rafmacalaba/data-use-mentions-extended huggingface dataset:
**Financial markets and monetary policy.** There are two assets in this economy. One is a bond denominated in domestic currency that is issued by the home government and pays gross nominal interest rate . The other is a bond issued by the foreign government denominated in foreign currency with nominal tt RR interest rate ∗, which is the sum of the constant “world” interest rate, , plus a debt-elastic country spread, tt . The central bank sets the gross domestic nominal WW RR TT interest rate, , according to an inflation-targeting regime (floating ER) or a peg ψψ to the foreign currency (fixed ER). Uncovered interest rate parity (UIP) holds so tt RR that either bond provides the same expected return when converted to the same currency.<sup>15</sup>

**Constant natural resource production and precautionary savings.** An assumption in our NK model worth highlighting is that the production of the natural resource good is constant at s , which allow us to abstract from concerns about intergenerational equity. On the surface, this simplification may seem unrealistic— QQ almost by definition—for producers of _non-renewable_ natural resources.<sup>16</sup> But historical data suggest it is actually a good approximation for countries with large reserves relative to current production, and active mineral exploration. Indeed, since 2000, the largest 10–20 producers of key commodities have shown _increased_ reserves and production, likely due to technological progress and new discoveries (see Appendix Figures 3 and 4).<sup>17</sup> For example, BP argues that “over the last 35

15 We define the Consumer Price Index (CPI) as the price index of a unit of consumption in the home country (basket of the home and foreign goods) and the Producer Price Index (PPI) as the price index of a unit of the home good (basket of all good varieties). The real ER (RER) is defined as the price of the
# **Figure 4 Net Transfers Received by Rural Elderly**

by Migrant Status of Adult Children <!-- Start of picture text --> 1995-1998 2000-2003<br>1/4 1/2 1 2 4 1/4 1/2 1 2 4<br>0 2000 4000 6000 0 2000 4000 6000<br>Pre-Transfer Income Pre-Transfer Income<br>Migrant Adult Children No Migrant Children Migrant Adult Children No Migrant Children<br>1500 1500<br>1000 1000<br>500 500<br>Net Transfer Net Transfer<br>0 0<br>-500 -500<br><!-- End of picture text --> Notes: Vertical lines indicate multiples of a nutrition based poverty line, which is equal to 875 RMB Yuan Per Capita in 2000 RMB. All values are in 2000 RMB Yuan per capita. We use the RCRE Rural Household Surveys for 1995 to 2003 from Anhui, Henan, Jiangsu and Shanxi and the matched RCRE 2004 Supplemental Rural Household Social Network, Labor Allocation and Land Use Survey.

31
to macroeconomic volatility and reliance on donor aid flows.

We carry out analysis at the most disaggregated level, using all 65 separate dimensions included in each assessment, rather than relying on the 25 high level performance indicators that aggregate some of the dimensions that have been used in previous analyses.

Missing data is categorized by PEFA assessors as “NA” (not applicable), “NU” (not used for the assessment), or “NR” (not rated due to insufficient information). Our treatment of missing data is in line with suggested practice (PEFA Secretariat 2009). We treat NA and NU scores as missing and exclude them from the analysis, but code NR as a 0 score, since in most cases NR will correspond to a function that is either not being carried out at all, or is being completed to such a poor standard that it would not achieve a D score. This approach avoids a systematic upwards bias in the scores of countries with poorer PFM systems, such as those we are primarily interested in.

Given the limited range of values, we follow the literature on similar constructed, ordinal data (Elbadawi and Randa, 2003; Bates, 2006) in testing for the presence of censoring. As described by Rigobon and Stoker (2006), the presence of censoring can be tested for using a simple Chow test on the stability of the coefficient estimates between a sub-sample of possibly censored data 6
age 60.<sup>76</sup> In countries with significant outmigration, whether international or intranational, grandparents may be the main caregivers in so-called “skipped-generation” households consisting of only older people and children.<sup>77</sup> Studies from Brazil, Mexico, and Thailand show that grandmothers’ care obligations affect both their participation and hours worked negatively.<sup>78</sup> Since families are often formed relatively early in life, many of these grandmothers in L/MICs often have significant potential working years ahead when they leave the workforce, reduce hours, or move to less-demanding activities. Spouse employment is also generally positively correlated: to retire is a joint decision, made with a view to spending time together.<sup>79</sup> A study from the Russian Federation showed that having a working wife is associated with a 13 percentage point increase in the probability that a pension-age husband will still be employed.<sup>80</sup> In urban China, a working spouse is associated with as much as 40 percent and 58 percent increases in the probability of men and women working, respectively.<sup>81</sup>

**Lack of safe and reliable transportation systems affects mature workers’ access to productive opportunities.** More care responsibilities, lower stamina and physical strength, and related vulnerability to crime means that mature workers depend heavily on quality transportation services. A survey of senior workers in the United States suggested that lack of reliable transportation was a

> 72 Atkinson and Sandiford 2016.

> 73 McKinsey Global Institute 2021.

> 74 Backhaus and Barslund 2021.

75 UN 2007.

76 Ferranna et al. 2022.

> 77 UNFPA 2012.

> 78 Attanasio et al 2022, Paweenawat and Liao 2021. A study from Mexico also showed that female labor participation of a mother was not negatively correlated with more children, but that co-habitation with grandmothers increased in response to more children (Schmieder 2021).

> 79 Giles et
(Figure 8). This shifts markedly among femaleheaded households, among whom 50 percent received no education.

<!-- Start of picture text --> Figure 2: Educational achievement of household head Figure 3: Household monthly income by number of<br>working members.<br>60%<br>IQD 400,000<br>50%<br>IQD 350,000<br>40%<br>IQD 300,000<br>30%<br>IQD 250,000<br>20%<br>IQD 200,000<br>10%<br>IQD 150,000<br>0%<br>IQD 100,000<br>IQD 50,000<br>IQD 0<br>0 1 2 3<br>All Female-Headed Number of working members<br>Data Source : CLCI Legacy Database. Data Source : CLCI Legacy Database.<br>None Primary Secondary Undergraduate Technical College<br><!-- End of picture text --> The average monthly income across the sample is IQD 137,041 / $115 (not shown). As expected, this increases with level of education and with household level of economic activity. Households with heads having an undergraduate degree earn 173% more than those having completed no education (not shown).

16
**Figure 8: Breakdown of the ninis population aged 15 to 24 in LAC, 2010**

<!-- Start of picture text --> Ninis<br>(aged 15 - 24)<br>100%<br><!-- End of picture text --> <!-- Start of picture text --> Start new HH<br>without children<br>0.7%<br>Start new HH with<br>children<br>1%<br>Remain in HH<br>45.5%<br>Start new HH<br>without children<br>4.4%<br><!-- End of picture text --> <!-- Start of picture text --> Look for work<br>13.2%<br>Do not look for work<br>16.5%<br>Look for work<br>0.4%<br>Do not look for work<br>0.3%<br>Look for work<br>0.7%<br>Do not look for work<br>0.3%<br>Look for work<br>11%<br>Do not look for work<br>35.5%<br>Look for work<br>0.6%<br>Do not look for work<br>3.8%<br>Look for work<br>1.1%<br>Do not look for work<br>17.6%<br><!-- End of picture text --> Source: authors’ own computation with data from CEDLAS and The World Bank (2015) In the remainder of this analysis, we develop the cross-sectional breakdown from Figure 8 into a more detailed, longitudinal analysis to observe how the composition of ninis across these dimensions
# **Figure 5.1: Learning Trajectories in India**

<!-- Start of picture text --> A: Percent who can do division, by grade B: Percent who can do division, by grade<br>( linear trend between Grades 6 and 10)<br>0<br>Source: Authors’ analysis of Indian ASER data (2008‐2012)<br><!-- End of picture text -->

# **Figure 5.2: Learning Trajectories in India, by State (2012)**

<!-- Start of picture text --> Source: Authors’ analysis of Indian ASER data (2012)<br><!-- End of picture text --> _PISA data across grades_ Another way to test the local linearity assumption is through inter-grade comparisons of 27
|**Court Registries**|**Ranking DB 2018**|
|---|---|---|---|
|New Zealand|1|France|25|
|Canada|2|Slovenia|46|
|HongKongSAR,China|3|Croatia|87|
|Georgia|4|Germany|113|
|Jamaica|5|Austria|118|
|Singapore|6|Bosnia and Herzegovina|175|

Source: Doing Business 2018 data obtained from www.doingbusiness.org

> 2 See for example Djankov et al. (2002), Klapper, Laeven and Rajan (2006), and Ciccone and Papaioannou (2007).

> 3 See Bruhn and McKenzie (2014) for a summary of the studies on Brazil, Colombia, and Mexico, and see Branstetter et al. (2013) for Portugal.

2
Annual Real Growth in Wages of Monthly Wage Earners, 1991 to 2004 __ 61_|

# **Acknowledgements**

I am grateful to Indermit S. Gill, Kazi Mahbub-Al Matin and Roberto N. Zagha for suggesting this work and providing advice throughout. Comments on an early draft from Barry Eichengreen, Ricardo Hausmann, Dani Rodrik and Andrés Velasco helped greatly to improve the paper. Norbert Schady and Carolina Sanchez-Paramo were very generous in providing me with Stata codes to calculate relative labor demand and supply. Numkrit Jeraputtiruk patiently explained to me how to analyze the Thailand Labor Force data.

4
Damania, Richard, Sébastien Desbureaux, Marie Hyland, Asif Islam, Scott Moore, Aude-Sophie Rodella, Jason Russ, and Esha Zaveri (2017). “Uncharted Waters: The New Economics of Water Scarcity and Variability.” Washington, DC: World Bank. doi:10.1596/978-1-4648-1179-1.

Davis, Jennifer, Alice Kang, and Jeffrey Vincent (2001), “How Important Is Improved Water Infrastructure to Microenterprises? Evidence from Uganda.” _World Development_ 29(10): 175367.

Distefano, Tiziano, and Scott Kelly (2017). “Are we in deep water? Water scarcity and its limits to economic growth.” _Ecological Economics_ 142 (2017): 130-147.

Escribano, Alvaro, J. Luis Guasch, and Jorge Pena (2010), “Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa: Cross-Country Comparisons Base on Investment Climate Surveys from 1999 to 2005” World Bank Policy Research Working Paper 5191.

Fisher-Vanden, Karen, Erin T. Mansur, and Qiong (Juliana) Wang (2015), “Electricity shortages and firm productivity: Evidence from China’s Industrial Firms,” _Journal of Development Economics_ 114(2015): 172-188.

Gillanders, Robert (2014), “Corruption and Infrastructure at the Country and Regional Level,” _The Journal of Development Studies_ 50(6): 803-819.

Haltiwanger, John, Ron S. Jarmin, and Javier Miranda (2013), “Who Creates Jobs? Small versus Large versus Young,” _The Review of Economics and Statistics_ 95 (2): 347-361.

Harris, Richard and Catherine Robinson (2003), “Foreign Ownership and Productivity in the United Kingdom using the ARD.” Review of Industrial Organization 22(3): 207-223.

Huergo, Elena and Jordi Jaumandreu (2004), “Firms’ Age, Process Innovation and Productivity Growth,” _International Journal of Industrial Organization_ 22:542-559.

Iimi, Atsushi (2011), “Effects of Improving Infrastructure Quality on Business Costs: Evidence
For the poverty targeting simulations, we examine impacts on the poverty gap rather than the headcount poverty rate (P0) because targeting areas based on their poverty headcount rates maximizes the impact of a transfer program on the poverty gap (Besley and Kanbur, 1991; Kanbur, 1986). Reporting the headcount poverty rate, on the other hand, could give a misleading picture of targeting effectiveness by rewarding methods that successfully identify households whose predicted consumption is just under the poverty line and would therefore exit poverty as a result of the transfer.

A key difference between the poverty targeting measure and all others previously considered is that it is averaged across individuals rather than areas. This implicitly weights areas according to their population, rewarding methods that more accurately estimate poverty rates for areas that have larger population. Weighting areas according to their population during model estimation could yield estimates better suited for the purpose of minimizing this indicator, and would be a useful topic for further research.

A final caveat is useful. The final performance of a specific ML method may depend on a combination of different factors related to data sources (including data-specific features), prediction targets, the prediction method, and performance metrics. While one method may work better for a specific application or according to a specific evaluation metric, it may perform worse for another. To better focus the comparison, we present in Table 2 a general comparison of the key features of the four methods that we use in this paper. To compare their performance, we use the same prediction targets (i.e., imputing into census data without any assets or poverty data) and performance metrics (as discussed above). However, they differ by design regarding the functional form (including whether random effects are employed), (assumed) data generation process, objective function, and other features such as how they select the predictor variables and whether they use different subsamples of the data in growing trees and making predictions. Finally, these ML methods offer various degrees of transparency and ease of
# 3.1 The External Finance Need of Exports

We start with the standard Rajan and Zingales (1998) classi...cation of industries according to their dependence on external ...nance. The Rajan and Zingales measure is de...ned as capital expenditure minus cash ‡ow, divided by capital expenditure, and is constructed based on US ...rm-level data. We use the version of the variable assembled by Klingebiel, Kroszner, and Laeven (2005), in which industries are classi...ed according to the 3-digit ISIC Revision 2 classi...cation. The Rajan and Zingales external dependence measure is reproduced in Table 1.

We combine this industry-level information with data on the structure of a country’s exports to develop a measure of a country’s external ...nance need of exports (hereafter EFNX) by following the approach of Almeida and Wolfenzon (2005). In particular, we construct the following variable for each country and period of time: where c indexes countries, t time periods, i industries, !<sup>X</sup> ict<sup>is the share of exports in sector i in total</sup> manufacturing exports from country c in time period t, and EDi is the Rajan and Zingales measure of dependence on external ...nance. Summing up across sectors in each country and year implies that our index is at country level, but potentially varies over time.

Armed with this variable, we would like to estimate the following equation: The left-hand side variable, FinDevct is a measure of a country’s level of ...nancial development. We condition on the vector of controls Zct, country ...xed e¤ects �c, and time ...xed e¤ects �t. Our hypothesis is that the e¤ect of ...nancial content of exports, EFNXct, on ...nancial development is positive (�> 0)
analysis for groups of advanced economies such as the EU. This raises at least two problems. First, the empirical results lack the external validity, particularly for developing countries. It is difficult for policy makers to interpret these results. Second, developing economies in Asia are large recipients of FDI, yet it is not clear how beneficial FDI is for recipient countries, especially those least inserted in GVCs.

The early research looked at the link between FDI in a sector and the performance of the recipient firm in that same sector. From a very direct point of view, relative to a counterfactual of no financing, a project is clearly better off with foreign investment. However, not all studies have found positive spillovers once other aspects are controlled for. Haddad & Harrison (1993) reject the hypothesis that FDI accelerates productivity growth using firm-level data in Moroccan manufacturing sectors. Employing panel data of Venezuelan plants, Aitken & Harrison (1999) find that the magnitude of the FDI spillovers is marginal, and the positive correlation between FDI and firm productivity is only robust for small enterprises. Evidence is mixed on the magnitude and even the direction of the FDI spillovers. Using plant-level panel data covering manufacturing firms in the United Kingdom during 1973 – 1992, Haskel et al. (2007) show that there is a robust and significantly positive correlation between the total factor productivity of a domestic plant and the foreign-affiliate’s share in that sector. However, Lu et al. (2017) find negative FDI spillovers among Chinese manufacturing firms. At the same time, FDI may be harmful if local firms lose market share to foreign-backed competitors (Aitken and Harrison 1999). Smeets (2008) and Gorg et.al. (2001) review possible sources of FDI induced spillovers through an evaluation of the empirical evidence on productivity and wages, among others. These surveys note that although theory can identify a range of possible spillovers, the empirical results paint a mixed picture at best.

# **Linking to upstream and downstream sectors, and
countries over the 2010-19 period, the period which matches the availability of FDI data while being representative in terms of the number of countries selected (Annex 3 provides the full list).

We average the data for indicators over two non-overlapping five-year periods (2010-2014 and 201519). The first period (2010-2014) is used for computation of the indicators to avoid any potential reverse causality issues (the lagged indicators are predetermined relative to the FDI and trade flows which are computed over the 2015-19 period). We do not use the information on indicators from the second period in estimations, but only for calculation of the thresholds for sector benchmarking exercise (see section below). Since we are interested in analyzing the key structural and macroeconomic factors associated with FDI and trade success from the perspective of the recipient country, we only use country level indicators and do not include any global or "push" factors.

11
economic factors are commonly identified as important determinants of revenue potential (see the survey in Gupta 2007): (i) per-capita income levels, (ii) sectoral composition of output,

> 3 Specifically, the analysis is based on data from the UNU-WIDER Government Revenue Dataset which was launched in 2014 and combines government revenue data from various sources (see Section IV for more details).

> 4 DEA and regression analysis represent alternative approaches to performance assessments which each feature their own benefits and weaknesses (for a detailed discussion, we refer to Thanassoulis 1993; Sickles and Zelenyuk 2019). Regression analysis features more tools to identify causal relationships in observational data. However, it also involves stronger assumptions on the structure of the underlying data than non-parametric methods, making the results sensitive to econometric challenges such as endogeneity and omitted variable bias.

4
# **Table 1:  Bivariate relationship between knowledge of conflict and region in the last year**

||**Barisal**|**Chittagong**|**Dhaka**|**Khulna**|**Rajshahi**|**Sylhet**|**Total**|
|---|---|---|---|---|---|---|---|
|Incident of conflict|_% reportin_|_g yes_||||||
|Anyone's money taken away against<br>his/her wish|13.33|20.28|17.55|17.23|10.29|9.06|15.29|
|Anyone opposed women working<br>outside the home|1.45|13.4|2.43|4.4|5.35|3.17|5.29|
|Anyone physically assaulted outside<br>the home|8.98|23.94|5.89|13.79|16.37|6.83|12.92|
|Anyone encountered harassment<br>while traveling|5.78|14.99|4.14|12.18|8.6|2.38|8.14|
|Anyone physically hurt for political<br>reasons|10.92|23.81|6.71|23.34|9.42|4.61|12.54|

Source: Authors‘ calculations based on World Bank Gender Norms Survey 2006 Note: The relationship between region and gender norms/violence discussed in greater detail in World Bank, 2008 _People seem to ―live in harmony‖ in the community but violence against women is high._ While perception and knowledge of violence in the community are low, levels of violence within the family
where _X1_ is the indicator for the period of birth occurrence, _X2_ is the indicator for exposure to the voucher program, and _Xi_ is the vector of the control variables in the model for birth _i_ from village _j_ in parish _k_ . The parameter _α0_ represents the likelihood of the outcome for nonexposed individuals at baseline, _α1_ is the difference in the outcomes for nonexposed individuals over time, _α2_ is the difference in outcomes between exposed and nonexposed individuals at baseline, _α3_ is the difference in the changes in outcomes between exposed and nonexposed individuals over time (i.e., the difference-in-differences estimate); _βi_ is the vector of parameters for the control variables in the model, and  _jk_ are the unobserved characteristics of individuals from the same village and parish that might be correlated with the outcome.

A key assumption of the difference-in-differences estimation is that preexisting trends between intervention and comparison groups are similar. Table 1 presents trends in the distribution of facility-based births occurring two years before the voucher program began among multiparous voucher and nonvoucher respondents who were interviewed in the 2010–11 survey. The results show no significant baseline differences between the two groups of respondents in the proportion of births delivered in public or private facilities in the two years preceding the voucher program launch.

Table 1 Trends in Facility-Based Births among Multiparous Voucher and Nonvoucher Respondents for Births Occurring Two Years before the Voucher Program, 2010–11

||Public|health facility||Privat|e health facility||
|---|---|---|---|---|---|---|
|Year of<br>birth|Voucher<br>respondents|Nonvoucher<br>respondents|_P_<br>value|Voucher<br>respondents|Nonvoucher<br>respondents|_P_<br>value|
|2006|50.0%|51.7%|0.83|<br>20.0%|12.8%|0.20
)|(-0.666)|
|Relative world prices of palm-soy/wheat-rice|-0.860|-3.606**|
||(-0.522)|(-2.200)|
|Relative world prices of palm/wheat|0.701**|0.816**|
||(2.129)|(2.571)|
|Intercept|<br>19.16***|<br>17.35***|
||(5.682)|(5.890)|
|Observations|1,292|1,292|
|Controls|||
|Year, Quarter, Product Fixed effects|Yes|Yes|
|Ramadan Dummies|Yes|Yes|