rafmacalaba/data-use-mentions-extended huggingface dataset:
Employers also highlight the demand for a range of skills. A review of 28 studies reveals remarkable consistency across the world in the skills demanded by employers. Although employers value all skill sets, there is a greater demand for socio‐emotional and higher‐order cognitive skills – the capacity to deal with complex information processing – than for basic cognitive or technical skills. These results are robust across economy size and level of development, sector, export‐ orientation, and occupations (Cunningham and Villaseñor 2014).

While most of the work correlating skills and labor market outcomes is based on US or European data, new data from developing countries permit the exploration of whether the skills important for success in developing country labor markets are the same as those identified in US and European labor markets. Both the OECD’s PIAAC<sup>4</sup> and the World Bank’s STEP<sup>5</sup> initiatives have begun to survey adults in countries as diverse as Bolivia, Colombia, Ghana, Sri Lanka, the Ukraine and Vietnam. These surveys collect data from adults on cognitive skills ‐ primarily literacy and skills used on the job – and non‐cognitive skills. The data analysis shows that both sets of skills affect wages and employment patterns. While these studies give the first insights into how these skills interact with labor markets, they suffer from a range of econometric issues that limit the robustness of their conclusions.

An early survey undertaken in Peru (ENHAB 2010) provides a richer set of variable that allow us to employ methodologies to overcome some of the econometric issues and perhaps present more robust results. Using these data, Diaz, Arias, and Vera‐Tudela (2012) explore the impact of cognitive and non‐cognitive skills on wages. The paper finds that an additional standard deviation in the overall non‐cognitive skill measure increases earnings by 9 percent, similar in magnitude to the effects of individual cognitive skills. Specifically, emotional stability<sup>6</sup> and
**Figure 5: Tiers of Cumulative Classes Completed: Transitions Throughout College.**

<!-- Start of picture text --> 100 100 Tier 1 100 Tier 2 to tier 1 100 Tier 1 to tier 2,3&4<br>Tier 2 Tier 3 to tier 1&2 Tier 2 to tier 3&4<br>90 90 Tier 3 90 Tier 4 to tier 1,2&3 90 Tier 3 to tier 4<br>Tier 4<br>80 80 80 80<br>70 70 70 70<br>60 60 60 60<br>50 50 50 50<br>40 40 40 40<br>30 30 30 30<br>20 20 20 20<br>Tier 1<br>10 Tier 2 10 10 10<br>Tier 3<br>0 Tier 4 0 0 0<br>2 3 4 5 2 3 4 5 2 3 4 5 2 3 4 5<br>Year Year Year Year<br>a. Persistence b. Dropout c. Catch−up d. Fall behind<br>Percent Percent Percent Percent<br><!-- End of picture text --> _Source_ : Calculations based on SPADIES for the 2006 entry cohort (first semester). _Notes_ : Each panel shows the probability that a student who ended the previous year in a given tier experiences the following outcomes in the current year: persist in the tier (panel a), drop out (panel b), catch up (rise) to a higher tier (panel c), or fall behind to a lower tier (panel d). For a given year and tier, probabilities add up to 100 across panels. For example, a student who finished year 1 in tier 3 is depicted in green. In year 2, she is 35, 23, 18, and 24 percent likely to persist in tier 3, drop out, catch up to tiers
>Evacuation Shelter Trained Committee Emergency Evacuation Shelter Trained Committee Emergency<br>routes community plan routes community plan<br>Urban Rural Total Urban Rural Total<br>c) By sex of head of household (2023)<br>63.3<br>63 63.1<br>50.1<br>42.7 45.1<br>25.825.225.6 23.7 23.5<br>23.2<br>11.7 11.5<br>11.3<br>Evacuation Shelter Trained Committee Emergency<br>routes community plan<br>Urban Rural Total<br><!-- End of picture text --> Note a): Results from the full sample of wave 2. Note b): Results from the full sample of wave 3. Note c): Results from the full sample of wave 4.

> 10 Subsequently referred to as shelter.

9
|Strong x 2021|0.138|-0.096|0.083|-0.015|-0.037|
|---|---|---|---|---|---|
||(0.091)|(0.061)|(0.114)|(0.101)|(0.117)|
|PSU dummies|Yes|Yes|Yes|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|Yes|
|Women controls|Yes|Yes|Yes|Yes|Yes|
|Partner controls|Yes|Yes|Yes|Yes|Yes|
|Observations|2,024|1,990|1,233|1,583|1,894|
|R-squared|0.221|0.149|0.283|0.196|0.136|

The dependent variables are the sub-indices of the female decision-making index (FDI). Standard errors clustered at the district level.

# b. Sensitivity checks

Our baseline results have established that the female decision-making index has significantly improved in medium conflict districts relative to low conflict districts. To check the sensitivity of the results, we proceed with a series of robustness checks based on column 2 (i.e., full sample) of the main results in Table 1.<sup>7</sup> We repeat the analysis reported in the main results while controlling for the intensity of conflict, controlling for children living in the household, excluding polygamous households, excluding women that were not privately interviewed, considering alternate measures of conflict intensity, excluding districts that saw extreme levels of conflict intensity, and excluding a district from the sample one-by-one. These checks, summarized in Table 4, prove that the results reported in this paper are satisfactorily robust in the face of alternate specifications and conditions.

_Table 4: Sensitivity checks_

||(1)
on banking regulation and supervision practices 160 jurisdictions. We use this survey to establish whether the central bank or a separate entity is responsible for financial supervision. This variable aims to analyze whether reserve management is different in central banks with more responsibilities and more complex governance structures. The survey's last edition was in 2019.<sup>23</sup>

**Table III.1  Financial Supervision and the Broader Governance Environment**

<!-- Start of picture text --> Independence of Corruption Governance Government<br> Variables<br>central bank Perceptions Index Pillar Score Effectiveness<br>Central bank is financial supervisor -0.086 -0.268*** -0.281*** -0.291***<br>Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e.,<br>extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant).<br><!-- End of picture text --> Regarding the impact on reserve management, the correlations suggest that countries where the central bank is also a financial supervisor tend to have an independent investment committee to which the middle office reports (see Table III.2). This arrangement may result in a lower risk appetite in managing reserves and imply that the central bank accounts for and manages the

> 22. Henry Thornton (1802) and Walter Bagehot (1873) developed the classic doctrine of lender of last resort. According to Bagehot, in a panic situation monetary authorities should lend unsparingly but at a penalty rate to illiquid but solvent banks. See Freixas et al. (2000) for further background. 23 _._ <u>https://www.worldbank.org/
The advantage of such a methodology is that the procedure is highly standardized and comparable across economies and entails lower measurement errors for what it intends to measure, and the indicators provide good reflections of the de jure regulatory environment. However, a limitation, as documented in the _Doing Business_ report (2019), is that these indicators assume that entrepreneurs have knowledge of and comply with applicable regulations. As a result, _Doing Business_ indicators cannot capture what is actually happening on the ground in real business settings. Hallward-Driemeier and Pritchett (2015) document significant differences between closely related indicators in _Doing Business_ (which focus on the de jure regulatory environment) and in the Enterprise Survey (which reflects the de facto regulatory environment). Another limitation is that the survey cases in _Doing Business_ are located in the largest business city of each country,<sup>5</sup> which cannot capture the heterogeneity across a country.

The Global Competitiveness Index is produced by the World Economic Forum (WEF). It aims to present comprehensive insights into a country’s drivers of economic growth. A portion of the Global Competitiveness indicators is based on the Executive Opinion Survey conducted by WEF in cooperation with its partner institutes in each country. In the 2018 survey, for example, more than 12,000 business executives from companies of various sizes and from the various sectors in 140 countries were surveyed. The other indicators are drawn from external data sources, such as the International Energy Agency and the International Telecommunications Union. The Global Competitiveness project uses detailed data to construct 12 pillars of competitiveness, and its indicators capture broader aspects of the business environment. However, because of the limited sample size in each country (for example, in 2018, the median number of respondents in a country was 83), the representativeness of the indicators drawn from the Executive Opinion Survey can be an issue.

We now turn to the construction of our variables. The business environment has many aspects. In this paper, we rely on the guidance of Reyes
food prices (through a large exchange rate devaluation), but also increased unemployment and reduced investment, and, therefore, likely affected poverty by other routes. In this example, an omitted variable (a financial crisis) induces a potentially spurious positive relationship between food prices and poverty, but one can also articulate biases in the other direction (e.g., expansionary monetary or fiscal policies), such that it is difficult to say in which direction OLS coefficients might be biased.

To address this concern we use a relatively simple instrumental variables strategy implemented with the two-step instrumental variables general method of moments (IV-GMM), using an optimal weighting matrix. The identification strategy rests upon the exogeneity of the international food price index (described above), which is assumed to only influence national poverty rates through its effect on domestic food prices. Hence, the IV-GMM approach results in an equation similar in structure to (1), but in which we use domestic price changes predicted by international price changes.

In addition to these basic least squares and IV-GMM approaches, we engage in several robustness tests. First, IV approaches can sometimes be sensitive to the choice of estimator. While results from standard IV and LIML estimators are also very similar (available on request), in our appendix we also report results for the Arellano and Bond (1991) dynamic panel estimator, in which lagged poverty rates are introduced.

Second, we conduct robustness tests to gauge whether the introduction of potentially confounding factors to the model has any influence on our core results. For example, increases in food prices might be strongly associated with increases in non-food commodities, so we control for changes in the overall terms of trade but also overall inflation, monetary expansion, exchange rate movements, changes in trade and foreign investment ratios, and non-agricultural growth rates.

Third, we test robustness of results to splitting up the sample by region and by time periods. The former is implemented to test whether any particular region might be driving the result, while the
|0.580***|0.600***|
|(Index)|(0.108)|(0.102)|(0.105)|
|Equitable treatment|0.126***|0.137***|0.132***|
|by the administration|(0.0313)|(0.0414)|(0.0333)|
|Policy credibility|0.348***|0.270***|0.314***|
||(0.0584)|(0.0744)|(0.0671)|
|Region|X|X|X|
|Urban/rural|X|X|X|
|Gender|X|X|X|
|Income|X|X|X|
|Marital status|X|X|X|
|No. of HH members|X|X|X|
|Income<br>X|X|X|X|
|Education<br>X|X|X|X|
|Employment status|X|X|X|
|Observations|5,916|5,916|5,916|
||Standard errors cl<br>*** p_<_0.|ustered by region in parentheses<br>01, ** p_<_0.05, * p_<_0.1||
||Data source: Wor|ld Bank survey on trust (2021)||

Table 6 shows the results of our main model with an additional control for individual positive self-assessment in column 2. The variable “positive perceptions of individual situation” corresponds to a self-assessment of individual’s living conditions (on a scale of 1 to 10, where 1 are the worst possible conditions and 10 the best). The dependent variable “institutional trust” refers to trust in the executive. This control variable helps 19
Services Make a Difference?,” _Demography_ , 38: 253-66.

- Frankenberg, E. and D. Thomas. 2000. _The Indonesia Family Life Survey (IFLS): Study Design and Results from Waves 1 and 2_ . March, RAND, Santa Monica, CA. DRU2238/1-NIA/NICHD

- Frankenberg, E., D. Thomas and K. Beegle. 1999. “The Real Cost of Indonesia’s Economic Crisis: Preliminary Findings from the Indonesia Family Life Surveys,” _Labor and Population Program Working Papers Series 99-04_ , RAND, Santa-Monica.

- Frankenberg, E., W. Suriastini, and D. Thomas. 2005. “Can Expanding Access to Basic Healthcare Improve Children’s Health Status? Lessons from Indonesia’s Midwife in the Village Program,” _Population Studies_ , 59(1):  5-19.

- Friedman, Jed and Norbert Schady. 2009. “How Many More Infants Are Likely to Die in Africa as a Result of the Global Financial Crisis?” The World Bank, Policy Research Working Paper 5023.

- Gertler, P. 2004. “Do Conditional Cash Transfers Improve Child Health? Evidence from PROGRESSA’s Control Randomized Experiment,“ _American Economic Review_ , 94(2): 336-341.

- Gertler, P., and J.W. Molyneaux. 1994. “How Economic Development and Family Planning Programs Combined to Reduce Indonesian Fertility,” _Demography_ , 31(1): 33-63.

- Glewwe, P., and E. King. 2001. “The Impact of Early Childhood Nutritional Status on Cognitive Development: Does the Timing of Malnutrition Matter?” _The World Bank Economic Review_ , 15(1): 81-113.

- Glewwe, P., Jacoby, H. and E. King. 2001. “Early Childhood Nutrition and Academic Achievement: A Longitudinal Analysis,” _Journal of Public Economics_ , 81:345-68.

- Jayne, T., J. Strauss, T. Yamano and D. Molla. 2002. “
Figure A1: Distribution of the propensity score <!-- Start of picture text --> 0 .2 .4 .6 .8<br>Propensity Score<br>Untreated Treated: On support<br>Treated: Off support<br><!-- End of picture text -->

# **B Annual Performance Assessment (APA) data and the time path of improvements in project LGAs**

We use information from the Annual Performance Assessment (APA) of the 18 local governments receiving the Urban Local Government Strengthening Program (ULGSP), compiled in the Quality Assurance Reviews (QAR).

We explore two main indicators available throughout the period. Disbursement Linked Indicator (DLI) 2 refers to whether “ULGAs have strengthened institutional performance as scored in the annual performance assessment” and is comprised of five sub-indicators (as described in Section 2 of the paper): Improved urban planning system; increased revenues from property taxes; efficient fiduciary system; improved infrastructure, implementation and O&M; and strengthened accountability and oversight systems. DLI 3 refers to whether "Local infrastructure targets as set out in the annual action plans are met by ULGAs using program funds." In Figure B1 below we present the mean score for four of the sub-components of DLI2 - we exclude the sub-component on efficient fiduciary system since maximum scores changed over time. As referenced in the timeline presented above, our first round survey was implemented around the same time of the 4th APA (early 2016), and the follow up around the 6th APA (early 2018). The evolution of mean scores is uneven over time. With the exception of the accountability index, that shows a remarkable increase between the 1st and 2nd APAs and then flattens out, the other sub-components are broadly stable over time, with ups and downs and no indication that all improvement happened in the very beginning of the ULGSP program.<sup>29</sup>

> 29We also present the overall changes in APA subcomponents in Table
percent (for a two-adult household) to more than 200 percent (for a five-adult-one-child household) higher than our estimated poverty lines. The poverty rate varies for different adult scale parameters, but less so for children. The shares of chronic poverty of total poverty, defined against an absolute poverty line, are positively related to the adult scale parameter, regardless of the poverty measure. More interestingly, income mobility could be classified as either upward or downward depending on the specific scale parameters that are employed. Our results are robust to different measures of poverty, income expectations, reference groups, functional forms, and various other specifications. There is significant heterogeneity in terms of economic growth and demographic composition among the regions of Russia, which is caused by geographical differences in relative prices and consumption preferences. Since the RLMS data are, unfortunately, not representive at the regional level, we are unable to offer this analysis. But given a larger panel survey that is representative at the regional level, a promising direction for further research is to apply our scale adjustments to better understand the effects of equivalence of scale on the composition of poverty and poverty dynamics. Such knowledge is essential for regional poverty comparisons and the development of well-targeted policy interventions.

22
to conduct a thorough demand survey. A demand or broader market survey may also provide guidance to the government on the following issues:

- to identify the nature and magnitude of the financing gap;

- the appropriateness of the fund as an instrument to support private investment and the timing of the fund;

- the design of instruments;

- the 1FF funding requirements; and

- the staffing levels and skill mix needed to manage those instruments.

12. Nevertheless, the development of such funds tends to be a complex and time consuming process, especially if contingent instruments are offered as the facility may need to build up institutional capacity. In the meantime, given that developing countries tend to be vulnerable to shocks, economic conditions may change and may adversely 4 Nevertheless, at the same time, government may have implemented a set of policy reform that improves the overall regulatory environment and allow, among other things, for cost covering tariffs. This would in turn reduce govemments' the need for the govemment to offer financial support to attract private resources into the sector.
ii PPE tt tt between the survival function, and the cumulative hazard function (see appendix A for tt detailed discussion). The estimated probability of early termination can be written as follows: SS(tt), � 0) . (2) ii ii **_4.2 Data_** PPE = 1 −SŜ (tt|tt

The data come from four sources: the World Bank’s Private Participation in Infrastructure Project Database (PPI),<sup>7</sup> the Polity IV Project,<sup>8</sup> _World Development Indicators_ (WDI),<sup>9</sup> and Laeven and Valencia (2020). The PPI database includes data on project characteristics agreed to at the time of the signing of the PPP contract or at the time of financial closure. These characteristics include the type of project, the sector, the contract period, the government level granting the contract, the sponsors, types of government support, the amount of investment commitments, and

> 6 The model was estimated in Stata using the stpm2 routine, written by Lambert and Royston (2009).

> 7 See https://ppi.worldbank.org/en/ppidata.

> 8 See http://www.systemicpeace.org/inscrdata.html.

> 9 See http://datatopics.worldbank.org/world-development-indicators/.

9
DEC) has established an open web facility, the XCO2 database,<sup>19</sup> that pre-filters the OCO-2 data and publishes monthly mean concentration anomalies for all terrestrial cells of a 25 km global grid (G25). The website will also publish annual change estimates for urban areas by statistical significance class and provide information that links G25 grid cell IDs to IDs for urban areas and national administrative units (levels 0, 1 and 2). We believe that this web facility will contribute to the global effort to reduce CO2 emissions as rapidly as possible. We provide more details in the Appendix.

> 19 Available online at https://datacatalog.worldbank.org/search/dataset/0062760.

34
|All co|untries|||Developin|g||

_Note:_ Each observation corresponds to a destination-year, represented as _it_ . “CR5” stands for the concentration ratio of the top 5 investing firms, with no restriction on the minimum number of foreign investors. “HHI” refers to the Herfindahl-Hirschman concentration index, calculated using the “CR5” sample. “log(# MNEs)” represents the natural logarithm of the number of foreign investors, and “log(K)” denotes the natural logarithm of the average capital expenditure. “Uncertainty” is based on the “T3” variable from the World Uncertainty database (Ahir et al. 2022), which counts the occurrence of the word “uncertainty” from Economist Intelligence Unit (EIU) reports for 143 countries since the 1950s. A higher value indicates greater uncertainty. Uncertainty in year _t_ is approximated as the logarithm of the average number of uncertainty-related words in EIU reports over the three years preceding the investment (standardized to have a mean of zero and a standard deviation of 1). The analysis includes country fixed effects and (world) region by year fixed effects. Countries are classified into 7 regions based on World Bank classifications. Destination-specific control variables include the logarithms of population and GDP (source: Conte et al. (2022)) and the average exchange rate over the three years preceding the investment (national currency/USD, Penn World Tables, mark 10.1, Feenstra et al. (2015)). Standard errors are clustered at the destination country level. Significance levels: *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

30
(2009) casts doubt on the robustness of these findings with respect to the functional form specifications adopted and the validity of the instrumental variables. An RD analysis of the same data finds less support for positive program impacts and provides new evidence that providing loans to men has reduced the labor supply of women. In another study based on a fairly simple comparison of villages that do or do not have microlending programs, Khandker, Samad, and Khan (1998) find that these programs have had positive impacts at the village level on income, production and employment. They also find that the Grameen Bank has had positive spillover effects on people not participating in the program.

On the other hand, de Mel, Duresh, McKenzie and Woodruf (2008) question the efficacy of targeting programs toward women. Their experimental evaluation of a program that randomized grants in Sri Lanka finds much lower rates of return on capital for female-owned enterprises in comparison to male-owned enterprises, which they attribute in part to differences in the returns for female and male dominated industries. They also show that the female entrepreneurs tended to come from less poor dual-earner families and that loans targeted at women may not have targeted the poorest families. de Mel et al. (2008) do not find any benefits of the program on children‟s school-going, but do not examine the full set of outcomes examined in the Pitt and Khandker study. Nevertheless, their evidence calls into question the justification for targeting credit programs at women. Whether micro-lending programs yield higher rates of return when targeted at women is likely to depend on the country context and the types of enterprises in which women are engaged.

Hussein and Hussein‟s (2003) survey of Pakistani microcredit programs similarly shows that microcredit programs do not necessarily benefit women or the poorest families, unless there is an explicit targeting mechanism in place to reach these groups. Even when the loans are targeted at women, it is not uncommon for women to relinquish control of the loans over to male household members. Hussein and Hussein