rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1 Introduction**

This paper asks whether we should expect to find the sharp polarization of labor markets found in high-income countries to emerge in the developing world, and then employs global census data to see whether we do.

For the United States, Katz et al. (2006); Autor (2010); Autor and Dorn (2013) document expanding job opportunities in both high-skill, high-wage occupations and low-skill, low-wage occupations, coupled with contracting opportunities in middle-wage, middle-skill white collar and blue-collar jobs. In particular, job opportunities are declining in middle-skill white collar clerical, administrative and sales occupations and in middle-skill, blue-collar production, craft and operative occupations. This especially hits the earnings and labor force participation rates of workers without college education and particularly men. Goos et al. (2014) document that this phenomenon has appeared in each of 16 European countries from 1993 to 2006: middle wage occupations decline as a share of employment in all 16 countries with an unweighted average of 8 percentage points while high wage and low wage occupations increased in the vast majority. Beaudry et al. (2013) show that the collapse of middle level paying jobs has now spread to the high-skill labor market. Even growth optimists, such as Brynjolfsson and McAfee (2014) predict major shifts in the composition of labor and the need for compensatory social policies to offset the resulting inequality.

From the prevalence of polarization across the advanced countries, Autor concludes that a common set of forces are at work. Of these, a leading explanation is the ongoing automation and off-shoring of middle-skilled “routine” tasks that were formerly performed by workers with moderate education. Routine tasks as described by Autor et al. (2003) are sufficiently defined that they can be carried out by a computer executing a program or alternatively, by a comparatively less-educated worker in a developing country who carries out the task with minimal discretion. These would include book keeping, clerical work and repetitive production tasks. Acemoglu and Autor (2011) offer a model where
.8 million in 2017. Additionally, taking the national share of out-of-pocket payments in total health care expenses (56 percent, from the WDI), it is estimated that households bear GEL 44.9 million in tobacco-related medical expenses.

> 13 In the common absence of price data from household expenditure surveys, unit values are calculated as household expenditure (amount paid), divided by quantity. To the extent that observations bundle together different goods or quality grades of the same good, unit values differ from prices paid. See Deaton (1990; 2018) for a comprehensive discussion on this issue and the proposed methodology to correct for simultaneity biases.

12
223|503|
||Stab|ility|Household P|ower Index|Work Rig|hts Index|
||Female|Male|Female|Male|Female|Male|
|Work|0.241|0.284|0.323|0.019|0.349|0.086|
||(0.140)|(0.087)|(0.129)|(0.093)|(0.104)|(0.094)|
|Cash|0.196|0.045|0.279|0.072|0.110|0.040|
||(0.163)|(0.111)|(0.142)|(0.097)|(0.113)|(0.111)|
|Test: Cash = Work|0.724|0.018|0.745|0.532|0.011|0.600|
|Shrp. q-val Cash=Work|1.000|0.026|1.000|0.271|0.146|0.280|
|Test: Male = Female|0.174|0.174|0.674|0.674|0.195|0.195|
|Shrp. q-val Male = Female|0.396|0.396|0.491|0.491|0.396|0.396|
|Observations|223|503|223|503|223|503|

**Notes:** This table reports the treatment effect of the employment and cash arms on psychosocial outcomes separately for male and female participants. Each column shows the OLS estimates of Equation (1) including dummies for each treatment arm and controlling for the baseline measure of the dependent variable (ANCOVA), camp and enumerator fixed effects, and sociodemographic controls as determined through a double-selection LASSO procedure (Belloni
Number of observations 250 1166 1166 1166 1166 1164 1166 Note: (1) Symbol + indicates correlation statistically significant at 0.1 level; * indicates correlation statistically significant at 0.05 level; ** indicates correlation statistically significant at 0.01 level; (2) CAP support are the sum of payments for the period 2008-2013; (3) Unemployment rate data is at NUTS2 level; (4) EU15 are Belgium, Denmark, Greece, Finland, France, Germany, Ireland, Italy, Luxumbourg, Malta, Netherlands, Portugal, Spain, Sweden and the UK Table 7. Cross-tabulation of per hectare Pillar 1 support (2008-2013) and farm economic size (2010)

|Pillar 1 support per||Farm size cl|assification(1|= smallest)|||
|---|---|---|---|---|---|---|
|hectare classification (1|||||||
|= lowest)|1|2|3|4|5|Total|
|1|0|7|103|48|75|233|
||0.00%|3.00%|44.21%|20.60%|32.19%|100.00%|
|2|36|31|70|47|49|233|
||15.45%|13.30%|30.04%|20.17%|21.03%|100.00%|
|3|50|54|31|55|43|233|
||21.46%|23.18%|13.30%|23.61%|18.45%|100.00%|
|4|56|58|19|44|56|233|
||24.03%|24.89%|8.15%|18
It is found that all estimates of the model parameters make economic sense (have expected signs). Given the controlled variables, ethnic minorities still have lower per capita expenditure than Kinh and Hoa people. Households of large size are more likely to have lower per capita expenditure than households of small size. As expected, assets are positively correlated with per capita expenditure. Households who have more working members or members with vocational training tend to have higher expenditure. Finally, the R-squared values are quite encouraging with the range from 0.43 to 0.7.

# _IV.3 Poverty estimates_

# _Regional estimates_

Table 1 presents the estimates of the poverty incidence of the eight rural regions. It shows that the estimates from the small area estimation exercise are very close to the estimates based on the 2006 VHLSS (both for the large and small models). While we observe a noticeable difference for the Central Highlands, the difference is not statistically significant. The standard errors for the Central Highlands estimates based on the 2006 VHLSS are rather large due to the small number of observations.

The poorest region is the North West with a poverty rate of above 50 percent. In regions with low levels of poverty, about 10 percent of the rural population lives below the poverty line.

**Table 1 The poverty incidence estimates of regions**

|Region<br>VHLSS 2006|Small area estimation|
|---|---|
|Red River Delta<br>11.0|11.3|
|[1.1]|[0.9]|
|North East<br>29.9|31.6|
|[1.8]|[1.6]|
|North West<br>56.4|57.3|
|[3.7]|[2.6]|
|North Central Coast<br>33.1|32.9|
|[2.4]|[1.7]|
findings suggest that higher network resilience against natural shocks is also likely to result in a reduction of baseline fragility of the network, and thus higher service quality for users. Moreover, we find robust empirical evidence of adaptation effects. This implies that at given levels of baseline fragility, grid operators can exploit experience to design measures specifically effective against natural hazards.

# References

- Allcott, H., Collard-Wexler, A. and O’Connell, S. D. (2016) ‘How Do Electricity Shortages Affect Industry? Evidence from India’, _American Economic Review_ , 106(3), pp. 587–624. Available at: http://www.aeaweb.org/articles?id=10.1257/aer.20140389.

- Bangladesh Bureau of Statistics (2010) ‘Report of the Household Income & Expenditure 2010’,

- _Bangladesh Bureau of Statistics. Ministry of Planning_ , (1), pp. 1–5. doi: 10.1007/s13398-014-01737.2.

- Bartos, M. _et al._ (2016) ‘Impacts of rising air temperatures on electric transmission ampacity and peak electricity load in the United States’, _Environmental Research Letters_ . IOP Publishing, 11(11). doi: 10.1088/1748-9326/11/11/114008.

- Dewan, A. _et al._ (2017) ‘Spatial and temporal analysis of a 17-year lightning climatology over Bangladesh with LIS data’, _Theoretical and Applied Climatology_ . Theoretical and Applied Climatology, pp. 1–16. doi: 10.1007/s00704-017-2278-3.

- European Commission (2018) _Study on the quality of electricity market data of transmission system_

- _operators, electricity supply disruptions, and their impact on the European electricity markets Final report_ . Available at:

- https://ec.europa.eu/energy/sites/ener/files/documents/dg_ener_electricity_market_data__final_report_-_22032018.pdf (Accessed: 18 December 2018).

- Government of Bangladesh (2008) ‘Cyclone Sidr
summarizes the data sources and variable de...nitions. To ensure adequate coverage for each country, we excluded information for nations that do not have at least three consecutive years of tari¤ and rainfall data. Table A.2 lists the countries and corresponding time periods covered in our sample, while Table A.3 details the product classi...cation we employ. To identify agricultural products we use the UN statistics division of the SITC classi...cation. For robustness, we also consider a broader de...nition that includes as well other agricultural products.<sup>16</sup> Irrigation data are available for 32 of the 70 countries initially considered. These data generally refer to the 2001-2009 period, but the extent of time coverage di¤ers across countries. We therefore use the average value of this variable among non-missing observations for each of the 32 countries as a (time-invariant) proxy for access to irrigation at the country-level.

Figure 1 provides illustrative evidence on the rainfall variable we employ for a subset of countries. The dashed lines in these diagrams refer to major, widely-documented droughts observed in each nation. Many of these extreme weather events were associated with the El Niño or La Niña phenomena, including Argentina (1999), Bolivia (1998), Brazil (1998, 2003), Colombia (1992, 1997), Chile (1998), Costa Rica (2001), Guatemala (1997, 2001) and Peru (2000).

# [Figure 1 about here]

Our theory applies to countries that have discretion to set import tari¤s on agricultural goods. A potential concern is that, in reality, tari¤ setting might be constrained by limits associated with GATT/WTO membership. As shown in Figure 2, however, countries typically set their tari¤s on agricultural goods well below the bound tari¤s imposed by such multilateral tari¤ agreements; see Gibson et. al (2001, pp. 20-21) for further data and
Third, the results emphasize the importance of integrating efforts to tackle chronic poverty, seasonality, and unpredictable shocks in a holistic way. This requires strengthening ASP systems to enable them to cover the chronic poor, reach households facing seasonal food insecurity, and expand during crises. This, in turn, hinges on the availability of regular resources, not just for extreme situations; strengthening delivery mechanisms; and developing adaptive social registries so that chronically-poor households as well as households in transitory need of assistance can be identified quickly.

This paper also demonstrates the potential of cross-country microdata for improving our understanding of the effects of seasonality. By harmonizing both the survey instrument and the data collection schedule, it is possible to pool data from Burkina Faso, Mali, Niger, Senegal and – to some extent – Chad, improving statistical power but also allowing the results from one country to be benchmarked against the others. Efforts to collect such consistent data in other countries in the future could help guide social protection and other policies if the welfare effects of shocks and seasonality are to be overcome and chronic poverty is to be reduced.

25
<mark>overwhelmingly procyclical, especially among African countries. Dabla-Norris, Minoiu, and Zanna (2010) find that bilateral aid flows are on average procyclical with respect to the business cycles in both donor and recipient countries; however, they also find that these flows become countercyclical when recipients face large adverse terms-of-trade shocks, thereby playing an important buffering role during downturns.</mark> <mark>It appears that no assessments of the cyclicality of private sector-focused impact investing by IFIs have yet been undertaken. This study seeks to fill that gap in the empirical literature by assessing</mark> the cyclicality of capital flows of IFC as the largest global development finance institution with a private sector focus <mark>and how the cyclical patterns of IFC investments in countries and regions are related to its portfolio asset quality and profitability. I</mark> n turn, the relation between IFC cyclical patterns and its financial performance would help assess the feasibility of a less procyclical investment strategy, particularly during downturns.

# **3 Empirical Approach**

## **3.1 IFC Investment Activity**

The study uses two different variables to model IFC investment activity: (i) _original commitments_ , which represent the IFC’s initial legal obligation to provide financial products to the client for a given period and (ii) _disbursements_ , which are principal outflows from the IFC to the client under the relevant investment agreement. It should be noted that commitments and disbursements data include both equity and debt<sup>4</sup> financing. The data also aggregates across long-term financing (LTF), short-term financing (STF), and guarantees. It should be noted that STF and guarantees, given their structure and tenor, do not disburse, therefore they are included only in commitments.<sup>5</sup> We differentiate commitments and disbursements, as the former denote more the moment of the IFC investment decision, while the latter are more related to the client’s decision to request a
Figure 3 Panel A shows a rapid rise of total crypto-assets activity over the past two years reaching a total of US$2.82 trillion year to date in 2021. Across all transactions, the breakdown by type of crypto-asset shows that the value sent in ether (40 % of overall volume 2021 year to date) and stablecoins (24% of overall volume 2021 year to date) has gained more compared to bitcoin (24% of overall volume 2021 year to date). DeFi and other crypto-assets activity represents 12% of crypto activity year to date. The on-chain stablecoin activity (US$ 602 billion 2021 year to date) is significantly less in comparison to total stablecoin transaction volumes (i.e., including off-chain which is US$ 2.8 trillion 2021 year to date).<sup>14</sup> This indicates that a majority of stablecoin volume is driven by intra exchange trade for settlement of crypto-asset trading.

Figure 3 Panel B shows a similar trend for volumes associated with smaller transaction sizes. Stablecoin activity remain relatively low with 16% of overall volume year to date. When looking at the crypto activity by transaction size, we find that while large value transfers ($2.69 trillion year to date) dwarf smaller transaction size transfers ($119 billion year to date), the smaller transaction size transfers have also been rising steadily in the past two years.

> 14 Estimate based on Coinmetric data.

16
households, because though they include two cells, the surveyor recorded all expenditure in the head’s cell. Finally, seven households could not be included due to missing information on some key variable. We end up with a total sample size of 1762 households kept in the analysis.

4 PSF Households with exceedingly high levels of expenditures have been screened and, when possible, their records corrected case-by-case based on information provided by other variables in the survey. No such work could be done on the ESPS data since the available information is more limited. The maximum level of annual expenditure per capita in PSF is found around 5,810,000 CFA (that is about $11,500 in 2006-2007). In ESPS the maximum amount is around 13,780,000 CFA ($27,300), but only eight households out of 13559 have values higher than the PSF 11
# **-** **<u>Table A4: Correlations with external (macro level) corruption indicators</u>**

# **<u>Panel A: Spearman's rank correlation</u>**

||**Overall Corruption**|**Petty**<br>**Corruption**<br>**Incidence**|**Petty**<br>**Corruption**<br>**Depth**|
|---|---|---|---|
|Control of Corruption (Worldwide<br>Governance Indicators; N=124)|0.544***|0.648***|0.633***|
|Corruption Perception Index||||
|(Transparency International;||||
|N=117))|0.437***|0.586***|0.549***|
|Corruption (ICRG; N=88)|0.208***|0.291***|0.303***|
|**Pa**|**nel B: Pearson’s correla**|**tion **<br>||
||**Overall Corruption**|**Petty**<br>**Corruption**<br>**Incidence**|**Petty**<br>**Corruption**<br>**Depth**|
|Control of Corruption (Worldwide||||
|Governance Indicators; N=124)|0.405***|0.564***|0.544***|
|Corruption Perception Index<br>(Transparency International;||||
|N=117))|0.330***|
For sampling in urban areas, each town (or city) was divided into enumeration blocks consisting of 200–250 households. Each enumeration block was classified as belonging to one of three income groups (low, middle, and high), based on the living standard of the majority of the people in the block. In rural areas, the list of villages from the Population Census 1998 was used as the sample frame. Urban blocks and villages were the primary sampling units for the survey. Sixteen households were selected from each sample village and 12 from each enumeration block, using a systematic sampling scheme with a random start. Because the PSLM survey reports an electrification rate close to 100 percent in urban areas, the analysis was restricted to the rural sample, which included about 4,300 households.

Data from a second survey, conducted in 2014 under the IFC’s Pakistan Off‐Grid Lighting Consumer Perceptions Study (IFC 2015), were used to gauge the impact of power outages on households. This survey explored the market potential for off‐grid solar lighting in Pakistan. It included 6,000 households, both on and off grid, across all four of the country’s provinces. This cross‐sectional survey lacks the multifaceted measurement of welfare used in the PSLM, but it provides important information on the quality of electricity supply, because it asked on‐grid households to report the average duration of outages.

Table 2 shows the geographic distribution and electricity access rate of the population surveyed by the PSLM. Punjab accounts for the largest share of the sample, followed by Sindh and Khyber Pakhtunkhwa. Balochistan has the lowest electrification rate and the smallest number of households surveyed. Sindh and Balochistan have the lowest access rates, at less than 75 percent in 2010.

The average electrification rate rose by just 2 percentage points between 2008 and 2010. It rose by about 17 percentage points in Balochistan and 4 percentage points in Sindh.

Table 3 compares mean outcomes of households and individuals by electrification status. It shows
levels. StarkH non<br>significant at all levels.<br>StarkL positive and<br>significant only at PSU<br>level (Text and Table<br>11).|Gini non significant<br>(Description of results in<br>text but not in tables).|
|**Inequality variable**|Comparison of an equal<br>and unequal society|Max and min income,<br>skewness, 20th and<br>80th percentiles|Gini|Gini (regional)|Gini (reference group)|Gini (World Bank)|Gini (Wu et al 2002)|Gini (Deninger and<br>Squire 1996 for EU)|Gini and Stark indices<br>of income overhang<br>(national, regional and<br>PSU level)|Gini|
|**Happiness variable**|Happiness and Life<br>satisfaction|Happiness|Life satisfaction|Life satisfaction|GHQ-12 and Life<br>satisfaction|Life satisfaction|Happiness|Happiness|Life satisfaction|Life satisfaction|
|**Model**|na|OLS|OLS|Ordered Probit and OLS|Ordered probit,<br>conditional fe logit and<br>re probit|Oredered Probit|Ordered logit|Ordered logit|Ordered probit|Ordered Logit|
|**Data**|Ad-hoc questionnaire in<br>two villages in Israel|US-GSS 1989-1996|8 countries study 1972-<br>1994|GSOEP 1985-1998<br>|BHPS 1991-2002<br>(Employed)|WVS ( 1980 – 1982,<br>1990 – 1991, and 1995<br>– 1997 waves)|US-GSS 1972-1997|EU Eurobarometer<br>1975-1992|RLMS 1994-2000|Latinobarometro 2004,<br>17 LAM countries|
|**Study**|Morawetz et al., 1977|Hagerty 2000|Hagerty 2000|Schwarze and Harpfer,<br>2002|Clark, 2003
|[0.001]|
|Mean outcome|0.032|0.032|0.032|0.032|
|Adjusted R<sup>2</sup>|0.003|0.053|0.066|0.100|
|Observations|16,458,902|16,394,979|16,458,819|16,394,924|
|Controls|No|Yes|No|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|Fixed effects|Municipality|Municipality|School|School|

_Source_ : Authors’ calculations from Guatemalan educational administrative records.

_Notes_ : The table reports difference in difference estimates of the effects of differential exposure to COVID-19 (see Section 4 for details). Each column presents results from a separate regression for each outcome. Clustered standard errors by municipality are shown in parentheses. Columns 1 and 3 estimates Equation (1) with no controls, Columns (2) and (4) include controls for gender, urban residence, level of schooling, grade, and whether the school provides bilingual instruction. We present q-values that adjust for multiple hypothesis testing in brackets, calculated using the method by Benjamini and Hochberg (1995) that controls for the false discovery rate described in Anderson (2008). Significance levels: ***p<0.01; **p<0.05; *p<0.10.

18
positive associations between psychosocial stimulation and a range of ECD outcomes (Avan et al., 2014; Armstrong-Carter et al., 2021). Yet global survey data that measure psychosocial stimulation— with minimum adequate levels of stimulation represented by the presence of four or more stimulating activities with a child in the past 3 days—suggest that in South Asia, levels of minimum adequate stimulation are low: on average, just 41% of children receive adequate stimulation from maternal caregivers, 12% from paternal caregivers, and 23% from other caregivers, signaling a broader need for increased stimulation for young children (Cuartas et al., 2020).

Few studies from South Asia have examined differences in psychosocial stimulation by child gender. A study in Bangladesh found that girls experienced higher levels of certain aspects of stimulation (i.e., responsiveness, encouragement, emotional, and learning opportunities) relative to boys (Hossain et al., 2018), though children were slightly older (aged 8 years) and authors caution that the magnitude of this association was substantively low.

Even fewer studies have examined whether the strength of the associations between ECD and psychosocial stimulation varies by child gender. Evidence for moderation by child gender from a recent study in rural Sindh is mixed: at age 4 years, home stimulation had a significant positive association with verbal skills for boys, but not for girls. However, there was no significant interaction between child gender and stimulation in predicting executive function skills (Rathore et al., 2023).

# **_Early Childhood Education (ECE) Enrollment_**

There is substantial support indicating that ECE is positively associated with outcomes for children’s early learning and development (Rao et al., 2021). Meta-analytic findings from the US also indicate that ECE is associated with a number of desirable medium- and longer-term outcomes, including reduced special education placement (d=0.33 SD), reduced grade retention (d=0.26 SD), and increased high school graduation (