rafmacalaba/data-use-mentions-extended huggingface dataset:
their companies’ business strategies, policy barriers, operational obstacles, and investments in the host economy. Information was collected on general characteristics and investments of the company, the importance and effect of global megatrends on the company’s business operations, FDI’s contribution to the host economy, and the importance of investment policy factors and operational obstacles faced by foreign-owned affiliates.

The survey was designed to generate results that are representative at the country level and comparable across countries. It targeted a statistically representative sample of existing foreign-owned affiliates across the 10 surveyed MICs. The target was to reach 125 interviews per sector (manufacturing and services). Each country sample comprises roughly 250 affiliates with at least some level of foreign equity ownership and at least five employees. The only exception is Nigeria, where, due to sampling frame limitations, the sample comprises 164 respondents (55 manufacturing and 109 services). Thus, across the 10 target countries, more than 2,400 responses were collected.

# **4. DESCRIPTIVE STATISTICS**

The GIC 2019 Survey attempted to draw a representative sample of foreign investors across the 10 surveyed emerging markets. This section outlines the profiles of the 2,424 survey respondents.

_Sector and subsector:_ Survey respondents operate across a wide range of sectors. By design, about half of surveyed affiliates were in the manufacturing sector, and about half were in services. Within each sector, the sample covers many subsectors: _Machinery and Equipment_ comprise the largest subsector within manufacturing, and _Wholesale and Retail Trade_ is the largest subsector within services in our sample. However, no one subsector accounts for more than 11 percent of the overall sample (see Table 1).

**Table 1. Respondents by sector and subsector**

|**Sector and Subsector**|**N**|**Share of total sample**|
|---|---|---|
|**MANUFACTURING**<br>|**1,190
in light of the results in column (1), we can safely conclude that institutional quality has much more explanatory power than Different-ness in Legal Origins.

# **_Robustness: cross-sections_**

One issue that one might have with our IV estimates is that our instrument is not time-varying, while our instrumented variable is time-varying. In order to address this concern we re-estimate the specification in columns 1-2 in Tables 4-6 on cross sections of 5-year averages. Our sample is 16 years long, so we take averages over three 5-year periods: 1985-90, 1991-94 and 1995-99. Our averaging procedure takes into account the fact that some year observations for a countrypair might be missing; in order to avoid dropping too many observations we average over the years for which data are available, within the 5-year period. We choose not to estimate specifications with tariffs in order to avoid too much imputation out of the tariff data, thus asking too much from already scant tariff data: some of the average tariff data points already exist for only one country as it is (see description in The Data subsection) ; moreover, many year observations for tariffs are missing.

The results are reported in Tables 8-10 and are in line with the estimates from the annual frequency sample. Interestingly, the estimates for the effect of different legal origins in Tables 8- 9 – when Bureaucratic Quality or Control of Corruption are controlled for – are much smaller than in the previous ones, sometimes small enough not to render statistical significance. The largest estimate is found in Table 8 in column 2: 18% less trade due to different legal origins. However, the estimates in Table 10 – when Protection of Property Rights is controlled for – are larger than before: 26% (OLS) and 32% (IV) less trade due to different legal origins.

25
the pattern of inequality during the crisis period cushioned the poverty impact of the crisis.

Table 3.6.Shapley Decomposition of Poverty Outcomes 1993-1996

|Measure|TotalChange|Growth|Inequality|
|---|---|---|---|
|Headcount|-11.04|-16.05|5.01|
|Poverty Gap|-5.70|-8.83|3.13|
|Squared PovertyGap|-3.14|-4.95|1.80|

Source: Computed Table 3.7. Shapley Decomposition of Poverty Outcomes 1996-1999

|Measure|TotalChange|Growth|Inequality|
|---|---|---|---|
|Headcount|4.79|10.00|-5.21|
|Poverty Gap|1.23|4.97|-3.74|
|Squared PovertyGap|0.47|2.61|-2.14|

Source: Computed Table 3.8. Shapley Decomposition of Poverty Outcomes 1999-2002

|Measure|TotalChange|Growth|Inequality|
|---|---|---|---|
|Headcount|-2.88|-5.99|3.11|
|Poverty Gap|-0.88|-2.97|2.09|
|Squared PovertyGap|-0.40|-1.55|1.15|

Source: Computed

# **_4.  Conclusion_**

This paper deals with the important issue of identification of pro-poor growth. This is essentially an exercise in social evaluation as it entails a comparison of social states characterized by various distributions of the living standards induced by the growth process. Starting with a general impact indicator defined as a weighted sum of individual impacts, the paper offers an integrative framework for a unified discussion of various concepts and measures of pro-poor growth emerging from the current literature. It is
through (4). We also repeat the analysis separately for urban areas and for rural and estate areas (grouped together) to check if prediction accuracy varies across sectors. Finally, focusing on the 55 sub-districts, we reduce the training sample size by half and quarter, to examine if the prediction accuracy is sensitive to the size of the training sample. As a robustness check, we also implement a flexible random forest algorithm using population density as the response variable, using the four sets of satellite indicators described above. For each model, we report out-of-bag _R_<sup>2</sup> , RMSE, and MAE.<sup>17</sup> Next, we focus on an overlapping sample between the sampled villages in the HIES and those in the 55 sub-districts for which proprietary features are available. These amount to 1,360 villages. In 414 villages of this sample, we model the HIES population density as a function of the all public and proprietary satellite indicators, and apply the procedure described above. Since this is a survey-based model, we use the inverse of village population for village _v_ as weights, which is given by _V illage Population_ <u>1</u> _v_<sup>.Inaddition,therecouldbefactors</sup> correlated with satellite-derived indicators that affect the probability of selection of the village into the HIES. We therefore adjust the weights based on the predicted probability of the village being sampled in HIES, based on satellite indicators available for all villages (Horvitz and Thompson, 1952; Wooldridge, 2002). This probability is obtained from the following probit model: where _INHIESv_ is a binary indicator for whether village _v_ is sampled into HIES, and _lassov_ represents all LASSO-selected variables from the Poisson model. We estimate this model using data for all 1,360 villages in the 55 sub-districts. The predicted probability of the � village selection into the HIES, _INHIESv_ is used for correcting the weights in the following <u>1</u> way :
keep increasing in our modeled data, possibly suggesting that the 2008-2014 successes may have partly been driven by a combination of slow economies and improvements due to ongoing urbanization that will likely halt as rural areas become less populated. Improved economic conditions in the top income countries may result in renewed upward momentum, albeit the increase is much slower than what is seen in the lower income countries. The slowdown resulting from modeled data indicates that stabilized levels may possibly be reached soon in high income level countries. Disregard of the decoupling discussion in high income countries, that evidently must result from policy intervention, we see that carbon output in the fourth income quintile, and China in particular, dominates the global result. In total, 57% of average global 2030 emissions, or 35.8 GT, is produced in these countries. Without improved policies, the fourth income quintile produces more than the 2014 global output.

# **6 Discussion and Conclusion**

Through this paper, we have discussed penalized non-parametric modeling of environmental output across economic development. This type of modeling works well for nonlinear processes given that they do not result in overly complex dynamics. We deployed the framework to study environmental data in a panel of 95 countries. We specifically modeled satellitederived deforestation, satellite-derived air pollution, and reported carbon emissions. To deal with heteroskedastic variance, we transformed the data to logarithmic degradation intensity of per capita GDP. Our out-of-sample shrinkage of fixed error components did not support time fixed effects.

Our results suggest that production gradually favors conserving the earth’s finite resources as GDP increases, but that this alone is not sufficient to offset the effects of scale growth. Instead, structural change in the economy shapes environmental output curves. This process shares similarities between sovereigns, but remains largely heterogeneous. These results do not support a single Environmental Kuznets rule. Instead, the results emphasize the importance of local economic conditions on environmental results. Across all data levels, some effects hold unambiguously. Poverty and income inequality correlate with higher pollution, higher deforestation
knowledge-capital model that incorporates spatial correlations in the independent variables and find that third-country characteristics exert significant effects on FDI flows. The linkage between host countries declines with bilateral distance among the host countries. Using sectoral FDI data, Blonigen et al. (2007) examine how investments in third countries affect a country's receipt of U.S. FDI. They find evidence of negative interdependence across proximate host countries, a result consistent with export-platform FDI theory, among European OECD members. The importance of third-country effects in inbound FDI is shown in Blonigen et al. (2008). The authors find a strong parent market proximity effect whereby parent markets' proximity to large third nations increases the volume of FDI. Similarly, Chen (2011) examines the cross-country interdependence in French multinationals' production networks using subsidiary-level data and finds strong spatial interdependence in multinationals' foreign production networks. The role of distance and transportation cost depends on the input-output linkages between subsidiary locations. MNCs are more likely to locate final-good production in countries with large market potential, vertically linked subsidiaries in proximate countries, and horizontally linked subsidiaries in remote locations.<sup>4</sup>

# 2.2 The Impacts of FDI in BRI Countries

The economic impacts of FDI have been studied in a large body of literature, through macro, crosscountry analysis as well as micro, firm-level research. At the macro level, numerous studies, including, for example, the work by Borensztein et al. (1998) and Alfaro et al. (2004), have examined the relationship between FDI and economic growth. Evidence suggests that FDI could exert a positive effect on economic growth when host countries meet certain economic conditions, including sufficient human capital stock and relatively developed financial markets. At the firm level, an extensive volume of research has examined the effects of FDI on host countries in a variety of dimensions including productivity, employment, wage rate, and export performance. The productivity effect of FDI, in particular, has attracted considerable empirical attention.

In this
for the Coordination of Humanitarian Affairs (UNOCHA), it is also the only source of information including IDPs (both from conflict and natural shocks), returnees from abroad and migrants, regardless of their legal status and reasons for displacement.<sup>1</sup> In addition, while UNOCHA supplies more frequent information on flows of internal displacement at the district level, the information cannot be disaggregated at the village level.

IOM did not collect data prior to the year 2018 in Badghis, suggesting that there was limited evidence of major displacement flows before then, hence justifying our focus on the 2018-2020 period.

Limitations in terms of measurement errors are nevertheless important to keep in mind and further make the case for better data on displacement in Afghanistan. Due to the absence of a clear definition of geospatial borders for IOM settlements, the possibility that settlement buffers could overlap cannot be excluded. This could bias the results if the weights of specific outcomes are artificially inflated by appearing multiple times in overlapping buffers for what should be a unique settlement. In other words, in denser areas, some of the buffers may overlap; hence the population summarized can be duplicated. Therefore, displacement numbers might also be overestimated by counting multiple times the same population. This, however, is not an important issue for our analysis, which focuses on the evolution of the displacement flows in settlements rather than absolute values. In addition, the survey relies on self-reported estimates, which can be associated with important measurement errors. There again, assuming that the bias remains constant throughout time, this issue is unlikely to affect the analysis dramatically.

The IOM Displacement Tracking Matrix (DTM December 2020) allowed us to identify 294 settlements in Badghis across the seven districts . As of December 2020, there were 294 settlements in Badghis for which IOM has collected data on inflows and outflows of persons (Figure 2).<sup>2</sup> Key indicators refer to IDP arrivals (Afghans who fled other settlements and presently reside in the assessed
presence in foreign-owned firms, as 70 percent of strikes occur in unionized enterprises (cf. ILO, 2011).

While the high and rising number of wildcat strikes are a symptom of the weaknesses of Vietnam’s industrial relations system, their consequences reach much farther. Strikes by workers (and lock-outs by employers) are typically taken as “last resort” measures, when no agreements can be reached on pay, working conditions or equally fundamental issues. Strikes are in most contexts an outcome of negotiations failing. Thus the pattern shown in Figure 7 raises the concern that the institutions for setting pay and other conditions of employment are not functioning in the manner expected, forcing workers to extreme measures. Whether wildcat or not, strikes and lock-outs have significant and harmful consequences. They lead to lost days of production, reduced profits and unpaid wages. That workers frequently resort to last resort measures and apparently have no recourse to less costly ways of voicing their concerns can in turn be seen as another symptom of the dysfunctional state of Vietnam’s industrial relations institutions.

**Figure 7 – Number of wildcat strikes in Vietnam by type of enterprise and annual CPI inflation**

<!-- Start of picture text --> 1000 25%<br>900<br>20%<br>800<br>700<br>15%<br>600<br>500 10%<br>400<br>5%<br>300<br>200<br>0%<br>100<br>0 -5%<br>State Enterprise FDI Private Enterprise Annual Inflation<br>Annual inflation<br>Number of wildcat strikes<br><!-- End of picture text -->

**Source: VGCL (2012) and World Development Indicators.**

**Notes: Annual inflation measured by changes in the consumer price index.**

Problems with this system have long been recognized. As a result, the 2012
both multi-ethnic societies with a history of civil conflict. The question they are interested in is whether news media contribute to nation-building in multi-ethnic societies. Using word counts and topic-modeling, they find that when an ethnic group is explicitly named in the newspaper article, it generates more and negative references in readers’ comments – referring either to the same ethnic group mentioned in the article or to another ethnic group. In other words, news media can negatively influence inclusive nationalist frames by triggering ethnicity specific reactions among their readers.

The question is if these NLP papers can be classified as “reflexive.” They substantially expand the kinds of data that can be analyzed to study discrimination and polarization and contribute towards our understanding of bias in settings that have not been previously analyzed, but the data are analyzed in a manner similar to quantitative survey data without any explicit attempt at cognitive empathy, process understanding, or participation. Comparing my own work in Parthasarthy et al (2019) and Sanyal and Rao (2019) in analyzing the same topic – Indian village meetings, it is clear that the close reading that Paromita Sanyal and I did of the transcripts, followed by hand-coding, discussion and iterative analysis (i.e. traditional qualitative work) brought us into closer proximity with the people who participated in these village meetings, than when my co-authors and I let machines do the coding for us. This is, more generally, true of the work done using NLP to study social media. Matamorez-Fernandez and Farkas (2021) conduct a systematic review of 104 studies, across disciplines, that study hate speech, and racial and gender bias in social media. They find “a lack of geographical and platform diversity” and “an absence of researchers’ reflexive dialogue with their object of study.” This is not by any means an indictment of machine learning, but simply emphasizes the point that a set of techniques by themselves cannot move us toward greater reflexivity. This is much more a function of the question being asked, the approach taken
from the margin of formalizing and will have lower treatment effects. Second, some businesses already have access to other forms of registration that offer partial benefits and for which the added benefits of the _entreprenant_ status will be lower. This includes two groups – those in the Dankopta market who are registered with the public company in charge of all markets (“ _Société de Gestion des Marchés Autonomes,_ ” or SOGEMA), and traders who have a access to a “trader card”. Third, we use our baseline data on formal and informal firms together with the species classification technique of de Mel et al. (2010) to identify which informal firms look similar to the formal “species”, and predict that they will be closer to this formalization margin. Fourth, we consider directly size and owner education, believing smaller, less productive firms are likely to be further from the margin where formalization can benefit them, so will respond less. Finally, if avoiding problems with tax inspections is a benefit of formalizing, we predict that firms that are less frequently inspected will see less benefit from formalizing.

Finally, the framework predicts that the informal firms that formalize as a result of our added interventions will be further from the margin than those who are already formal and those who would formalize without the added help. That is, the interventions should be bringing in smaller, and less like the formal type to begin with, firms.

# **5.2 Estimation**

To analyze the impact of the program on formalization rates, our estimation is at the firm level and involves the following specification for firm _i_ : Where ��,��� is the outcome variable (formalization), �1� is an indicator for being assigned to treatment group 1, �2� an indicator for being assigned to treatment group 2 and �3� an indicator for being assigned to treatment group 3. �� is a vector of strata dummy variables (one dummy variable for each triplet of businesses) (
a result of, or in order to avoid, the effects of armed conflict, generalized violence, human rights violations, protection concerns, or natural and human-made disasters.

## Construction of total inflow

We construct the variable _Net Inflow_ as the cumulative net inflow (labeled _net_inflow_ ) at final date (December 2020). For each settlement _i,_ the variable is hence defined with _local net inflowi= number of returnees from abroadi + number of returning IDPsi + number of new IDPs in settlementi - (Out Migrantsi + Fled IDPsi)_

# Appendix B: Geospatial Data and Methodology

The geospatial methodology used for this analysis extracts monthly descriptive statistics of Nighttime Lights data to individual buffers around IOM settlement locations. This process is known in Geographic Information Systems (GIS) as “zonal statistics”, and is a commonly used method for collecting raster, or gridded data (e.g., NTL, precipitation) into zones or areas of interest to the user (e.g., buffers, administrative districts). This general approach was used in Google Earth Engine, a free cloud computing and data repository, which enables largescale, satellite imagery and other analyses, including the extraction of zonal statistics.<sup>48</sup>

> 1 Corresponding author, <u>adahmaniscuitti@worldbank.org</u>
# **Figure 11. Composition of government expenditure in small states and other EMDEs**

_The share of capital expenditure in total government expenditures was slightly higher in small states than in other EMDEs during 2011-22. While varying greatly, small states’ interest payments were on average comparable to those of other EMDEs, and, as in other EMDEs, they increased markedly between 2019 and 2023. Small states spend more on health and education, as well as public investment, than other EMDEs._

## **A. Current and capital expenditure, 2011-22**

<!-- Start of picture text --> Percent of total spending Current expenditure<br>Capital expenditure<br>100<br>80<br>60<br>40<br>20<br>0<br>Small states EMDEs excl. small<br>states<br><!-- End of picture text -->

## **B. Net interest payments**

<!-- Start of picture text --> Percent of GDP Small states<br>2.5 EMDEs excl. small states<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>2011 2019 2023<br><!-- End of picture text --> <!-- Start of picture text --> C. Composition of expenditure, 2011-21 D. Expenditure on healthcare and education by<br>region, 2011-21<br>Percent of GDP Small states Percent of GDP Health Education<br>EMDEs excl. small states<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>Health Education Public LAC small EAP small Other small<br>investment states states states<br><!-- End of picture text --> _Sources_ : GFS (
<!-- Start of picture text --> 200 Climate change<br>impacts<br>Low<br>150 High<br>131.5<br>100<br>67.7<br>50 42.0<br>32.2<br>0<br>Pessimistic Optimistic<br> people living in extreme poverty<br>due to climate change (million, 2030)<br>Additional<br><!-- End of picture text --> Figure 5: Effect of socioeconomic development to the number of people falling into extreme poverty in 2030

# **4 Discussion and conclusion**

Building upon the methodology developed in (Hallegatte et al., 2015; Rozenberg & Hallegatte, 2015; Hallegatte & Rozenberg, 2017), this study uses household survey data from the 2020 Global Monitoring Database to update climate-driven poverty projections for 2030.

Several similarities in results compared to projections from a previous study (Hallegatte & Rozenberg, 2017) can be observed. For example, we find that health impacts are the dominant channel in most scenarios, pushing the largest number of people into extreme poverty. Impacts through increased food prices are the most uncertain ones (i.e., widest range of distribution), and could have an effect on poverty even bigger than health impacts in the most pessimistic cases.

Under the high climate change impact scenarios, we find that climate-driven increase in prevalence of diseases on average leads to 44 million people falling into extreme poverty. This projection is slightly higher than the previous projection (around 30 million people), although it is still within a similar order of magnitude.

This analysis does more than updating previous results and provides further results. For instance, we explore the dominant climate change impact channel in each region. Health impacts are particularly important in relatively richer regions (East Asia & Pacific and Latin America & Caribbean), while impacts of food prices dominate in poorer regions (South Asia and Sub-Saharan