rafmacalaba/data-use-mentions-extended huggingface dataset:
should be noted that employment estimates from the two sources are not strictly comparable, and any differences may be the result of several factors, including the aforementioned recall bias, differences in the number, sequence and wording of questions, differences in the timing of surveys, and important distinctions between the populations represented.<sup>13</sup> More details on this comparison exercise can be found in Annex 4. The analysis suggests that despite some differences in magnitude, the HFPS and LFS capture similar employment dynamics in most countries with overlapping data (Figure A4.1). The share of respondents currently working in the HFPS is strongly correlated with the employment population rate reported in the ILO database (Pearson correlation coefficient=0.604, _p<_ 0.001; R<sup>2</sup> =0.869 controlling for country fixed effects; see Figure A4.2). Furthermore, discrepancies in terms of how closely employment trends mirror each other at the country level tend to be less consequential in country or population group aggregates, which is the level of analysis in this paper (Figure A4.3). The number of LICs with LFS data remains very small, and as such HFPS data continue to be one of the few available sources of information for many developing countries throughout the pandemic.

# **4 Employment dynamics during the pandemic**

This section uses the most comprehensive and up-to-date cross-country data available to (i) update the phone survey estimates of the initial impacts of the pandemic for a considerably larger set of countries vis-`a-vis analysis in previous papers; (ii) trace the evolution of employment, work stoppages and acquisition of new jobs; and (iii) examine transitions across sectors and types of employment throughout 2020 and 2021, focusing on the question of the quality of the employment gains following initial losses.

# **4.1 What are the main patterns of initial employment losses and subsequent employment gains?**

There are several ways through
## **3.4 Dismantling of coercive institutions**

The advent of democracy in South Africa was followed by numerous interventions aimed at leveling the historically uneven bargaining field. Labor market outcomes were affected via two channels. First, changes in legislation governing union activity directly caused an increase in the bargaining power of unionized labor, _β_ . Second, the abolishment of mobility restrictions for workers in the former homelands, along with the elimination of the restrictions on the type of jobs they would gain access to, led to an increase in the reservation wage of unionized workers, _<u>w</u>_ <u>.</u> Straightforward computations show that, all else being equal, both these forces contribute to an unambiguous increases in the negotiated wage and union utility, while lowering domestic profits and employment levels.

## **3.5 International price shocks**

Consider now the effects of movements in international prices, which we assume are driven by exogenous shocks to global demand, _a_ , and/or foreign marginal costs, _w_<sup>_∗_</sup> .<sup>14</sup> From (8) we observe that: An increase in global demand or a positive shock to foreign marginal costs leads to a higher international price. This leads to an increase in domestic production and quasirents, and hence to a higher negotiated wage. From (9) and (10), we further observe that these positive effects on union wages are strictly increasing in _β_ . The more powerful the union, the stronger its ability to benefit from these resource windfalls.

# **4 Data**

For the main analysis in this paper, we build a community-level panel data set using the community profiles from the 1996 and 2011 population censuses run by Statistics South Africa. In their original format, the community profiles (at the sub-place level) provide aggregated category counts for each variable in the census. South Africa is divided into 9 provinces
> - 17 -

total nonfuel exports. In total, the 20 countries listed in Table 3 accounted for $113 billion or approximately 89 per cent of all labor intensive exports from developing countries. 14/ For many of the largest exporters of labor intensive manufactures textiles and clothing were dominant early in the expansion and then declined in importance as countries shifted to other items. For example, from 1965 to 1975 labor intensive manufactures rose from 46 to 79 per cent of Taiwan's total nonfuel exports and the share of textiles and clothing within these items rose from 3 to 28 percent. From 1975 to 1986 the share of all labor intensive products in Taiwan's exports continued to rise (to 85 per cent), yet the share of textiles and clothing in the labor intensive group fell by about 12 percentage points. This same general pattern is evident in many of the developing countries' export profiles. 15/ In the initial stages, textiles and clothing appear to have been a major element or catalyst for the initial export expansion and then were replaced by other labor intensive products.

The question of which labor intensive products replace textiles and clothing as "dynamic" elements in developing country exports is addressed in

- 14/ The subject is not explored in this paper, but there is considerable evidence that the export performance statistics reported in Table 3 are linked to domestic policy measures in developing countries. For example, the rapid 40 point increase in labor intensive products share in total Dominican Republic exports over 1975-1986 can be tied directly to internal measures, such as the establishment of export processing zones, aimed at trade expansion. See World Bank (1988b) for an evaluation of how domestic policies influenced the export performance of the Caribbean Group for Cooperation in Economic Development (CGCED) countries.

- 15/ Among the five largest exporters of manufactures China is an exception as the share of textiles and clothing in the labor intensive group rose throughout 1965-1986 and reached 47 per cent in 1986. This may be explained by a difference in timing
route length-time trend interaction is not clear, measuring the net

> 25 For example, the annual growth rate of the vehicle fleet fell from 5.9% in 1965 to 1.9% in 2002 in Austria, from 6.0% to 1.1% in Denmark, and from 5.0% to 2.0% in the United States.

> 26 Appendix B of Kopits (2004) shows the sensitivity analysis of the Base Model results with respect to the specification of this variable and the countries included in the sample.  In general, the coefficient increases in magnitude as more years are included in the average growth rate and if South Korea is dropped from the sample. All other coefficient estimates remain stable across specifications.  In the interest of maintaining the largest sample size possible, we continue to use VEHGROWTH in our estimation.

> 27 The magnitude of the coefficient on total route length (and, hence, the overall elasticity) approximately doubles in magnitude when LIVER is included in the estimation (Model 3 in Table 7).

28 Mahalel and Szternfeld (1986) hypothesize that drivers may also feel safer on wider roads and reduce cautionary behavior.

> 29 Similarly, using monthly data for 18 counties in Norway, Fridstrom and Ingebrigsten (1991) also found improvements to the national road network had no beneficial effect on safety rates.

20
- Wilkie, D. S., Starkey, M., Abernethy, K., Nstame Effa, E., Telfer, P., & Godoy, R. (2005). Role of prices and wealth in consumer demand for bushmeat in Gabon, Central Africa. _Conservation Biology_ , _19_ (1), 268–274.

- Wilkie, D. S., Wieland, M., Boulet, H., Le Bel, S., van Vliet, N., Cornelis, D., BriacWarnon, V., Nasi, R., & Fa, J. E. (2016). Eating and conserving bushmeat in Africa. _African Journal of Ecology_ , _54_ , 402–414.

- Willis, J., Ingram, D. J., Abernethy, K., Kemalasari, D., Muchlish, U., Sampurna, Y., Midoko Iponga, D., & Coad, L. (2022). WILDMEAT interventions database: a new database of interventions addressing unsustainable wild meat hunting, consumption and trade. _African Journal of Ecology_ , _60_ , 205–211.

- Wright, J. H., Malekani, D., Funk, S. M., Ntshila, J., Mayet, L., Mwinyihali, R., Fa, J. E., & Wieland, M. (2022). Profiling the types of restaurants that sell wild meat in Central African cities. _African Journal of Ecology_ , _60_ (2), 197–204.

- Yocum, L. F., Vanegas, L., & Day, B. A. (2022). From the forest to the fork: Why we need to “reframe conservation”’ for conservation behavior change campaigns. _Applied Environmental Education & Communication_ , _21_ (1), 3–6.

**Data Availability:** All anonymized data are publicly available (Cisse et al., 2024).

**Code Availability
the overall trade restrictiveness is marginal in our sample.

# 7 Concluding Remarks

A set of consistently estimated import demand elasticities at the tariff line level is critical to understand the consequences of trade liberalization. This paper is the fi rst attempt to provide a theoretically consistent methodology to estimate import demand elasticities for a wide variety of countries at the six digit level of the HS (4,625 tariff lines). Moreover, the methodology can be easily implemented with existing trade data.

We fi nd that the sample average import demand elasticities is -1.67, while the sample median is -1.08. There is wide variation in import demand elasticities across countries and tariff lines, and the estimates exhibit some interesting patterns. First, homogeneous goods have more elastic import demand than differentiated goods. Second, the average estimated elasticities decrease as we increase the level of aggregation at which they are estimated. Third, large countries tend to have more elastic import demands. Fourth, more developed countries tend to have less elastic import demand.

In addition, we calculate trade restrictiveness indices (TRIs) and welfare losses associated with the existing tariff structures for 88 countries. We use a methodology that is both theoretically sound, and more importantly consistent with our estimated elasticities. Results show that both simple and weighted average tariffs tend to underestimate the distortion imposed by the tariff regime by 30 percent on average. Because of the large variance in the United States tariff schedule, underestimation of trade restrictiveness is among the largest. The simple and import-weighted tariffs in the United States are around 4 percent, whereas the TRI is close to 15 percent. With welfare losses at around $7 billion per year, the United States is the country in our sample whose tariff structure imposes the largest welfare costs on its nationals. It is followed by China, Mexico, India and Germany. Finally, given the high TRI of the US, the $7 billion per year deadweight loss estimate is also larger then previous fi ndings of the literature which mainly use
1.3 joint trade-fiscal reform: a case study

19 consumption of good k, raising revenue through the VAT, vk. However, when the good is in the informal sector, there is no increase in revenue from increased consumption. If the VAT base of formal goods is small (that is, the informal sector is large), revenue following a coordinated tariff and tax reform could decrease. This theoretical postulate guides the analysis of the revenue implications of tax policy reform in Nepal, a country with a large informal sector that is hard-to-tax.<sup>22</sup>

# 1.3.2 _Simulation Model_

The empirical analysis in this section draws on simulations conducted using the TRIST developed by the World Bank (Brenton et al. 2011). It uses a partial equilibrium model that quantifies the effect of trade reform scenarios on imports, revenue and production (please refer to the appendix) for the simulation model and an illustration). The model makes the following key assumptions: (1) it is derived from standard consumer theory and elasticities play a central role in determining the magnitude of demand response to price change; (2) there is imperfect substitution between imports from different countries, following Armington (1969), and each product is modeled as a separate market; (3) the economy is small and open such that all changes in tariffs are passed on, but change in demand by consumers in the small country does not affect world prices.

Percentage change in the price of good j from country i (∆pij) when tariff and other domestic taxes are lowered is as follows: the prime indicates post-reform values of tariff (τ), excise duty (e) and the VAT (v).

> 22 Keen (2007) argues that the theoretical result of Emran & Stiglitz (2005) does not fully take into account the efficacy of VAT as a taxation device. It is not just a tax on final consumption, but a charge on all imports and sales at every
In the sections that follow, we will use the latest version of the WVS-EVS to address the two questions stated earlier: the definition and relation between bonding and bridging indicators; and the relation between these indicators and life satisfaction in the context of world regions characterized by different histories, cultures and levels of development.

# **3 Data, model and variables**

The data we use include all rounds of the WVS-EVS carried out between 1980 and 2008. The data set was created by combining the 1981-2004 World and European integrated data file (version 2006.04.23) and the last round of the World Value Survey following the instructions provided by the World Values Survey organization (www.worldvaluessurvey.org). The resulting dataset includes five rounds of the World Values Surveys and three rounds of the European Values Surveys for a total of 98 countries, 1639 regions, 24 recorded years and 355,298 observations. This is the largest dataset of its kind to date.

Our final objective is to assess the impact of bonding and bridging attitudes on life satisfaction worldwide. For this purpose we use what can be described as a standard model when working on happiness cross-country and longitudinally with the WVS-EVS. The model is as follows: where _H_ stands for happiness, _B_ is a vector of variables representing bonding and bridging attitudes, _X_ is a vector of control variables representing individual and household characteristics, _C_ is a vector of countries dummies, _Y_ is a vector of years dummies, _α_ , _β_ , _γ_ , _η_ , _δ_ , are the parameters to be estimated and _μ_ is the error term. The subscript _i_ stands for individuals and the subscripts _c_ and _t_ stand for countries and time (years) respectively. Therefore, in the most general model, we pool all available observations for all countries and years and estimate the happiness equations on individuals with countries and years fixed effects. The same equation is applied to the full set of countries available and to 16 separate groups of countries representing different geographical areas, recent histories
preferences for certain types of benefits will be more likely to take jobs that offer those benefits. They may also, however, point to loss aversion – workers who are in jobs that have certain types of benefits may seek to avoid losing those benefits. Disentangling these two possibilities would improve our understanding of the drivers of the preference heterogeneity that we document. Second, most research on preferences for job benefits, including this study, focuses on workers’ preferences. In a separate study, we also examine employers’ preferences and willingness to offer different types of job benefits, which could provide useful evidence for policymakers who seek to encourage provision of those benefits (Kumar et al., 2019).

Finally, our sample was confined to adults who were working at the time of the survey; thus, we excluded the majority of adult females. The findings suggest that working women’s preferences for job attributes differ from men’s in at least one key way: women have a stronger preference for lower working hours, likely due to the social norm that they are responsible for household duties. Had we included non-working adults as well as working adults, it is possible that our overall results would have reflected a higher valuation of shorter working hours. Had we included additional, related job attributes, such as flexible work schedule and ability to work from home, these might also have yielded high valuations, particularly among women. A fruitful area for research would be to conduct a stated preference study of women who are not in the labor force, to identify what types of job attributes might induce them to join the labor force (or might induce their families to be more willing to allow them to participate). Such an experiment would have to be carefully designed to ensure that the vignettes provided give non-labor force participants a sufficient understanding of working conditions to allow them to make realistic tradeoffs.

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_5_

# 11. COLLECTING DATA ON HOUSEHJOL 4<sup>D ENERGY USE</sup>

# Comtmon Methodologies

'Typically, information relating to residential energy sector consumption has been dealt with through country energy balaices, national budget surveys and micro-studies. Each of these has certain advantages which, however, are outweighed by their drawbacks. Their limitations render them inadequate tools for crafting a usable household energy database. Instead, it wil be suggested that a dedicated, nationwide household energy survey is the most effective means of generating relevant information for planners, policymakers and evaluators.

The choice of methodology and the scope of the data-gathering endcavor will be significantly influenced by a variety of factors. These have been summarized in the upcoming Commonwealth Scicnce Council's Biomass Handbook and include the following:

- geographical area to be covered and ease of communication

- number of regions investigated

- number of communities/eco-systems investigated per region

- relative reliance upon secondary & primary data sources

- precision and breadth of coverage

- number of persons to be questioned per socio-economic or other category

- number of topics explored per person

- level of detail & accuracy required for each topic

- number & timing of interviews per person.

Planners should compare these trade-offs with the proposed uses of data and with their available resources in order to select the optimal methodology.

Regardless of the methodology chosen, household energy data collection can not be a one-shot affair. The institution responsible for information gathering and analysis should be involved in the creation, management and updating of a database on household energy. If external assistance will be used for household energy data gathering, then staff training in survey work and data analysis should be part of the package. Once created, the database should be accessible and easy to update, e.g. on a microcomputer with user-friendly software.

# National=nerzv Balances

An energy balance presents quantities of each major fuel (by weight or by energy value) consumed by specific sectors (industrial, commercial, government, household) per year. A representative energy balance
,5|0,0024|5,8|0,20|3,6|0,44|3,0|0,035|
|2013|2,8|0,0022|5,2|0,17|2,8|0,43|2,6|0,035|
|2014|4,6|0,0020|5,3|0,16|3,4|0,42|3,6|0,034|
|2015|4,4|0,0018|5,2|0,14|3,4|0,39|0,7|0,034|
|2016|2,7|0,0019|7,1|0,13|3,6|0,37|1,3|0,032|
|2017|2,3|0,0021|4,5|0,15|3,0|0,39|2,9|0,030|
|2018|2,6|0,0018|4,4|0,15|1,5|0,38|5,2|0,030|
|2019|3,0|0,0017|4,0|0,14|2,3|0,38|2,5|0,031|
|2020|3,0|0,0018|3,1|0,13|2,0|0,36|2,6|0,032|

Source: Own elaboration with data from the International Financial Statistics (IMF).

In Subsection 7.2, we use the rate of return of the project to estimate NPV target functions for each country. This rate (5.88%) is obtained through Equation B, where pqt, ct and It are shown in Table 10 and δt is obtained using Brazil’s inflation
geo-referenced individual-level indicators from the Demographic and Health Surveys. We use flexible binned specifications that simultaneously consider the impact of temperature, precipitation, and air pollution. The latter is a crucial dimension of environmental degradation and one of the negative consequences of transport and industrial emissions. We also assess the effectiveness of adaptation actions for children’s diarrhea incidence by studying their effectiveness in mitigating the adverse effect of weather on diarrheal diseases. Finally, we employ climate simulations to project the incidence of child diarrhea in Cambodia under different climate and development scenarios. We consider two extreme climate scenarios, also called Shared Socioeconomic Pathways (SSPs): SSP1.19, characterized by very low greenhouse gas emissions, and SSP3.70, marked by high emissions. Combining our estimates of the impact of weather and toilet ownership on diarrhea incidence with the IPCC climate scenarios, we forecast the effectiveness of different toilet ownership scenarios in reducing the

> 2There are several water-related diseases with a moderate-to-high incidence in Cambodia: diarrheal diseases, typhoid fever, leptospirosis, melioidosis, viral hepatitis, and schistosomiasis (McIver et al., 2016a).

> 3The share of people in urban areas in Cambodia has grown steadily since the 1980s. In 2022, the percentage of people living in urban areas was 25.1% in Cambodia; it was only 10% in 1980 (World Bank, 2023a).

2
Figure 6 – Demeaned Net Migration Flows for Work Only and Demeaned Share of Unpaid Family Workers by Governorate <!-- Start of picture text --> cairo<br>6-Oct<br>alexandria<br>ismailia<br>giza port saidqualiobia<br>red seasuez kafr el sheikh<br>north sinhelw a in<br>matrouh<br>south sinaidamietta luxor new valley<br>bani suef<br>aswan<br>dakahlia fayoum<br>assiut<br>qena garbeyya menoufia<br>sharkia meniabeheira<br>sohag<br>-.1 -.05 0 .05 .1 .15<br>dunpaidempag<br>denetmig2 Fitted values<br>10<br>5<br>0<br>-5<br>-10<br><!-- End of picture text --> Source: Authors’ calculations using Labor Force Survey

# **Household Food Production for Consumption**

Migrants are usually motivated by a better living standard and a higher income to offset the impact of inflation and soaring food prices. Many Egyptian households produce much of their own food, reducing the incentive to migrate.

A ratio (constructed with HIECS 2005) of household food consumption from its own production over total household food consumption, plotted against net migration (to work) rates, yielded a negative correlation. Governorates where households rely on their own food production tend to have lower migration rates (see Figure 7). The ability to purchase food at low prices or low opportunity cost reduces the likelihood of migration.

17
**Figure 2. Distribution of students in single-gender or mixed education among countries participating in TIMSS 2019**

<!-- Start of picture text --> Grade 4<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Mixed Boys-only Girls-only<br>Grade 8<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Mixed Boys-only Girls-only<br>Percent<br>Saudi Arabia Islamic Republic of IranKuwait Bahrain QatarPakistan United Arab Emirates Malta Ireland SingaporeTurkeyHong Kong, SARChile New Zealand EnglandSpainCanada Finland Russian Federation GeorgiaUnited States Oman South Africa Northern Ireland Australia Austria Serbia GermanySlovak RepublicCroatia Republic of KoreaFrance Lithuania BulgariaBosnia and HerzegovinaJapanMontenegroHungaryNorth Macedonia Kazakhstan Denmark Czech RepublicLatvia Armenia CyprusRepublic of AzerbaijanBelgium (Flemish)Kosovo ItalyAlbania Chinese TaipeiMorocco Netherlands NorwayPhilippinesPoland PortugalSweden<br>Percent<br>Saudi Arabia Islamic Republic of Iran Jordan Oman Kuwait Bahrain Egypt Qatar United Arab Emirates Ireland Republic of Korea New Zealand Singapore Hong Kong, SAR Israel Australia England Turkey Chile Lebanon Malaysia Chinese Taipei Japan Kazakhstan United States Georgia South Africa Russian Federation Lithuania Portugal Sweden Cyprus Finland France Hungary Italy Morocco Norway Romania<br><!-- End of picture text --> _Source_ : TIMSS 2019 data.

6