rafmacalaba/data-use-mentions-extended huggingface dataset:
>70<br>60 1.5<br>50<br>40 1<br>30<br>20 0.5<br>10<br>0 0<br>2011 2012 2013 2014 2015<br>Source: authors based on BREB data<br>(moving average)<br>month in the preceding year<br>Average number of outages per<br>Average number of loadsheds per month (12 month moving average)<br><!-- End of picture text --> Storm related power outages are not decreasing during the same time frame.

While falling load shedding volumes suggest that the reliability of power supply has improved overall, resilience to natural shocks has not. During the same time period, storm-induced outages have been stable or even increasing in frequency – at the country level, and in all divisions where data is available (Figure 3.6). Over the period 2000-2017, outages were reported on 575 storm days. Bangladesh

> 15 “Sustainable electricity for all” initiative: SE4ALL database, IEA and World Bank

> 16 Storm-related outages are not available in Barisal. Load-shedding data in Dhaka did not pass robustness checks. Therefore, those divisions were excluded from the comparison between load shedding and storm-related power outages.

13
., Jaravel, X., Petkova, N., and J. Reenen (2019). “Who Becomes an Inventor</u> in America? The Importance of Exposure to Innovation.” _Quarterly Journal of Economics_ , 134(2): 647–713.

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defined (e.g., arithmetically or geometrically)? Should we look at the changes in absolute numbers or the changes in relative numbers over time, or both (i.e., the absolute number of the poor in the population or the percentage)?

The importance of employing appropriate metrics to monitor progress cannot be overemphasized.<sup>11</sup> We turn to discussing some of these issues with concrete data. Table 3 provides the latest poverty numbers using the international poverty line of $1.9/day in 2011 PPP from the WDI database for four countries: Brazil, China, Ethiopia, and India (i.e., 2015 for the first three countries and 2011 for India). Judging by the headcount poverty rate (Table 3, Panel A, Row 1)— or the percentage of the population who are poor—China at 1 percent and Brazil at 3 percent have far lower poverty rate than the 21 percent for India, and 27 percent for Ethiopia. Brazil and China are thus strong performers in terms of having a smaller percentage of their population that are poor. Yet, these four countries vary widely in population size, which leads to the interesting result that 1 percent of the population in India (12 million) is even larger than 10 percent of the population in Ethiopia (10 million) (Panel A, Row 5). Consequently, although Ethiopia’s poverty rate is onefourth higher than India’s, its (absolute) number of poor is just around one-tenth that of the latter (Panel A, Row 2). Similarly, Ethiopia has more than 20 times the poverty rate of China, but has only three times as many poor.

As such, while driving down the poverty rate by one additional percentage point may represent similar progress on this metric for each country, when translated into the number of people living

> 11 Indeed, Easterly (2009) famously points out that, depending on whether we target a relative change
a 500 m radius (from 6 to 3) and then use the centroid as the crash location.

We define a correct geoparse as one located within 500 m of the coordinates in the truth dataset. As a benchmark, we compare our algorithm to the Location Name Extraction tool (LNEx), which was shown to have better accuracy than other geoparsers <mark>[40]</mark> . As LNEx and other geoparsers are not designed to extract one unique location from text [26,40,47], we first judge performance by examining whether any location references are near the true coordinates. Next, we define the crash location as determined by LNEx to be the centroid of all locations it finds in the tweet and compare this with the unique location identified by our algorithm.

- (c) **Identify unique reports.** To avoid over-counting, we develop a clustering algorithm that uses time and location to identify which tweets refer to the same crash. In Figure 1, five tweets report a crash within two hours of each other, referencing different landmarks that are all close together. To develop reasonable parameters for clustering, we manually identify tweets that report the same crash in the truth dataset based on the time, location and crash characteristics. The 4,192 crash reports are clustered into 2,648 unique crashes. For unique crash clusters, 97% of tweets reported

6
# **1. Introduction**

Workers earn higher wages in bigger cities. One explanation is the learning advantages that bigger cities provide, as well as the larger array of opportunities for workers to match with a firm. However, an alternative explanation for the higher observed wages is the sorting of more skilled workers into bigger cities. We know workers in bigger cities exhibit higher educational attainment and tend to work in occupations that demand more skills. Yet, we continue to know little about the contribution of internal migration to the sorting of more skilled workers into bigger cities.

Recent studies that examine differences in skills between workers in big and small cities rely on rich administrative worker-level panel data that follow workers throughout their careers (Combes, Duranton, Gobillon, and Roux, 2012, Carlsen, Rattsø, and Stokke, 2016, Matano and Naticchioni, 2016, De la Roca and Puga, 2017). However, sorting may take place prior to entering the labor force as more skilled individuals may leave their small towns upon college graduation and move to a big city to commence their job search and career. Furthermore, sorting may occur even earlier as high-ability high school graduates may move to a big city to pursue a college degree and remain there after graduation to work. The sorting of skilled individuals into big cities can, therefore, occur prior to labor market entry and may be a dynamic process.

In this paper we study the dynamic sorting that leads to differences in skills across cities of different sizes and its consequences on estimated agglomerative effects for work, college, and high school city, using rich administrative data for young college-educated workers in Colombia. For college graduates who work as employees in the formal sector of the economy, we observe their wages, as well as their college, degree, and major. We also observe a rich set of background characteristics, including parental income and education, as well as individual scores in the mandatory national high school exit exam, Saber 11, which measures academic readiness for higher education
at time t+ 1; εt+1 follows a stationary process and represents measurement error; and where β measures the extent of risk sharing. Theoretically, in a model with complete international financial markets and perfect risk sharing, the coefficient β, which captures the degree of countries’ uninsured idiosyncratic consumption risk, is equal to zero. In practice, earlier studies interpret β pragmatically and argue that this coefficient can be used to measure the degree of risk sharing. The smaller the extent of idiosyncratic comovement between country-specific consumption and output growth the smaller β, and the greater the degree of international risk sharing (Asdrubali, Sørensen and Yosha 1996).<sup>10</sup>

> 8 The data set by Lane and Milesi-Ferretti (2007) on financial assets and liabilities ends in 2011. This data set is constructed using information from the IMF Balance of Payments as well as other sources. As a result, it offers a better coverage for developing economies in the earlier years of our sample compared to the IMF Balance of Payments. We use growth rates of each variable in Lane and Milesi-Ferretti’s (2007) data set to extend the IMF Balance of Payments data backwards.

> 9 The Appendix presents the list of countries. For comparison and further robustness, we also pool this country group with advanced economies. In that case, the sample includes 110 countries. On average, total remittances have been less than 0.2 percent of GDP in advanced economies, so we do not include them in the baseline. In the case of aid flows, WDI reports data for less than one-third of the advanced economies and only for the 1990-2004 period, so they are not included in the baseline. Aid flows to advanced economies averaged about 0.6 percent of GDP during the 1990-2004 period.

> 10 The extent of risk sharing can be calculated as (1 −β) (Artis and Hoffmann 2012; Sørensen et al. 2007). If β= 1, then there is no risk sharing.
# **Annex 1. Detailed Information on Transport Expenditures in LSMS Surveys (12 Countries in LSMS Collection)**

|**Survey Question**|**Albania**|**Burkina Faso**|**Cambodia**|**Ethiopia**|**Iraq**|**Malawi**|
|---|---|---|---|---|---|---|
|**_Non-food Expendit_**|**_ures during..._**||||||
|Past 7 days||Urban transport by taxi, bus,<br>tricycle or other||Transport||1. Public transport - Bicycle<br>Taxi<br>2. Public transport -<br>Bus/Minibus<br>3. Public transport - Other<br>(Truck, Oxcart, etc.)|
|Past 1 month|1. Fuels and lubricants<br>for personal vehicles<br>2. Passenger transport<br>by road (bus, minibus,<br>taxi, etc.) or railway<br>(EXCLUDE expenses to<br>travel to school and<br>health care facilities)||Gasoline, diesel, and<br>lubricant, and gas||(Include a lot of detailed<br>subcategories)<br>1. Fuel and oils for personal<br>transport equipment<br>2. Maintenance and repair<br>of personal transport<br>equipment<br>3. Wages of transport<br>services<br>4. Other transport services|1. Petrol or diesel<br>2. Motor vehicle service,<br>repair, or parts<br>3. Bicycle service, repair, or<br>parts|
|Past 3 months||1. Bus<br>
of errors are lowest for plots without a legal document (17%), followed by those with documents issued to men and women jointly (30%), individually to women (35%), and individually to men (54%). This is consistent with the notion that information on nature of land ownership documents held or owners’ identity may not be fully shared among household members (Ashraf 2009) with men generally more secretive about their documents and more likely to falsely claim having a document.<sup>17</sup> As mortgages and transfers are rare events, the sample sizes commonly used in household surveys may make it difficult to arrive at precise estimates, possibly magnifying the effect of measurement error. Of the plots registered by our survey households, 60 had a mortgage registered (with the entry dating from 2013 in 32 and from after 2013 in 28 cases). But only for 22 of these (37% of the total) did household survey respondents report existence of a mortgage or having taken out a bank loan. Routine availability of administrative data can thus help reduce measurement error by cross-checking survey information against registry entries and by stratifying household survey samples, allowing to combine data and make inferences on distributional aspects, heterogeneity of impacts, and the extent to which land or credit market activity triggered processes such as gentrification that are impossible with administrative data on their own.

# **4.4 Interpreting the results**

If, as suggested by the above figures, policy reform lowered barriers to titling, one would expect to see differences in observable characteristics between plots registered in pre- and the post-reform periods. To test this, we regress the type of titling (systematic/sporadic before or after the policy change) on geographic plot attributes in particular distance to CBD and size. Results in appendix table 2 suggest that plots that entered the registry sporadically before reform were larger and closer to the CBD while focus thereafter shifted towards smaller plots (a coefficient much larger in absolute terms than
|77.68|86.65|
|Uganda|3,271,840|15.94|1,815|55.59|81.76|98.95|
|_Overall_|3,905,006|17.95|14,839|38.45|53.22|68.13|

_Note_ : To protect confidentiality household location coordinates in LSMS-ISA data are not exact, but rather based on a random distortion of 0-5km. Data on extent of tree cover by country and percent tree cover relative to country land area derive from Hansen et al. (2013). Note that “tree cover” is not the same as “forest cover” in these data. “Tree cover” refers to the biophysical presence of trees, which may be a part of natural forests or tree plantations. Information on household distance to forest are based on the authors' calculations from LSMS-ISA data sets (World Bank, 2015) and “MOD44B MODIS Vegetation Continuous Field Coll. 5–2000 through to 2010:�Percent Tree Cover” (DiMiceli et al., 2011).

26
# **{Tables 4 and 5 here}**

Among the first six categories that are based on raw census data, three categories (raw scaled, R&R not scaled, and R&R scaled) are constructed through the summation of bilateral raw numbers and disaggregations of some aggregate categories in the original censuses. Since these categories together constitute around 45 percent of migrants in each census round, the original bilateral portion of each cell was compared with the final number assigned to them after the various calculations as a check on accuracy. For each decade, therefore, the overall percentage contribution of the raw bilateral data to the total is calculated (table 6).<sup>23</sup> In each census round, at least 92 percent of all those categories are derived from the raw data.

# **{Table 6 here}**

# _Simulating Missing Data_

Finally, to examine the reliability of the estimated missing census data and test the methodologies, several scenarios are assumed. All bilateral observations for a single year for four countries (Australia, United States, Switzerland, and Chile) in different parts of the world are deleted and the missing cells are filled using one of five methods.<sup>24</sup> The first simulation assumes that all bilateral data for 2000 are missing but that the total number of migrants is available. The missing bilateral numbers then have to be filled using the propensity measure (equation 1 in appendix 4) based on the data available in other years. The second and third simulations assume that the total is missing as well, and interpolation is used to fill in all missing data for 1960 and 2000. The fourth and fifth simulations remove all data for all years and then fill the missing years using data for the remaining portion of the subregion (table 7).

# **{Table 7 here}**

The simulations perform well. The four countries are examined one at a time, starting with Australia. The correlation
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 informal housing, we restrict our sample for the estimation, by excluding the Sub-Places in the bottom quintile of property prices _Ps_ and for which more than 5% of dwellings are reported to live in informal housing<sup>43</sup> We also exclude rural sub-places (i.e., those that are large, with a small share than can be urbanized). We find: log � _Ns_<sup>_FP_</sup> � = _−_ 3 _._ 51 (0 _._ 97) + 0 _._ 25 (0 _._ 07) log ( _Ps_ ) _−_ 0 _._ 98 (0 _._ 07) log ( _qs_ ) + 0 _._ 92 (0 _._ 08) log � _Ls_<sup>_FP_</sup> �, with standard errors in parenthesis. We find that coefficient γ3 is close to 1 and coefficient γ4 is close to _−_ 1. Coefficient κ and _a_ in the construction function are retrieved from γ1 and γ2, with _a_ = 1 _−_ γ2 and

> 43Our data set for dwelling sizes only provides the average dwelling size at the Sub-Place level, aggregating formal and informal housing.

32
The global interest rate factor displayed particularly large swings until the mid-1980s, partly due to sharp fluctuations in national interest rates (Cook and Hahn 1989). During this period, some countries focused on money supply targets (Brimmer 1983; Friedman 1982), such that high rate volatility reflected the impact of financial markets on interest rates rather than central banks’ decisions to adjust policy rates. The global rate factor was relatively stable in the 1990s and early 2000s before becoming more volatile again around the 2009 global recession. After a protracted period of stability in the 2010s, it has again displayed significant volatility since the beginning of the COVID-19 pandemic.

The behavior of the global inflation and output factors (in the bottom two panels of the Chart 12) is also consistent with some well-known global events. For example, the global inflation factor fell sharply just before or during global recessions, especially those associated with the 2008-09 global financial crisis and the COVID-19 pandemic, but also around the 1975 and 1982 global recessions. In addition, the global inflation factor fell during periods of sharp declines in oil prices (in 1986, 1990-91, 1997-98, 2001, 2008, 2014-16, and 2020).<sup>41</sup> The global output factor (measured by the highly volatile monthly IP series) shows even more short-term volatility, but also plunged during global recessions and rebounded in subsequent recoveries. The collapse and subsequent spike in output around the pandemic is particularly noteworthy and much more extreme than any other period in the sample—consistent with the evidence in Section III showing the unusual behavior of output and employment during the most recent rate cycle.

Although movements in the interest rate factor are mirrored by those for the global inflation and output factors during specific periods (such as the collapse in the global factor in all three variables during the 2008 recession), the correlations across the three global factors are fairly low at 0.110.26 for the full period. To better understand the evolutions of these variables over time, it
the Move: Functional and Management Perspectives on Transformations of European Urban Infrastructures_ (Ashgate).

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_Journal of Planning Literature_ , _27_ (2), 153–169. https://doi.org/10.1177/0885412211431984 39
# **4.5 Discussion**

Taken together, our cross-sectional results imply a long-run elasticity of rural wages with respect to land productivity, _εw,π_ = _d_ log( _w_ ) _/d_ log( _π_ ) =<sup>_d_lo</sup> _dT_<sup><u>g(</u></sup><sup>_w_</sup><sup><u>)</u></sup> _/_<sup>_d_lo</sup> _dT_<sup><u>g(</u></sup><sup>_π_</sup><sup><u>)</u></sup> , of around 0.35. Badiani (2009) examines the response of unskilled wages (across sectors) to long-run changes in exogenous agricultural productivity using a district-level panel constructed from successive rounds of the NSS employment-unemployment survey dating back to 1983. Reassuringly, she obtains an estimated wage elasticity remarkably close to 0.35.

We do not directly estimate the impact of weather _shocks_ on labor productivity, primarily because reliable, high frequency, and relatively recent data on rural wages in India is lacking (see Himanshu, 2005, for a critique of alternative sources). However, we obtain an indirect _d_ log( _Y_ <u>)</u><sup><u>)</u></sup> estimate using ∆log( _w_ ) _≈ εw,π dT_ ∆ _T,_ where<sup>_d_lo</sup> _dT_<sup><u>g(</u></sup><sup>_Y_</sup> is the output response to temperature shocks already estimated. Using this formula, a 1<sup>_◦_</sup> C increase in temperature, for example, would reduce agricultural wages by about 10 percent in the absence of adaptation.

Finally
Policy Research Working Paper 9783

# **Abstract**

Amid noticeable improvements and achievements in the reproductive, maternal, neonatal, child health, and nutrition landscape in Bangladesh, existing evidence suggests that further accelerated progress hinges on strategic investment decision making. Addressing the top service utilization determinants that are both context- and time-specific is one cost-effective way of improving the unmet reproductive, maternal, neonatal, child health, and nutrition outcomes in a short timeframe. Against this backdrop, using machine learning analysis, the overall aim of this study was to help Bangladesh identify priority investment areas that could accelerate reproductive, maternal, neonatal, child health, and nutrition utilization, quality, and outcomes over the short run, by comparing the relative importance of demand- and-supply-side determinants of key reproductive, maternal, neonatal, child health, and nutrition indicators over the past decade (across two time points). Two rounds of the Bangladesh Health Facility Survey and the Demographic and Health Survey (2014 and 2017) were analyzed. The findings indicate that the relative importance of the demand-side determinants (except wealth and education status) have recently declined. Conversely, investments in key supply-side determinants (for example, availability of skilled staff, readiness for care, and quality of care) could provide a thrust toward further increases in utilization. Immediate attention is needed to address the regressive role of wealth status on utilization through, for example, demand-side financing that goes beyond user fee exemptions. Further, developing strategies to improve the engagement of community health workers in reproductive, maternal, neonatal, child health, and nutrition utilization and tapping into the potential of mobile health technology to support community health workers’ performance and women’s awareness could help to boost utilization patterns.

This paper is a product of the Health, Nutrition and Population Global Practice. 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