rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Can Public Works Programs Reduce Youth Crime? Evidence from Papua New Guinea’s Urban Youth Employment Project**

_Oleksiy Ivaschenko, Darian Naidoo, David Newhouse, and Sonya Sultan_<sup>1</sup> JEL Classification Codes: J30, J68, C9 Keywords: Public works, Job Training, Crime, Active Labor Market Programs, Papua New Guinea 1 Oleksiy Ivaschenko (Corresponding author), Sr. Economist, Social Protection and Labor Global Practice, World Bank; email: <u>oivaschenko@worldbank.org; Darian Naidoo, Consultant, World Bank; email: dnaidoo@worldbank.org; David</u> Newhouse, Sr. Economist, Poverty Global Practice, World Bank; email: dnewhouse@worldbank.org; Sonya Sultan, Sr. Social Development Specialist, Social Development Practice, World Bank; email: <u>ssultan@worldbank.org. The</u> views presented in this paper are solely those of the authors, and should not be attributed to the World Bank. We would like to express thanks to Ingo Wiederhoefer, Sonya Woo, Bassam Ramadan, and Tom Gillis for their enthusiastic support for and contributions to the evaluation. We would like to also like to thank Victor Canales, Francesca Tinibar, and Serrah Nao for managing the data collection, and Dung Doan for preparing the baseline questionnaire. Juan Carlos Parra Osorio, Ravi Corea and Prathip Manuel also provided useful comments and support provided with data analysis. We gratefully acknowledge funding was provided by the Rapid Social Response Trust Fund.
and application in the Middle East and North Africa region. Sci. Total Environ. 628, 1149–1164.

- MacDonald, A. M., Bonsor, H. C., Dochartaigh, B. É. Ó., Taylor, R. G., 2012. Quantitative maps of groundwater resources in Africa. Environmental Research Letters, 7(2), 024009.

- McCallum, A. M., Andersen, M. S., Giambastiani, B. M., Kelly, B. F., Ian Acworth, R., 2013. River–aquifer interactions in a semi‐arid environment stressed by groundwater abstraction. Hydrological Processes, 27(7), 1072-1085.

- Mendonça, R., Müller, R. A., Clow, D., Verpoorter, C., Raymond, P., Tranvik, L. J., Sobek, S., 2017. Organic carbon burial in global lakes and reservoirs. Nature communications, 8(1), 1694.

- Meunier, S., 2019. Optimal design of photovoltaic water pumping systems for rural communities – a technical, economic and social approach. Doctoral dissertation. Université Paris-Saclay.

- Meunier, S., Heinrich, M., Quéval, L., Cherni, J.A., Vido, L., Darga, A., Dessante, P., Multon, B., Kitanidis, P.K., Marchand, C., 2019. A validated model of a photovoltaic water pumping system for off-grid rural communities. Appl. Energy 241, 580–591. https://doi.org/10.1016/j.apenergy.2019.03.035.

Mukherjee, A., 2018. Overview of the groundwater of South Asia. Springer.

- Murray, B.B.R., Zeppel, M.J.B., Hose, G.C., Eamus, D., 2003. Groundwater‐
Another potential source of bias in our results could lie in the importance of cross country investments in the form of _Foreign Direct Investments_ . In table 8, we investigate the robustness of our findings to the inclusion of FDI links using UNCTAD’s data. We find that accounting for cross-country investments only reinforces the specific role of trade in intermediate inputs in the increase of GDP comovement within the country-pairs included in our sample. However, because the coverage of such data is limited, it is important to keep in mind that the sample is different than in previous analysis – see table 1 for an overview of data coverage for each country pair. Over and beyond the role of Global Value Chains, our results highlight the negative but insignificant relationship between Foreign Direct Investments with GDP comovement.

**Table. 8.** The impact of financial integration: Foreign Direct Investment

|||corr|GDP<sup>_HP_</sup>|||corr|GDP<sup>_BK_</sup>|||corr|∆GDP||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|ln(_TP_)|**0.121**<sup>_∗∗_</sup><br>|**0.161**<sup>_∗∗_</sup><br>|||0.095<br>|**0.133**<sup>_∗∗_</sup><br>
_Science must be understood as a social phenomenon... not the work of robots programmed to collect pure information. Scientific findings should not be elevated to the status of immutable truths._ _—_ Stephen Jay Gould _, The Mismeasure of Man_

# **1 Introduction**

There are 37 weather stations in the World Meteorological Organization’s (WMO) database on the entire continent of Africa. These stations provide weather data for 1.1 billion persons living on the continent (Tzachor et al., 2023). This figure stands in contrast to the 636 weather stations across the European Union and the United States, which provide weather data for 1.2 billion persons in those two regions (Tzachor et al., 2023). Further, because of their uneven dispersion across the African continent, the weather stations that exist cover only about 40 percent of the African population. Weather stations on the continent are often so far apart that the data collected are of limited use, a condition exacerbated by under-investment in their maintenance, which results in a deterioration of the frequency and quality of data reporting. Only one in five weather stations in Africa met the WMO’s reporting standards as of 2019.

The lack of station data across Africa means that, for much of the continent, the truth of the weather is unmeasured. The emergence of Earth Observation (EO) and improvements in weather modeling have enabled the development of remotely sensed weather datasets to fill this gap. These products provide an estimate of the on-the-ground conditions of weather, albeit detected from a distance. These EO products can provide myriad measures of weather phenomenon, like precipitation, temperature, wind speed, or humidity. Each EO product uses a different combination of weather sensors and methods for interpolating and interpreting the data from those sensors. And so, while each EO product endeavors to measure the objective truth of weather conditions, each produces its own “truth.” In theory, these “truths” should all be the same. In
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long discussed that the road freight efficiency has been deteriorated with more empty trucks observed.

44. The paper recast light on the question of how road freight costs are determined using very large, unique shipping data from Eastern Europe and Central Asia. The data covers over 10,000 shipping operators during the period from 2010 to 2020. Freight costs and time, and other basic characteristics were recorded for over 83,000 shipping routes observed.

45. It was found that economies of scale in both volume and distance matter to freight transport costs. The elasticity with respect to freight volume (tonnage) is high at about 0.3 to 1.0. Thus, the load factor is an important element to determine freight costs. The elasticity of distance is relatively small but significant. It varies from 0.04 to 0.16, depending on specification. Longer distance shipping tends to be cheaper. The elasticities also differ across types of commodities.

46. The impact of speed or the quality of roads looks complex and heterogeneous among different types of commodities. According to the OLS results, transport efficiency matters to capital goods, such as equipment, minerals, metals and materials. On the other hand, consumer goods, agricultural commodities and food products are less sensitive to driving speed. However, it seems that there exist important unobservables that affect both driving speed and freight costs. When the carrier-specific fixed-effects are incorporated, it is found that the transport costs decrease with driving speed for particular types of cargos, such as agricultural commodities, food products, and other consumer goods. The elasticities are estimated at 0.03 to 0.11. If observed driving speed is considered as a proxy for road conditions, it can be concluded that the significant road corridor investment in the region contributed to increasing shipping efficiency and lowering average freight costs.

47. There are other important determinants of freight costs, including terrain conditions, such as elevation. In mountainous areas, freight costs tend to be higher. In
imports subject to AD only in effect Flow: imports subject to any newly initiated TTB investigation Flow: imports subject to newly initiated AD investigation only**

<!-- Start of picture text --> Peru<br>percent<br>25<br>20<br>15<br>10<br>5<br>0<br>1993 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Philippines<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>1997 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>Thailand<br>percent<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br><!-- End of picture text --> <!-- Start of picture text --> All trading partners' exports under any TTB in effect<br>China's exports under any TTB in effect<br>Other emerging economies' (non-China) exports under any TTB in effect<br>High income countries' exports under any TTB in effect<br><!-- End of picture text --> Notes: Shares of nonoil imports, constructed by the author with policy data from Bown (2012) and trade-weighting with HS-06 import data from UN Comtrade via WITS, following Appendix equation (A2).

22
# **For online publication: Appendix**

## **Data**

This section provides details on other data sources employed.

### **Credit: Reserve Bank of India**

We obtained data on bank branches from the Reserve Bank of India.<sup>27</sup> We obtained the number of commercial (which include both private and public sector banks) by district for the years 2002 and 2012 for both rural and urban areas.<sup>28</sup> . This measure provides a proxy for banking activity in the district.

### **Weather: University of Delaware**

We also aim to include control variables for adverse weather conditions in the years preceding the year under consideration. We utilized two databases of the University of Delaware’s Global Climate Database: Earth Precipitation: 19002017 Monthly Time Series in Grid, and Earth’s Air Temperature: 1900-2017 Time Series in monthly grid.<sup>29</sup> These databases contain monthly precipitation and air temperature for the 1900-2017 period, based on weather station data, but interpolated to a 0.5-degree by 0.5-degree latitude/longitude grid (with the grid nodes centered on the 0.25 degree).

Using the 2001 district census boundaries, we average the precipitation/air temperature data of all data points within each district boundary to create district-level averages for all the years between 1970 and 2012.

Using these district-level time series, we classify years (in each district) as to whether or not adverse weather conditions prevail, and then aggregate this information (at the district level) over five years.

We define an rain adverse event as a year in which the annual rainfall is below the 20th percentile or over the 80th percentile of the long term (30

> 27 see: https://www.rbi.org.in/ under the annual publications tab.

> 28The data from 2012 used the 2008 district boundaries; we mapped up the data points
the selected dimensions and indicators for the region. The Arab MPI was based on three dimensions: education, health, and living conditions.

Table 4. Dimensions and indicators of deprivation for household poverty in Arab multidimensional <u>poverty index</u>

|Dimensions<br>|Deprivations|
|---|---|
|educa<br>tion|Years of schooling<br>School attendance|
|ealth|Child mortality<br>Nutrition|
|h|FGM/early pregnancy|
|s|Electricity|
|ion|Sanitation|
|dit|Water|
|con|Floor/roof|
|ng|Cooking fuel|
|Livi|Overcrowding<br>Assets|

Source: Authors’ compilations based on UN (2017).

Constructing the MPI using MICS or DHS could potentially provide an important alternative to monetary poverty in MENA countries where collection of HBSs is not possible, or where existing surveys are old. Figure 10 shows the most recent years for HBS, MICS, and DHS in MENA countries. Algeria, Iraq, the West Bank and Gaza and Tunisia could benefit from more recent MICS data compared to their outdated HBSs. However, the situation in the most data-deprived countries, such as the Syrian Arab Republic or Libya, cannot be improved given that there are no recent DHS or MICS surveys. In addition, the MPI, depending on deprivations selected, may not be very sensitive to economic changes in the country. For example, number of years at school may not change quickly during the economic crisis.

It is also important to remember that tracking multidimensional poverty over time requires comparable DHSs or MICSs. Comparability also requires identical definition of deprivations in the different years, including definitions of indicators, cut-offs, and weights. Finally, countries will have to conduct DHSs and MICS regularly and frequently, which is not always the case. For example, MICSs were conducted only every six to seven years in the MENA region.

Besides availability of microdata, several important technical
<mark>Maccini, Sharon, and Dean Yang (2009). “Under the weather: Health, schooling, and economic consequences of early-life rainfall.”</mark> _<mark>The American Economic Review</mark>_ <mark>99(3): 1006-1026.</mark> Marques, H. (2015). “Does the gender of top managers and owners matter for firm exports?” _Feminist Economics_ 21(4): 89–117.

<mark>Mascia, Danilo V. and Stefania P.S. Rossi (2017). “Is there a gender effect on the cost of bank financing?”</mark> _<mark>Journal of Financial Stability</mark>_ <mark>31: 136-153.</mark> <mark>Moro, Andrea, Tomasz Piotr Wisniewski, and Guido Massimiliano Mantovani, (2017). “Does a manager's gender matter when accessing credit?” Evidence from European data.”</mark> _<mark>Journal of Banking & Finance</mark>_ <mark>80: 119-134.</mark> <mark>Muravyev, Alexander, Oleksandr Talavera, and Dorothea Schäfer (2009). Entrepreneurs' gender and financial constraints: Evidence from international data.”</mark> _<mark>Journal of Comparative Economics</mark>_ <mark>37(2): 270-286.</mark> Niederle, M. and L. Vesterlund (2007). “Do women shy away from competition? Do men compete too much?” _Quarterly Journal of Economics_ 122(3): 1067–101.

Paris, Lori D. and Diane L. Decker (2012). “Sex Role Stereotypes: Does Business Education Make a Difference?” _Gender in Management: An International Journal_ 27(1): 36–50.

Pham, T., and O. Talavera (2018). “Discrimination
_NTMit_<sup>_out_, corresponding to the share of HS-10 digit products subject to at least one NTM</sup> within the corresponding five digit industry _i_ in the manufacturing plant-level data, in year _t_ (see sections 2.4 and Appendix B.1 for details on the manufacturing data).

Based on the output NTM variable, we compute the input NTM measure, _NTMit_<sup>_in_.</sup> To that end we construct input-output (IO) tables from the plant-level data. For each five digit industry _i_ , input NTM is defined as the weighted sum of output NTM in each five digit supplying industry _k_ : where _wik_ is the share of _k_ in total input value in industry _i_ . Our approach results in much more granular IO tables to construct upstream linkages, compared with the trade reforms literature (Javorcik, 2004; Bourlès et al., 2013). In particular, the weights _wik_ are obtained from detailed base year information on nine digits raw inputs used by each plant (see sections 2.4 and Appendix B.1). While this data would allow us to compute plant level input NTM variable, we prefer to aggregate the plant level information at the five digit level to minimize measurement error.<sup>12</sup> The results of the analysis below are qualitatively similar when using plant- instead of industry-specific input NTMs although the estimates are less precise, consistently with the larger noise in the data (results available from the authors upon request).

The evolution of output NTM in our sample is depicted by the solid line in Figure 1. The figure suggests a widespread and increasing incidence of NTMs for Indonesian manufacturing plants. Over the years of the sample, the share of products subject to NTM went from 60 to almost 80 percent. The increasing incidence of NTMs for Indonesian plants is also reflected in our measure of input NTM. In Figure 1, the dashed line represents the average share of intermediate inputs subject to at least one NTM.
, DC: Inter-American Development Bank.

Solon, Orville, Kimberley Woo, Stella A. Quimbo, Riti Shimkhada, Jhiedon Florentino, and John W. Peabody (2009). “A Novel Method for Measuring Health Care System Performance: Experience from QUIDS in the Philippines,” _Health Policy and Planning_ 24:1-8.

Spiller, Pablo S. (1990). “Politicians, Interest Groups and Regulators: A Multiple Principals Agency Theory of Regulation, (or Let Them Be Bribed),” _Journal of Law and Economics_ , 33:65-101.

Spiller, Pablo S. and Santiago Urbiztondo (1994), “Political Appointees vs. Career Civil Servants: A Multiple-Principals Theory of Political Institutions,” _European Journal of Political Economy_ .

USP DQI (U.S. Pharmacopeia Drug Quality and Information Program) (2009), “Matrix of Drug Quality Reports Affecting USAID-assisted Countries,” Washington, DC: U.S. Agency for International Development and United States Pharmacoepia. <u>http://www.usp.org/pdf/EN/dqi/ghcDrugQualityMatrix.pdf</u> ______________________________________________________________________________________

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Europe<br><!-- End of picture text --> Source: Global Competitiveness Report (2002) Figure 2.3: _Ranking of countries according to the efficiency of courts to enforce contracts_ <!-- Start of picture text --> Contract enforcement index<br>12<br>9.7<br>10<br>8<br>6 5.2 5.45.55.55.65.86.16.77.07.07.17.17.47.67.97.98.08.18.18.28.38.48.48.48.68.88.9<br>4.5<br>4 3.6 3.7<br>2<br>0<br>Kyrgyz Serbia and Albania Slovenia Bosnia i SEE Poland Macedonia, Kazakhstan Bulgaria Uzbekistan ECA Romania Moldova CEE CIS Czech Rep. Hungary Slovak Belarus Azerbaijan Croatia Latvia Ukraine Europe Georgia Lithuania US Armenia Russia Turkey Estonia<br><!-- End of picture text --> Source: World Bank, Doing Business Database; Bank staff calculations 30
URB) their mean expected future reliance on research is 5.34 (0.28) as compared to 6.72 (0.16) for EP, EDU, HNP, POV and SP (and the difference is significant; t=4.31). Even with the signs of convergence we have seen, the hard sectors can be expected to persist in their relatively low reliance on research for their work.

# **4. Accessing research within the Bank**

How do the Bank’s practitioners obtain the research they use? In addition to papers and reports, there are learning programs, seminars, formal and informal contacts with researchers, and formal and informal networks. The survey data on self-assessed familiarity with Bank

> 22 All but one of these sectors is mapped to SDN; the mean future reliance scores are 5.49 (s.e.=0.23) and 6.25 (0.14) for SDN and non-SDN sectors, and the difference is significant (t=2.85). Note that the staff in sectors mapped to SDN need not be in the SDN VPU; many are in regional units. However, there is very little difference in the score between staff in these sectors who are in the SDN VPU versus other VPUs; the mean scores are 5.52 and 5.47 respectively (and the difference is not significant; t=0.09). It is a ―sector effect‖ rather than VPU effect.

12
bureaucratic system**

7 Another study also examines politician incentives and GDP manipulation. Chen, Qiao, and Zhu (2021) focus on the effect of different stages of politicians’ careers on local officials’ GDP manipulation in China. We differ from this study in many ways. First, we focus on the political cycle effects. Second and more importantly, we explore how performance manipulation is related to political rivals’ manipulation, especially comparable rivals, as well as the degree of political competition, but not geographical neighbors. We are thus able to offer evidence of direct political mechanisms of tournament competition in inducing performance manipulation. Third, we painstakingly construct more comprehensive measures of career track based on _real power_ rather than just administrative ranks, and use it to provide evidence that performance manipulation pays for career advancements. Fourth, we show that the ratchet effects and the opportunity to scapegoat predecessors explain why some bureaucrats would restraint performance manipulation. Finally, we offer improvements in empirical details: (1) we use a relatively long period of data, which improves the credibility of the results as studies have shown that the studies on local political incentives are fragile with data with different time coverage (Shih et al. 2012; Landry 2003); (2) we employ prefecture-level data in contrast to the county-level data of night light data to predict GDP, which represents an improvement in predicting real GDP since Gibson et al. (2021) caution that the predictive performance of the night light data (i.e., DMSP) is less accurate for lower-level spatial units, for lower density areas, and for smaller areas.

8 For the general literature in this area, see Nordhaus (1975), MacRae (1977), Rogoff (1990), Shi and Svensson (2006), and Labonne (2016).

9 Yu, Zhou, and Zhu (2016) and Shi and Xi (2018) employ similar spatial econometric models to estimate the strategic competition among city governments for GDP growth and coal mine deaths respectively,
||Mar-Jun 2021(RRPS R5)|47.9||Fullyopen**|
|---|---|---|---|---|
||Jul-Nov 2021(RRPS R6)|80.1||Fullyopen**|
|Malawi|||||
||Apr-Nov 2019(LSMS W5)|66.9||Pre-COVID|
||Apr-May2021(HFPS R10)|83.7||Fullyopen**|
|Nigeria|||||
||Jan-Feb 2019(LSMS W4)|73.2||Pre-COVID|
||Oct 2020(HFPS R6)|59.0|54.3|Partiallyopen|
||Mar 2021(HFPS R11)|81.6||Fullyopen|
|Uganda|||||
||Mar 2019-Feb 2020(LSMS W8)|85.4||Pre-COVID|
||Oct-Nov 2020(HFPS R4)|53.8||Partiallyopen|
||Mar-Apr 2021(HFPS R6)|31.5||Partiallyopen|
||Sep-Nov 2021(HFPS R7)***|65.2||Partiallyopen|

_Notes:_ * estimates for Kenya refer to children aged 10-17 years. Estimates for Malawi refer to children aged 15-18. ** with academic break. *** Uganda R7 asked about school attendance in May 2021.

Before the onset of the pandemic and according to the last pre-COVID 19 multi-topic household survey available to the research team, adolescent girls were less likely to be in school than adolescent boys at the aggregate level, but this masks important disparities across countries. Particularly, adolescent boys were more likely to attend school than adolescent girls in Kenya (92.5 vs. 91.1 percent), Malawi (73.5 percent vs. 60.5 percent) and Nigeria (74.6 vs. 71.7 percent). Only Uganda showed the opposite pattern (83.6 vs
in both urban and rural areas the vast majority of households with access to electricity had connections (i.e., chose to connect). This is not true for the other infrastructure sectors. A greater proportion of rich households than poor households chose to install in-house water taps, sewer connections, and telephones. In rural areas very few of the poor households with access to water, sewer, and telephone service actually had '9 It should be noted that some such households may have consciously made this choice, i.e., located their home in a place without access because rents or land values were cheaper there.

20 Information on community access to private telephone service is not available for Nepal, and the sewer access variable is missing for Kazakhstan.

> 21 The LSMS community surveys provide an imperfect measure of access. The surveys make it possible to determine whether an infrastructure network is available within each respondent's community, but having a network in the area does not necessarily mean that it is technically or financially feasible to extend the network to all homes in the area. Despite this

10