rafmacalaba/datause-encoder-data huggingface dataset:
# **II. Global Poverty, Concepts and Data**

_Conceptual Framework_ When counting the total number of poor people in the world, the World Bank uses an international poverty line that is constructed to reflect a constant real value across countries (World Bank, 1990, Chen and Ravallion, 2001, Ravallion, Chen and Sangraula, 2009, Ferreira et al., 2016). Many other institutions, including the United Nations as expressed in the Millennium Development Goals (MDGs) and now the Sustainable Development Goals (SDGs), have also adopted the use of this absolute international poverty line.<sup>5</sup> In addition to maintaining the real value across countries, Ferreira et al. (2016) further make the case that the latest value of the international poverty line of $1.90 (in 2011 PPP terms) maintains, on average, the real value of the previously defined international poverty line of $1.25 (in 2005 PPP terms) among the world’s poorest countries.

The Final Report of the Atkinson Commission on Global Poverty (World Bank, 2017) recommends that for the purposes of assessing the goal of eradicating extreme poverty by 2030, the value of the international poverty line should remain fixed in real terms up to that point in time.<sup>6</sup> Using the $1.90 measuring stick, 767 million people in 2013 are estimated to be in extreme poverty (World Bank, 2016). Despite this large count of people who live in extreme poverty, the proportion of the population that is living in extreme poverty has fallen from more

> 5Millennium Development Goal 1.A was stated as cutting poverty in half as defined by the proportion of people living on less than the World Bank international poverty line of $1.25 (in 2005 PPP values). Similarly, target 1.1 of the SDGs is to eradicate extreme poverty, again as defined by the international poverty line. For more details on the SDGs, see: https://sustainabledevelopment.un.org/?menu=1300. 6 More specifically, Atkinson (2016) recommends updating the value of the international poverty line in each and every country only by their national measure of inflation (in most all cases, as measured by the consumer price index).

5
are civil servants receive further benefits that are not likely to be monetized in regular earnings (e.g., retirement benefits; job security) and hence not included in the response to the earnings questions – and these are therefore not included in our analysis.

Indeed, in our data we find that teachers are significantly more likely to report receiving benefits such as paid leave, medical benefits, and a pension than other workers (Table A3). Across our sample, teachers are 31 percentage points more likely to receive paid leave, 9 percentage points more likely to receive medical benefits, and 30 percentage points more likely to have a pension. On average, even teachers on fixed term (as opposed to permanent) contracts are more likely to receive benefits: for example, 49 percent of teachers on fixed term contracts report access to paid leave versus only 38 percent of other wage workers (Table A4).<sup>17</sup> As a result of these other benefits, our earnings differential estimates likely represent an underestimate of the teacher premium.

In our analysis, we separately consider monthly and hourly earnings since hours worked differ systematically between wage workers who are teachers and those who are not. For all but one survey we analyze reported earnings along with the corresponding time unit of the earnings, for example, daily, weekly, monthly, quarterly, or yearly. Using these two indicators, we first derive implied monthly earnings. For example, if an employee reports her quarterly earnings, then her monthly earnings are calculated as the quarterly earnings divided by three. If daily earnings are reported, we multiply the daily earnings by how many days the person usually works in a week, then multiply that number by four to get monthly earnings. To estimate hourly earnings, we divide the monthly earnings by four times the reported weekly working hours for each worker. When daily working hours are reported, we calculate the weekly working hours by multiplying the number of days worked in a week. The Gambia survey did not report working hours, so we exclude it from any analysis involving hourly earnings. (Detailed data processing notes can be found in Appendix Table A1.) When reporting earnings and performing the multivariate analysis, we limit the sample to wage employees (i.e., who self-identified as a paid employee) with at least some secondary

> 17 In separate data on a wider range of benefits but asked only of teachers, we find that teachers – particularly civil servant teachers – across 10 African countries do report receiving a wide range of benefits (Table A5). For example, 54 percent of teachers in Benin (and 90 percent of civil service teachers) report receiving a housing allowance; those numbers are 67 percent (88 percent) in Côte d’Ivoire. The set of benefits vary across countries, but at least 30 percent of civil service teachers report at least three benefits in each of the 10 countries, and in one (Senegal) they report seven benefits.

11
**Table 3.4: Protection offset share for riverine flooding for each coastal province and return period**

||||**Returnp **|**eriod inyear**|**s**||
|---|---|---|---|---|---|---|
|**Province**|**5**|**25**|**50**|**100**|**250**|**500**|
|Quang Ninh|0|50|1|1|1|100|
|Hai Phong City|0|0|0|0|5|100|
|Thai Binh|0|0|0|0|5|100|
|Nam Dinh|0|0|0|0|5|100|
|Ninh Binh|0|0|0|0|5|100|
|Thanh Hoa|0|0|5|100|100|100|
|Nghe An|0|0|5|100|100|100|
|Ha Tinh|0|0|5|100|100|100|
|Quang Binh|0|100|100|100|100|100|
|Quang Tri|0|100|100|100|100|100|
|Thua Thien - Hue|0|100|100|100|100|100|
|Da Nang City|0|100|100|100|100|100|
|Quang Nam|0|100|100|100|100|100|
|Quang Ngai|0|100|100|100|100|100|
|Binh Dinh|0|100|100|100|100|100|
|Phu Yen|0|100|100|100|100|100|
|Khanh Hoa|0|100|100|100|100|100|
|Ninh Thuan|0|100|100|100|100|100|
|Binh Thuan|0|100|100|100|100|100|
|Ba Ria-Vung Tau|0|100|100|100|100|100|
|Ho Chi Minh City|0|0|0|5|100|100|
|Tien Giang|0|5|100|100|100|100|
|Ben Tre|0|5|100|100|100|100|
|Tra Vinh|0|5|100|100|100|100|
|Soc Trang|0|5|100|100|100|100|
|Bac Lieu|0|5|100|100|100|100|
|Ca Mau|0|5|100|100|100|100|
|Kien Giang<br>Source: Van Ledden et al. 2020.<br>Note: Each number represents the sha|0<br>re of modelle|5<br>d riverine flood|100<br>extent that is a|100<br>ctually expecte|100<br>d to occur in a g|100<br>iven province|

Source: Van Ledden et al. 2020. Note: Each number represents the share of modelled riverine flood extent that is actually expected to occur in a given province for a specific return period taking into account the current state of the existing flood protection infrastructure. These offsets are designed to be used specifically with the flood maps described in section 2.1 and should not be used in combination with other flood maps or for other applications.

This newly created body of flood protection infrastructure knowledge allows for refinement of the flood exposure analysis in Vietnam. Ideally, it would be used to rerun the flood modelling. However, this is very difficult for several reasons. First, it would require manual modelling of all the categorized flood protection measures and their effectiveness. Furthermore, in order to incorporate structures as small as an individual dike, the resolution of the flood model would need to be increased drastically. Given the countrylevel scope of this analysis, this would result in large increases in the computational power required. Finally, and most crucially, re-implementing the flood model at a higher resolution requires a digital terrain model (DTM) of equally high or higher resolution (Rentschler et al. 2019a for an example of the creation of a detailed flood map which incorporates man-made structures at the city level in Kampala). In the 19
2011 and would continue to do so after the reform was implemented in April 2011. As Monsignor Escobar Alas himself put it: “We have expressed on several occasions our concern that so-called the focalization of the liquified gas subsidy is, with all due respect to the Government or the Ministry of Economy, not the right measure.”<sup>3</sup> It is worthwhile spending some time on the Archbishop’s views as those of someone who has legitimacy among the population at large. In fact, the most recent survey of the Latin American Public Opinion Poll shows that Salvadorans have more confidence in the church than in any state institution, with the exception of the armed forces.

The dissatisfaction with the reform of the LPG subsidy apparently played a role in the Congressional elections held on March 11, 2012, in which the ruling party (Frente Farabundo Martí para la Liberación Nacional, FMLN) suffered significant losses. Reflecting on the reasons for the electoral outcome representatives from both the FMLN and the opposition party ARENA agreed that the gas subsidy reform had played a role. The head of the ARENA group in Congress, Donato Vaquerano, argued that the subsidy reform had been a “colossal mistake” by the FMLN. The head of party organization at the FMLN, José Luis Merino, recognized that the subsidy reform “undoubtedly had an effect among urban sectors who have resented the measure.”<sup>4</sup> The president of Congress, Sigfrido Reyes, also of the FMLN, lamented that the defeat of his party had been due in part to “serious mistakes [including] the change in the gas subsidy [which] increased tremendously the price of gas for domestic use.”<sup>5</sup> Other analysts also agreed that the gas subsidy reform had “hurt” the FMLN.<sup>6</sup> Even more puzzling than the overall lack of popularity was the fact that the reform was particularly unpopular among the poor. For example, in January 2011 among those respondents in the bottom 40 percent of the income distribution only 28 percent were satisfied while among those with an income in the top decile almost 50 percent were satisfied with the reform. Overall, the satisfaction of respondents that were expected to benefit from the reform was no different from the satisfaction of those that were expected to lose 10
where _Sp_ , _Ss_ and _St_ stand for the total number of years of schooling for each successive level. Care has to be taken regarding the foregone earnings of primary school-aged children. In the empirical analysis that follows we have assigned only three years of foregone earnings to this group, following tradition (see, for example, Psacharopoulos (1995)).

The costs incurred by the individual are her foregone earnings while studying, plus any tuition fees or incidental expenses incurred during schooling. Since schooling is mostly provided free by the state, at least at the basic education level, then in practice the only cost in a private rate of return calculation is the foregone earnings. The private benefits amount to what a more educated individual earns (after taxes), above a comparable group of individuals with less schooling. This more or less refers to adjacent levels of schooling; for example, tertiary versus secondary school graduates. Although convenient because it requires less data, this method is slightly inferior to the full discount method (Psacharopoulos (1995)); in fact, it assumes flat age-earnings profiles for different levels of schooling (Psacharopoulos and Layard (1979)).

From equation (1) the return to potential experience is given by: which needs to be evaluated at a given value of _Xi_ . For each sample we use the average years of potential experience as the evaluation point. It is important to stress that in the empirical part when we refer to potential experience we are referring to the estimates based on equation (1).

# **III. DATA**

Our findings are the result of studying a large database constructed from existing national household surveys through the use of the International Income Distribution Database (I2D2) World Bank (2014), initially compiled by the World Bank’s World Development Report unit over the period 2005-2011, and now under the World Bank’s Poverty and Inequality Unit (since 2012). These data have been used in recent _World Development Reports_ and also in several _Human Development Reports_ (see, for example, United Nations Development Program (UNDP) (2011). For a detailed description of the sample, see Montenegro and Hirn (2009). The database covers economies from developed and developing regions, with no censoring of any kind in the sample selection. For most of the economies it covers at least one point in time, and in many cases several points in time. An enormous effort was undertaken in standardizing the variable definitions across economies and time periods. The original data set includes 1,018 economies-years that represent 160 economies. Not all of the economy-year data points are included in our analysis because some surveys lack key variables. The basic specification (the one that requires the minimum set of variables, and hence the one that has the most estimates) was calculated for 819 economy-year points, and covers 139 economies.

5
<!-- Start of picture text --> Panel (a): Weekly marginal effects of COVID-19<br>20<br>10<br>0<br>-10<br>-20<br>01feb2020 01mar2020 01apr2020 01may2020<br>weeks<br>Panel (b): Cumulative effects of COVID-19<br>70<br>50<br>30<br>10<br>-10<br>-30<br>-50<br>-70<br>01feb2020 01mar2020 01apr2020 01may2020<br>weeks<br>hotel airport zoom skype<br>percentage change<br>percentage change<br><!-- End of picture text --> Note: Weekly change in demand is calculated by applying the size of the effect identified in Table 1 to the weekly global growth in the number of confirmed cases.

Figure 3: Weekly changes in global demand for services due to COVID-19 In Tables 2a and 2b, we report results using indicator variables for country-specific “milestones” with regards to the spread of the pandemic. In this exercise, we focus on the differential implications of the first, 10<sup>th</sup> and 100<sup>th</sup> confirmed cases. Such "milestones” influence the behavior of individuals and induce governments into action by making the spread of the pandemic more salient, thereby impacting demand for various services.

Table 2a shows that hotels and restaurants, transport and tourism services experience a significant fall in demand as the number of confirmed cases reaches 10. While the demand starts falling when countries register their very first case, the effect becomes statistically significant for 10 or more cases. Columns 1-6 show that demand for hotel and restaurant services decreases by 19
The results in this section are derived from micro-data in the Population and Housing Census (RGPH) 2014 and the National Survey of Budget, Consumption and Standard of Living of Households (EBCNV) of 2015, which has the typical structure of HBSs. Details on the regressors included in the models are reported in INS (2020).

Mapping the poverty rates in Tunisia demonstrates that there is a high concentration of poverty in the North-West and Center-West landlocked regions. Poverty rates decrease moving towards the coastal North-East, Center-East and the Greater Tunis regions, although there are pockets of relatively high poverty rates there as well. Southern regions (South West and South East) are characterized by diverse levels of poverty.

The performance of models is tested by comparing the poverty rates obtained with the poverty mapping method with the EBCNV 2015 survey estimates at the regional and governorate levels. This comparison is feasible because the EBCNV 2015 is representative at both regional and governorate levels.

As shown in Table 1, poverty rates estimated on the basis of a household survey are more precise at the national level only. In fact, at regional levels, estimates from poverty mapping (Census 2014) show coefficients of variation (in the range of 8%-19‰) that are lower compared to survey estimates (EBCNV 2015), with the coefficient of variation in the range of 24%-69‰.

**Table 1. Poverty rates** **<u>by region</u>**

|**Region**|**E**<br>**Poverty**<br>**rate**|**BCNV 2015**<br>**Standard**<br>**error**|**cv**|**Pove**<br>**Poverty**<br>**rate**|**rty mappin**<br>**Standard**<br>**error**|**g - Cens**<br>**cv**|**us 2014**<br>**Absolute**<br>**difference**|
|---|---|---|---|---|---|---|---|
|**TUNISIA**|**15.2**|**0.227**|**0.015**|**15.1**|**0.356**|**0.024**|**0.1**|
|**Greater Tunis**|5.3|0.365|0.069|6.1|0.050|0.008|0.8|
|**North-East**|11.6|0.600|0.052|11.9|0.223|0.019|0.3|
|**North-West**|28.4|0.762|0.027|25.8|0.353|0.014|2.6|
|**Centre-East**|11.4|0.535|0.047|11.7|0.197|0.017|0.3|
|**Centre-West**|30.8|0.742|0.024|29.3|0.392|0.013|1.5|
|**South-East**|18.5|0.635|0.034|17.8|0.296|0.017|0.7|
|**South-West**|17.5|0.614|0.035|18.2|0.251|0.014|0.6|

Source: our elaborations from INS (2020).

11
are also more likely to start their sexual life at a later age. However, this differential pre-existed the HIV/AIDS epidemic, since it can be found among individuals born before 1971 (older than 15 in 1986, when the information campaigns where started).<sup>20</sup> Since the analysis of HIV incidence has shown that the effect of education in reducing the risk of becoming HIV positive was only present among young individuals, and in particular among females, the two last rows of table 4, as well as table 5, further scrutinize sexual behaviors by birth cohort and gender. For both age groups (born after 1970 and born before 1971), there is a positive and signi fi cant relation between education and the probability to have ever used a condom and the likelihood to have used a condom during the last sexual intercourse. However this positive gradient is substantially steeper for individuals born after 1970. The levels of condom use are also much higher among young individuals. These results are informative in order to understand why it is only among young individuals that education has an effect in reducing HIV incidence.

For both genders, condom use is positively related to educational levels. However, there is a difference in the sign of the education gradient for the number of partners. Among females, education reduces the number of partners, while more educated men tend to have more sexual partners, in particular when they are older than 29. This difference might explain that the effect of education in reducing HIV incidence is stronger for females.

Looking at sexual behavior con fi rms that more educated individuals have been more responsive to the threat of the HIV/AIDS epidemic. Condom use seems to be a mechanism where the differences by education levels are very pronounced. In addition, for females, the number of partners is negatively associated with schooling. The combination of higher use of condom and a reduced number of partners might explain why the effect of education in reducing HIV prevalence and incidence is concentrated among females.

The fact that this analysis is based on self-reporting might cast some doubts since more educated persons are more likely to know the "good answer". However, since their answers are con fi rmed by the lower HIV prevalence and incidence associated with education described previously, this gives credence to the analysis. The studies of the HIV outcomes and of the sexual behaviors are mutually reinforcing in establishing that more educated individuals have been more responsive to the HIV/AIDS campaigns and in documenting the behavioral mechanisms involved.

> 20 In general, it does not seem that the initiation of the sexual activity has been modi fi ed with the arrival of the epidemic: younger individuals started slightly earlier, on average, than older ones.

17
1998 and 2010 have reduced formal manufacturing firms’ revenues by about 2% per year. Severe reductions in manufacturing output due to extreme heat have also been documented among Chinese plants (Zhang et al. 2018) and at the global level (N. Pankratz, Bauer, and Derwall 2023). For the U.S., Addoum, Ng, and Ortiz-Bobea (2020) find no significant impact of temperature exposure on establishment-level sales or productivity using granular climate data from 1990 to 2015. Beyond temperatures, Keiller and Van Reenen (2024) demonstrate that natural disasters adversely affect firm performance. Cevik and Miryugin (2022) find that firms in more climate vulnerable countries have a lower profitability and access to debt financing. A detrimental impact of climate risks on the firm’s financial position is also documented by Venturini (2022), Bansal, Ochoa, and Kiku (2016), Addoum, Ng, and OrtizBobea 2020, and N. Pankratz, Bauer, and Derwall (2023). Huang, Kerstein, and Wang (2018) find that the impact of climate risks on firms is larger in climate vulnerable sectors such as food, healthcare, communications, business services, or transportation. Recent studies also use transcripts from conference calls to quantify the degree of exposure of firms to different climate risks and link it with firm-level outcomes (Li et al. 2020; Sautner et al. 2023). These single-country studies carefully identify local marginal effects but are unable to sketch out the cross-sectional gradient of how firms worldwide are coping with similar shocks.

Several studies also use survey data to measure how rising heat affects labor productivity. Somanathan et al. (2021) collect high-frequency survey data in India. They document that an additional day above 35<sup>_◦_</sup> C in the six preceding days has caused a 2.7% decrease in daily output among textile workers in sites without climate control while worker efficiency has still declined by 1.1% in climate-controlled garment plants. The latter stems from absenteeism, with each heat day raising worker absenteeism by 0.5% and reducing output by 1.4%. Similarly, Graff Zivin and Neidell (2014) find that extreme heat reduces hours worked in heat-sensitive industries in the U.S. Adhvaryu, Kala, and Nyshadham (2020) exploit differences in temperatures across workplaces within the same factory in India due to selected use of lighting technology—low heat-emitting LED bulbs—and find large adverse efficiency 6