rafmacalaba/data-use-mentions-extended huggingface dataset:
42 support the poorest diets because of limited sources of income. Food gathering is often impossible and there is less space for home gardens and livestock which can be good stable sources of nutritious food in view of especially short term variations in income.

Indeed, data from Indonesia (Chernichovsky and Meesook; 1988), for example, indicate that while the urban population is wealthier and better off in terms of protein and vitamin C consumption, it **it** worse off in terms of consumption of calories and other micro nutrients. One of the reasons for this is that absolute and relative prices are different betveen rural and urban areas. These differences move consumption away from grains towards foods that are rich in fat and protein. The price differentials 1-tween the areas, higher prices in urban areas, seem, in this case, to outweigh the higher Income in the urban areas as far as food consumption is concerned. Moreover, the evidence suggests that a higher proportion of the population in the urban areas is at risk from malnutrition.

Urbanization offers, however, means for efficient market interventions to cope with nutrition because of the population's dependence on the market for food. It is, therefore, easier to implement subsidies in urban areas than in rural areas. A serious policy issue Is how to keep urban food prices low without affecting rural households who sell the produce (e.g. Gittinger et al. 1987).
first countries to establish public credit registries were in Western Europe – Germany in 1934 followed by France in 1946. By the mid-1960s, three other European countries – Italy, Spain and Belgium – had also established PCRs. Early adopters included the former French

> 5 This data is based on the 2002 survey of PCRs conducted jointly by the Credit Information Group in the Vice Presidency for Financial Services (FSE) and by the Doing Business Unit in the Vice Presidency for Private Sector (PSD) as well as from previous FSE surveys on this topic. Not all countries responded to the 2002 survey, especially in Africa and Asia, so the actual number of PCRs is likely to be slightly greater than 58.

4
initial female employment rate is 46% and the initial male

> 29 According to the assumptions made.

30 Note that this weight depends on the number of workers of both genders in better employment, not the gender gap in better employment. 31 This assumes the human capital is evenly spread across the population. In reality, there is likely to be some positive sorting, such that people with higher human capital are more likely to be in better employment. 32 Most importantly, I assume that the labor share =0.5. The depreciation rate is 3.6%, capital-to-output ratio is 2.2, human capital growth rate is 0.5%, TFP growth rate is 1.5% and investment rate trends up from 26% of GDP initially to 30.1% by 2040. Population demographics are taken from the UN. Baseline GDP growth is initially about 5.5%, but slows to 4.5% by 2050. The ββ male LFP rate is assumed constant at 72%.

33 The Philippines was chosen because of the other gender work in that country and an existing LTGM calibration. However, its LTGM calibration is fairly similar to that in other developing countries, and so this exercise is likely informative for other countries. For example, the assume labor share of 0.5, is similar to the cross- country average in Penn World Tables 10.

12
cent of the population, and the province with the smallest is Corrientes (0.5 percent).

Figure 1: Percentage of Natives by Province <!-- Start of picture text --> (7.6,8.7]<br>(6.5,7.6]<br>(5.4,6.5] 7.9<br>(4.3,5.4]<br>(3.3,4.3]<br>(2.2,3.3] 6.6 6.1<br>(1.1,2.2]<br>[0.5,1.1]<br>No data 3.9<br>1.3 1.2<br>1.9<br>1.3<br>0.5<br>1.2<br>1.2 1.5<br>1.6 1.1<br>1.9<br>2.4 2.2<br>1.9<br>4.5<br>8.0<br>7.2<br>8.7<br>3.6<br>2.9<br><!-- End of picture text --> _Source:_ REDATAM INDEC Census Argentina (Instituto Nacional de Estad ́ıstica y Censos, 2010). _Disclaimer:_ Falkland Islands - Islas Malvinas: A dispute concerning sovereignty over the islands exists between Argentina who claims this sovereignty and the U.K. which administers the islands.

How does our measure of Native self-identification relate to native ancestry? To answer this, we study the correlation of our self-identified measure of Native prevalence with a measure of prevalence based on genetic studies (Wang et al., 2008; Corach et al., 2010; Avena et al., 2012; Parolin
Finally, other constraints from the supply side related to higher reservation wages could explain the high unemployment rate for female job seekers. It is influenced by high opportunity costs, especially because of preferences for public sector employment, the working conditions in the private (informal) sectors, and other explanations presented in section 3 of this report. Female unemployment in Tunisia is thus a complex challenge and cannot be fully explained by one factor alone. Closing the gender gap in employment requires a comprehensive, multisectoral approach aimed at tackling binding constraints from both the demand side and the supply side.

Policy implications: Addressing discrimination in the Tunisian labor market In the IT sector, where discrimination is widespread, it is important to undertake prevention measures. Enacting laws that prohibit any form of discrimination in employment is an important first step. Tunisia currently scores 75 on the World Bank’s Women, Business, and Law index,<sup>18</sup> indicating that the country’s economy gives women only three-quarters of the legal rights of men. Although this average is significantly higher than the MENA average of 47.37, there is room for improvement.

Currently, Tunisia’s laws do not mandate equal remuneration for work of equal value, and women are not allowed to work in the same industries and same night hours as men.<sup>19</sup> In addition, because this study finds that veiled women are 8.5 percentage points less likely than nonveiled women to receive a callback from employers, it is important to gradually influence the customs around inserting photos in applicants’ resumes. More also needs to be done to ensure that women are not discriminated against at the interview stage or throughout their career progression based on their appearances, religious views, and family circumstances.

In instances in which discrimination in the workplace appears to be the result of statistical discrimination (for example, employers believe that female applicants are still, on average, less qualified or less productive than male applicants), Tunisia’s
Estimating the Economic and Distributional Impacts of the RCEP Table 2. Impact on trade by economy. Percentage change relative to ‘Business-as-Usual’ scenario, 2035

|**Region**||**E**|**xports**<br>|||**Im**|**ports**<br>||
|---|---|---|---|---|---|---|---|---|
||**Tariffs**<br>**alone**|**RCEP**|**ROO**<br>**liberaliz-**<br>**ation**|**Productivity**<br>**kick**|**Tariffs**<br>**alone**|**RCEP**|**ROO**<br>**liberaliz-**<br>**ation**|**Producti-**<br>**vity kick**|
|Australia-New|||||||||
|Zealand|0.2|3.6|4.3|4.7|0.2|3.2|4.1|4.6|
|China|0.7|1.7|2.4|4.0|0.6|1.8|2.5|3.9|
|Indonesia|0.3|3.0|3.7|5.5|0.2|2.6|3.6|5.0|
|Japan|3.0|7.6|9.0|8.9|2.6|5.2|6.2|6.4|
|Cambodia|0.4|3.
had the highest median vulnerability and increase in vulnerability. By region, Latin America and the Caribbean had the highest vulnerability and second highest increase in vulnerability. Among exporters, as expected, the Middle East and North Africa, with some of the major oil exporters, had the largest median vulnerability in magnitude. These countries also dominated the vulnerability index for highincome net oil exporters. Figure 3 illustrates the distribution of vulnerability in 2011 by oil trade status, and Figure 4 provides the distribution of the change in vulnerability between 2009 and 2011.

Figure 3: Countries’ Vulnerability by Oil Trade Status in 2011 <!-- Start of picture text --> 40 40 38 38<br>35 Net importers Net exporters<br>35 32 35<br>30 30<br>25 25<br>20 17 20 18<br>15<br>15 15<br>10 10 8<br>5 2 5<br>0 0<br>0 to 2.5 2.5 to 5 5 to 10 10 to 20 Above 20 Below -30 -30 to -15 -15 to -5 -5 to 0<br>Vulnerability as % of GDP Vulnerability as % of GDP<br>% of countries<br><!-- End of picture text --> _Source_ : Author’s calculations based on data from EIA 2013b.
Policy Research Working Paper 8940

# **Abstract**

Rapid economic growth over the past two decades lifted millions of people out of poverty in Central Asia. But the uneven spread of prosperity left many communities struggling to catch up. To support lagging regions within countries, each of the region’s five national governments has made convergence a pillar of their development strategies. An imperfect patchwork of household surveys allows policy makers to monitor progress and identify some spatial disparities. But these share an important weakness: none of the official surveys in the region is representative when disaggregated to the level of districts. Islands of poverty and prosperity are thus lost in the averages—leading to targeting inaccuracies that can slow the pace of poverty reduction. This study partially addresses the challenge. The accuracy of key welfare indicators is sharpened well beyond what could be achieved for any country alone by: i) unifying survey data from across the region and ii) applying the techniques of small-area estimation. The results provide detailed measures of welfare that in turn can be disaggregated for each district in Central Asia. Comprehensive maps of where the poor and the middle class live are presented, for the entire region and individually for each country.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at wseitz@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors.
to the European countries in our sample, fossil fuels account for 38% and renewables for 34%. Brazil and India are two countries that rely heavily on one energy type: hydroelectricity in Brazil (71% of electricity generation) and coal-fired generated electricity in India (73% of electricity generation). We gathered data about the price of oil, natural gas, and coal from the IMF Primary Commodity Prices. They are not available at a daily frequency, which prevents us from estimating a model of demand (by generators) and supply of the fuel inputs. Nevertheless, we can examine how these prices evolved during the pandemic using the monthly level fuel prices from major energy hubs.

# _2.1. Electricity generation and electricity mix_

As shown in the summary statistics in Table A. 1 and Table A. 2, the average daily generation in our sample was 953 GWh, with a max of 14,019 GWh. Capacity averaged 92 GW, with a max at 1,225 GW. Looking at the electricity generation by source, renewables saw a large increase after the pandemic started, led by solar and followed by hydropower and wind. Coal has seen the largest decrease in power generation.

Figure A. 2 presents daily generation in GWh. The figure shows that electricity generation is strongly seasonal. This is because the demand and generation profiles of renewables (hydro, solar and wind) are seasonal, depending on weather patterns and the more abundant availability of sunlight and wind at certain times of the year. Our econometric models (described below) must therefore control for month, temperature, and diffusion of air conditioning and various other types of heating/cooling equipment in the country.

Figure A. 2 also shows that all regions faced a significant drop in electricity generation during the first wave of COVID-19 – approximately starting in March 2020. All regions returned to their original level of electricity generation in the following months but recovered at different speeds. The United States recovered quickly, returning to its previous peak in
(1), we have estimated regressions that exclude either industrial emissions sources or income. When we exclude the income terms, we find a very small effect (median change of 3%) on parameter estimates for power plants, steel mills, cement plants and refineries. When we exclude the industrial facilities, we find that the income parameters change slightly. For case (2), we have estimated regressions that exclude all righthand variables except income, time trend and seasonal controls (by hemisphere). As expected, with all collinear variables dropped, we find an increase in the significance and size of the EKC parameters.

Figure 8 displays the estimated EKC with industrial emissions sources, without them (case 1), and without any righthand variables except the EKC terms (and the trend and seasonal controls) (case 2). With industrial sources excluded, the EKC peaks sooner and declines more sharply at higher incomes. At present, we cannot say how much of this change reflects estimation bias and how much reflects the collinearity-related upward bias that would accompany exclusion of properlyinstrumented facility variables. In any case, all three estimates carry the same basic message: The EKC reaches a peak in the range [$40,000 - $50,000] which is above the 90<sup>th</sup> percentile internationally.

Case (3) warrants more detailed attention because of potential simultaneity bias in the relationship between income and CO2 emissions. A substantial literature has used inventory-based emissions estimates to study this relationship at the country level (e.g., Apergis and Payne 2014). Results have differed substantially by country and time period (Ben Youssef et al. 2016). The research presented in this paper is different from previous work in at least three relevant ways. First, it uses direct, satellite-based observations of CO2 rather than estimated emissions inventories. Previous studies have risked introducing some technical element of simultaneity _by construction_ , because their emissions inventories derive entirely from measures of activities that provide components of
income inequality is slightly greater in Botswana. The Index of Economic Freedom clearly indicates a higher fiscal burden<sup>8</sup> for South Africa, but also less government interventions in South Africa. The Polity IV data set shows, in line with previous data sets, that Botswana has more

> 7 Galbraith and Kum (2005) estimate gross household income inequality from a regression between the Deininger and Squire (1996) inequality measures and the UTIP-UNIDO pay inequality measures. 8 The index of fiscal burden measures the burden a government imposes on its citizens. The following variables have been integrated in the index: top income tax rate, tax rate an average taxpayer faces, top corporate tax rate and government expenditures measured as a percentage of GDP. To get the index, the scores of the income tax rate and the corporate tax rate are measured separately and then averaged to get a single taxation score. The final score for the fiscal burden consists of the averaged scores for income and corporate taxation and for government expenditures.

28
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 22. Hidden costs in power generation in Cameroon and comparator countries**

<!-- Start of picture text --> a. AES Sonel b. Selected power utilities in Central and West Africa<br>180 350<br>160<br>300<br>140<br>250<br>120<br>100 200<br>80 150<br>60<br>100<br>40<br>20 50<br>0 0<br>Collection Inefficiencies Underpricing Losses Collection Inefficiencies Underpricing Losses<br>Source: Briceño-Garmendia, Smits, and Foster 2009.<br>Percentage of revenues Percentage of revenues<br><!-- End of picture text --> The power supply in Cameroon is unreliable. Investment climate surveys suggest that firms encountered around 128 outages in 2009, almost as twice the average for Africa’s middle-income countries, enduring blackouts of four hours each time. On average, at least 16 days a year are spent without power due to outages (World Bank 2009c). To combat erratic power supply, many firms generate their own power. It is estimated that as much as 31 percent of the country’s installed capacity is selfgeneration. Suppressed demand for power is around 241 GWh and is expected to progressively increase with growing domestic demand (World Bank and IFC 2010).

Investment climate surveys from 2007 determined that unreliable power was one of the top five constraints to business activity. Around 67 percent of firms reported that power was a major constraint to business, similar to other fragile states but worse than the averages for Africa’s low- and middle-income countries and resource-rich countries.

Firms that generate their own power face high costs. Self-generation is estimated to cost the private sector $0.46 per kilowatt-hour, four times the tariff charged by the power utility, which pushes up the prices of the firms’ products. If the price premium of
; economic<br>needs tests|E.g., foreign insurance firms not<br>permitted to offer certain types<br>of coverage or product<br>innovations; price controls|
|**Non‐discriminatory**|E.g., A limit of three mobile<br>phone providers permitted to<br>operate in the country|E.g., all retail banks required to<br>maintain a minimum level of<br>capital, independent of type of<br>legal entity|

Source: Francois and Hoekman (2010)

**Both entry and conduct regulations in services have a knock‐on effect on the economy.** As Figure 4.1 shows, both types of barriers are negatively correlated with firm‐level TFP. A recent study by Van der Marel et al. (2016), using data from millions of European firms, shows that reducing service restrictions to the level of the three least regulated EU member states (UK, Denmark, Sweden) would increase the productivity of firms operating in both services and manufacturing by up to 5.3 percent within two years of implementation. While removing both entry and conduct barriers in services increases firm productivity, the effect is larger for conduct barriers. Barriers to operations prevent services firms from growing in the post‐entry phase and therefore prevent the Schumpeterian dynamic of ‘creative destruction’.

23
period, we also observe a divergence between developed and emerging economies with the emerging economies clearly outpacing developed economies. The euro zone, which includes some of the major trade partners of Morocco, was the worst performer among the group of countries considered in terms of average growth.

**Figure 1 - World GDP growth 2000-2010**

<!-- Start of picture text --> 10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-2.0<br>-4.0<br>-6.0<br>-8.0<br>-10.0<br>World Developed countries<br>Emerging countries Euro zone<br><!-- End of picture text --> Source: Morocco Tableau de Bord (2000-2010) During the same period, Morocco experienced significant annual changes in GDP growth (Figure 2) but remained in line with world growth overall, with a growth rate hovering around 4-5% a year. The significant annual fluctuations in the output of the Moroccan economy are explained by the volatile performance of the agricultural sector. Despite accounting for only about 15% of GDP, the agricultural sector accounts for a much larger share of the annual variations in GDP. For example, growth in agricultural production accounted for 27% of total growth in 2003 and 35% in 2009.<sup>5</sup> Interestingly, Morocco has been able to counter balance the effects of the 2001 and 2007-2010 global crises thanks to exceptional agricultural years that happened in conjunction with (or following) the two global crises; as shown in Figure 2, the agricultural sector exhibits exceptional growth rates in 2001, 2008 and 2009.

> 5 Our estimates based on official statistics (Tableau de Bord, High Commission for the Plan, 2012
|**Gaining all type of work experiences**|4972|0.0440|0.2052|0<br>1|2322|0.0349|0.1835|0<br>1|2650|0.0521|0.2222|0<br>1|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Understanding the meaning of life**|4972|0.0603|0.2381|0<br>1|2322|0.0633|0.2436|0<br>1|2650|0.0577|0.2333|0<br>1|
|**Gain self-esteem**|4972|0.1185|0.3232|0<br>1|2322|0.1016|0.3022|0<br>1|2650|0.1332|0.3399|0<br>1|
|**Living in rural areas**|4972|0.4055|0.4910|0<br>1|2322|0.3876|0.4873|0<br>1|2650|0.4211|0.4938|0<br>1|
|**Living under the poverty line**|4972|0.1961|0.3971|0<br>1|2322|0.2046|0.4035|0<br>1|2650|0.1887|0.3913|0<br>1|

_Note:_<sup>_1_</sup> _For the definition of actual work experience adopted see the data section. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._

*
For ‘Getting Credit’, as the most important DBI for economic growth overall, it appears that the Legal Rights Index, followed by the Credit Information Index are the most important. Citizen coverage under a public registry or a credit bureau is only marginally or not at all significant. Finally, the two marginally significant sub-components for the ‘Protecting Investors’ index are the Disclosure Index and the Shareholder Suits Index.

With these results, policy advice can be given as follows: the most important DBI to focus on is ‘Getting Credit’, closely followed by ‘Enforcing Contracts’. As a general rule, DB-inspired reforms are more likely to have an effect on economic growth when they focus on reducing the cost aspect of the regulatory environment.

But does reform really bring about economic growth? Table 4 aims to answer this question by focusing on the sub-components of two indicators: ‘Starting a Business’ and ‘Enforcing Contracts’. This analysis is limited by the availability of data. The DBIs were first published in 2004 with fewer countries than more recently, and with less data coverage. Focusing on the change of these two indicators between 2005 and 2006 allows for substantial data coverage. They are also among the most important indicators for economic growth, as shown by figure 1.

Data availability allows for calculating average growth over 4 years from 2006. The same control variables are included as in the analyses for figures 1 and 2, but in addition, the initial value of the respective DBI sub-components in 2006 will be included as controls as well. 151 countries can be included for the ‘Starting a Business’ components and 150 countries can be included for the ‘Enforcing Contracts’ component.

The table suggests that, indeed, DBI reform can stimulate economic growth. The general gist of figure 2 is upheld: DBI reform has a particular impact on economic growth when focusing on reducing the cost of doing business, followed by the time required for dealing with regulatory requirements. The strength of the results is striking. The coefficients