rafmacalaba/data-use-mentions-extended huggingface dataset:
manufacturing in total employment reached about 20 percent before it declined due to industrial upgrading and wage increases.

It seems reasonable to suggest that the Leading Dragon Phenomenon alone can create sufficient labor intensive manufacturing jobs for developing Sub-Saharan African countries to bring them to par with most industrial countries. Even if China‘s manufacturing sector sheds 10 percent of its total employment in the next few years, a pool of 8.5 million jobs will be ready to relocate overseas. The number could almost double employment in manufacturing in African countries in a few years, jumpstarting its process of industrialization. Similarly, in the medium term, India‘s manufacturing sector will prepare a large pool of labor intensive manufacturing jobs for relocation. Evidently, the sheer number of jobs the Leading Dragon Phenomenon can create is unprecedented and will be adequate to industrialize not only Sub-Saharan Africa but also other lower wage countries in the medium term.

# **4.5: Outward Foreign Direct Investment in the Manufacturing Sector will accelerate the leading dragon phenomenon in Sub-Saharan Africa**

SSA countries are not alone in competing for the millions of jobs relocating from China. The scarcity of local entrepreneurial skills and investment capital are invariably the top two constraints for a competitive manufacturing sector. Evidently, the availability of OFDI can enable SSA to overcome these constraints and take advantage of the Leading Dragon phenomenon to finally participate in the global production of labor-intensive products. Outward FDI from developing and transition economies reached $388 billion in 2010 with China at the helm and Brazil, Russia, India and Korea (BRICK) the other key sources of FDI (UNCTAD data) (Figure 4.4). 60 percent of the OFDI from developing countries went into other developing countries, mostly in the form of Greenfield investments which can typically open the door for South-South relocation of various industries from China and other BRICSs (M&A and Greenfield data).

**Figure 4.4 Outward FDI (OFDI) from BRICK Countries,**

48
_MACHINE LEARNING AND HIGH-FREQUENCY SURVEYS IN PAPUA NEW GUINEA 13_ varying distribution of the month-on-month inflation process as an autoregressive moving average process with fractionally integrated generalized autoregressive conditional heteroskedasticity (ARMA-fiGARCH) following Baillie et al. (1996). The estimation is detailed in the Appendix.

# **III. Application to Papua New Guinea’s Fresh Produce Prices**

With a population of over 11.87 million people (2021), PNG is the largest and most populated island state in the Pacific. Yet, the country is known to be one of the most data-scarce and impoverished in the world. The World Bank’s last poverty assessment was performed well over a decade ago in 2009 and estimated that 38% of the population lived under the US$1.90 poverty line at the time. The country performs poorly on a number of social development indicators, but accurately tracking development trends remains difficult because of a lack of representative primary data (Edmonds et al., 2018).

In PNG, the traditional consumer price index (CPI) is produced at an aggregate level, using data from a few urban areas. This does not properly reflect the majority of the population as the World Development Indicators estimate that 87% of PNG’s population lives in rural areas (2021). Traditional price data collection also aims to follow a deliberate sampling and measurement process that is not well suited for monitoring during crises situations, when price levels may rise and fall rapidly. For instance, at the time of writing, the latest CPI for PNG lags by 6 months.

Scarce analyses point out that food markets in PNG are likely to be poorly integrated and disrupted by recent macro-shocks (Tracer, 2005; Huffaker et al., 2021; Davila et al., 2021), and as such, high volatility due to local shifts in supply and demand can be expected. With 87% of the population in rural areas, and highly localized price dynamics, a local-area CPI would be needed to more adequately describe price
countries, heterogeneity across households, heterogeneity across goods, and heterogeneity across sources of gains (namely, consumption and income effects). We illustrate these varying forms of heterogeneity by slicing the results from our simulations in different ways.

To begin, Table 2 shows that the aggregate gains are highly heterogeneous across countries. While the gains can exceed 5% of real household income, as in Zambia (6.93%), Bhutan (6.53%), Jordan (6.55%), Cameroon (6.25%), and Ecuador (5.05%), there are two countries with income losses, namely Burundi (-3.23%) and Ghana (-0.50%) and a few other countries with small gains (e.g., Cambodia, 0.17%). Figure 7 clearly illustrates the cross-country heterogeneity with a map of the relative magnitudes of the gains from trade. We can also use these data to show an interesting result: there is a positive correlation between the aggregate gains from trade and the log of per capita GDP (Figure 8). Roughly speaking, this suggests that richer countries stand to gain more from unilateral agricultural liberalization than the poorest ones.

Second, we examine household heterogeneity and explore distributional impacts, which is one of the main advantages of the Household Impact of Tariffs database. There is a burgeoning literature on this topic, including Porto (2006) and more recent contributions from Fajgelbaum and Khandelwal (2015), Atkin, Faber and Gonzalez-Navarro (2018), Faber (2014) and Atkin and Donaldson (2015), Antras, de Gortari and Itskhoki (2018), and Galle, Rodriguez-Clare, and Yi (2017), and Artuc, Porto and Rijkers (2019).

We start by plotting the developing world distribution of the gains from trade to show how HIT data can be used to analyze global implications of trade with a specific focus on farm households.<sup>8</sup
Second, while past studies have focused on individual characteristics such as rural background, education, age, and gender, they have not addressed the role of individual motivation and personal norms. Yet, different strands of the literature indicate that such factors may have an important effect on career choice. Deci (1975) was one of the first to recognize the role of professional commitment, or intrinsic motivation, while Dixit (1997; 2001) and Wilson (1989) emphasize its role in organizations and its importance for public service delivery. Benabou and Tirole also attribute a central role to intrinsic motivation and contrast it with motivation triggered by extrinsic incentives.<sup>5</sup> Studies applied to the health sector also underline the importance of worker motivation . In recognition of these findings, we will analyze and discuss the effect of individual motivation on the willingness to work in a rural area.

The remainder of the paper is organized as follows. Section 2 presents the basic characteristics of the data, how we collected the contingent valuation data and details of the survey. Section 3 discusses the econometric strategy, while section 4 reports the results. In section 5 we discuss the policy implications and carry out a simulation exercise. Section 6 concludes.

# **2. The Ethiopian Context and the Cohort Survey**

The cohort survey was implemented in Ethiopia, a country with some of the worst health outcomes in the world.<sup>6</sup> With the majority of the population living in rural areas , and with substantial urban-rural disparities in living standards - the Household Income and Expenditure Survey (2000) indicates that average annual household expenditure in urban areas is almost twice that in rural areas (CSA & ORC Macro, 2001) - the health challenges are particularly severe in the rural areas. Endeavours to meet these challenges are hampered by the limited nature of health resources. Public spending is very low: in a recent report per capita health expenditures are estimated at around 4 USD or 25 USD PPP,
be more collaborative in their businesses in the Interpersonal or Personal Initiative condition. In the control condition, on average women worked with 0.24 other people outside the household on their business, and this did not differ in either intervention condition ( β PI=0.04, t(2474)=0.95, _p_ =.342; β II=-0.12, t(2474)=-0.29, _p_ =.772).

The Personal Initiative intervention did not have a significant effect on other relational outcomes, nor did the Interpersonal Initiative intervention affect relational outcomes related to women’s relationship to their broader community, including social standing ( _d_ PI=0.03, SE=0.05, _p_ =.527; _d_ II=0.05, SE=0.05, _p_ =.318), or social cohesion ( _d_ PI=-0.05, SE=0.05, _p_ =.300; _d_ II=0.00, SE=0.05, _p_ =.982). In addition, there was no effect of either intervention on women’s control

> 13 Monetary amounts are PPP-adjusted USD terms, set at 2016 prices and deflated using Niger’s CPI published by the World Bank. In 2016, 1 USD = 242.553 XOF PPP.

19
-|---|---|---|
||**2008**|**2011**|**2013**|**2008**|**2011**|**2013**|**2008**|**2011**|**2013**|
|**INDH**|0.020|0.103|˗0.300***|˗0.014|0.058|˗0.244**|˗0.042|˗0.036|˗0.021|
||(0.093)|(0.096)|(0.100)|(0.093)|(0.082)|(0.101)|(0.046)|(0.039)|(0.031)|
|**Community poverty rate**|˗0.009|˗0.037|0.109***|˗0.013|˗0.051**|0.072**|0.005|0.010|0.001|
||(0.029)|(0.032)|(0.030)|(0.031)|(0.025)|(0.032)|(0.015)|(0.011)|(0.010)|
|**N (Observations)**|678|604|608|678|604|608|638|642|657|

_Source:_ Authors’ calculations based on ONDH impact evaluation panel survey. _Notes:_ *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors clustered on commune level. Quantile regression standard errors are bootstrapped with re-sampling at the commune and household levels. Logit
conservation progress has been highlighted in numerous studies. However, inconsistencies in data across countries and administrative boundaries hinder the accurate assessment of ecosystem health and biodiversity trends (Urbano et al., 2024; Geijzendorffer et al., 2016; Pereira et al., 2013). Moreover, the growing gap between emerging threats and infrequently updated data, often managed by underfunded institutions, presents an ongoing challenge.

To help address this gap, we have produced new public datasets by utilizing millions of georeferenced species occurrence records from the Global Biodiversity Information Facility (GBIF) (Dasgupta et al. 2024a). Using machine-based pattern recognition, we developed a comprehensive database covering the occurrence regions of nearly 600,000 species. These maps were validated by comparing them with expert-reported maps for mammals, ants, and vascular plants, revealing strong correlations in global distribution patterns. Our expanded database includes a broad array of species across terrestrial, freshwater, and marine environments, encompassing arthropods, mollusks, invertebrates, plants, fungi, and more, alongside traditionally studied amphibians, birds, fish, reptiles, and mammals. Figure 1 illustrates the composition of this expanded database, revealing new global distribution patterns that can inform conservation planning.

In this paper, we focus on the implications of our results for biodiversity protection in transboundary regions,<sup>2</sup> joint marine regions,<sup>3</sup> non-determined legal status areas,<sup>4</sup> and fragile and conflict-affected situations areas (FCS).<sup>5</sup> Conflicts and coordination failures in such areas have numerous direct and indirect impacts on biodiversity (see Rist, Norstrom and Queiroz (2024) for a review of the relationships between biodiversity, peace and conflict). These are often difficult to address in non-determined legal status areas and FCSs because of weak governance, limited administrative capacity, and insufficient institutions and policies. The effective protection of biodiversity in transboundary areas must rely on cooperation among the parties involved.

> 2
according to the margination index produced by CONAPO (Consejo Nacional de Poblacion) and then turn to the sample of sixth graders enrolled in indigenous schools in 2007-2008. Note that we follow sixth graders to whatever school they attend after sixth grade; the secondary and high schools that students attend thus are not restricted to be in marginalized areas.

Tables 6a to 6c presents the effects of PROSPERA on enrollment in seventh grade, ninth grade and twelfth grade. As described earlier, we measure enrollment using attendance the day of the ENLACE test application. One advantage of this measure is that it may have less biases than selfreported data on children’s enrollment reported by parents in household surveys, which has been the usual source of data for previous evaluation studies. Tables 6a and 6b show state fixed effects models and school fixed effects models. The results indicate that the impact of PROSPERA on enrollment in secondary school is about 8 percentage points for seventh grade, 9 to10 percentage points for ninth grade and decreases to about 5 percentage points for twelfth grade. The results based on propensity score matching (Table 6c) are similar but about 1 percentage point smaller for enrollment in seventh and ninth grade.

Table 6a: Impacts of PROSPERA on School Enrollment All 6th Graders in Medium, High and Very Highly Marginalized Communities 2008 State Fixed Effects

||7th grade|9th grade|12th grade|
|---|---|---|---|
|Variables|(1)|(2)|(3)|
|PROSPERA beneficiary<br>2008|0.081***|0.101***|0.045***|
||(0.006)|(0.006)|(0.006)|
|Female beneficiary|0.005|0|0.002|
||(0.007)|(0.007)|(0.008)|
|Female|-0.014**|
**Table 4. Proportions of Non-Zero Investments Among Firms in Six African** **<u>Countries</u>**

|Employment|Cameroon|Ghana|Kenya|Uganda|Zambia|Zimbabwe|
|---|---|---|---|---|---|---|
|1≤ ≤5|0.21|0.31|0.44|n.a.|0.29|0.53|
|5<≤20|0.29|0.44|0.40|n.a.|0.29|0.51|
|20<≤100|0.24|0.48|0.41|n.a.|0.28|0.63|
|>100|0.38|0.20|0.44|n.a.|0.38|0.71|
|Total|0.29|0.32|0.42|0.52|0.31|0.66|

_Note:_ The table is based on data from a sample of 821 firms. _Source:_ Bigsten et al. (2005).

One of the main objectives of carrying out investment is to get access to better technology. There are other ways by which African firms can access better technology, however. Foreign direct investment (FDI) can lead to transfers of technology if multinational enterprises transfer technological information to their subsidiaries. Firms that export learn about general conditions and available technology in the international market. Technology licensing arrangements can be established to transfer technology directly. However, so far direct transfers of technology via technology-contracts and the like to African manufacturing firms have been very limited (see e.g. Bigsten and Kimuyu, 2002, on Kenya). Because of this, trade or FDI are potentially important channels for technology transfers in Africa. While there
coverage**<br>(Threshold and hazard<br>bias)|• Subject to reporting in aggregate data<br>sources and reports<br>• Salient shocks likely covered but data<br>less sensitive to idiosyncratic or small<br>shocks<br>• Coverage varies by shock type|• Based on “grassroots” reports elicited<br>from those affected by shocks<br>• Granular and able to cover small and<br>localized shocks<br>• Shock recording subject to questionnaire<br>design (list of shocks, number of shocks<br>recorded)|
|**Population coverage**<br>(Population coverage<br>biases)|• Coverage<br>depending<br>on<br>comprehensiveness<br>of<br>coverage<br>in<br>underlying data sources (e.g. news<br>reports) but not limited to a specific<br>population of interest<br>• under-coverage<br>of<br>poor<br>and<br>marginalized population groups within<br>countries likely|• Limited to (stratified) sample of target<br>population<br>• Potential for gaps in coverage wherever<br>shock impacts not well represented by<br>sample<br>• Poor and marginalized population groups<br>explicitly covered|
|**Temporal coverage**<br>(Temporal coverage<br>biases)|• Continuous coverage but subject to<br>improvements in quality of reports in the<br>long run|• Intermittent coverage limited to years in<br>which survey was conducted and/or<br>recall period of survey questions|
||**Detail and accuracy**||
|**Detail of available**<br>**information**<br>(Missingdata biases)|• Dependent on information reported in
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 22. Benin’s pattern of capital investment in infrastructure and that of comparator countries** Investment in infrastructure sectors as percentage of GDP, by source

<!-- Start of picture text --> 2.5 Benin LIC-Non-fragile<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Public ODA Non-OECD Private<br>% of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ Private investment includes self-financing by households.

# How much more can be done within the existing resource envelope?

About $101 million of additional resources could be recovered each year by improving efficiency (table 19). The most vexing problem is underrecovery of the costs of generated power. Every year up to $50 million is lost due to underpricing of power. Underpricing of water services is also substantial, costing Benin $11 million per year. Reducing distributional losses could save another $11 million in power and $3 million in water. Collection of bills for these services needs to be improved. Benin is losing $5 million per year in the power sector and $3 million per year in the water sector because of undercollection of bills for services rendered. Overstaffing in the ICT sector is quite significant. Cutting staff levels to an economically viable benchmark could save an estimated $17 million in ICT.

Underexecution of the capital budget does not seem to be an issue for Benin, based on fiscal data from 2000–05 gathered by the AICD, but there is evidence that the public investment program has slowed down over the past couple years. The general budget execution rate, 97 percent in 2001, dropped to just 55 percent in 2010. Of particular concern is that execution rates for investment spending are persistently below 70 percent, averaging 63
4 An evaluation in South Africa showed that job seekers who went through a more deliberative written exercise, including setting realistic goals, thinking through obstacles, and envisioning how to overcome these obstacles, were more likely to both find employment and use more formal channels in the process (Abel et al, 2017). Female job seekers may need to engage in the job search process for longer showing greater persistence or “grit” to succeed (Duckworth, 2016). Goal setting can help here as well by both creating realistic targets, and as a reminder to follow through.

In 2016, the Kurdistan Regional Government (KRG) laid out a strategy to accelerate and prioritize job creation in the private sector (Kurdistan Regional Government, 2016). This poses several challenges towards increasing female participation in the labor force. For example, one reason why increased educational attainment by women in the region has not translated into increased labor force participation is that the contraction of opportunities in the public sector has not been met by an increase in the formal private sector (Assaad et al, 2018). However, women still prefer to work in the public sector. While greater completion of tertiary education in the region is an influential factor, the favorable working conditions such as shorter working hours, job security and fringe benefits are also important reasons as to why women are more likely to prefer the public sector (Assaad and Barsoum, 2019). Wage discrepancies between men and women are also lower in the public sector (Said, 2014).

In KRI, while women’s participation in the private sector has increased incrementally, labor force and demographic surveys indicate these rates are still low. For example, labor force and demographic surveys indicate the participation rate was 20.1% in 2012, 14.9% in 2013, 20.6% in 2014, 24.2% in 2015, and 24.3% in 2017 (compared to about 55% for men in all of these surveys). More importantly,
expenditure per capita and the location of refugee settlement sites. I estimate a household fixed effects variant of equation (1) to eliminate the concern from such characteristics and present the result in column (3). To partially circumvent the remaining concern from time-varying factors, I additionally control for many (12) household- or zone-level time-varying variables in the household fixed effects specification and report the result in column (4). The estimates in these last two specifications remain consistent with the estimate in column (2), confirming that hosting refugees has a significant negative effect on household consumption expenditure per capita. Specifically, on average, the estimated elasticity of household consumption expenditure per capita to refugee intensity is about -0.19.

This finding is not in line with the existing empirical evidence on the short-term household consumption impact of hosting refugees (Kreibaum, 2016; Rozo and Sviastchi, 2018). Kreibaum (2016) shows that hosting Congolese refugees in Uganda is beneficial and Rozo and Sviastchi (2018) find that hosting Syrian refugees in Jordan is immaterial. Unlike the case in my paper, it is important to note that these studies employ repeated cross-section data and the overwhelming majority of refugees considered in Rozo and Sviastchi (2018) are out-of-camp refugees.

Additionally, the documented adverse consumption effect of hosting refugees is not consistent with 80%-90% of the existing evidence if the broader literature that examines the shortor long-term welfare impacts of hosting refugees, expellees, or internally displaced persons is considered, as per the survey in Verme and Schuettler (2021).

As there are differences between rural and urban areas in, among others, the economic structure, the skill levels and skill mix of individuals, and access to and type of financial services, which may be useful in smoothing household consumption, the aggregate estimate may mask important difference in the consumption impact of hosting refugees between these areas. Thus, I next examine if there is heterogeneity in the estimated impact according to urbanity. Columns (5) and (6)