rafmacalaba/data-use-mentions-extended huggingface dataset:
2003 and 2013 in manufacturing as a whole and in four subcategories of manufacturing, namely heavy manufacturing, light manufacturing, clothing, and food.<sup>8</sup> The rate of growth of firms and employees in the clothing industry is significantly higher in the north than in the south. Figure 3 (panels d, e, and f) provides corroboratory evidence based on district maps. District hubs for heavy and light manufacturing activities are scattered evenly between the north and the south. However, the top one-third of districts experiencing employment growth in the clothing sector between 2003 and 2013 are predominantly located in the north. Overall, the growth of manufacturing employment in the north is predominantly led by the clothing sector.

# **3. Spatial Differences in Intersectoral Linkages**

To estimate spatial differences in intersectoral linkages, we construct five-sector national I-O tables based on 2004 and 2013 supply and use tables for Ghana (GSS 2006, 2021). The five sectors are agriculture; mining; “other industry,” which includes industrial subsectors other than mining; wholesale and retail trade (WRT) services; and “other services,” comprising services subsectors other than WRT services.<sup>9</sup> This five-sector classification allows us to examine the changing patterns of intersectoral linkages between mining and other sectors. Table 2 reports five-sector national I-O tables for 2004 and 2013. The unit for inputs and outputs are in billion 2004 cedis for 2004 and million 2013 cedis for 2013. In 2007, due to inflation, the Ghana cedi was devalued. The current cedi is 10,000 times the old cedi (before 2007). We converted the figures to million cedis in both years, and then applied the sectorlevel deflators from the ETD to have them in 2004 constant prices. In addition, GSS rebased Ghana’s national accounts series from the 1993 base year to 2006. In our analysis, both 2004 and 2013 I-O tables use the rebased figures. For robustness, we compare the value-added shares
characteristics which are relevant for the reservation wages in various setups; while the second strand documents that both unemployment duration and insurance potentially affect the reservation wage, despite the sign and the direction of the causality in case of the unemployment duration has been frequently contested.

# **3. Data**

We make use of two yearly waves of the Labor Force Survey (LFS), 2016 and 2017. We restrict the sample to working-age population, i.e. 15-64. The reservation wage, as survey question, has been asked for the first time in 2016. This is advantageous in the sense that this study is the first to use this quite new information, though disadvantageous in the sense that panel structure of our data cannot be imposed, despite LFS – for the rest of the variables – is on disposal since 2006. However, each year is composed of four quarterly survey waves, providing a total of eight periods for analysis. Further however, households are interviewed at most four times, every second quarter, and then they drop out.

4
Unsurprisingly, the rankings of countries are different for the two indexes. A particularly interesting case is El Salvador, a lower-middle-income country in Latin America.<sup>27</sup> This country ranks as number 5 of 145 countries based on the SCI scores. This would place this country in a surprisingly higher position than many richer countries, including its regional upper-middleincome neighbor—Colombia—which is ranked at number 10. Yet, according to a recent evaluation for 44 indicators concerning children in the 2030 SDG agenda by UNICEF, El Salvador has inadequate data for tracking progress on more than half (i.e., 27) of these indicators. The same figure for Colombia is much lower at just 14 indicators (UNICEF, 2018). The SPI ranks El Salvador as 48 while the ranking of Colombia changes to 12.

Another example is Botswana, an upper-middle-income country in Africa. This country has an SCI rank of 122, near the bottom of the SCI scores. This places Botswana lower than Madagascar (number 89) and Zimbabwe (number 105), both low-income countries in Sub-Saharan Africa. Indeed, Botswana’s GDP per capita was $15,807 in 2011 PPP, which was respectively 11 times and 8 times higher than that of Madagascar and Zimbabwe (World Bank, 2018b). Furthermore, a recent study suggests that Botswana has accomplished both upward consumption mobility and pro-poor growth in the past decade, which reduced its headcount poverty rate to 18 percent (using the international poverty line of $1.9/ day in 2011 PPP dollars). The opposite story held Madagascar, where the country has undergone much downward mobility, which drove its poverty rate up to as high as 82 percent (Dang and Dabalen, in press). Zimbabwe, on the other hand, has a higher income level than Madagascar, but is observed to have implemented at most one household consumption survey during the period 1990-2012; the corresponding figure for Botswana is two

> is that, since the SPI is the
16|56.11|24.95|5800|
|meal than you felt you needed because there<br>was not enough food|(1.51)|(2.14)|(2.62)||
|You or any household member ate fewer meals|26.96|53.85|26.88|5792|
|a day because there was not enough food|(1.36)|(2.16)|(2.55)||
|There was no food at all in your household|19.77|36.28|16.51|5788|
|because there were no resources to get more|(1.06)|(2.09)|(2.34)||
|You or any household member go to sleep at|8.97|35.94|26.97|5797|
|night hungry because there was not enough|(0.68)|(2.07)|(2.18)||
|food|||||
|You or any household member went for a|6.35|25.98|19.63|5795|
|whole day and night without eating because|(0.58)|(1.90)|(1.98)||
|there was not enough food|||||
|_Average _|34.28|54.31|20.03|5789|

Notes: Responses indicating more severe outcomes are operationalized in this module as responses of “once or twice”, “3 to 10 times”, and “more than 10 times”. Estimates are unweighted, and clustered standard errors are reported in parentheses.

# **Reduced Coping Strategy Index (RCSI)**

|**Statement**|**Share of r**<br>**choosing**<br>**
Disasters: A Review of Models and Empirical Studies.” _Review of Environmental Economics and Policy_ 13 (2): 167–88. https://doi.org/10.1093/REEP/REZ004.

- Bouillon, Steven, Alberto V. Borges, Edward Castañeda-Moya, Karen Diele, Thorsten Dittmar, Norman C. Duke, Erik Kristensen, Shing Y. Lee, Cyril Marchand, Jack J. Middelburg, Victor H. Rivera-Monroy, Thomas J. Smith II <mark>I and R</mark> obert R. Twilley. 2008. “Mangrove Production and Carbon Sinks: A Revision of Global Budget Estimates.” _Global Biogeochemical Cycles_ 22 (2). https://doi.org/10.1029/2007GB003052.

- Bowen, Glenn A. 2007. “The Challenges of Poverty and Social Welfare in the Caribbean.” _International Journal of Social Welfare_ 16 (2): 150–58. https://doi.org/10.1111/J.1468-2397.2006.00453.X.

- Brinkman, Richard M., Stanislaw R. Massel, Peter V. Ridd and Keita Furukawa. 1997. “Surface Wave Attenuation in Mangrove Forests.” In _Pacific Coasts and Ports ’97: Proceedings of the 13th Australasian Coastal and Ocean Engineering Conference and the 6th Australasian Port and Harbour Conference_ , 2: 941–49. https://search.informit.org/doi/10.3316/INFORMIT.036154632045063.

- Cavallo, Eduardo and Ilan Noy. 2011. “Natural Disasters and the Economy — A Survey.” _International Review of Environmental and Resource Economics_ 5 (1): 63–102. https://doi.org/10.1561/101.00000039.

- Chen, Xi and William D. Nordhaus. 2011. “Using Luminosity Data as a Proxy for Economic Statistics.” _Proceedings of the National Academy of Sciences_ 108 (21): 8589–94. https://
it is not unheard of to interpolate "missing" data in this way, it is surprising that no documentation is available setting out the methods used. Perhaps even more surprising is that WHO should attach so much importance to the values of the index for the 172 countries where index values were estimated.<sup>2</sup> The concerns that several health ministers have expressed about the WHR rankings are, in such circumstances, understandable.

The paper begins in section II with a discussion of the ethical premise underlying the WHO index-that households ought to be expected to pay for health care in line with their ability to pay. The paper suggests that the ability-to-pay principle is best understood in terms of a desire on the part of policymakers to limit the impact of health care payments on the distribution of disposable income. Whilst there appears to be a good deal of support for the general principle, the proportionality version of it adopted by the WHO is harder to defend. The paper goes on in section III to draw out the properties of the WHO index and in section IV argues that they are highly unattractive. The index is unable to distinguish between progressive and regressive payments, and is also unable to distinguish between cases where households on l Discussion papers have been promised but have yet to appear on the WHO website. One might even have hoped that they would have been written and distributed in time to provide the basis for a discussion of the methods prior to the work being undertaken.

> **2** In the 1991 World Bank's _World Development Report,_ for example, poverty indices were estimated for 22 countries out of 86, but the 22 countries covered 76% of the population of the 86 countries, and only regional aggregates were published (the country-specific values were never made public precisely because they were felt to be too imprecise) (cf. Ravallion et al. 1991).
**Table B.5:** Clinic Quality

||Median|Mean|SD|Min|Max|N|BL data|
|---|---|---|---|---|---|---|---|
|(1) Clinic service provision index<br>1|0.034|0.068|1.143|_−_6.872|3.576|254|Yes|
|(2) Clinic aware of free health care †|1|0.803|0.398|0|1|254|Yes|
|(3) Employee satisfaction index|0.295|0.012|0.975|_−_3.652|1.443|254|Yes|

†: dummy variable; §: likert scale (1-4); _¶_ : likert scale (1-5); _♦_ : measured across all health episode types (ante and post natal care, child birth, under-2 vaccinations, and illness and injury); w , 1 , 6 , and 12 stand for measures referring to the last week, one month, six months and twelve months respectively.

**Table B.6:** Health Service Delivery

||Median|Mean|SD|Min|Max|N|BL data|
|---|---|---|---|---|---|---|---|
|(1) Absenteeism index<br>1 _♦_|_−_0.239|0.057|1.070|_−_0.663|7.471|2874|Yes|
|(2) Paid for treatment<br>1 _♦_†|0|0.404|0.491|0|1|2872|Yes|
|(3
4 Thus, while the shock has a persistent impact on GDP decades after the COVID-19 shock is gone, there are no persistent _differences_ after ten years.

Our study makes several contributions to the literature, for example on the macroeconomic impact of pandemics (e.g., Barro et al., 2020; Coibion et al., 2020; Fernandez-Villaverde and Jones, 2020; Guerrieri et al., 2020; Jorda et al., 2020; Ng, 2021).<sup>5</sup> Given new technologies for mitigating schooling loss, these should be taken into account when assessing the impact of future pandemics on human capital.

While other studies on the global learning losses due to COVID-19 have appeared independently of this study – Azevedo et al. (2020, 2021), Hanushek and Woessmann (2020) and Psacharopoulos et al. (2021) estimate the impact of disruptions to schooling – we are to our knowledge the first to thoroughly quantify the human capital effects of COVID-19 by income level: (i) considering _both_ education and employment/experience losses; (ii) relying on returns to education and experience that were consistently estimated for the world using 1,084 household surveys across 145 countries;<sup>6</sup> (iii) performing a thorough analysis of the education and experience shocks using evidence on school closures, access to and effectiveness of distance learning, and on disruptions to employment for different types of workers;<sup>7</sup> (iv) examining the output dynamics associated with the shocks; and (v) studying how they vary by gender. Lastly, Hanushek and Woessmann (2020) study OECD economies only and Fuchs-Sch ̈undeln et al. (2020) and Jang and Yum (2022) focus on the distributional effects of school closures.

We also contribute to a sparse literature on the relationship between experience and economic development (Manuelli and Seshadri, 2014; Lagakos et al., 2018b,a; Islam et al.,
employees. Education and Health employment data are drawn from the Country Economic Memorandum of 7 February, 1994, Education corresponds to "Education Culture and arts" for 1992, while Health is "Health care, social security, physical culture and sports." Military employment data relate to armed forces under joint Turkmen/Russian command.

Ukraine Unemployment rate is drawn from Economist Intelligoence Unit 1995-96. Unemployment reflects only official unemployment for 1995, and probably underestimates the level of unemployment. . Central Govemment, Local Government, Education and Health employment data are from Ukraine: Public Expenditure Review- Public Administration and the Civil Service of May 1996 and relate to 1994. Local employment corresponds to 'Regional and local administration funded from local budgets' and is for the year 1994. Education and Health employment is also from the same source and relates to 1994. In Ukraine delivery of these services is done by both State Owned enterprises as well as the govemment administration. Military employment data do not, include paramilitary troops, e.g., the Internal Security Troops (15,300) and the National Guard (700). Wages and salaries as percentage of GDP (and therefore GDP estimate), Average wage and average wage as multiple of per capita GDP is taken from EMTPM's David Wood's report of 6/24/96 on Ukraine: Policy notes - Public Administration and the Civil Service and relates to 1994.

Uzbekistan Unemployment is from IMF Report SM/96/134 of June 12, 1996 and relates to 1995. It lists 187,700 unemployed people, of which 156,700 not officially registered in the unemployment registers of the employment offices. In addition, an estimated 50-60 000 are on forced (unpaid leave) and another million people is considered "disguised unemployment in the agricultural sector. Data on paid employment in non-agricultural activities are taken from Statistical Handbook 1995: States of the Former USSR and are for 1994. Central Government, Education and Health employment are taken from IMF Report No. 96/
while less than _5%_ live in conditions of extreme poverty.

**Table 3.1: Distribution of poverty across rural and urban areas in Panama**

||**Extre**|**me poor**|**Po**|**or**|**Non**|**-poor**|**T**|**otal**|
|---|---|---|---|---|---|---|---|---|
||**m**|**%**|**m**|**%**|**M**|**%**|**m**|**%**|
|**Rural**|0.52|88.1|0.25|59.5|0.44|25.7|1.21-|<br>44.5|
|Urban|0.07|11.9|0.17|40.5|1.27|74.3|1.51|55.5|
|Total|0.59|100.0|0.42|100.0|1.71|100.0|2.72|100.0|

_Source:_ **Encuesta de Nivel de Vida, Panama, 1997.**
**National**<br>**consumption**<br>**aggregates**|<br>**(1)-(2)**|
|||**(1)**|**(2)**|**(3)**|
|Afghanistan|2012|n.a.|n.a.|n.a.|
|Bangladesh|2010|28.87|27.49|1.37|
|_- Rural_|_2010_|_26.12_|_24.89_|_1.23_|
|_- Urban_|_2010_|_2.74_|_2.6_|_0.13_|
|Bhutan|2012|0.02|0.01|0.01|
|India<sup>1</sup>|2011/12|217.52|239.1|-21.58|
|_- Rural_|_2011/12_|_191.75_|_196.7_|_-4.95_|
|_- Urban_|_2011/12_|_25.76_|_42.4_|_-16.64_|
|Maldives|2009|0.02|0.02|<0.00|
|Nepal|2010|4.09|4.2|-0.11|
|Pakistan|2010/11|11.96|10.26|1.69|
|Sri Lanka|2012/13|0.43|0.38|0.05|
|Total||262.91|281.47|-18.55|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). Population numbers were obtained from PovcalNet accessed on September 25, 2017.

18
serve as legal witnesses (Hakim and Aziz, 1998). The second category is associated with the enforcement of _purdah_ (the seclusion and hiding of women from men) or the seclusion of women (Amin 1995). All of these restrictions curtail women's decision making about factors that affect their lives: education, employment, and other economic and social activities, as well as medical services.

Efforts to increase women’s social status in Pakistan have achieved some success, particularly efforts to provide education for girls (Hou 2010). Most recently, the Benazir Income Support Program (BISP) was established to provide cash transfers to the female heads of the poor families. This program provides Rs. 1,000 ($12) per month to eligible families, and cash is delivered to the doorstep to ensure that women receive it. The main objective of making women the beneficiaries is to empower them.

# **3. Methods**

# 3.1. Data

The data for this study come from the Pakistan Social and Living Standards Measurement Survey (PSLM) 2005-06, which is a large household survey of more than 15,000 households on a range of social sector issues, including education, health, immunization, pre/postnatal care, family planning, and household consumption. More specifically, a maternal history was recorded for all women between 15 and 49 years old. Details about maternity health care utilization were collected for women who had delivered a child within three years of the date of interview. After excluding cases with missing values, a total sample of 5,061 women (from 4,515 households) is included in this study.

# 3.2. Dependent variables

Four dependent variables are used for this study: prenatal care, institutional birth, skilled birth attendance, and postnatal care. They are all treated as dichotomous variables indicating the use or non-use of the service. Institutional birth is defined as 1 if birth takes place in a government or private hospital or clinic, and
11 implementation, and that we have quarterly data prior to UC and monthly data thereafter, the constrained version of eqn (1) includes six dummies: the first equaling one when the quarter is three or more years before implementation ( _UC_<sup>-3</sup> ); the second equaling one when the quarter is two years before UC implementation ( _UC_<sup>-2</sup> ); the third equaling one when the month is between zero and 12 months after implementation ( _UC_<sup>0</sup> ); the fourth equaling one when the month is between 12 and 24 months of implementation ( _UC_<sup>1</sup> ); the fifth equaling one when the month is between 24 and 36 months of implementation ( _UC_<sup>2</sup> ); and the sixth equaling one when the month is 36 months or more after implementation ( _UC_<sup>3</sup> ). If UC has a causal effect on _y_ , we would expect  3 and  2 to be zero, and at least some of  0 ,<sup></sup> 1<sup>,</sup>  2 and  3 to be nonzero, depending on the time profile of UC impacts.

# IV. **DATA**

Our data are from Thailand’s Labor Force Survey (LFS) conducted by the National Statistics Office of Thailand (NSO). The data were collected quarterly from 1985 to 2000, and then monthly from January 2001 (UC was launched in the first provinces on April 2001) until the present. Each round of the survey covers all provinces and all industries in the entire country. The sample size is between 300,000 and 460,000 persons per year, with some rotation across surveys. The LFS gives a weight variable which allows us to gross up to the population. In this paper, we use the data from
enced by discoveries of deposits and less of ambient infrastructure quality. Within the service sector, we leverage the detailed nature of the data to classify the data into three categories (subsectors), FDI in: financial services, technology services, and other services.<sup>13</sup> Our final dataset is a district-year panel between 2003 and 2018.

In addition, we compile data on the staggered arrival of submarine Internet cables to the coasts of various African countries, as well as the connections to landlocked countries, using data from www.africabandwidthmaps.com,<sup>14</sup> www.submarinecablemap.com, and other country-specific sources. We complement this with data on terrestrial fiber backbone network from www.africabandwidthmaps.com. Figure 1 shows trends in the number of countries connected to at least one submarine cable between 2003 and 2018.

We complement the Internet data with additional data on infrastructure such as electricity grid network from https://gridfinder.org/, and road density from the Global Roads Inventory Project.<sup>15</sup> Also, we use data on the quality of governance from the World Bank’s World Governance Indicator database<sup>16</sup> and finally firm-level data in nine countries<sup>17</sup> from the World Bank Enterprise Survey (WBES).

Further, to avoid zero-inflated data, we restrict our analysis to subnational districts that received at least one FDI during the sample period. Our final data is a balanced panel of 401 subnational districts in 32 countries over a 16-year period.<sup>18</sup> Summary statistics of the full data are presented in Table 1.

# **4 Empirical Strategy**

In the 1990s, satellite transmission was the main source of Africa’s Internet connection. It was characterized by high connection costs and slow speeds, which affected the utilization of Internet services for productive uses. To address these issues, several countries partnered with telecom companies
**Table 5. Tax compliance across the sectors of the economy** **<u>(measured at median income)</u>**

|**Economic Sector**|**NACE code**|**Share of employees**<br>**in the EU-SILC data**|<br>**Tax compliance**<br>**(median imputed tax**<br>**income/ median**<br>**survey income)**|
|---|---|---|---|
|**Agriculture**|a|3.55%|92.33%|
|**Mining, manufacturing,**<br>**electricity, and water supply**|b – e|28.66%|85.13%|
|**Constructon**|f|9.13%|74.06%|
|**Trade**|g|16.92%|96.67%|
|**Transport**|h|7.30%|68.37%|
|**Accommodaton and food service**|I|2.67%|81.79%|
|**Informaton and communicaton**|j|3.69%|65.72%|
|**Financial services**|k|1.63%|76.93%|
|**Real**<br>**estate,**<br>**professional, rental**|l–- n|6.37%|84.18%|
|**Public administraton**|o|5.90%|81.81%|
|**Educaton**|p|4.72%|94.05%|
|**Human health
the firm increases, as shown in Figure 1. According to calculations using data from World Bank Enterprise Surveys, women-led businesses constitute 22 percent of formal businesses globally.<sup>3</sup> However, the share of women-led firms diminishes from 24 percent among small firms to 19 percent among medium-sized firms, and further to 14 percent among large firms. This trend is observed worldwide, with a less marked pattern in the Middle East and North Africa and South Asia, where there overall share of formal women-led firms is smaller and legal constraints for women are more pronounced (World Bank 2023). The data for Figure 1 include only formal firms with at least five employees, since there is no equivalent cross-country dataset for informal or smaller firms. Nevertheless, the gender-based size 3 The definition of a “woman-led firm” varies in the literature. To construct Figure 1, the definition adopted includes firms that have either more than 50 percent female ownership or a female top manager. Alternative definitions yield a similar pattern by firm size across regions. In the sample used to prepare the graph, the share of firms with a female top manager (as defined by Islam et al. 2020) is 18 percent; the share with more than 50 percent female ownership is 15 percent, the share with both a female top manager and at least one female owner (as defined by Fang et al. 2022) is 14 percent; and the share with both a female top manager and at least 20 percent female ownership is 12 percent. Finally, the share with at least one female owner is 36 percent; only for this variable the pattern by size is not as clear. Fang et al. (2022) argue that the presence of a female manager better captures firms that are led by women than the presence of least one female owner. The shares reported above are consistent with data collected directly from business registers in eighty-one economies by the We-Data project (see Meunier et al. 2022), which shows that women