rafmacalaba/data-use-mentions-extended huggingface dataset:
silent on how such skills affect country participation in GVCs especially in manufacturing GVCs. The lack of empirical evidence on this area hampers the design of policies which aim at improving country participation in GVCs and ‘social upgrading’ (Grundke et al., 2017b).

Recently, researchers such as Grundke et al. (2017b) attempted to estimate the effect of skills on country participation in GVCs. However, there is no consensus on the direction of causality. This is because higher skill endowment is expected to contribute to the participation, competitiveness and positioning of countries in the global value chains (Kowalski et al., 2015). At the same time, countries are likely to gain substantial skill-benefits from higher engagement in GVCs (Del Prete et al. 2016). Del Prete et al. (2016) provide suggestive evidence that skill is one of the fundamental determinants of country participation in GVCs for North African countries, using recently released multi-regional Input-Output tables from UNCTAD-Eora. However, they pointed out that the ability to benefit and retain the benefits from GVC participation depends on country specific characteristics such as the level of human capital development.

While existing studies agree on the importance of skills in country GVC participation, they seldomly specify which skills matter for which types of GVC participation. To fill this gap in the literature, Grundke et al. (2017b) investigate the extent to which different skills and their distributions relate to global value chains in order to identify the specific skills that affect participation in GVCs. They find that cognitive skills, ICT skills, management and communication skills, and readiness to learn new skills have significant positive effects on integration into GVCs. However, this relationship is stronger for cognitive skills than for the other skill indicators. Furthermore, Grundke et al. (2017a) explore how the skill composition of a country's workforce (both types of skills and their distribution) shapes specialization in specific industries as well as their competitiveness and integration into GVCs. They find that industry competitiveness and positioning in GVCs depend
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# Figure 15a: A Complete Decomposition

<!-- Start of picture text --> 0.0 ____ _-_ _<br>C)~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~T<br>0<br>00<br>on<br>'.4-4<br>0<br>-2.0<br>% 43 b \ 0 ,° + ¢ oR{pc. c O @ @ <1 4 @ 4° e 4> 9<br>Percentiles<br>Sp-alphas and beta 9961976<br><!-- End of picture text --> Source: "Pesquisa Nacional por Amostra de Domicilios"<sup>(PNAD), 1976 and 1996.</sup>
our paper are labelled. Source: WDI (we use NY.GDP.PCAP.PP.KD for GDP PC, SL.TLF.CACT.FE.NE.ZS for Female LFP and SL.TLF.CACT.MA.NE.ZS for male LFP).

**Methodology.** The simplest estimates of the impact on standards of living from closing gender employment gaps in MENA build on the Gender Employment Gap Index (GEGI) of Pennings (2022). That paper uses a standard Cobb-Douglas production function and the assumption of a constant long-run MMPK or savings rate to show that the long-run GDP per capita gains from closing gender gaps _equal_ the size of the employment gaps themselves (expressed as a fraction of baseline employment). However, because the policy conversation tends to focus on shorter time horizons and MENA exhibits relatively low rates of private sector investment, we also produce

> 1Note that for the rest of the paper we focus on employment rates, rather than labor force participation, due to the difficulty of measuring job searching efforts in developing countries.

2
Combes and Gobillion, 2015), although the trend has generally been towards smaller spatial units or using individual firm or worker data. In this paper we use 110 districts, corresponding to administrative boundaries, covering the entirety of Ghana. Using data aggregated from firms for areas rather than firms themselves implies the possibility of what is called the Modifiable Area Unit Problem (MAUP). This refers to the negative influences that the use of discrete spatial boundaries can have on the conclusions drawn from empirical analysis. Auvray & Agha (2018) discuss the MAUP and consider two effects: the zoning effect and the scale effect. The zoning effect is that the particularities of a border are arbitrary and can spuriously influence growth measures in specific locations. Auvray & Agha (2018) used simulations to show that the division of one total area into x number of subareas of equal size can lead to substantially different results for measuring spatial concentration depending on the shapes of the areas.

The second MAUP effect is the scale effect. Auvray & Agha (2018:7) note that for a given area, empirical results are affected by the level of geographic aggregation. Briant et al (2010) notes that MAUP is more of a concern in testing for agglomeration economies when the size of the geographical areas is large. Briant et al (2010: 291) describe the Zones de Emploi regions in France “fairly small”, with an average size of around 1500 square kilometers and find that the MAUP is less of a concern when dealing with smaller areas. The average size of the Ghanaian districts we use in this paper is around 50% larger than the French regions and thus the MAUP may not be too much of a concern. Briant et al (2010) also note that the MAUP is less of concern when the independent and dependent variables are aggregated in the same way, as is done in our empirical analysis below. The districts we use are also of a similar size to the areas used in much of the research in this literature
Using respondent’s municipality of residence I match homicide rates by municipality-year. For the LAPOP sample, homicide rates have a mean value of 0.75 and standard deviation of 0.52 (with a maximum value of 3.1 homicides per 1,000 inhabitants). Crime statistics merged to the Latinobar ́ometro sample are fairly similar: mean value of 0.8 and standard deviation of 0.57 (and maximum of 4.2).

I also take into account differences across municipalities and years in central government unconditional transfers (FODES, by its acronym in Spanish) per capita received the year before the implementation of the survey.<sup>8</sup> Data comes from the Instituto Salvadore ̃no de Desarrollo Municipal.<sup>9</sup> FODES represent the main financial resource for municipalities. The central government allocates 6% of the national budget to municipal governments, 80% of these funds are intended to fund investment and 20% expenses. Importantly, the allocation mechanism for these funds is based on population (50% ), equity (25%), poverty (20%), and territorial size (5%) of each municipality. For the LAPOP sample, FODES Transfer per 1,000 inhabitants (in 1992) has a mean value of USD 45, a standard deviation of USD 39, and maximum of USD 206. On the other hand, Latinobar ́ometro sample shows a mean value of USD 46, a standard deviation of USD 46, and maximum of USD 478.

# **3 Empirical Strategy**

Exploiting several waves of two public opinion surveys (i.e., LAPOP and Latinobar ́ometero) from 2004 to 2018, I estimate the following equation: where _yimt_ is one of each of the reported attitudes by individual _i_ , living in municipality _m_ , and year _t_ . The variable _Econmt_ is the proxy of economic activity (NTL density) or per capita household income for a restricted sample. Both vary
education are based on children's recalled information, and thus may contain non-negligible measurement error (Emran and Shilpi (2021), Torche (2019)). This would tend to bias downward the estimated parents-children persistence in education and, in turn, lead to downward biased estimate of the share of the intergenerational transmission in sibling correlation. The DHS data on parents are not based on recall, and thus are much more reliable.

There are 53 countries in our sample. We use 230 waves of DHS surveys to build our data base. We exclude 42 countries where at least one DHS survey is available but the sample size is small. The cut-o for inclusion is a minimum of 1000 observations in the sample. The trade-o between country coverage and sample size is well-appreciated in the literature. For a recent analysis of intergenerational educational mobility covering a large number of countries (153), see Narayan et al. (2018), but, as noted earlier, they do not provide estimates of sibling correlation.<sup>28</sup> In each wave of DHS, our sample includes children of age 16-28 in the survey year. The exclusion of relatively older age cohorts in each wave is motivated by two considerations. First, it reduces the possibility of sample truncation due to grown-up children leaving the household for work or to start a family. Second, as noted earlier, we would like to exclude children who are born far apart as they are likely to face dierent family, neighborhood, and school environments. Among our 53 countries, there are 6 countries with fewer than 2000 observations, and 22 countries with sample size more than 5000. The total number of observations in our data set is 544624. The country level estimation samples include children from the 1960s to 1990s birth cohorts. But in many countries, the number of observations for the 1960s birth cohort is small because only a limited number of DHS surveys were administered in these countries in the 1990s and earlier. For the analysis
STEVEN PENNINGS 20

# Appendix A. Summary of Data Sources

- US import prices and US producer prices (dependent variables in regressions): confidential BLS microdata (see www.bls.gov/bls/blsresda.htm).

- Import and export values (for import shares): Feenstra Trade Database (http://cid.econ.ucdavis.edu/).

- Nominal bilateral exchange rates, country CPI, US CPI: Gopinath and Rigobon (2008) (http://www.aeaweb.org/articles.php?doi=10.1257/aer.100.1.304). Chinese CPI: IMF IFS.

- US Real Trade Weighted Exchange Rate (TWI), US GDP: Federal Reserve of St Louis Economic Database (FRED).

- Manf. output data and materials prices (series: PIMAT) from NBER-CES Manf. Ind. Database (http://www.nber.org/nberces/).

- NAICS6 measure of producer prices: bls.gov.

- Construction of Figure 1 (Aggregate Data). Manufacturing import prices index from Canada and Europe and all imports from Japan downloaded from bls.gov/mxp/. Annual average indices deflated by annual US CPI from FRED. Bilateral ER data and foreign CPI from FRED, annual averages. Competitors’ RERs data: Broad Real US TWI from FRED. Country trade weights for removing the bilateral rate and rescaling the TWI: Federal Reserve H.10 release.

# Appendix B. Data Construction

B.1. **Import price data.** The import price variable is constructed as the change in the log of the price of each individual good over its life in the sample (mean of around 2 years) in USD, from the first reported price to the last new price, which is essentially a pooled cross-section regression. The models in Sections 2 and 4 suggest I should deflate by unit labor costs (ULC). However, they are not available (or
of Ghana: Accra (and neighboring Tema), Kumasi, Takoradi and Cape Coast. The sampling scheme is based on a stratified random sample of urban households from the 2000 census in Ghana. Thus, the data are roughly representative of urban labor markets. The data span a 7 year period from 2004 until 2010, but the 2007 wave is a recall wave in which no indicators of subjective wellbeing were collected and is therefore excluded from the present analysis.

Personal Characteristics and Labor market Indicators: The survey contains the standard measures of age, years of education, gender, whether the respondent is married and is the head of the household, city of residence and ethnicity. We further construct height Z-scores conditional on age and gender as a proxy for the general health of the respondent.

For wage employees the earnings variable measures real monthly wage income, while for the self-employed the real earnings measure is based on enterprises profits. Thus, earnings for self-employed workers reflect both the returns to capital and the returns to labor. In addition to these earnings measures, and hours worked, the survey includes three questions that allow greater precision in the measure of conditional wages: tenure in the present job; years in the formal sector, and whether or not the present position is an apprenticeship.

We are also able to construct a measure of household assets from questions on whether the family owns a bike, telephone, electric stove, motorcycle, car etc. It does not include assets.

Satisfaction Measures: Our subjective wellbeing measures are provided by the answer to the question: “All things considered, how satisfied are you with your current work? (/life?/financial situation?)”. In all three cases, the options given to respondents were: “1.Very Dissatisfied, 2. Dissatisfied, 3. Neither Satisfied Nor Dissatisfied, 4. Satisfied, 5. Very Satisfied”. Information on subjective job satisfaction was collected in all waves (except the recall wave), while information on life- and financial
# evaluation of DICIPE.

We collected two rounds of data to measure the impacts of the DICIPE program, before the start of the intervention (baseline) and three years later (medium-term follow-up). Baseline data collection took place between September and December 2016 with 4,687 caregivers and children, while endline data collection was carried out from December 2019 to April 2020 with 3,765 caregivers and their children. We complement these survey data with classroom observation and qualitative interviews with caregivers, facilitators, and school administrators from a subsample of communities. This additional data collection was carried out between June and July 2019. We define the youngest child in the household aged 36 to 59 months at baseline as the “target child” that the intervention targeted. Baseline characteristics depict acute deprivation among the target children and families of our sample. The average target child is stunted and the vast majority of caregivers are illiterate (81%).

The impacts of the DICIPE preschool construction program on primary school-age outcomes are positive and large. First, take-up of preschool education increased massively after construction. Compared to a small base of 2% of children in control communities enrolled in preschool, the intervention increased preschool enrollment rates in treated communities by 73 percentage points. The program also had significant positive effects on enrollment in and progression through primary school: children in treated communities were 6 percentage points more likely to be enrolled in primary school (an effect of about 10%) and 3 percentage points less likely to repeat a grade (an effect of about 20%) compared to children in control communities. Overall, we document intent-to-treat (ITT) impacts of 0.16 of a standard deviation (SD) on a primary school enrollment index.

Second, we assess children’s cognitive and social-emotional skills and document ITT impacts of 0.16 SD on a skills index. The magnitudes of these estimates are consistent with other early childhood interventions in the literature (see, for example, Bos et al. 2024; Berlinski et al. 2009
as possible.

# _iii. Corruption_

33. From inception, oil production in Nigeria has been associated with a widening of the income gap as well as increasing poverty and corruption of state institutions. Weak control on petroleum governance made corruption possible (see Gillies 2008 for a review). Rent-seeking in regulation and at political levels could continue for decades. According to international rankings, Nigeria‘s corruption level is perceived to be among the highest in the world. It constantly was ranked among the bottom three of Transparency International‘s Corruption Perception Index (CPI) from 19992005, after which its ranking began to improve. Particularly the 2010 CPI report indicates that Nigeria was ranked 134 of the total 178 countries (Transparency International 2010). Perceived improvements are the result of strenuous efforts by the Economic and Financial Crimes Commission (EFCC) to arrest and prosecute those guilty of advance fee fraud, money laundering, bribery, and other financial crimes. The EFCC has strategically targeted high-profile political office holders, especially state governors, who frequently were accused of money laundering by the President and Minister of Finance. The former governors of Bayelsa and Plateau were impeached following the EFCC‘s charges of money laundering, although the methods by which this impeachment was accomplished fell short of strict constitutional and legal stipulations.

34. Following the government‘s removal of the EFCC chair, Nuhu Ribadu, in December 2007, the anticorruption fight appears to have weakened. U.S. Secretary of State Hilary Clinton alluded to 17
(see below). Finally, we use the Sakernas survey to generate variables used as controls in the econometric analysis. These variables include the age composition of the population and the share of urban population in each province and year, and the composition of industrial employment by educational level and by gender in each year of data.

Second, we constructed a variable indicating the skill content of occupational groups. Data on types of skills by occupation are not available for Indonesia. Many studies in a similar situation rely on the U.S. Department of Labor’s O*Net database, which collects occupation-specific attributes for detailed occupations in the United States. However, the United States and Indonesia differ greatly in their levels of development and production technologies, which could affect both the levels and rankings of the skills used in each occupation. Another possibility is to use information from the World Bank’s STEP (Skills Towards Employment and Productivity) survey of working-age people (15-64), which covers Vietnam and the Philippines in East Asia. Unfortunately, the survey mostly covers urban areas, which makes it less relevant for Indonesia where only half of the population is urban. On balance, we decided to use the 2015-2016 STEP survey of the Philippines for our main results and the U.S. O*Net database as a robustness exercise.<sup>3</sup>

> 3 We decided to use the STEP survey of the Philippines instead of Vietnam because for the latter country the survey only covers the main cities.

6
relational affordances (e.g., social capital, social influence) were also strengthened.

For this analysis, we first compute the standardized main treatment effects at <mark>follow-up</mark> across all psychosocial outcomes of the Psychosocial arm compared to the Control arm (Table 1, column 3), as well as the marginal effects, i.e., added value, of the psychosocial interventions by taking the difference between the Full and Capital arms (Table 1, column 4).<sup>9</sup> We rank-order the main treatment effects to highlight those with the highest effect size estimates. Second, we assess the extent to which the psychosocial variables positively predict, or correlate with, women’s offfarm business revenues, using data from the control group at <mark>follow-up</mark> (Table 1, column 5). We focus on women’s business outcomes because they were a primary target of the program and a proximal step on the path to reduce poverty. We present bivariate correlations for comparability across outcomes and interpretability in terms of effect size. Finally, given different limitations of these analysis approaches, we group psychosocial variables according to a triangulation exercise: we consider the outcomes with the greatest likelihood of being mechanisms to be those that show the strongest and most points of supportive evidence across these three analyses (i.e., columns 3- 5 of Table 1).

# **_Results_**

> 8 These estimates assume 50% annual dissipation of impact and 5% discount rate. They are conservative given that the benefits only include effects on consumption and not on other indicators of wealth (e.g., assets).

> 9 We present both main and marginal effects because they have different interpretations. The comparison of the Psychosocial arm versus the Control arm represents the causal effects of the psychosocial interventions _plus_ the core economic component. The difference between the Full arm effect minus the Capital arm effect represents the marginal effect of the
Taken together, non-determined legal status territories, areas with fragile and conflict-affected situations status, joint marine regimes, and transboundary river basins cover significant portions of the world's terrestrial and marine areas. Non-determined legal status territories cover approximately 4.6 million square kilometers, while areas with fragile and conflict-affected status extend over 37 million square kilometers. Figure 3 shows the geographic distribution of FCS countries across the world. International joint marine regimes cover over 400,000 square kilometers, and transboundary river basins encompass a vast area of nearly 62 million square kilometers. These regions, often characterized by geopolitical tensions and environmental challenges, have substantial importance for international cooperation and conflict resolution. According to population data from WorldPop (Tatem 2017; WorldPop and CIESIN 2018), an estimated 2.1 million people reside within non-determined legal status territories, while around 936 million people live in areas marked by fragile and conflict situations. Furthermore, over 3.5 billion people inhabit regions dependent on shared water resources in transboundary river basins, underlining the complex interplay between population distribution and resource management in these geopolitically sensitive and overlapping areas. Figure 4 illustrates the log human population density in transboundary river basins across the world.

# **The Role of Human Population Density**

Understanding the relationship between biodiversity and human population density is crucial for addressing the ongoing biodiversity crisis. As human populations grow and expand, the pressure on ecosystems intensifies, making it essential to explore how population density impacts biodiversity conservation. Human activities, particularly in densely populated areas, have been a major driver of ecosystem alteration, from habitat loss and fragmentation to overexploitation of resources. The clearing of forests and wetlands for agriculture, urban development, and infrastructure projects has directly contributed to the decline in species diversity and the disruption of critical ecological processes.

Human population density often correlates with higher levels of resource consumption, which in turn often leads to greater environmental degradation. For example, densely populated urban areas tend to have higher pollution levels, increased waste production, and more extensive land conversion
institutions are supportive of rule of law principles and permit access to non-<br>discriminatory judiciary|Fraser Institute: Economic Freedom of<br>the World 1997|
|||<br>Category V-C|
|Institutions of Dem<br>|ocracy<br>||
|Political Rights|Average county scores for the years 1990-99|Freedom House: Annual Survey of<br>Freedom|
|Civil Liberties|Average county scores for the years 1990-99|Freedom House: Annual Survey of<br>Freedom|
|Press Freedom|Average county scores for the years 1990-99|Freedom House: Press Freedom 1999|
|Decentralization an|d Bureaucratic Culture||
|Investment Profile|A measure of government’s attitude to inward investment as determined by four sub-|Political Risk Services: International|

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