rafmacalaba/data-use-mentions-extended huggingface dataset:
digit disaggregated level of the Statistical Classification of Economic Activities in the European Union (NACE); and (2) by occupational categories up to the three-digit level of the International Standard Classification of Occupations (ISCO-08). Because the ISCO classification scheme changed significantly between 2010 and 2011,<sup>7</sup> we restrict our final sample to the period from 2011 to 2018. In addition, the dataset includes information on the wage decile of the worker in the national wage distribution for individuals who are wage employees.<sup>8</sup> Finally, the dataset provides information for each observation within destination countries at a subnational, regional level: the European Commission’s second level of nomenclature of territorial units for statistics (NUTS2). This allows us to measure immigrants’ presence and labor market outcomes at a rather disaggregated geographical level, and to conduct our estimation at the regional level within European countries. For our analysis, we restrict the sample to the 124 NUTS2 regions in Western Europe, which includes the EU15 countries, Switzerland, and Norway. The share of migrants in the New Member States (NMS13) countries is very small, so the sample captured in the EU Labour Force Survey (EU-LFS) can be noisy.

# 3.2. Measuring Economic and Health Exposure to COVID-19 and Correlation with the Task Content of Occupations

Labor-force surveys are not yet available to provide post-COVID-19, harmonized information of labor-market outcomes for the European Union, at the sub-region level. We categorize economic exposure to COVID-19 using information on workers’ occupations and labor market outcomes _prior_ to the pandemic. This approach reflects the method used in the literature (e.g., Dingel and Neiman, 2020; Garrote Sanchez et. al., 2020) to measure risk exposure before an event takes place. We observe the detailed occupational categories in the data, including the three-digit ISCO-08 categories, which we use to estimate the economic exposure of different types of workers to COVID-19.

> 7 The scheme changed from ISCO-88
21 labor costs are perceived as being low, the likelihood of future investments does increase significantly (a favorable labor cost receives a high score and hence a positive sign on that coefficient). But once again, the influence of PAST and RIVAL remains largely unchanged. Thus the inference again is that perceptions of labor cost while important are not the essence of the privately-held information but rather a complement to that information. The terms interacting perceived labor costs with PAST and RIVAL are positive, though the interaction with past is not statistically significant. In contrast, therefore, to perceived FDI policy (which becomes less importance when private information is available), labor costs actually grow in importance when privately-held information exists.

Finally, a larger market size also improves the improves the attractiveness of a country to Japanese investors (the results are not presented in the interests of space but are available on request from the authors). The interaction with RIVAL has no influence, but the interaction with PAST has a negative and significant sign (at the 5 percent level). Thus, past presence reduces the influence of a large market size.

# **Conclusions and discussion**

Using a firm-level data set, we explored the empirical importance of privately-held information in foreign investment location decisions. Though the limitations of a one-time survey did not permit us follow an information "cascade" over successive generations, the value of private information, which is central to the cascade phenomenon was consistently and impressively evident The data permitted us, moreover, to distinguish between two types of private information: one that was obtained through direct experience in the host country and the other that was inferred
research studies suggest that greater discrimination against women persists in unorganized sector of labor markets in India, the analysis on differences in gender wage gaps in organized and unorganized sectors of labor market becomes difficult due to lack of availability of data.

# 2.2.3 Pakistan

Pakistan’s women’s participation in the labor force is among the lowest in the world. The WEF’s Global Gender Gap Report (2017) ranks Pakistan at 143 of 144 countries surveyed, just ahead of the Republic of Yemen. Pakistan has more men than women working in any occupation and the level of women’s participation in the labor force is low at 25 percent (as opposed to a male participation rate of 75 percent) in 2015. The cultural barriers and are prevalent, emphasizing women’s primary role as homemakers and caregivers, coupled with high levels of early marriage, discourage women from entering the workforce, particularly as employed women shoulder the same burden of household-related unpaid work as do their non-employed counterparts.<sup>16</sup> Though female labor market participation has increased from 13.7 percent in the 1990s to 21.1 percent in 2007 and further to 25 percent in 2015 in Pakistan, it is still very low as compared to other South Asian countries. Nearly 82 percent of the employed women are in agriculture/ forestry/ hunting & fishing industry division followed by manufacturing (13.9 percent). Within manufacturing, the textile and garment sector is the most significant employer for women in Pakistan. About 55 percent of women employment in the manufacturing sector is in the garment industry, while the corresponding figure for men is 19 percent.<sup>17</sup> The largest proportion of women workers are employed in manufacture of wearing apparel (42 percent) followed by agricultural, forestry and fishery (40 percent) and manufacturing of textiles (30 percent). As per UN estimates,<sup>18</sup> the highest percentage share of women working in agriculture sector
Price of intra-African calls, mean||**1.10**|0.72|n.a.|

_Source:_ Minges and others 2009. Derived from AICD national database, http://www.infrastructureafrica.org/aicd/tools/data. _Note:_ Mbps = megabit per second; — = data not available; n.a. = not applicable.

## **Table 7. High international call charges driven by both technology and market power**

|$|**Percent of**<br>**cases**|**Call within**<br>**region**|**Call to the**<br>**United**<br>**States**|**Internet**<br>**dial-up**|**Internet**<br>**ADSL**|
|---|---|---|---|---|---|
|Without submarine cable|67|1.34|0.86|68|283|
|With submarine cable|33|0.57|0.48|47|111|
| Monopoly on international gateway|16|0.70|0.72|37|120|
| Competitive international gateway|16|0.48|0.23|37|98|

_Source:_ Minges and others 2009.

# Financing Malawi’s infrastructure

To meet its most pressing infrastructure needs and to catch up with developing countries in other parts of the world, Malawi needs to expand its infrastructure assets in key areas (table 8). The targets outlined in table 8 are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting the targets, which can be
1965 and 2009, as a share of GDP, agriculture value-added decreased from 50 to 32 percent, but the transformation was not in favor of manufacturing which also decreased from 11 to 7 percent. In Tanzania, between 1990 and 2006, the share of agriculture decreased slightly from 46 to 45 percent and of manufacturing declined from 9 to 7 percent (World Development Indicators, 2011). These patterns also corroborate the empirical analysis by McMillan and Rodrik and are elaborated in Lin and Lin and Monga (2011).

Trends in the allocation of labor usually reflect value addition at the sector level as also noted by Kuznets. In the developed countries, in the aftermath of the Industrial Revolution as the share of manufacturing increased, labor moved from agriculture to manufacturing to support growth in the latter. This pattern continued until the 1960s when rising wages in manufacturing dampened the sector‘s growth and created space for the service sectors. Today, the predominance of services is notable in all Western countries (Figure 1.4).

7
--|---|---|---|
|Country risk score|-0.484***|-0.828***|-0.638***|-0.880***|-0.712***|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.3  Transparency of Reserve Management Policies and the Broader Governance Environment**

|Variables|Independence of<br>central bank|<br>Corruption<br>Perceptions Index|Governance<br>Pillar Score|Government<br>Effectiveness|
|---|---|---|---|---|
|Transparency of reserve management policies|0.298***|0.117|0.082|0.084|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.4 Transparency of Reserve Management Policies and Country Risk**

<!-- Start of picture text --> Country risk<br> Variables<br>score<br>Transparency of reserve management policies -0.222*<br><!-- End of picture text --> _Note: The significance level of the pairwise correlations
15 To reflect access to public services, indicator variables are included that take the value of one if clean water<sup>32</sup> , sanitary latrines,<sup>33</sup> or electricity are present in the community, and zero otherwise. To proxy a household’s integration in the overall economy, the community data were further augmented with the distance of the centroid of the community (in meters) to the nearest part of the nearest primary or secondary road, as designated in the VMAP0 dataset (NIMA 1997). These measures were computed that using Arc View 3.2. In addition, the estimated travel time to the nearest town or city of at least 25,000 people, the nearest city of at least 100,000 people, and the nearest city of at least 500,000 people were included. The travel times were computed in Arc View from a friction grid that assigned differing travel times to various classes of roads, urban areas, water bodies, off‐road, and crossing international boundaries.<sup>34</sup> To further control for geological and agro‐ecological characteristics at the community level, elevation figures were included, taken from the GLOBE 1 kilometer elevation database (GLOBE Task Team et al. 1999). Elevation differences were also incorporated as a measure of terrain roughness. These were computed using the SRTM3 data (Jarvis et al. 2006) by subtracting the minimum elevation in a 1 kilometer grid from the maximum elevation in the 1 kilometer grid. Finally, urban/rural and regional indicator variables help capture unobserved (time invariant) location specific characteristics related to the urban and rural livelihood systems as well as those related to regional variation in the agro‐ecological, economic and political environment. Year dummies were included to control for the survey year.

The disaster data were mapped into the household survey data at the commune level, which are on average only 30.3 kilometer squared.<sup>35</
- Betts, A., Omata, N., Rodgers, C., Sterck, O., & Stierna, M. (2018). _Self-Reliance in Kalobeyei? Socio-Economic Outcomes for refugees in North-West Kenya._ Oxford Department of International Development. Oxford: Refugee Studies Centre.

- Betts, A., Sterck, O., & Omata, N. (2018). _Refugee Economies in Kenya._ Oxford Department of International Development. Oxford: Refugee Studies Centre.

- Verme, P., & Schuettler, K. (2019). The Impact of Forced Displacement on Host Communities : A Review of the Empirical Literature in Economics. _Policy Research Working Paper, 8727_ .

UNHCR Kenya. (2021, September). _Kenya: Registered refugees and asylum-seekers._ Maffioli, E. M. (2019). Relying solely on mobile phone technology: Sampling and gathering survey data in challenging settings. _Working paper_ .

- Himelein, K., & Kastelic, J. G. (2021). The socio-economic impacts of Ebola in Liberia: Results from a high frequency cell phone survey, round 5. _Working paper_ .

- Lau, C. Q., Lombaard, A., Baker, M., Eyerman, J., & Thalji, L. (2019). How representative are SMS surveys in Africa? Experimental evidence from four countries. _International Journal of Public Opinion Research, 31_ (2), 309–330.

- World Bank. (2018). _Listening to Tajikistan - Household survey: Background,_

- _implementation, and methods._ Washington, D.C.: World Bank Group.

World Bank. (2021). _How Covid-19 continues to affect livelihoods in Kenya._ 23
31 the details, especially in which hospitals are affected most. There are many possible explanations of these differences. The hospital inventory is intended to be a census while the household data come from a survey; though conducted in every province, the household survey was not fielded in every district. The hospital results tell us what happened to the hospital‘s outcomes when its autonomy status changed, while the individual results tell us what happened to the individual‘s use of and spending on hospital care when the lowest-level hospital in his district was autonomized. This need not be the hospital the individual typically uses or used in the next treatment episode; indeed, the person may typically use or may have used next a hospital in a neighboring district or even neighboring province. The two datasets may also differ in their accuracy, though it is hard to say which is likely to be better. Hospitals might keep better records than households of their utilization and spending, but there again some of the hospital data – especially the financial data – were found to be problematic. Hospitals may deliberately underreport or overreport depending on the incentives they face, while households face no obvious incentive to deliberately mislead the enumerator, at least on this set of issues. Households could, however, be confused about the type of hospital they went to, while a hospital is very unlikely to be confused about whether it is a district or provincial hospital.

The bigger question worth addressing is why the results are not more clear-cut and stronger. The results point to some volume effects, but one might have anticipated stronger and more clear-cut effects. Why did the policy not lead to increases in bed numbers and bedoccupancy rates? Why did it not lead to increases in hospital efficiency? This question is clearly of some interest given the motivation for hospital autonomization worldwide. Why did we not find a stronger and more clear-cut effect on out-of-pocket spending? And why were the effects on quality and resource-intensity so muted?
and Gabon). Therefore, the Ideal Point Distance suggests the difference between the preference for the US-led liberal order for any two countries in any given year.

Observe that the voting dataset does not simply measure similarity across all votes. Rather, it estimates the distance of voting toward a specific topic. Anchoring the content of the estimates in one topic helps address the issue that the G-7 donors do not always vote the same way.

To understand how preferences in our sample changed compared to the five donors over time, we sum the yearly Ideal Point Distance between each country and the donors. Since we want a _closeness_ indicator rather than a _distance_ indicator (i.e., we want our IV to be positively associated with relative closeness to the donors), we multiply the summed Ideal Point Distance by the negative one.

# **International Trade Closeness IV**

Henisz, Zelner, and Guillén (Henisz et al., 2005) observe that countries implementing economic reforms often imitate their trade-related peers. They argue that "the intensity of trade transactions reflects the density of the social network in which a given country is embedded [...] and therefore the level of formalized conformity within the network."<sup>5</sup> To capture changes in closeness through trade, we compute the percentage of trade that corresponds to exchange with the donors for each recipient country each year. The exchange data comes from the United Nations Comtrade dataset via the Database for International Trade Analysis (BACI) (Gaulier & Zignago, 2011). We also generate a panel dataset on the existence of Trade Agreements with the European Union. The source of the data is the EU Commission's "current state of play" agreements in place (see Appendix Table A.6). This

> 5 Furthermore, they argue that "...policies directly reflect the level of formalized conformity within a trade network. In a world characterized by uncertain cause-effect relationships, the policy initiatives undertaken by "relevant others
community, social and personal service activities|1644|744|45.3|
|15|Activities of private households as employers|672|372|55.4|
|16|Extraterritorial organizations and bodies|1956|1920|98.2|

Source: ILOSTAT database, International Labor Organization The existing literature and the results above suggest that gender differences in the labor market in South Asia are highly prevalent. Gender differences loom large in female labor force participation and earnings across different sectors and jobs in South Asia. Though the combined effect of economic development, rising education among women, and declining fertility to an extent explains increase in female participation rates in some South Asian countries, there still remains several prominent constraints to increasing female labor force participation such as women’s domestic responsibilities, limited mobility, which restrict the subset of jobs considered appropriate for women, and also impede their access to information.

# **3. Development of the GTAP-Gender Data Set for South Asia**

Data on the wages and number of employees for different sectors were first obtained, separately for men and women. These sectors were then mapped for which data were available to the 57 sectors in the standard GTAP database. In the GTAP-Power data set, electricity is further broken down into several types of renewable and non-renewable sectors, so the male and female wage bill shares were used corresponding to ISIC sector 35 to all the electricity sectors in GTAP-Power.

24
(Panel A). Regarding the detectable effects for these three wealth indices, they ranged between 0.10 and 0.12 (equivalent to 14-30% increases). Given the variation in the data, the sample would not have been able to detect these small effects. In addition to the household wealth indices, we analyzed other social variables that are meaningful on their own: migration, children’s education, ownership of livestock and whether the household was credit constrained. We do not observe statistically significant changes (p < .05) in any outcomes. The point estimates indicate treated households were 54.9% less likely to report a household member to have migrated (3.9 percentage points with respect to a control of 7.1 percent) which is a substantial change, but only marginally statistically significant (p < .10). The 2013-2014 cohort was also more likely to report having a household member enrolled in secondary school (18.6% increase with respect to a control of 59 percent) and enrolled in highschool (28.8% with respect to a control of 40.0 percent). Again, these are substantial changes in magnitudes but only marginally statistically significant. The point estimates for most of these social outcomes were similar for the 2011-2012 cohorts, though these were not statistically significant.

# _Heterogeneous Effects_

We tested for impact heterogeneity for our welfare indices across regions, deforestation risk and payment magnitude. There were smaller effects on the housing index in the Yucatan Peninsula in both cohorts and in the same region in the later cohort (see **Appendix Table A8** ). There were also consistently more positive effects for the housing and asset indices in the central region of the country, although the total effect (the sum of the threshold and the differential impacts) indicates very small

> 17 Previous work based on the survey of applicants from the 2008 cohort found that the program induced significant increases in the time dedicated to the forest and that the ratio of the cost
unit cost is assigned to each of the 78 countries of interest; and 2) different assumptions on sample size (5,000; 7,000; 9,000; 11,000) and annual growth rate of unit costs (0 percent; 2 percent; 2.5 percent; 3 percent). Depending on the assumptions, the estimated total cost of survey operations varies from USD 371 million to over USD 1.1 billion. The median total cost of all the options presented in Table 5 is USD 695 million, coincidentally very close to the paper’s preferred estimate of USD 692 million. Using the income class-specific average unit costs or the global average unit cost that grow at an annual rate of 2.5 percent and interviewing 7,000 households per survey, the total survey implementation cost estimates decrease slightly to USD 651 million and USD 623 million, respectively. These changes are mainly driven by the fact that the average unit cost in sub-Saharan Africa is higher, and the region accounts for 39 of the 78 countries of interest.

Needless to say, these estimates based on different notions of average unit cost mask important details. For example, while assigning regional average unit costs to countries, we assume the same unit cost for Low Income (e.g., the Democratic Republic of Congo) and Lower Middle Income (e.g., the Republic of Congo) countries in the same region. On the other hand, assigning income class-specific average unit costs to countries implies that the unit cost in the Democratic Republic of Congo is the same as in Tajikistan and Haiti, as they are also Low Income countries. Mindful of these considerations, we still rely on regional averages for our baseline estimates to impute unit costs. This is a simple and transparent measure that captures regional heterogeneity in survey costs.

14
Table D.8. Placebo Test for All Pairs of Neighbors Whose Difference in Altitude is between the Following Thresholds

||_Altitude_|_difference betwee_|_n 15 and 20 masl_|_Altitude_|_difference between_|_20 and 100 masl_|
|---|---|---|---|---|---|---|
||Altitude|Night Lig|ht- Arcsine (2013)|Altitude|Night Lig|ht- Arcsine (2013)|
||Any neighbor|Any neighbor|Both neighbors outside|Any neighbor|Any neighbor|Both neighbors outside|
||pair|pair|guerrilla area|pair|pair|guerrilla area|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Difference|17.83***|0.0206***|0.0239***|47.71***|-0.0114***|-0.0172***|
||(0.0322)|(0.00521)|(0.00525)|(0.201)|(0.00384)|(0.00430)|
|Neighbor pairs|2,914|2,914|2,515|11,811|11,811|8,742|
|||_Wealt_|_h Index (2007)_||_Wealt_|_h Index (2007)_|
|||(7)|(8)||(9)|(10)|
|Difference|-|0.0149|0.0202**|-|-0.0456***|-0.0468***|
||-|(0.00921)|(0.00980)|-|(0.00501)|(0.00583)|
|Neighbor pairs