rafmacalaba/datause-encoder-data huggingface dataset:
affected social groups (e.g. beneficiaries) if the moderator is categorical in nature (e.g. SES). If the moderator is continuous in nature, then the evaluator can employ a range of methods, for example QTE as described above, to generate patterns of benefits across the distribution of individuals or groups along the continuum.

In module 3, the enhanced CBA APP provides the outputs of the economic analysis. In summary, the APP allows for cost-feasibility analysis (i.e. comparing the overall costs of the project against the budget), cost-savings analysis (i.e. comparing the costs of the project against the savings generated from avoided crimes), cost-effectiveness analysis (i.e. comparing the costs of the project against the number of units of output such as the number of crimes prevented) and CBA (i.e. comparing the costs of the project against the overall benefits – avoided crimes and other benefits such as enhanced safety). Tables and plots are also included to display, for example, net costs, net benefits, cost-benefit ratio, net benefits by bearers and potential efficiency gain (PEG - as described above).

# **_Data employed to test modules 1 to 3 using the enhanced CBA APP_**

We employ data from the Pathways to Prevention Project (Homel et al., 2006) to test modules 1 to 3 of the enhanced CBA APP. The Pathways to Prevention project was established over twenty years ago as an early intervention, developmental crime prevention initiative focussed on the transition to school in one of the most disadvantaged urban areas in Queensland, Australia (Homel et al., 2001). Here, the goal was to provide positive pathways to individuals at-risk of later learning and behavioral problems that may eventually lead to crime and deviance. The project comprised two components: (i) a preschool intervention program (PIP), which was a child-focused and school-based set of activities; and (ii) a family independence program (FIP), which was a family-focused and community based set of activities run by Mission Australia (Manning et al., 2006).

In this paper, we focus exclusively on the PIP component. PIP aimed to enhance the participant’s readiness to succeed at school. Through a sequence of structured small-group interactions with either specialist teachers or program staff, PIP aimed to develop a participants: (i) communication skills by introducing more abstract language, complex vocabulary and appropriate grammar formats as part of the preschool experience, and (ii) social skills to improve a participants ability to better interpret social interactions, overcome emotions that are unproductive (e.g. anger and anxiety) and develop strategies for dealing with problems that often occur during exchanges with peers (Manning et al., 2006).

23
# - B.3. Causal Impact of the Covid 19 Lockdown on Congestion

After the World Health Organization (WHO) declared Covid-19 a global pandemic on March 11, 2020, many countries implemented restrictive policies of various degrees (stringent vs. lenient) and scope (national or subnational) to prevent further spread of the virus. These include, among others, social distancing, quarantine, isolation and community containment measures. As early as March 17, 2020, the French government banned all non-essential trips and required people leaving their homes for essential journeys to remain within a 1-km perimeter for up to 1 hour while carrying a signed and dated exception form. The initial 15-day stay-at-home order was later extended until May 5, 2020. The governor of New York, where the number of Covid-19 infections and deaths had drastically increased meanwhile followed suit and ordered that all nonessential businesses be shutdown on March 20, 2020. Both Colombia and India enforced similar countrywide stringent lockdown actions starting on March 25, 2020.

Using the above Bayesian structural time series model, we estimate the causal effect of the Covid-19 lockdown policies on traffic congestion in Bogotá, New Delhi, New York City, and Paris. All four metropolitan areas are consistently ranked among the most congested cities in the world. For example, Bogotá topped the Inrix rank of the world most congested cities in 2019, with drivers loosing on average the equivalent of 7 days and 23 hours a year (Reed, 2019).<sup>8</sup> Traffic congestion in New York City, the fourth most congested place in the United States according to Inrix, cost the city about $US11 billion in 2019. In Paris, a typical driver lost about 165 hours a year due to traffic congestion, making the city the 7th most congested in the world in 2019. Similarly, New Delhi was ranked the 8th most congested city of the world, according to the 2019 TomTom traffic congestion index. Together, while congestion remains a pressing policy challenge in these cities, existing policies are either politically difficult to adopt (e.g., congestion pricing) or have yet to deliver on their promises.

> 8 Inrix ranks cities according to the number of hours spent in congestion (see www.inrix.com).

20
# TABLE 7

# The demand for tasks and exports Exporter dummy

(log employment)

|||exp|orter dum|my||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|A) Skilled and Unskilled Labor||||||
|log highly-skilled|0.67***|0.53***|0.53***|0.53***|0.53***|
||(0.238)|(0.197)|(0.198)|(0.199)|(0.197)|
|log skilled|0.61***|0.45***|0.45***|0.44***|0.44***|
||(0.196)|(0.146)|(0.146)|(0.147)|(0.146)|
|log unskilled|-0.32|-0.58***|-0.58**|-0.58**|-0.57**|
||(0.219)|(0.225)|(0.227)|(0.227)|(0.226)|
|B) Production and Non-Production Labor||||||
|log production|0.32**|0.03|0.03|0.03|0.03|
||(0.161)|(0.031)|(0.034)|(0.034)|(0.034)|
|log non-production|0.11|-0.01|-0.01|-0.02|-0.02|
||(0.104)|(0.081)|(0.079)|(0.080)|(0.080)|
|C) Tasks||||||
|log managers|-0.13|-0.20|-0.21|-0.21|-0.20|
||(0.185)|(0.174)|(0.174)|(0.174)|(0.173)|
|log engineers|0.84***|0.69***|0.69***|0.69***|0.68***|
||(0.291)|(0.248)|(0.249)|(0.250)|(0.248)|
|log services|0.40**|0.25|0.24|0.22|0.22|
||(0.185)|(0.174)|(0.172)|(0.175)|(0.174)|
|log blue-collar|-0.33|-0.61**|-0.60**|-0.60**|-0.59**|
||(0.240)|(0.249)|(0.251)|(0.251)|(0.250)|
|log maintenance|-0.08|-0.14|-0.13|-0.14|-0.14|
||(0.183)|(0.186)|(0.185)|(0.185)|(0.184)|
|_N_|20544|20544|20544|20544|20544|
|Firms|5226|5226|5226|5226|5226|
|frm and year FE|Yes|Yes|Yes|Yes|Yes|
|log employment|No|Yes|Yes|Yes|Yes|
|industry-specifc trends|No|No|Yes|Yes|Yes|
|frm-specifc trends|No|No|No|Yes|Yes|
|imported inputs|No|No|No|No|Yes|

NOTES: IV-FE regressions of (log) employment on an exporter dummy. Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends; column (5): adds the share of imported inputs. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

21
# **Closing the Gaps:**

# **The Role of Screening Questions and Self-Reporting in Measuring Women’s and Youths’ Employment and Work**<sup>**1**</sup>

Ivette Contreras<sup>2</sup> Lelys Dinarte-Diaz<sup>3</sup> Amparo Palacios-Lopez<sup>4</sup> Valentina Costa<sup>5</sup> Steffanny Romero<sup>6</sup>

## **JEL classification** : O1, J2, C8

**Keywords** : labor gender gaps measurement, labor age gaps measurement, survey methods, informality, gender norms.

1 We are thankful to Damien de Walque, Kathleen Beegle, Aletheia Donald, Jason Kerwin, Sylvan Herskowitz, and Diego Zardetto for their time and feedback. We also appreciate the comments we received from the participants of the different conferences and seminars where we presented this paper, including the World Bank Research Department, World Bank Data Group Seminar, Statistics Canada’s 2022 International Methodology Symposium, European Survey Research Association Conference 2023, Northwestern Global Poverty Research Lab (GPRL) and Innovations for Poverty Action (IPA)’s Methods and Measurement Conference, Allied Social Science Associations (ASSA) 2024 Annual Meeting, and the Washington Area Labor Economics Symposium (WALES). We are grateful for the support of the International Fund for Agricultural Development and the World Bank Research Support Budget. The Institutional Review Board (IRB) of _Universidad Francisco Gavidia_ in El Salvador reviewed and approved this research project's protocol, which is registered under ID #011-2021. This study trial is also included in the AEA RCT Registry under the unique ID #EARCTR- <mark>0009541</mark> . The findings, interpretations, and conclusions expressed in this report are entirely the authors and do not necessarily represent the views of the World Bank, its affiliated organizations, Executive Directors, or the governments that they represent.

> 2 Corresponding Author. The World Bank. Email: icontreras@worldbank.org.

3 The World Bank, IZA, CESifo, HiCN. Email: ldinartediaz@worldbank.org.

> 4 The World Bank. Email: <u>apalacioslopez@worldbank.org.</u>

5 The World Bank. Email: vcosta@worldbank.org.

> 6 The World Bank. Email: sromeroesteban@worldbank.org.
Table 6: SGMM estimates for labor productivity growth effect of BRICS’ direct investment in selected African countries, 2001-2012

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Ln GDP per worker (lag)|-5.444***|-5.405***|-5.609***|-5.529***|
||(0.628)|(0.610)|(0.215)|(0.220)|
|Ln BRICS’s direct investment|0.206|0.215|0.252***|0.264***|
||(0.155)|(0.157)|(0.073)|(0.075)|
|Ln Education|||6.220***|4.392***|
||||(1.384)|(1.121)|
|Ln Openness||||-1.392<br>(0.993)|
|Governance||0.037**|0.034***|0.021***|
|||(0.018)|(0.009)|(0.007)|
|No. of observations|160|160|97|97|
|No. of countries|16|16|16|16|
|No. of instruments|12|13|14|15|
|Sargan test (Probability > χ2 )|0.195|0.19|0.268|0.294|
|Arellano-Bond (Probability > Z)|0.598|0.575|0.467|0.552|
|Time dummy|Yes|Yes|Yes|Yes|

Note: _All values are based on two-step SGMM. Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Arellano-Bond test that average auto-covariance in residuals of order 2 is 0 accepts H0: no autocorrelation_ Table 7 presents results for a model in which G7 countries’ FDI is the only measure of foreign investment impacts on labor productivity growth. In this case, FDI from G7 countries induces productivity growth substantially only when human capital and openness are controlled for. The magnitude of effect ranges between 1.5 and 1.6 percent for every 10 percent increase in FDI stock (columns 3 and 4). However, the explanatory power is lower when the measure of openness is included in the model (column 4), which is contrary to the findings in Table 6 for BRICS’ FDI effects. This finding could be attributed to the possibility that both FDI and volume of trade are proxies for a country’s level of openness or integration in the global economy. Consequently, in addition to their direct effect on labor productivity growth, they both capture the indirect openness effects. Moreover, a large proportion of Africa’s trade is with developed 21
**Table 3.8  Example of spending by economic nature**

|**Functional**<br>**Code**|**Functional**<br>**Classification**<br>**(as defined in the**<br>**Guide)**|**Economic**<br>**Code**|**Economic classification**<br>**(as presented in**<br>**Government budget**)|**IMF economic**<br>**classification**|**Data**<br>**(LCU)**|
|---|---|---|---|---|---|
|0434|Electricity|261100|Personnel|Employee compensation|300|
|0434|Electricity|362110|Expenditure on materials for<br>operation|Use of goods and services -<br>maintenance|200|
|0434|Electricity|362212|Maintenance of IT equipment|Use of goods and services -<br>maintenance|1000|
|0434|Electricity|362220|Vehicle maintenance|Use of goods and services -<br>maintenance|50|
|0434|Electricity|362140|Fuel|Use of goods and services -<br>other|1500|
|0434|Electricity|362620|Postal charges|Use of goods and services -<br>other|15|
|0434|Electricity|362820|Transport charges|Use of goods and services -<br>other|1000|
||||...|||
||||To include all economic<br>classifications|||

# **_Consolidating data in template 10_**

After the relevant budget lines have been extracted, all functional codes mapped to the relevant COFOG categories are added and input into the corresponding cell of template 10. It is then necessary to calculate the totals for each economic classification (by function), by simple addition taking care of the units of the data, and input them into the corresponding cell of template 10. As discussed, this template should include each year‘s budget estimates, budget releases, and actual expenditures (audited expenditures) when available. If data collection covers 5 years, this implies that template 10 will need to be filled 15 times.

In countries with a dual budget system, template 10 needs to also be filled in separately for each budget type (which doubles the occurrences of template 10 from 15 to 30 for a 5-year data collection period). This makes the data generation process easier and more transparent. It also allows the classification of expenditures to be assessed. In other words, quantify the amount of current expenditures included in the DB as well as the capital expenditures included in the RB (see section 3).

46
Figure 20: Main and Other Crops Grown by the Poor/Non-poor, 2014/15

# (a) Main Crops

<!-- Start of picture text --> 60%<br>Non-Poor Poor<br>40%<br>20%<br>0%<br>Sorghum Millet Sesame Groundnut Other Crops*<br><!-- End of picture text --> _Note_ : * Other crops include those listed in Figure 19b.

# (b) Other Crops

<!-- Start of picture text --> Non-Poor Poor<br>4%<br>3%<br>2%<br>1%<br>0%<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15. _Note_ : * Other crops besides those listed in Figure 19a.

** Other crops besides those already listed in Figure 19b.

Most of the households engaged in crop production grow only the subsistence crops of sorghum and millet (Figure 21a). More than half the households, 56 percent, that engage in crop production grow only these two crops and nothing else. Most of these households grow either millet or sorghum exclusively, 32 percent of households grow only sorghum, 22 percent grow only millet, and only 2 percent grow both. This means there is very little geographic overlap between these two crops.

Households growing only sorghum or only millet have higher rates of poverty than households growing other crops (Figure 21b). Those engaged in millet production have especially high rates of poverty. These households are concentrated in Darfur and Kordofan. Households engaged in growing the other two common crops—sesame and groundnut—have significantly lower rates of poverty, while households engaged in all other types of crop production have the lowest rates of poverty. These other crops are primarily cash crops. This strong positive correlation between poverty and growing 23
telephone assets that were created prior to 1998 - has also lacked the financial resources to make any further investments.

 Second, in addition to these privatization related initiatives, the existing social funds principally  FSDC, FIS and FONAPAZ increased their investments efforts in the infrastructure sectors (Figure 2.1). Overall, the investments of these three funds in energy, water and sanitation services more than quadrupled between 1996 and 1998. However, this reflected an overall increase in social fund expenditure; rather than a shift in the portfolio of projects towards infrastructure sectors. Moreover, there is evidence that the water, sanitation and electricity investments of social funds have begun to tailoff since 1999.

**Figure 2.1: Total social fund investments in rural infrastructure since 1993**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>Electricity<br>25<br>Water and sanitation<br>20<br>15<br>10<br>5<br>0<br>1993 1994 1995 1996 1997 1998 1999 2000<br>US$ million per year<br><!-- End of picture text --> <!-- Start of picture text --> Sources: FIS, FONAPAZ, FSDC<br><!-- End of picture text --> It is interesting to explore the relative importance of resources generated by the privatization process, those channeled via the social funds, and other sources of finance for rural expansion of utility services (Table 2.4). By far the largest volume of resources has gone to water and sanitation, US$153 million, versus US$99 million for electricity and US$7.5 million for public telephones.

In electricity, the volume of resources devoted to rural electrification tripled in the years before and after the Peace Accords. As well as this overall increase, the composition of financing has changed substantially. Up until 1996, about two thirds of the investment in rural electrification came from the state-owned operator INDE, whose program has since been dramatically reduced in scale. This reduction has been more than offset by a quadrupling of social fund investments, and by the beginning of the PER. The latter, which has only disbursed about 20% of its programmed expenditure to date, will become increasingly important over time as social fund investments appear to be tailing-off.

In telecommunications, the rural investment activities of GUATEL were substantial prior to the Peace Accords but came to a halt following the privatization and sector restructuring exercise that divested the state-owned company of all its assets except for the rural telephones, thereby curtailing its ability to finance further projects. While GUATEL continued to operate existing rural telephones, FONDETEL became responsible for constructing further rural telephones, which it did by contracting with private sector operators. The FONDETEL program rapidly succeeded in almost doubling the number of rural public telephones (from 2,000 to 3,600) in a very short period, with a fraction of the resources absorbed by GUATEL in the earlier period (US$7.5 million versus US$46.0 million), largely due to its ability to leverage private capital.

11
dards harmonization depends on the level at which the harmonized standard is set. Unlike the case of a customs union, standards harmonization has a market integration e¤ect that creates scale economy bene...ts for the ...rms of not just participating but also third countries. The impact on the ...rms of a speci...c country depends on how the costs of meeting the new harmonized level of the standard compare with the bene...ts from economies of scale in integrated markets. We ...nd that if ...rms from one set of countries ("developing") have a higher variable cost of meeting a standard and reap fewer scale economy bene...ts in integrated markets than ...rms from another set of countries ("developed"), then the former can su¤er a decline in exports to the integrated market when harmonization raises some destination countries’standards.

As in the case of a free trade area, the economic impact of a standards MRA depends critically on the choice of rules of origin. For the participating countries, an MRA is in e¤ect a downward harmonization of standards since ...rms are now free to meet the least costly of the initial standards: trade is stimulated not only by market integration but also by the reduced stringency of the standard. The analytical implications for imports from third countries di¤er dramatically with rules of origin. If ...rms of third countries are denied the bene...ts of the MRA and must continue to meet the original standard in each market, then they will face unchanged absolute conditions but su¤er a decline in relative competitiveness - and hence a decline in exports to the region. In contrast, if the ...rms of non-participating countries are also entitled to access the entire region by conforming to the least costly standard, then they too reap bene...ts. In this case, it is shown that ...rms from the set of countries ("developing") where it is more costly to meet a standard may bene...t more, because there is a greater reduction in their compliance costs thanks to the decline in the stringency of the standard.

In order to test the empirical validity of these propositions, we construct the ...rst dataset that directly identi...es policy initiatives of di¤erent types on standards for manufacturing industries in 42 countries over the period of 1986-2001.<sup>8</sup> These include all OECD countries and 14 developing countries that are the largest exporters of manufactured goods outside the OECD and account for over 80% of non-OECD manufactured exports. The

> 8 In contrast, the existing Perinorm dataset describes standards and technical regulations, but does not allow an unambiguous measurement of harmonization or mutual recognition.

5
following Bayes factor: This expression is not that different from the one in our equation (6), provided we take _g_ = 1 _/n_ . In that case, the Bayes factor in (6) becomes: which behaves very similarly to the BACE procedure in (7) for practically relevant values of _n_ (as in the examples here). This will be crucial in explaining the similarity of the results with the FLS and SDM prior settings mentioned before.

It also becomes immediately clear that the necessity of choosing prior settings implicit in using BMA is not really circumvented by the use of BACE, in contrast with the claims in SDM. In fact, BACE implicitly fixes _g_ in the context of our BMA framework. The fact that this is hidden to the analyst does not make the results more robust with respect to this choice. It even carries a substantial risk of conveying a false sense of robustness to the applied analyst.

Now we can examine more in detail how the various prior choices translate into model size penalties. From (6) we immediately see that if we have two models that fit equally well ( _i.e._ , _Ri_<sup>2=</sup><sup>_R_</sup> _j_<sup>2), then the Bayes factor will approximately equal</sup><sup>_g_(</sup><sup>_ki−kj_)</sup><sup>_/_2 (as</sup><sup>_g_tends to be quite small).</sup> If one of the models contains one more regressor, this means that the larger model will be penalized by _g_<sup>1</sup><sup>_/_2</sup> .

For _n_ = 88 and _k_ = 67 (as in the SDM data) this means that the choice of _g_ = 1 _/n_ leads to a Bayes factor of 0.107 and choosing _g_ = 1 _/k_<sup>2</sup> implies a Bayes factor of 0.015. Thus, the model size penalty is much more severe for _g_ = 1 _/k_<sup>2</sup> in the context of these types of data. The size penalty implicit in the BACE procedure is the same as for _g_ = 1 _/n_ .

We can also ask how much data evidence is required to exactly compensate for the effects of prior odds under different specifications. Posterior odds will be unity if the Bayes factor equals the inverse of the prior odds, thus if where the prior odds are given in the previous subsection, as a function of prior mean model size _m_ , as well as _k_ and the sizes of both models.

Typically, we have no control over _n_ and _k_ , but we do need to select _g_ and _m_ . To give more insight into the tradeoff between _g_ and _m_ , Figure 4 plots the contours in ( _g, m_ )-space that correspond to _n_ = 88 _, k_ = 67 _, ki_ = 8 _, kj_ = 7, and _Rj_<sup>2= 0</sup><sup>_._75 for different ratios</sup><sup>_R_</sup> _i_<sup>2</sup><sup>_/R_</sup> _j_<sup>2that would</sup> make the two models equally probable in the posterior. These plots are provided for fixed and random _θ_ model priors with the Bayes factor in (6).

It is obvious from the left figure for the fixed _θ_ case that there is a clear trade-off between _m_ and _g_ : larger _m_ , inducing a smaller size penalty, can be compensated by a small _g_ , which increases 9