rafmacalaba/data-use-mentions-extended huggingface dataset:
skills that are needed to improve one’s chances of obtaining a good job that is less likely prone to automation. Those in vocational secondary have substantially lower cognitive skills according to an analysis using PISA data (Bentaouet Kattan, Macdonald and Patrinos 2018).

Also of importance are so-called non-cognitive skills, or socioemotional skills, which include the “Big 5” personality traits of extraversion, conscientiousness, openness to experience, emotional stability and agreeableness (Heckman 2011). There is evidence that non-cognitive skills – particularly openness to experience – are positively associated with being in a non-automationprone occupation (Bentaouet Kattan, Macdonald and Patrinos 2018). Also, vocational education programs tend to emphasize technical skills over cognitive _and_ non-cognitive skills, placing such graduates at a further disadvantage. Frey and Osborne (2017) and Brynjolfsson and McAfee (2014) find that non-cognitive skills such as creativity, social-intelligence and advanced perception will be humans’ comparative advantage over machines given current technological trends.

# **Implications for Education Systems**

Education systems are faced with the double challenge of improving the quality of education to make it relevant to the labor market given the automation challenge and giving students the ability to navigate the post-truth society. As daunting as that challenge might be, it turns out that the solution to both problems is more and better education. The challenge, however, is that this is expensive and difficult. At the same time, governments have to increase educational attainment, improve cognitive skills, and teach non-cognitive skills. These are expensive and difficult tasks, and the evidence base on how to improve non-cognitive skills is limited.

The labor market returns to schooling are high and this alone justifies expanding access to education. For compulsory schooling, the guidance is clear. It is government’s responsibility to finance schooling. At the higher education and post-compulsory levels, the onus is not on government to fully provide and finance it, but certainly to put in place the conditions for increasing access to quality opportunities. This is an area where financial innovations make
7|

Sources: The statistics of migrants and non-migrants are computed from the VMS 2004 and the VHLSS 2004 respectively. Notes:<sup>*1</sup> See the text for the definition of “State”, “Foreign”, “Private” and “Agriculture” categories.<sup>*2</sup> The unemployed category is defined as “those who are not working but sought employment.”<sup>*3</sup> Monthly incomes between non-migrants and migrants are not comparable due to the differences in definition and in sample coverage. Whereas the monthly incomes for non-migrants are based on wage employments in the VHLSS 2004, non-monetary compensation for migrants is converted with equivalent monthly values in the VMS 2004.
|**2004**|
|Contributions (% of GDP)|0.27|0.49|0.61|0.66|0.68|<br>0.87|<br>1.06|
|Contribution rate (%)|6.0|6.0|6.0|6.0|7.0|<br>7.0|<br>8.0|
|Contribution base (% of GDP)|4.6|8.1|10.1|11.0|11.4|<br>12.4|<br>13.2|
|Determinants of contribution base:||||||||
|Membership (thousands)|1,347|2,064|2,193|2,253|2,214|2,304|2,402|
|Contribution ceiling/Average covered wage|-|2.0|1.9|1.8|1.6|<br>-|<br>2.5|
|Covered wage bill (% of GDP)|22.1|22.4|22.2|23.5|23.9|<br>24.2|<br>-|
|Memo item:||||||||
|Contribution base/Covered Wage Bill|20.7|36.1|45.7|46.9|47.5|<br>52.4|<br>-|

Source: HFSA raw data.

21. A recovery of the covered wage bill would also contribute to larger contributions and more rapid growth of the second pillar. As shown in Table 7, the base of contributions to the second pillar has been growing in line with the increase in membership, and is now more than half of the covered wage bill. At the end of
The **epidemic context** , in particular, drives community responses and the types of activities undertaken. In Sub‐Saharan Africa community responses are often described in terms of PLHIV and OVC support, whereas in Asia, examples tend to highlight responses driven by group most at risk of HIV infection, such as sex workers and MSM.

Community responses have also evolved as the epidemic has evolved. NGOs working on AIDS issues in Brazil focused initially on fighting social exclusion and pressuring the government to be more responsive. NGO leadership subsequently was instrumental in creating the legislation to mandate nationwide HIV testing of blood donations, and NGOs created home‐care programs, established HIV support groups, and launched preventive efforts. In Zimbabwe, the Families, Orphans and Children Under Stress (FOCUS) program has evolved from an initial focus on home‐based care and awareness raising to encompass support for affected children. As treatment has become more widely available, community organizations have increased their involvement in treatment‐related activities or changed the emphasis of their work. For example, in South Africa, the Treatment Action Campaign has expanded its work on treatment access to include mobilizing community involvement in treatment education and treatment adherence.

Responses are also characterized by **location** . A mapping of community responses (M’Zamani et al. 2007) in three settings in South Africa ‐a large urban township, a small town, and a rural area ‐found very few organizations operating in the rural area. In the rural area, community response, instead, emerged as outreach conducted by churches and home care provided by a local NGO. In contrast, there were 67 and 104 organizations in the town and urban township, respectively, involved in a range of activities. These included support groups, home‐based care, spiritual support, condom distribution, and provision of transport to clinics for PLHIV.

Another review of a subset of faith‐based organizations in a South African database (Birdsall 2005) found that the nature of services provided
APPENDIX bonds with a much longer maturity, since average maturities were much longer then. From 1942 on, we rely on the 10-year government total bond return series provided by the CRSP database, which builds on a comprehensive collection of US Treasury bond prices.

## <u>UK government bonds, monthly, since 1815</u>

The return index on long-term UK government bonds is from GFD and was compiled from Neal (1990) for 1815-1823, from The Times (1824-1844), from the periodical The Banker’s Magazine (1844-1852), and from the Central Statistical Office of the British government (1853 onwards). Whenever possible, we use an index of 10-year bonds, but in the 19<sup>th</sup> century, the available indices also contain bonds with a much longer maturity, since average maturities were much longer compared to today.

## <u>S&P US Stocks Total Return Index, monthly, since 1815</u>

From 1815 to June 1962, we use the US stock market returns index provided by Schwert (1990). From July 1962 onwards, we use the S&P US Stocks Total Return Index from Standard & Poor’s retrieved from GFD.

## <u>FTSE UK Stocks Total Return Index, monthly, since 1815</u>

This index is from GFD and compiled from the following sources: Rostow and Schwartz (1953) which contains data on Bank of England Shares and the East Indies Company from 1815 to 1850, Hayek (1935) from 1851-1867, and Smith and Horne (1934) from 1874 to 1922. Additional sources are the periodical Banker’s Magazine (1907-1933), The Economist (1933-1962), and The Financial Times from 1950 onwards. For more recent decades, the data comes from the Central Statistical Office of the British government for the years 1939-1988 and from Eurostat from 1989 until today.

## <u>UK inflation/CPI series, monthly</u>
Our perspective on EWAP dynamics in EMDEs is motivated by the literature on structural change. A key dynamic among EMDEs is the movement of economic activity out of agriculture and into non-agriculture. Labor markets in these two sectors have very different operating features: agricultural labor markets are dominated by self-employment in low-density rural areas, whereas non-agricultural labor markets are dominated by formal or informal employment in higher-density urban areas. Additionally, many policy and institutional differences are likely to have very different effects on labor markets in these two sectors. For these reasons, the process of structural change will plausibly give rise to dynamics in aggregate labor market outcomes, as activity switches from largely self-employment in rural areas to formal or informal employment in urban areas. Long-run labor market outcomes will thus reflect the steady state outcomes in the nonagricultural sector.

Our methodology for studying EWAP dynamics draws heavily on the growth literature that studies convergence patterns in cross-country panel data sets. Specifically, following the literature on conditional convergence, we include country fixed effects in our convergence regressions to allow for the possibility that each country is converging to its own steady-state level of EWAP. When implementing this procedure, we also control for two time-varying driving forces: productivity and population.

Our analysis of EWAP dynamics proceeds in two steps. The first step runs convergence regressions for EWAP. A by-product of this first step are country-specific steady-state values for the EWAP. In the second step, we examine the correlation between these steady-state levels and various indicators.

Our first step delivers three key results. First, while there are many countries with very similar steady-state EWAP levels, there are many countries with steady-state EWAP levels that display large deviations from the mean. Second, higher population growth is associated with significantly lower EWAP. Third, higher productivity growth is also associated with significantly lower EWAP. This third finding suggests that EMDEs may face a tradeoff between employment and productivity. This would be the case if these countries tend to have many individuals employed in low-productivity activities, as eliminating these jobs would increase productivity growth via composition effects
**|-201.8|18.4|18.5|18.9|20.3|23.8|1.3|
|Mean age of all<br>members squared|-140.4**|157.1*|16.4|17.0|17.9|20.1|28.6|1.7|
|Number of<br>household males<br>over 15 years|29.5*|71.0|20.2|20.2|20.2|19.1|20.2|1.0|
|Capital<br>Mean education<br>of household<br>males over 15<br>years|28.2|25.8|15.7|17.9|20.1|22.3|23.8|1.5|
|Mean education<br>of household<br>males squared|27.9|-64.5|16.4|17.0|17.9|20.1|28.6|1.7|
|Value of farm<br>equipment|1.3|-2.1|9.8|20.8|18.4|11.7|39.2|4.0|
|Value of<br>enterprises<br>owned|5.1*|-4.4|10.6|7.6|13.5|19.8|48.5|4.6|

Notes: Income shares calculated from tobit results in Table 7; income shares of governorate-level dummy variables are not reported.

> * Coefficient in income equation significant at .05 level.

> ** Coefficient in income equation significant at .01 level.
women in the treatment and control groups who completed each stage of the program.<sup>13</sup> 61 percent of those assigned to the franchise treatment attended the initial two-week business training course at least once; 39 percent of those assigned to the franchise treatment completed the franchise-specific business training and launched a microfranchise. Though these modest take-up rates are not out of line with those observed in comparable training programs (McKenzie and Woodruff 2014), they have important ramifications for the interpretation of intent-to-treat estimates of program impacts (a point we return to below). Unsurprisingly, the take-up rate is 11The average level of education among women aged 18-20 in Nairobi is 10.6 years; 28 percent are currently married or living with a partner, and 26 percent have had a child (Kenya DHS 2014). Thus, relative to the general population of comparably-age women in Nairobi, our sample is slightly less educated, less likely to be married or cohabitating, and more likely to have had a child. These differences likely reflect the program’s focus on Nairobi’s poorest neighborhoods.

> 12Women assigned to the control group come from slightly larger households, and are somewhat more likely to have given birth prior to the program. Women assigned to the cash grant treatment had, on average, about half a year less schooling than those assigned to the franchise treatment and the control group. Controls for those variables that are not balanced across treatments are included in our main specifications (though results are nearly identical when controls are omitted).

> 13The table is based on administrative data from the implementing NGO and the franchise partners, though self-reports line up with administrative records.

14
# **4. International allocation of pension funds**

Pension assets are a large component of global capital markets and have grown in size over the last two decades (Figure 1). Although total pension assets reached $48.1 trillion at the end of 2019, the growth has not been constant over time and it has been mostly uneven across countries. For example, while pension contributions peaked in 2005, these declined after the 2008 Global Financial Crisis (GFC) and have not reached pre-crisis levels since (Figure 2). Following the drop in contributions, and exacerbated by lower portfolio valuations during the GFC, pension assets experienced a decline in size relative to global GDP between 2008 and 2011. After 2012, global pension assets experienced modest growth, rising from 25% to 30% of GDP at the end of 2019.

According to the OECD Global Pension Statistics, during the last 20 years, pension funds across the world have significantly increased their share in foreign holdings. In 2002, pension funds had 16% of their portfolio allocated to international investments. Foreign exposure rose consistently in the next two decades, reaching 36% of total assets by 2019 (Figure 3). Although the level and pace at which pension funds have increased their foreign assets vary across regions, the data suggests that over time, pension funds systematically gain exposure to international assets (see Figure 4). For example, while pension funds in ECA and MENA countries had almost no foreign exposure in 2002, the share of foreign assets grew to 11% and 20% respectively by 2019. We find similar patterns in Latin America, Western Europe and East Asia and Pacific regions.

**Figure 1.** Pension assets

**Figure 2.** Pension funds’ contributions
Unlike tenure insecurity due to village land reallocations, land requisition by higher levels of government for non-agricultural uses has a different impact on migration decisions. As it relies on the state’s eminent domain power and its timing and scope are beyond farmers’ control, the prospect of land requisition is a threat to land rights that farmers cannot guard against by focusing on agricultural production and staying in the village. As a result, the risk of losing land through requisition significantly increases individual farmers’ migration rates (Ma and Mu, 2020).

# **3. Migration Trends**

Reduction in the barriers to population mobility created by the _Hukou_ and land tenure systems, and the shift of labor out of agriculture and into higher productivity employment opportunities in urban and coastal areas, is recognized for its important contribution to China’s economic growth since 1990. From the village-level survey data of the Research Center for Rural Economy (RCRE) in China’s Ministry of Agriculture, it is evident that fewer than 10% of the registered rural population worked as migrants in 1993. The migrant share of the registered population increased steadily to 38% by 2017, with 22% working outside of their home counties and 11% outside of home provinces (Figure 1). Expanding the reach of telecommunications and investments in transportation infrastructure were enabling factors that facilitated increases in both rural-to-urban migration and the duration of migrant employment. China’s internal migrants, like economic migrants around the world, move to places with higher wages and more economic opportunity.

Below we discuss the strengths and weaknesses of using census versus individual and household survey data, respectively, for the study of internal migration in China. China-specific issues related to sampling and definition of household membership are highlighted. Long-term patterns in rural-to-urban migration trends are summarized using village, household and individual data from surveys conducted by the Research Center for Rural Economy (RCRE) at China’s Ministry of Agriculture. These data have the benefit of using a consistent standard for defining a migrant over three
national poverty rate in replication _r_ . The average post-transfer national poverty rate across all replications provides our estimate of how poverty will have changed as a result of the transfer scheme. This expected poverty rate can be compared to the original estimate of national poverty from the poverty map, and to the estimate of the poverty associated with an untargeted lump-sum transfer.

# _Calculating “Equivalent Gains”_

In thinking about the “performance” of the transfer scheme we are interested not only in the poverty impact of a specific scheme, but also in how much more “expensive” a given poverty reduction is without targeting as opposed to with geographic targeting. To explore the latter we apply a variant on the simulation procedures described above whereby we calculate how much smaller the overall budget _S_ can be in order to achieve the same poverty impact with optimal targeting as with the untargeted uniform lump-sum transfer.

# _Distance from Perfect Targeting_

As has been emphasized in section II, the poverty map cannot provide reliable estimates of poverty below some level of aggregation. This is because the idiosyncratic component of the overall standard error on the poverty estimate becomes progressively larger as poverty is being measured over progressively smaller groups. For policy makers therefore, the poverty map cannot be viewed as a tool to assist with the identification of, say, poor households or small groups of households. A rough rule of thumb is that poverty map estimates become unreliable for communities of less than 1000-5000 households.

21
*<u>Table 2. Evolution of key indicators, 2006–2014</u>**

|**Indicator**|**Start of PBS**<br>**(2006)**|**2014**|**Source**|
|---|---|---|---|
|Child mortality 1-4 per 1,000|72|31|DHS|
|Contraceptive acceptance rate (modern method)|14%|40%|DHS|
|Access to antenatal care|28%|41%|DHS|
||||Education|
|Net enrolment rate for grades 1–8|77.5%|90%|Management<br>Information<br>System(EMIS)|
|Gross gender parity index for grades 1–8|0.84|0.92|EMIS|
|Improved sanitation|38%|66%|DHS|
|Cereal productivity in quintals per hectare|15|20|Agriculture Sample<br>Surveys|

30
Page 10 One could easily test the hypothesis that providers of small amounts of money would require an exposed agent. On income tax returns a field could be created where taxpayers could voluntarily indicate the amount of money they would be willing to invest into projects via the government. The money could be collected with the tax bill and returns from such voluntary investment could be automatically credited to future tax bills.

Somehow we might expect that not too much money would be raised that way. If so, this would support the view that the financial advantages of government finance are based on coercive powers and not on a superior system to exploit the low cost of risk-bearing of tiny investors. In fact, it would become clear that the low cost funds cannot be tapped unless the intermediaries have good incentives to use the money well, which gives by necessity rise to significant costs of risk-bearing on the part of the exposed intermediary. In turn this requires risk-adjusted rates of return on financial instruments. Crucially, we cannot consider the cost of funds independent of the incentive system, under which intermediaries collect them. This is similar to modern theories of intermediation, which emphasize the monitoring problem of small investors in intermediaries and not only optimal risk diversification issues (Hellwig 1990).

# **111.4. Incentives of the government as intermediary**

For the Arrow and Lind argument to still hold the government as intermediary would need to have better incentives to invest well than private parties. Arrow and Lind state "that many of the uncertainties which arise in private capital markets are related to what may be called moral hazards. Individuals involved in a given transaction may hedge against the possibility of fraudulent behavior on the part of their associates. Many such risks are not present in the case of public investments and, therefore, it can be argued that it is not appropriate for the government to take these risks into account when choosing among public investments." It seems Arrow and Lind assume that the government will in fact do what it ought to do,
brought more credibility to the findings. The findings from Vietnam are used widely in the Voices of the Poor global studies and findings are quoted in the WDR2000.

Govenmment comments and feedback were taken into account in the final drafts of the PPA site synthesis reports. But Government involvement in drafting was really far more crucial in the national poverty assessment. This is the document which Government has indicated that they will take as their own and use for their own planning purposes.<sup>An</sup> abridged version of _Vietnam: Attacking Poverty_ (about half the length of the original report) is now being produced and will be adopted as an official document of the Government of Vietnam.

**_Diagram 2: Feeding PPA findings into national documentation and plans_**

<!-- Start of picture text --> .............................<br>.................................... . . .. Global .<br>.... -. Voices of the<br>Poor<br>Tra Vinh .. -- ---** S..~~~~~~~~~~~~~~..............<br>Site Report .. ;,<br>World<br>1 ^ ~~Tra Vinh.--.-- .Wol<br>Synthesis *-: Development---- .. *.<br>TraVinhNtReport Nona Report 2000<br>Sit Rp rt PPA .............<br>2 synthesis \ \9<br>Three National<br>12 site Synthesis
246,000 people (CBS, 2022a; ICIMOD, 2019a). A brief snapshot of Nepal’s brick industry is summarized in Table 1.

**Table 1: Snapshot of Nepal’s brick industry**

|Parameter|Year|Value|Data Source|
|---|---|---|---|
|Number of brick kilns|2018|1,349|(ICIMOD, 2019a)|
|Distribution of kilns by province<sup>a</sup>|2018|P1 (8.8%), P2 (30.2%), P3 (21.2%),<br>P4 (6.4%), P5 (23.4%) and P7 (9.8%)|(ILO et al., 2020)|
|Annual production|2018|5.14 billion|(ICIMOD, 2019a)|
|Value of annual output<sup>b</sup>|2020|NRs 14 billion|(CBS, 2022a)|
|Contribution to GDP|2020|4%|(CBS, 2022a)|
|Coal consumption|2018|504,750 tons|(ICIMOD, 2019a)|
|CO2emissions|2016|2.2 million tons|(Eil et al., 2020)|
|Total industry employment<sup>c</sup>|2018|186,150|(ILO et al., 2020)|
|Brick price index (2015=100)|2021|127|(CBS, 2022b)|
|AAGR<sup>d</sup>of the construction industry|2013-22|6.3%|(CBS, 2022b)|

_Notes_ :<sup>a</sup> P1 is Koshi, P2 is Madesh, P3 is Bagmati, P4 is Gandaki, P5 is Lumbini and P7 is Sudurpachim provinces. Karnali province reported only 2 kilns in the operation.<sup