rafmacalaba/data-use-mentions-extended huggingface dataset:
Trehan-Waish and Hakkio-Rush findings is no coincidence. If the auxiliary assumptions two approaches are satisfied, the two tests are equivalent. That is, if Gt + r,B,, is cointegrated with Tt and the cointegration vector is (1,-i), then must be stationary. The cointegration of G, + rB,l and T, with a cointegration vector (1,-1) means that the left-hand side of (38) is stationary. Under the assumption that r, is stationary with unconditional mean r, r, - r and hence (r,-r)B,1 are stationary. Therefore, G, + r,B, -T is stationary if and only if rBt,- - SURP, is stationary.

What if the assumption of a constant expected real rates of return on government debt is not provide a good characterization of the data?'<sup>6</sup> The Hakkio-Rush and Trehan-Walsh cointegration tests, as well as the Hamilton-Flavin test, are no longer valid.

Wilcox (1989) presents a stationarity test that does not require expected real interest rates to be constant. In the presence of stochastic expected real rates, one can use the _ex post_ real rates to discount the government debt outstanding in period t to a fixed point, say, period 0. Then the time series characteristics of the discounted debt series q,,iB,+i can be examined. If this series is stationary with mean zero, then the NPG condition (25) must hold. Applying this test to US fiscal policy for the period 1960-1984, Wilcox finds that the discounted debt series is not (mean) stationary. We can not be sure from this evidence that recent US fiscal policy is not sustainable, however, since mean-zero stationary of q,,iB,,i is only _a sufficient,_ 6 Using data for eighteen OECD countries, Rose's (1988) econometric examination of nominal interest rates and inflation rates implies that _real_ interest
— —-—<br>Improved cooking fuel & no garbage burn} $ —_ ><br>HH member smokes}3 ~*~ —<br>Functioning refrigerator in household} 3 —— ae<br>Female —- +<br>Single birth ——— ——<br>Urban —_<br>Total HH size (N)jo +<br>Total children under five in HH (N)j.Q ——<br>Total HH income per month (1 = 1000 PHP) (log) s ><br>In poorest quintile of total HH income per capita] 6 ——<br>Mother has high school or tertiary education} % ——<br>Mother is married oe<br>Mom wasa teenager at time of birth ——<br>Mother hasa job or business >t<br>0 1 2 3 4 50 1 2 3 4 5<br>Relative Risk (95% Cl)<br><!-- End of picture text -->

Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for significant risk factors are labeled. Age, age squared and age cubed (in months) is also used as a control but is not shown in the chart. Unadjusted regression models do not control for socio-economic factors.
– outright discriminate against mature workers.<sup>102</sup> A survey in EU countries found that a majority of respondents thought that being over 55 years of age put job applicants at a disadvantage.<sup>103</sup> An experimental study in Sweden showed significant and independent negative correlation between being older and the chances of being called for an interview, and callback rates began to decline for workers already in their early 40s.<sup>104</sup> A similar approach for the United States provided robust evidence of age discrimination against older women, but not against older men.<sup>105</sup> In Deloitte’s 2018 Global Human Capital Trends report, 20 percent of business leaders viewed older workers as a competitive disadvantage, and in countries such as the Netherlands, Russia, and Singapore, this percentage was far higher; some 15 percent also felt the older employees to be blocking rising (young) talent.<sup>106</sup>

**Firms—particularly small and microenterprises— generally invest little in skills development for their workforces, despite the importance of skills for firm productivity.** Individuals’ low investment in their skills development is thus mirrored in underinvestment by employers. The World Bank’s enterprise surveys show that only one-third of firms offer training to their workforce, and those that do, invest in a minority of their workers (Figure 8a and b). Across country income groups, small and micro-firms are the least likely to offer training, and firms in L/MICs are less likely to offer training compared to firms in HICs, for each size class of firms.

**Figure 8: Access to training through employers is limited, especially in L/MICs**

- a. Average share of firms providing training and b. Average share of firms providing training, by share of workers receiving training (in firms where firm size and income group offered), by income group

<!-- Start
public health shocks, new disease cases per 100,000 (100K) population. National new cases per 100K population intensified considerably over time

> 5 Examples include the Economic Tracker of Chetty, Friedman, Hendren, and Stepner (2020); the Johns Hopkins Coronavirus Resource Center; and the University of Washington COVID-19 Policies Database from Adolph, Amano, Bang-Jensen, Fullman, and Wilkerson (2021).

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34% in field offices (the remaining 1% were in WB offices in developed countries, mainly Europe). The fact that familiarity is lower for those in country offices suggests that the advances in communication technology—the WB has a well-developed ―intranet‖ to facilitate this—have not eliminated the advantages of physical proximity.

It is of interest to look for other evidence for or against the view that stronger incentives for learning translate into knowledge. An objective clue to how much the Bank’s internal system for knowledge management responds to differences in the incentive for learning can be found in how the demand for paid cross-support from the Bank’s research department varies with answers to the value question. Across VPUs, the mean score for the value of WB research is highly correlated with the per capita demand for cross-support from DECRG (using the data in Tables 1 and 12); the correlation coefficients between the three-year mean of cross-support per staff member GG+ and the mean value score is 0.72, and it is also 0.72 with the proportion of VPU staff giving a value core over 5. This is consistent with the view that incentives for learning in the Bank do generate a response.

# **7. Conclusions**

One can think of two stylized models of how research is used by development practitioners. In the first, they have a demand for knowledge that does not stem from its direct bearing on their work. Much development research is a public good. Practitioners might read research findings to better understand the world in which they work, even when that understanding is essentially irrelevant to the specifics of that work. Alternatively, in the second model, research has a direct value in the work of practitioners—such as by informing project choices at the entry stage and assessing impacts later on—and research findings are sufficiently relevant and accessible to assure that practitioners become well informed. To enhance the quality of development aid, we hope that the second model is the best characterization. But is
correlated with increased teacher absence, suggesting that increased spending on hiring additional teachers was accompanied by increased inefficiency, which may limit the extent to which additional spending may improve outcomes. Second, increases in the frequency of inspections are strongly correlated with lower teacher absence, suggesting that of all the investments in improving school quality, the one that was most effective in reducing teacher absence was improved administrative monitoring of schools and teachers. We calculate that the fiscal cost of teacher absence is over $1.5 billion per year, and estimate that investing in improved governance by increasing the frequency of monitoring would be over ten times more cost effective at increasing student-teacher contact time than doing so by hiring additional teachers.

come from just one state, compared to the estimates in this paper that use panel data from 190 districts across 19 states.

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effective rate of protection _(ERP)_ for 3-digit Indian manufacturing industries in four sub-periods: **1980-81** to **1985-86, 1986-87** to **1990-91, 1991-92** to **1994-95,** and **1995-96** to **1999-00.23** We also use tariff data from Topalova and Khandelwal (2011) which are available for every year but only from **1987** to **1999** therefore reducing our sample size. However, these data have rich cross-sectional and time-series variation **-** which the ERP data lack. We estimate all our main specifications with both measures of trade protection. We use the FDI deregulation variable from Sivadasan **(2009)** which is a dummy variable indicating which 4-digit industries were FDI-deregulated in **1992.24** We combine this dummy variable with the **ASI** data to calculate the proportion of employment in a 3-digit industry that was exposed to FDI. Hence, our **_Nj_** measure of FDI deregulation is given by _FDIjt FDIktyt_ where **_k_** indexes the 4-digit **Ek=1** Ykt industries within the 3-digit industry **_j_** and _Y_ refers to employment.

> 23Das **(2003)** calculates these ERP measures of trade for **72** 3-digit industries. In order to use all industries in our analysis, we use the average of his ERP measure for the corresponding 2-digit industry and in some cases, the economy-wide average. Note that for any given industry, the ERP has a constant value in all years within each **of** the four sub-periods.

> 24 The announcement of this reform was made in August **1991,** but its implementation began only in **1992.**

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more in such vouchers, depending on the outcome of the meeting. These vouchers were issued by Jordan Petroleum Refinery Company. They were widely known in Jordan and could be exchanged for gasoline in petrol stations throughout the country. The value of the JD 10 voucher was equivalent to slightly more than the daily minimum wage, or about five days of participants’self-reported mean per capita household expenditure. Each invitee who agreed to participate was left with two receipts (one for the JD 5 voucher and the other for the JD 10 vouchers), which they were of the transfer, the government also decided to set up a National Unified Registry (NUR) of the poor and vulnerable as a common platform for eligibility for social assistance programs, with several checks of living standards. Similar instruments (e.g. Cadastro Unico in Brasil, Ficha de proteccion social/Registro social de hogares in Chile) have been the backbone of transparent social assistance programs around the world (see Silva, Levin, and Morgandi 2013; Lindert et al. 2007; and Ministry of Social Development of Chile 2015). Many other developing countries have, in recent years, accompanied subsidy reforms by measures to enhance transparency. This includes, for example, earmarking increased funding for education and infrastructure linked to fiscal savings from subsidy reform, creating a website where each person could compute his/her score or enter their national ID and verify his/her (in)eligibility/information, or displaying the list of social assitance beneficiaries in a public place (IMF 2013).

5
a similar LGA outside of the region was not provided, we replaced it with the next most similar within the region. If any of these listed LGAs was actually in the program, then we moved down the list to the next most similar not in the program. Similarly, if any of these LGAs were participating in the Tanzania Strategic Cities Program (another major World Bank project), then we moved down the list to the next most similar LGA not in the program.

2. Propensity score list: The 2 LGAs with the closest propensity scores.

Finally, we selected the comparison LGA for each program LGA with the following procedure (with allowed repetition of non-program LGAs among comparators) in three steps:

1. If the top comparison LGA in both lists was the same, then that one was chosen.

2. If Step 1 did not hold, then if there was any overlap for the top 2 comparison LGAs across the 2 lists, then we chose the LGA highest on the survey list for which there is overlap between the survey list and the propensity score list.

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Policy Research Working Paper 8630

# **Abstract**

This paper presents a new family of Stata functions devoted to small area estimation. Small area methods attempt to solve low representativeness of surveys within areas, or the lack of data for specific areas/sub-populations. This is accomplished by incorporating information from outside sources. Such target data sets are becoming increasingly available and can take the form of a traditional population census, but also large scale administrative records from tax administrations, or geospatial information produced using remote sensing. The strength of these target data sets is their granularity on the subpopulations of interest, however, in many cases they lack the ability to collect analytically relevant variables such as welfare or caloric intake. The family of functions introduced follow a modular design to have the flexibility with which these can be expanded in the future. This can be accomplished by the authors and/ or other collaborators from the Stata community. Thus far, a major limitation of such analysis in Stata has been the large size of target data sets. The package introduces new mata functions and a plugin used to circumvent memory limitations that inevitably arise when working with big data. From an estimation perspective, the paper starts by implementing a methodology that has been widely used for the production of several poverty maps.

This paper is a product of the Poverty and Equity 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 http://www.worldbank.org/research. The authors may be contacted at mnguyen3@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,
<!-- Start of picture text --> BPD<br>Village Government Structure<br>Accordingto Village Law (Law No. 6 2014) Village Head dnp<br>And Relevant Regulations Chairperson<br>Village<br>Secretariat BPD Member<br>(Secretary)<br>Head of General Head of<br>Affairs Financial Affairs<br>PlanningHead Affairsof |<br>H. PHONE NUMBER COLLECTION<br>ENUMERATOR: PLEASE REPEAT THE FOLLOWING PROCESS TWO TIMES. ONLY ASK FOR<br>THE PHONE NUMBER OF PEOPLE AGED 18 YEARS OR OLDER.<br>1. lam going to tell you a letter: [XXX randomized according to census prevalence XXX].<br>Now, please look into the contact list on your phone.<br>Dia. | Is there anyone living in 1. Yes, name:<br>your village whose name —_| 3. No PROCEED TO NEXT RANDOMIZED<br>starts with [XXX]? LETTER<br>7. REFUSE TO ANSWER => PROCEED TO<br>NEXT RANDOMIZED LETTER<br>Dib. | Are you willing to share the<br>contact number of this. 1. Yes<br>nerson? 3.No > D2a<br>Dic. What is the phone number of ; \ L<br>this person?<br><!-- End of picture text -->
|Railway power / 100 million kWh|3.77|
|---|---|
|Waterway diesel/Mt|11.29|
|Waterway fuel oil/Mt|21.42|
|Aviation kerosene / Mt|25.6|

Source: Wang (2011).

The building sector is divided into urban residential, rural residential, general public construction and large-scale public construction according to the type of building. The enduse energy demands are space heating, cooling, lighting, cooking, hot water and other equipment. The base year data are provided in Table B5.

**Table B5** Baseyear data for the building sector (unit: Mtce)

|**Base year calibration**<br>**(Mtce)**|**general**<br>**public**<br>**construction **|**large-scale**<br>**public**<br>**construction **|**urban**<br>**residential**|**rural**<br>**residential**|
|---|---|---|---|---|
|**Heating**|5141|855|10971|5412|
|**Refrigeration**|1490|480|516|130|
|**Lighting**|1055|313|544|523|
|**Others**|1073|149|787|361|
|**Cooking and hot**<br>**water**|1363|250|4267|2296|
|**Total**|10121|2048|17086|8721|

The main sources of energy demand data are energy balance tables available from China’s energy statistical yearbook. For energy consumption in the industrial sub-sectors and technologies we used several sources such as _China Statistical Yearbook 2006-2011_<
time cost raster by assigning a speed to cross each pixel based on Tobler’s hiking function (1993) with a weight derived from historical land cover class (1900) following Ali et al. (2015). We use land cover from the HYDE model (Goldewijk et al. 2011) and we use slope data from Verdin (2007).

<mark>Hiking = 6 ∗ e</mark> −3.5 |s+0.05|∗0.6

where s is mean slope.

We then calculate the distance between each district’s centroid and each linear network. For robustness, we also consider the Euclidean distances from each district’s centroids to the actual GQ highways, which are available from Ghani et al (2014).

Similar to Faber (2014), we address the concern of non-random local route placements on the way between targeted city nodes by constructing a hypothetical network based on Euclidean distance and least cost path based on elevation and slope. Faber uses a simple land cover model based on the engineering literature (see Jha et al., 2001; Jong and Schonfeld, 2003; cited in Faber 2014) and includes measures of slope, development, water and wetland, where the algorithm prefers short and flat routes.
Policy Research Working Paper 9885

# **Abstract**

Does the internet enable the recruitment of transnational terrorists? Using geo-referenced population census data and personnel records from the Islamic State in Iraq and the Levant—a highly tech-savvy terrorist organization— this paper shows that internet access has facilitated the organization’s recruitment of foreign fighters from Tunisia. The positive association between internet access and Daesh recruitment is robust to controlling for a large set of observable and unobservable confounders as well as instrumenting internet access rates with the incidence of lightning strikes.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at abonfield@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 the World Bank or the governments they represent._ Produced by the Research Support Team
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# <u>Indicator definitions</u>

Tables 1, 2 and 3 also show the definitions of the indicators. In choosing exact definitions of indicators, we have been guided by the same initiatives that guided us in our choice of indicators (see above), but also by the constraints imposed by the data and a desire to have a common definition irrespective of the data source. Our definitions sometimes differ from those used in reports and online tools derived from the same surveys we have used (e.g. DHS reports,<sup>16</sup> DHS STATcompiler,<sup>17</sup> MICS reports,<sup>18</sup> UNICEF website,<sup>19</sup> the WHS reports,<sup>20</sup> and the World Bank’s ‘Health, Nutrition and Population Statistics by Wealth Quintile’ databank,<sup>21</sup> which contains the data from the DHS and MICS reports), and from the WHO’s Health Equity Monitor<sup>22</sup> which also contains summary statistics at the population level and for wealth quintiles based on analysis of microdata from the DHS and MICS surveys. We summarize the differences in definitions between our definitions and others’ in Annex Table A1. Important examples to highlight include:  Generally, when computing percentages of the population covered by certain services (e.g. fully immunized), we exclude cases with missing information from the denominator. The DHS and MICS reports, by contrast, typically do not, and instead treat missing values the same as if the respondent had answered _No_ when asked about having accessed the respective service. As a result, DHS and MICS reports typically have a lower service coverage rates than us.

> 16 <u>https://dhsprogram.com/publications/publication-search.cfm?type=5.</u>

> 17 <u>https://www
# V. Discussion and Conclusion

Big data is increasingly generating attractive opportunities for multi-sectoral collaborations involving government agencies, the private sector, and research institutions. Collectively, these actors are working to identify, organize, and harness the potential of large-scale administrative or private sector data. For developing countries, this technological progress offers an opportunity to leapfrog from the scarcity of traditional household travel surveys—deemed to be costly and often inappropriately administered—to high dimensional and cost-effective big data resources. For instance, call detail records (CDR) are increasingly used as reliable alternatives to conventional travel surveys, either exclusively (Alexander et al., 2015; Çolak et al., 2015) or in combination with other data sources (Wang et al., 2012; Jiang et al., 2013; Iqbal et al., 2014; Widhalm et al., 2015).

The quest for better understanding the causes and consequences of mobility and from or locational behaviors of individuals or congestion arising traffic flows, reflecting firms, has generated promising prospects for policy design. The availability of largescale data sets at high spatial and temporal resolution is useful to detect and predict mobility patterns and associated externalities. It also supports transport research and enables policy makers to assess the welfare impacts of their interventions, through various channels including the reduction of travel delays, accidents and pollution and the stimulation of output and labor productivity. Planners can capitalize on the predictive modeling techniques inherent to big data analytics to improve real-time traffic management and provide their constituents with accurate information on congestion, alternative routes, and multimodal transportation options. Big data may also help design the transition towards more energy-efficient transportation systems by providing real-time behavioral responses (via geocoded social media sensors) to congestion externalities. To illustrate this point, Wang and Moriarty (2018) present a smartphone-based personal travel assistant (PTA) which estimates the energy cost of an individual trip in Beijing, taking into account traffic conditions and modal choice.

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