rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure 2 Employment Rates by Age Cohort in China, Indonesia, Korea, the UK and the US**

<!-- Start of picture text --> China (CHARLS) Indonesia US<br>1 1 1<br>.8 .8 .8<br>.6 .6<br>.6<br>.4 .4<br>.2 .2 .4<br>0 0 .2<br>45 50 55 60 65 70 75 80 85 90 45 50 55 60 65 70 75 80 85 90 0<br>age age 45 50 55 60 65 70 75 80 85 90<br>Urban Male Urban Female Urban Male Urban Female age<br>Rural Male Rural Female Rural Male Rural Female Male Female<br>China (CHNS) Korea UK<br>1 1 1<br>.8 .8 .8<br>.6 .6<br>.6<br>.4 .4<br>.4<br>.2 .2<br>0 0 .2<br>45 50 55 60 65 70 75 80 85 90 45 50 55 60 65 70 75 80 85 90 0<br>age age 45 50 55 60 65 70 75 80 85 90<br>Urban Male Urban Female Urban Male Urban Female age<br>Rural Male Rural Female Rural Male Rural Female Male Female<br>Lowess Plot bw=0.3<br> Working / Total<br> Working / Total<br>Working / total<br> Working / Total<br><!-- End of picture text --> Notes: Employment rates by age cohort are calculated using non-parametric locally weighted regression (LOWESS) with a bandwidth of 0.3. The following data sources are used: China: 2009 CHNS and the 2008 CHARLS pilot; Indonesia: 2007 Indonesia Family Life Survey (IFLS); Korea:
characteristics. Students' parents and teachers answered a survey at the same time. This information was needed to control for teacher and socioeconomic characteristics.

The C.E.E sample consists of students from 198 schools randomly chosen from four different types of schools: Urban, RURAL, NATIVE and CONAFE from five different states.' The participation of each school type, relative to the total is shown in Table 2.2 Table **2. Number of schools by type and state, 1992**

|State|**Urban**|**Rural**|**Native**|**CONAFE**|**Total**|
|---|---|---|---|---|---|
|Chiapas|6|13|14|5|38|
|Guerrero|4|14|12|4|34|
|Hidalgo|3|11|12|8|34|
|Oaxaca|7|17|15|12|51|
|Michoacan|7|15|10|9|41|
|Total|27|71|64|44|198|

Source: PARE's database.

# **3.** Impact of the PARE **program on learning and achievement**

# 3.1 Control and the experimental groups

The literature generated by the PARE points toward a mixed conclusion about the impact of the program.<sup>3</sup> This was partly due to incomplete and faulty implementation, especially in urban areas. By design, the program intended to provide a number of simultaneous actions (components), which together would impact on educational outcomes. For pedagogical reasons the total was to be greater than the sum of the parts. The actions were to affect the behavior of students, parents, teachers, principals and supervisors; they were to provide the target schools with supplies, didactic materials and 1 CONAFE stands
necessarily be a remittance transaction. It may just be a cash withdrawal by a foreign tourist.

It has also become difficult for central banks to identify the source country of remittances, as well as the number of transactions that occur through the different financial instruments used by migrants to send remittances. According to the survey, only 50% of central banks have information on the number of remittance transactions (drafts, electronics wires, etc) that they cover in their statistics. For other countries, remittance data is available only on an aggregated basis.

10
indexed bonds and floating rate notes in local currency issuance policies. Therefore, the decision horizon was extended to five years in order to analyze the cost-risk characteristics of these instruments more precisely.

In cost-risk modeling of public debt, many developed debt management offices use longer decision horizons to get a better picture of the medium- and long-term policy responses. Although, the flexible nature of the model enables us to easily extend the decision horizon, volatile historical data regarding macroeconomic and financial variables, mostly attributable to financial crises and regime changes before 2002, and the limited extent of future projections for fiscal data make simulations for horizons greater than 5 years difficult.

The granularity of the model was modified from monthly to quarterly in order to simplify cash-flow modeling. Although a monthly frequency to calculate cash flows is very useful for a highly detailed understanding of government’s financing strategy, i.e., annual financing plan, from a longer-term strategic perspective, such a high degree of model granularity leads to a higher degree of complexity and potentially unwieldy calculations.

Typically, debt managers focus on annual cost and risk figures and combine annual observations with averages across years. With the adjusted cost and risk metrics, annual figures are taken into account, instead of using cumulative (or average) figures for the decision horizon. This prevents the possible information loss that may arise from the aggregation approach. Although it may be reasonable for debt management offices that have a long decision horizon to focus on averages, in the Turkish case the relatively short length of the decision horizon creates an incentive to analyze each year’s expected debt dynamics separately in a more detailed manner. This helps identify possible concentrations in cost and repayment profiles in specific years.

# **3.3 Debt Structure Metrics**

A main objective of cost-risk modeling is to provide guidance for the formulation of strategic benchmarks. Strategic benchmarks describe the policies designed to reach the desired debt structure and they are typically expressed as numerical targets for key portfolio indicators (IMF and the World Bank,
less likely to be applied to fast-growing commodities). Regional shifts of production create divergent interests between "old" and "new" producers, and therefore reduce their ability to lobby for transfers. Finally, commodities that face import competition receive more protection

> 22 In most cases wage equations have been estimated using cross-sectional rather than panel data, and do not therefore correct for unobservable individual attributes. The South Indian panel data enabled accounting for both unobservable fixed household effects, as well as correction for parental endowment effects, and therefore represent stronger evidence for the persistence of discrimination.

27
is 458—of 98 maternity leave days, 60 parental leave days reserved for mothers, and 300 unallocated parental leave days. Total leave days for fathers is 60 days, comprised solely of parental leave days reserved for fathers.

> 5 The complete list of assumptions for the indicators can be accessed at https://wbl.worldbank.org/en/methodology.

> 6 The full dataset of legal bases can be accessed at

> https://wbl.worldbank.org/content/dam/sites/wbl/documents/2021/02/WBL1971-2022%20Dataset.xlsx.

> 7 This is based on the assumption that mothers are more likely than fathers to take the parental leave that is not assigned to either parent. In our analysis, we test the robustness of the results to this assumption.

> 8 Maternity Protections Act; sections 3 and 6.
60043|8309|23829|71957|0.002|0.229|0.052|0.085|0.027|0.073|0.08|
|TLU|0.096<br>(0.007)|0.107|0.151|0.031|0|0.003|0.857|0|0.223|0.717|0.689|0.046|
|N|[920,978]|[20,22]|[16,17]|[6,7]|[10,11]||||||||

_Notes:_ Authors’ analysis based on data described in the text. Clustered-robust standard errors are in parentheses. Outliers are excluded (as explained in Appendix B.1). Sampling weights are accounted for. Test (1) is a joint test that the means of the nondemobilized and demobilized of both groups are equal to the mean of the civilians ([A]=[B]=[C]=[D]=[E]). Test (2) compares the mean of the CNDD nondemobilized with the mean of civilians ([A]=[B]). Test (3) compares the mean of the FNL nondemobilized with the mean of civilians ([A]=[D]). Test (4) compares the means across nondemobilized ([B]=[D]). Test (5) compares the means across demobilized ([C]=[E]). Test (6) compares the nondemobilized and the demobilized within the CNDD+ ([B]=[C]), while test (7) compares the nondemobilized and the demobilized within the FNL ([D]=[E]). The number of households in each group is in square brackets. It may vary as outliers or missing values are not taken into account when
<!-- Start of picture text --> China Indonesia Malaysia Philippines Thailand Vietnam<br>TFP growth Growth in Capital Accumulation<br>Growth in labor Growth in Human Capital<br>5<br>4<br>3<br>2<br>1<br>0<br>2000-2007 2008-2014 2000-2007 2008-2014 2000-2007 2008-2014 2000-2007 2008-2014 2000-2007 2008-2014 2000-2007 2008-2014<br><!-- End of picture text -->

**S** ource: Penn World Table 9.0. Human capital H in the Penn World Table 9.0 is assumed to enter production as in the following production function Y= AK<sup>α</sup> (Hl)<sup>1−α</sup> . Years of schooling for Indonesia between 2011 and 2014 are extrapolated. Output, capital and TFP are measured at constant national prices.

# **3. Labor allocation across sectors**

In EAP, labor productivity improvement was driven largely by labor productivity growth within sectors (Figure 7).<sup>3</sup> The three sectors we consider are agriculture, industry, and services. The reallocation of labor away from less-productive sectors to more productive sectors (“static reallocation”) also contributed to improving productivity, especially in Indonesia, Thailand and Vietnam. Limited evidence of faster productivity growth (“dynamic reallocation”) is available only for China.

**Figure 7 Growth in labor productivity: contributions of intersectoral reallocation and within-sector productivity growth (annualized, percent)**

> 3 This is consistent with the evidence from Foster-McGregor and Verspagen (2016). The authors note that withinsector productivity growth explains most of the productivity change for middle to high-income countries. Conversely, structural transformation plays a larger role as economies move from low- to middle-income status. This can be explained by the fact that high-income economies tend to display lower dispersion in productivity across sectors.

9
interviews were always administered in private and were attempted to be administered simultaneously and with a gender match-up between the enumerator and respondent.<sup>12</sup> Regarding agricultural land, following the creation of a roster of all owned and/or cultivated agricultural parcels and the identification of those that are “owned” by at least one household member, this common list of owned parcels that is generated as part of the household interview was fed forward to each individual interview in that household.<sup>13,14</sup> Furthermore, the focus on “personal” ownership of and rights to land in the IHPS meant that the phrasing of the questions with respect to the IHS4 was different for a range of questions on:

- (i) **reported** ownership (i.e. _Who in this household owns this [PARCEL]?_ in the IHS4 versus _Are you among the owners of this [PARCEL]?_ in the IHPS);

- (ii) **economic** ownership (i.e. _If this [PARCEL] were to be sold/rented out today, who would decide how the money is used?_ in the IHS4 versus _If this [PARCEL] were to be sold/rented out today, would you be among the individuals to decide how the money is used?_ in the IHPS);

- (iii) **documented** ownership (i.e. Who is listed on the title or ownership document as owner of this [GARDEN]? in the IHS4 versus _Is your name among the names listed on the ownership document for this [PARCEL]?_ in the IHPS), and

- (iv) right to **sell** (i.e. _Does anyone in the household have the right to sell this [PARCEL]?,_ followed by _Who can decide whether to sell this [PARCEL]?_ in the IHS4 versus _With regard to this [PARCEL]
foodstuffs), textiles and apparel, and footwear. Third, while the high-income economies have relatively little binding overhang in any sector, there is evidence of significant overhang for emerging and developing countries across all sectors. However, the greatest amount of overhang is in agriculture, suggesting that this is the sector in which emerging and developing economies have the greatest discretion to raise applied tariffs from 2013 levels while maintaining their WTO commitments.<sup>15</sup> For high-income economies in agriculture, we return to this issue in our discussion of specific duties and quantitative restrictions below.

Figure 3 presents an alternative approach to the tariff data by examining the share of HS06 imported products within a sector for which the MFN applied rate is defined as being a tariff peak, or a tariff applied at or above the threshold level of 15 percent. For high-income economies, nearly 30 percent of products in the foodstuffs sector had an applied MFN tariff in 2013 of 15 percent or more. Similar peak tariffs can be found for high-income economies in 18 percent of products in the animal sector, 15 percent in footwear, 13 percent in vegetables, and 6 percent in textiles and apparel. The distribution of peak tariffs across sectors is quite similar for emerging and developing countries; it is simply that in emerging and developing countries the share of products within each sector that has such high tariffs is significantly larger. Nearly 70 percent of footwear products in developing countries had applied MFN tariffs at rates that are higher than 15 percent in 2013. Recent empirical research has sought to explain aspects of the considerable cross-sectional variation in MFN applied tariffs and WTO tariff bindings. One important example is Broda, Lim ̃ao and Weinstein (2008), which exploits the variation in applied rates to assess implications of the terms-of-trade theory across a number of different empirical contexts. They examine tariff variation for a number of WTO _non-member_ countries and conclude that market power helps to explain applied tariffs for countries unconstrained by trade agreements.<sup>16</sup> An alternative
|0.095|0.056|0.094|0.358|0.23|0.063|0.018|

Standard errors in parentheses Source: Authors’ calculations

# **3.3. Price Volatility**

High price volatility, and in particular that of food products, is costly for the poor. It makes it difficult to plan ahead and to adjust to fluctuating market signals. Episodes of extreme volatility, especially when these imply food prices spikes are an important threat to food security in a country like South Sudan, in which the average share of food in total expenditures is 80 percent. The negative impact is exacerbated by little access to savings or credit that would allow households to smooth consumption decisions over time (e.g.: by buying and storing food when prices are low to be consumed later when prices are high). As a consequence, in this context, food spikes result in hunger and malnutrition in the absence of other interventions, making it a crucial issue for policymakers. In addition to the spatial dimension of price differences discussed above, food and energy prices in South Sudan have fluctuated substantially within each state capital over time.

Has price volatility increased after the imposition of trade restrictions with Sudan? To answer this question, we first measure the volatility associated with the prices of each product in a given state capital by calculating the standard deviation of the monthly rates of change of prices for each product for the period pre-border closure, and post-border closure. Second, we test for a difference in the standard deviation of price changes before and after border closure.<sup>14</sup> Figure 28 to Figure 34 show the pre and post border closure price volatility for those food products for which there was available price data spanning both sub-periods. We report the state capitals in which the differences in prices volatility were statistically significant.<sup>15</sup> Whenever there was a significant change in price volatility after the border closure,
Policy Research Working Paper 8818

# **Abstract**

Using unique direct observations of patient-provider interactions linked to patient exit interviews and detailed household surveys, this paper assesses the relationship between patient wealth and the quality and price of antenatal care in the Democratic Republic of Congo. Overall, the analysis finds a significant wealth-quality gradient, with a standard deviation increase in wealth being associated with an increase of 4 percentage points in protocol compliance. This increase in compliance represents 8 percent of the average quality of care received by women in the lowest wealth quintile. Over half of the wealth-quality gradient is driven by lower facility quality in poorer areas. However, the analysis also finds statistically significant within-village and even within-facility wealth-quality relationships. Within villages, wealth-quality gradients are primarily driven by wealthier women seeking care at higher-quality even if more distant facilities. Within the same facilities, poorer women tend to receive worse care, but on average they also pay less for the same quality of care compared with wealthier women. The price gap increases in the local ratio of wealthy to poor households, suggesting that providers do not charge different prices only for redistributive reasons.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gshapira@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and
quality upgrading, we rely on several data sets that span a broad sample of countries (28 developed and 31 developing economies) over a 15 year the period from 2000 to 2015.<sup>4</sup> Export quality is derived using detailed HS 10-digit US import data and combined with data on robot use at the country-industry level. To address potential endogeneity concerns, we instrument robot adoption of the home country-industry using robot diffusion among their foreign customers located in other world regions, using their initial foreign trade network defined

> 3 See for example, Acemoglu et al (2020); Acemoglu and Restrepo (2020); Brynjolfsson and McAfee (2011); de Vries et al. (2020), Dixon et al. (2021); Frey and Osborne (2017); Graetz and Michaels (2018); Kromann et al. (2020); Maloney and Molina (2019); Presidente (2017).

> 4 Our data reflects a considerable period of time (16 years) which is important, as it has been shown that the performance implications of technologies and complementary investments in organizational technology can take years to realize (Bresnahan et al 2002).

3
the use of the assigned share of revenue. Additionally, PB may mean slightly different things in different municipalities. Finally, the large municipalities for which I have PB data could be unique, casting doubt on the validity of the results in less-urban contexts. I address these concerns in this section by considering a second measure of equal access to political power for which I have data for all municipalities.

If the poor still have less political power than the rich under PB, my analysis would underestimate the importance of the political power channel. Ideally, I want to examine the effects of income and inequality on revenue use in political spheres where the poor have as much influence as the rich. As a robustness check, I divide the sample into municipality-years with socialist party mayors and those without, and estimate the same model. On average, about 17% of municipalities have a socialist party mayor, defined as an elected mayor whose party affiliation is one of the following five: PDT, PT, PSB, PPS, or PCdoB.<sup>40</sup> These are left-wing, grass-roots parties. There is not a significant relationship between per capita expenditure and having a socialist party mayor, after controlling for municipality fixed effects, median income, and population. However, these parties have historically pursued policies aligned with what blue-collar workers indicate they want in opinion polls. Socialist party mayors are likely to be elected precisely because the poor are equally- or relatively more-politically involved compared to the rich. An advantage of this analysis is that I have accurate, time-varying data for the full sample. I leverage off of within-municipality changes in the party in power over time.

Appendix Table A.2 presents these regression results. I examine the same three dependent variables as in the PB analysis, and the findings are nearly identical. Whether or not there is a socialist party mayor, richer municipalities are less likely to use an exogenous revenue increase to boost education spending or pre-primary enrollment, and more likely to improve pre-primary quality. These
Policy Research Working Paper 8676

# **Abstract**

The labor force of each industrial country is being shaped by three forces: ageing, education and migration. Drawing on a new database for the OECD countries and a standard analytical framework, this paper focuses on the relative and aggregate effects of these three forces on wages across different skill and age groups over 2000 to 2010. The variation in the age and educational structure of the labor force emerges as the dominant influence on wage changes. The impact is uniform and egalitarian: in almost all countries, the changes in the age and skill structure favor the low-skilled and hurt the highly skilled across age groups. Immigration plays a relatively minor role, except in a handful of open countries, like Australia and Canada, where it accentuates the wage-equalizing impact of ageing and education. Emigration is the only inegalitarian influence, especially in Ireland and a few Eastern European countries which have seen significant outflows of high-skilled labor to Western European Union countries.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at cozden@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced
0.518**|-0.556**|-1.150**|-0.549**|-0.587**|
||(0.459)|(0.222)|(0.239)|(0.511)|(0.246)|(0.264)|(0.573)|(0.270)|(0.287)|
|Constant|1.144***|1.147***|1.148***|1.195***|1.201***|1.200***|1.653***|1.642***|1.633***|
||(0.339)|(0.337)|(0.336)|(0.379)|(0.376)|(0.375)|(0.439)|(0.434)|(0.432)|
|Observations|963|963|963|770|770|770|486|486|486|
|Mother controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Child controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Table VII presents estimated effects of maternal mental health on child vocabulary at age 5, separated by whether the mother lives with a partner and his drinking likelihood. We estimate the model selected from LASSO procedure. Selected control regressor set includes mother's age, wealth index, living in urban area, child's age, consumption of durable goods index, household size, number of children younger than 5 years in the household, and Height for