatments of missing PODES years. All regressions include district, year, mother’s age fixed effects and an urban residence dummy; and <u>do not</u> include district-specific trends. Columns 1-3 repeat the estimates from Table 2 without district trends. Columns 4-6 report estimates when we infer inbetween PODES years and columns 7-9 report estimates when we fit a linear projection between non-missing PODES years. Across the board, we note that the general story and overall statistical significance do not change whichever preschool data we use. In the restrictive approach of using only PODES years, we introduce identification issues in the DDD-FE strategy, as we can only leverage variations as mothers transition in and out of eligibility over
These patterns confirm findings in Mattoo et al. (2008).

The progress within the ten years closest to migration is presented in Figure 2 where we plot the change in OP (i.e. ΔÔP) between years 1990 and 2000 in the y-axis and the original ÔP in 1990 in the x-axis for the 1985 arrivals. The same exercise is performed for the 1975 arrivals in Figure 3a. The negative correlation between the _entry level_ OP and _change_ in the OP over the next decade in Figures 2 and 3a show that there is significant improvement in the occupational placement levels for skilled immigrants coming from different countries. Additionally, this negative correlation suggests the existence of a convergence effect such that the differences in

> 20 For comparison, as of Census 1990, the weighted average occupational index calculated for all US-born skilled males (irrespective of wage or employment status) is about 77.

12
|2 month lagged autocorrelation of monthly outgoing duration (after first month with activity)|-0.039|(1.098)|
|---|---|---|
|Magnitude of strongest repeating temporal pattern in daily outgoing calls (Fourier transform)|0.004<sup>***</sup>|(0.001)|
|Magnitude of third strongest repeating temporal pattern in weekly outgoing calls (Fourier transform)|0.007<sup>***</sup>|(0.002)|
|2 month lagged spearman autocorrelation of monthly outgoing calls (after first month with activity)|13.496|(325.088)|
|Correlation of monthly outgoing SMS with 1 month lagged outgoing duration|-5.137<sup>*</sup>|(2.899)|
|Correlation of monthly outgoing SMS with 2 month lagged outgoing calls|13.600|(320.260)|
|Missing: correlation of monthly outgoing SMS with 1 month lagged outgoing duration|-4.941<sup>*</sup>|(2.898)|
|Constant|6.980<sup>**</sup>|(2.922)|
|Observations|7,068||

_Note: Standard errors computed based on the final logistic model; they do not adjust for model selection._ *p**p***p<0.01 Source: authors’ analysis from telecom data.

# **(b) Stepwise OLS FE**

|**Stepwise OLS FE**|||
|---|---|---|
||Coefficient|<br>SE|
|Fraction of outgoing duration during working hours|0.098***|(0.025)|
|SD of daily outgoing calls|0.556|(0.
population and the average years of secondary education of the female population. Our third model (M3) focuses on standard policy indicators (Barro, 2000). It includes the inflation rate (GDP deflator) as an indicator of macroeconomic stability, the adjusted ratio of the country’s volume of trade to its GDP as an indicator of the degree of openness of the economy, and the ratio of public consumption to GDP as an indicator of the burden imposed by the government on the economy.<sup>22</sup> Lastly, the fourth model (M4) is taken from López and Servén (2009). It includes the inflation rate, the average years of secondary female education, and a lagged composite index of public infrastructure.<sup>23</sup>

# **IV. Growth, poverty and inequality: Estimation results**

We turn to the estimation of (12) under alternative model specifications and using different econometric approaches. As summarized above, the GMM estimators of Arellano and Bover (1995) and Blundell and Bond (1998) attempt to deal with the endogeneity of regressors typical of dynamic panel data models like (12) using internal instruments. However, when the cross-sectional dimension of the sample is not large relative to its time dimension -- a common situation with macroeconomic panel data -- these GMM estimators can behave poorly (Bun and Sarafidis, 2015). In this setting, it is not obvious that GMM should be preferred to more conventional estimation methods, such as OLS with time and/or country dummies. For this reason, we report both sets of estimates, which helps also assess the robustness of the results.

# **IV.1. Pooled-OLS and Within-Group estimates**

Table 2 presents robust pooled-OLS and within-group (WG) estimates of the four models considered (M1, M2, M3 and M4). Time dummies are included in all models. As noted above, we use the headcount poverty rate (with poverty line of US
percentiles, predictions from MODIS AOD and the Google Traffic congestion index come closer to the CAMS-3 observations than the Sentinel-5P aerosol index or NO2 measure.

Our PM2.5 results are basically replicated for NO2, with some notable differences. Google Traffic nearly matches CAMS-3 in the higher percentiles, while the predictions from Sentinel-5P are notably conservative beyond the 90th percentile. By implication, Sentinel-based inferences about hazard levels on the ground might be drawn from percentiles rather than absolute numbers. In any case, Sentinel-5P clearly has two useful roles to play: It can identify areas polluted by non-traffic sources, and it is available globally while Google Traffic is limited to major urban areas in some countries.

# **6.  Application Benefits and Costs**

To summarize, we find that Sentinel-5P, MODIS and Google Traffic all provide useful information for air pollution monitoring in the Dhaka metro region. MODIS, S5P and Google Traffic all track CAMS-3 PM2.5 with high significance; S5P and Google Traffic perform similarly for CAMS-3 NO2. Google Traffic can track air pollution in metro Dhaka at higher spatial resolution, but for vehicular sources only. MODIS and S5P capture the effects of emissions from other sources. The two satellite platforms also measure pollution at all locations, while Google Traffic is often confined to the major metro areas of developing countries.

Use of the three information sources does involve trade-offs in the dimensions of spatial resolution, update frequency and cost. The effective spatial resolution of Google traffic is 100 m. Outputs from Google Earth Engine are rasters with 1 km pixels, built from Sentinel-5P imagery with 5.5 km resolution and MODIS imagery with 250 m resolution. While the underlying MODIS imagery is higher-resolution, its repeat observation frequency is much lower than Sentinel’s. Information from all three sources is available at no acquisition cost, but translating the raw data into usable outputs requires knowledge of Javascript, GIS technology, and a modular programming language (Python or R).

Overall, the evidence from Dhaka suggests that these three information sources should be viewed
**Table A1.3.** ITT effects on crop productivity and net crop income.

||Crop prod<br>in kg|uctivity<br>/ha|Net crop<br>in ET|income<br>B/ha|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|T|172.320|89.55|161.82|-2.37|
|(extension and<br>extension + video)|(129.40)|(78.93)|(1,110.26)|(703.02)|
|p-value|0.185|0.258|0.884|0.997|
|Endline control mean|1,832|.24|14,87|5.23|
|Additional controls|No|Yes|No|Yes|
|(Pseudo)R-squared|0.005|0.273|0.000|0.284|
|Observations|2,160|2,160|2,160|2,160|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Outcomes refer to productivity and income obtained from the main crops maize, wheat, teff, barley and sorghum. T subsumes T1 and T2. Additional control variables identical to those listed in notes of Table 2. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

37
**Figure 21: Total Factor Productivity Change over Time for Selected Countries**

<!-- Start of picture text --> 6<br>5<br>4<br>3<br>2<br>1<br>0<br>2010 2011 2012 2013 2014 2015 2016 2017<br>Bulgaria Mexico Peru<br><!-- End of picture text -->

**Figure 22: Malmquist Indices and Sub-Components Over Time**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Total Factor Productivity Pure Efficiency<br>Technological Change Scale Efficiency<br><!-- End of picture text --> Source: Authors’ elaboration.

# **3.3 Electricity Spending Trends**

As with roads, budgetary expenditure on electricity fails to fully capture resource flows to the sector due to widespread reliance on off-budget vehicles. Due to the prevalence of electric utilities, many of which are state-owned enterprises across the developing world, budget spending on electricity only tells a small part of the expenditure story for the sector. During the last 25 years, there has also been a substantial scale-up in PPPs, particularly for power generation projects. Finally, donors – from the OECD and beyond – have also contributed to spending in the sector, especially in the lower income country group. This means that budget expenditure needs to be combined with data from utility financial statements, as well as public private partnerships and donor engagements, to build-up a comprehensive picture of financial flows to the electricity sector.

By integrating off-budget data sources, a more complete picture can be provided. Over the period 20102018, public expenditure for the reporting 48 countries averages 0.35 percent of the GDP with a clear rising trend
, which translates to a 9km resolution over India. We use data on temperature, dew point, surface pressure, wind direction, wind speed, and precipitation over the full sample. We calculate relative humidity from dew point, temperature and surface pressure. We aggregate these weather variables to the district level by averaging over all grid-points that fall within a district boundary using the same population weighting we use to calculate inversions.

# **3.4 Air pollution data**

We use pollution data from the global data produced by the Van Donkelaar research group (Van Donkelaar et al., 2019). This provides monthly average pollution levels on a 0.01<sup>_◦_</sup> _×_ 0.01<sup>_◦_</sup> grid covering the entire planet from 1998 to 2020. We calculate annual average pollution in Indian districts by assigning grid-points to their respective district and then averaging across all grid-points within a district in each month. We weight grid-points by the population

> 11Data available here: https://cds.climate.copernicus.eu/cdsapp!/dataset/reanalysis-era5land?tab=overview

9
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, and interoperable), and be safe to use (be impartial, confidential, and appropriate). Too often, we find, the data produced by governments do not satisfy these conditions and thus are not conducive to transforming development outcomes. The data may be of poor quality, siloed in various administrative systems, not shared with the public, not readable by computers, and so forth.

We restrict our analysis to data collected by government agencies, such as surveys, censuses, and administrative data, with a focus on low and middle-income countries. This means that we will neglect private sector data, citizen generated data, and data from high-income countries. We believe that the case for improving the stock of high-quality data and the safe use of data is particularly pertinent for governments in low and middle-income countries. Our objective is to provide a series of examples that illustrate the conditions under which development data can generate value. The realized social value in these examples is large and typically occurs in nonmonetary dimensions, such as improved health and safety.

Several other frameworks exist that list features conducive for data to be valuable. Most of these frameworks have been developed by national statistical offices or international organizations to guide data producers (see for example Statistics Canada (2017), OECD (2011), 2
._ 50) to free trade ( _τ_ = 0). As we mentioned above, a positive productivity shock in the Home country leads to an increase in the domestic output and to a decline in its price. The terms of trade of the other country improves, raising employment, investment, output and consumption in the Foreign country. The improvement in the terms of trade of the Foreign country is larger under free trade than under low trade integration. The different behavior of the Foreign terms of trade depends on the different dynamics of the intermediate goods prices at different degree of trade openness. Trade costs introduce a wedge between the Home and the Foreign price of the same intermediate good traded across countries. Only under free trade the two prices are equated. Thus, only under free trade the decline in the domestic price of the intermediate good traded to the other country is fully transmitted into its foreign price. Therefore, free trade strengthens the response of the Foreign variables to a domestic productivity shock and through them produces an amplification mechanism which reinforces the reaction of all the domestic variables but consumption. Under free trade, the response of domestic consumption to the productivity shock is weaker because of the

> moments are obtained shocking productivity in both countries. All series have been logged (except net exports) and Hodrick-Prescott filtered with a smoothing parameter of 1600.

> 7The statistics for capital liberalization are reported in Table 5. The statistics for trade liberalization are reported in Table 6. Volatility is measured by the theoretical standard deviation. Contemporaneous comovement is measured by the theoretical correlation. The simulated moments are obtained shocking productivity in both countries. All series have been logged (except net exports) and Hodrick-Prescott filtered with a smoothing parameter of 1600.

14