rafmacalaba/data-use-mentions-extended huggingface dataset:
# **References**

- **Al-Karablieh, Yazan, Evangelos Koumanakos, and Stefanie Stantcheva.** 2021. “Clearing the Bar: Improving Tax Compliance for Small Firms Through Target Setting.” _Journal of International Economics_ , 130(1): 103452.

- **Andresen, Martin Eckhoff, and Emily Nix.** 2022. “What Causes the Child Penalty? Evidence from Adopting and Same-Sex Couples.” _Journal of Labor Economics_ , 40(4): 971– 1004.

- **Angelov, Nikolay, Per Johansson, and Erica Lindahl.** 2016. “Parenthood and the Gender Gap in Pay.” _Journal of Labor Economics_ , 34(3): 545–579.

- **Artavanis, Nikolaos, Adair Morse, and Margarita Tsoutsoura.** 2016. “Measuring Income Tax Evasion Using Bank Credit: Evidence from Greece.” _Quarterly Journal of Economics_ , 131(2): 739–798.

- **Bana, Sarah H., Kelly Bedard, and Maya Rossin-Slater.** 2020. “The Impacts of Paid Family Leave Benefits: Regression Kink Evidence from California Administrative Data.” _Journal of Policy Analysis and Management_ , 39(4): 888–929.

- **Bartel, Ann P, Maya Rossin-Slater, Christopher J Ruhm, Meredith Slopen, and Jane Waldfogel.** 2024. “The Impact of Paid Family Leave on Employers: Evidence from New York.” _Community, Work & Family_ , Forthcoming.

- **Baum, Charles L., and Christopher J. Ruhm.** 2016. “The Effects of Paid Family Leave in California on Labor Market Outcomes.” _Journal of Policy Analysis and Management_ , 35(2): 333–356.

- **Bergolo, Marcelo, and Guillermo Cruces.** 2014. “Work and Tax Evasion Incentive Effects of Social Insurance Programs: Evidence from an Employment-Based Benefit Extension.” _Journal of Public Economics_ , 117: 211–228
**Table 1: International Tourist Arrivals, 1995‐2017**

|Region<br>1995 2000 2005 2010 2017<br>Tourist Arrivals (Millions)|
|---|
|East Asia & Pacific (excl hi income)<br>43.57 62.85 91.04 119.85 172.30|
|Europe & Central Asia (excl hi income)<br>32.28 50.36 80.95 105.42 144.45|
|Latin America & Carib (excl hi income)<br>32.66 39.76 46.37 51.35 83.30|
|Latin America & Caribbean<br>47.00 55.65 64.12 72.04 112.44|
|Middle East & North Afr (excl hi income)<br>13.83 22.30 33.28 55.26 47.74|
|Middle East & North Africa<br>24.18 37.37 53.99 87.83 89.19|
|South Asia<br>3.81 4.83 6.37 9.23 22.85|
|Sub-Saharan Africa<br>12.96 17.58 23.58 31.46 42.36|
|World<br>523.91 677.39 808.77 956.37 1341.46|

**Source** : World Bank, World Development Indicators.

# **2.3 Digital tools are increasingly used in tourism planning**

Digital platforms in the travel and tourism industry are increasingly used, both by travelers and by businesses engaged in the industry. Digital platforms and online travel agents are major players. For instance, Booking.com comprises 29 million accommodation listings in 154,000 destinations worldwide and operates in 190 countries. Similarly, TripAdvisor reports 8.4 accommodation listings in 156,000 destinations spread across 49 markets, with 490 unique monthly visitors that provide more than 250 reviews
12 <u>Table 1: Data points which changed by more than 20 percent due to changes in underlying sources</u>

|<br>Indicator|<br>2018 survey identifier|<br>2019 survey identifier|<br>x����|<br>x����<br>�x����|
|---|---|---|---|---|
|a_bp_treat|AUT_2006_EUBM_v01_M; AUT_2006_EHIS_v01_M|AUT_2006_EUBM_v01_M|0.07|-0.06|
|a_inpatient_1yr|BEL_2000_MCSS_v01_M; BEL_2000_ECHP_v01_M|BEL_2000_ECHP_v01_M|0.10|-0.01|
|a_inpatient_1yr|BEL_2003_EUBM_v01_M;BEL_2003_WHS_v01_M<br>|BEL_2003_WHS_v01_M|0.14|-0.02|
|a_inpatient_1yr|CHL_2011_National Socioeconomic Characterization<br>Survey_v01_M; CHL_2011_ISSP_v01_M|CHL_2011_ISSP_v01_M|0.10|0.01|
|a_inpatient_1yr|DNK_2003_WHS_v01_M; DNK_2003_EUBM_v01_M|DNK_2003_WHS_v01_M|0.07|-0.03|
|a_inpatient_1yr|FRA_2003_EUBM_v01_M;FRA_2003_WHS_v01_M|FRA_2003_WHS_v01_M|0.10|-0.01|
|a_inpatient_1yr|IRL_2000_MCSS_v01_M; IRL_2000_ECHP_v01_M|IRL_2000_ECHP_v01_M|0.09|-0.02|
|a_inpatient_1yr|IRL_2003_EUBM_v01_M; IRL_2003_WHS_v01_M|IRL_2003_WHS_v01_M|0.11|-0.02|
|a_inpatient_1yr|ITA_2003_WHS_v01_M;ITA_2003_EUBM_v01_M|ITA_2003_WHS_v01_M|0.10|-0.02|
|a_inpatient_1yr|JPN_2011_ISSP_v01_M; JPN_2011_KHPS_v01_M|JPN_2011_KHPS_v01_M|0.05|-0.02|
|a_inpatient_1yr|KAZ_2002_HBS_v01_M; KAZ_2002_WHS_v01_M|KAZ_2002_HBS_v01_M|0.05|-0.02|
|a_inpatient_1yr|LUX_2003_WHS_v01_M; LUX_2003_EUBM_v01_M|LUX_2003_WHS_v01_M|0.15|-0.03|
|a_inpatient_1yr|NIC_2001_DHS_v01_M; NIC_2001_EMNV_v01_M|NIC_2001_DHS_v01_M|0.06|-0.06|
|a_inpatient_1yr|PRT_2000_MCSS_v01_M;PRT_2000_ECHP_v01_M|PRT_2000_ECHP_v01_M|0.05|-0.02|
|a_inpatient_1yr|PRT_2003_WHS_v01_M; PRT_2003_EUBM_v01_M<br>|PRT_2003_WHS_v01_M|0.09|-0.01|
||RUS_2011_WBHHS_v01_M;||||
|
POLICY RESEARCH WORKING PAPER 2572

# Summary findings

The rural economy of developing countries has long been regarded as synonymous with agriculture but in recent years this view has begun to change. Such diverse activities as government, commerce, and services are now seen as providing most income in rural households. Applying decomposition analysis to two new nationally representative sets of household data from Egypt and Jordan, Adams examines how different sources of income-including nonfarm income-affect inequality in rural income. He concludes:

- Nonfarm income has different impacts on poverty and inequality in the two countries. In Egypt the poor (those in the lowest quintile) receive almost 60 percent of their per capita income from nonfarm income. In Jordan the poor receive less than 20 percent of their income from nonfarm income. So nonfarm income decreases inequality in Egypt and increases it in Jordan.

* Access to land accounts for this difference between the two countries. In Egypt the cultivated land base is totally irrigated and very highly productive. Egypt's large rural population seeks access to land but because the land-to-people ratio is so unfavorable, only a minority of rural inhabitants actually own land. The rest-especially the poor-are forced to seek work in the nonfarm sector. By contrast, only 30 percent of Jordan's cultivated land base is irrigated and crop yields are low. So Jordan's

rural population does not press for access to land because the attractive economic rates of return are found in the nonfarm sector. Unlike Egypt's rich, rural Jordan's rich earn less than 10 perceht of their total per capita income from agriculture and more than _55_ percent of it from nonfarm sources.

* The poor in both countries depend heavily on government employment to decrease inequality. Government wages provide 43 percent of nonfarm income for Egypt's rural poor and 60 percent of Jordan's. But since both governments already employ far more workers than they can possibly use, advocating increased government employment to reduce inequality would not be wise policy advice. From a policy standpoint, it would be better to
Rodrik, D. (2008). “Normalizing Industrial Policy”, Commission on Growth and Development Working Paper No. 3, Washington DC.

Statistics South Africa. (2018) Quarterly Labour Force Survey. [online] Available: <u>http://www.statssa.gov.za/?page_id=1854&PPN=P0211. [Accessed: 25 April 2018]</u> Simatele, M. (2015). Market Structure and Competition in the South African Banking Sector. _Procedia Economics and Finance_ , 30: 825-835.

van Seventer, D., F. Hartley, S. Gabriel, and R. Davies (2016). ‘A 2012 social accounting matrix (SAM) for South Africa’. WIDER Working Paper 2016/26. Helsinki: UNU-WIDER. Available at: <u>https://www.wider.unu.edu/database/2012-social-accounting-matrix-southafrica.</u> Wittenberg, M. (2014). ‘Wages and wage inequality in South Africa 1994-2011: The evidence from household survey data’. REDI 3x3 Working Paper 4.

31
2

# **1. Introduction**

There is wide agreement that achieving net-zero emissions globally requires a rapid increase in the pace and scale of adoption of green technologies. However, to date there has been relatively little empirical evidence on the drivers of green technology _adoption_ , with much of the literature focusing instead on green _innovation_ , measured by patenting (e.g. Popp, 2002, 2019; Aghion et al., 2016; Dechezleprˆetre, Glachant and M ́eni`ere, 2013; Acemoglu et al., 2012).<sup>1</sup> As a result, there remains an open debate over the importance of energy and carbon prices in influencing green technology adoption. Empirical papers on green technology adoption have typically focused on a specific technology in a specific setting (e.g cooling and heating equipment in the US by Newell, Jaffe and Stavins, 1999) and find contrasting results about the role of energy or carbon prices.<sup>2</sup> This paper aims to fill this gap by providing new empirical evidence on the diffusion of a comprehensive range of green technologies across establishments over several countries over time. We follow a growing literature that infers the spread of new technologies through their real-time footprint in the demand for new tasks or skills in the text of job postings (e.g. Acemoglu et al., 2022; Goldfarb, Taska and Teodoridis, 2023). We use a rich and high-frequency dataset of some 580 million online job postings in 16 advanced economies from Lightcast from 2014 to the end of 2022.<sup>3</sup> We create a list of keywords relating to green technologies from the European Patent Office (EPO) Y02 classification of patents related to ‘climate change mitigation technologies’ and classify job postings as green technology-related if they mention these keywords (or close synonyms). We then match the job postings data to firm-level data including balance sheet information and energy consumption from Factset and the Carbon Disclosure Project (CDP
8|**38**|6|
|||**Burkina Faso**|Mainly thermal|Other developing<br>regions|
|Average effective power tariff|U.S. cents|30.0|17.0|5.0–10.0|

_Source:_ Eberhard and others 2008; World Bank 2007. Derived from the AICD electricity database (www.infrastructureafrica.org/aicd/tools/data). Outages data from the 2006 and 2009 Enterprise Surveys.

# **Challenges**

Burkina Faso’s power supply is insufficient to meet increasing demand. Installed generation capacity is 13 megawatts (MW) per million people, which represents about 2 percent of the average in Africa’s MICs and just about half of the installed generation capacity of other LICs (table 12). Residential power consumption is just one-third of other LICs. Only about 18 percent of the population has access to electricity (about 40 percent in urban areas and 3 percent in rural areas). Per capita consumption is 44 kWh in Burkina Faso, compared with 100 kWh in Cameroon, 200 kWh in Senegal, and 270 kWh in Côte d’Ivoire. The majority of the population (about 90 percent) still relies on wood energy (firewood and charcoal).

Under these circumstances, extending power supply—either by increasing installed capacity or via trade—is at the top of the government agenda. Improving the operational efficiency of SONABEL and revising the prevalent tariff scheme are also necessary before or as part of the expansion process.

In recent years, SONABEL has faced system transmission and distribution losses of over 60 percent, much higher than the internationally accepted norm of 10 percent. In addition, tariffs barely recover the operating costs of the expensive SONABEL power (table 13). The hidden costs of these operational inefficiencies (box 1) are approximately equivalent to 37 percent of SONABEL’s revenues. These hidden costs, though lower than the average in low-income African countries represent
they were able to assemble. The approach taken can, however, provide a useful starting point for further work using a better data set, particularly for regulation.

# **5.2.3 Pargal (2003)**

The Pargal paper discusses private investment in five infrastructure industries in nine major Latin American countries between 1980 and 1998. The strength of the Pargal study is that the data available to her are significantly better than for the previous studies in providing numerical indicators of the key variables at issue. In addition, the data allow her to put _dates_ on major changes that enable much more powerful use of panel data estimation. Even so, the variables on regulation and liberalization are still far from ideal and cause problems in interpreting some of the regression results. Nevertheless, this paper provides much the best study as yet for the effect of independent regulation on electricity industry outcomes – at least for Latin America.

The study combines the high quality Calderon-Easterly data set on Latin American infrastructure investment (public and private) with regulatory variables derived from the Guasch data set on Latin American concession contracts. Since the dates of legislation liberalizing the sector and establishing the regulator are known, each sector can be given a dummy of zero for the years before the change and one thereafter rather than a simple time-invariant zero-one dummy. Standard control variables relevant to an investment equation are also included (eg real GDP, lagged capital stock and real interest rates).

Although the data on regulation and other institutional features is far superior to the BHS and ZKP studies, there are still significant problems in establishing a robust estimate of the effects of regulation (and key governance features. These are general issues but we will concentrate here on the implications for the power sector:

- (i) As Pargal recognizes, liberalization in the power sector was usually accompanied by restructuring of vertically integrated utilities<sup>46</sup> .  Indeed, the same law would almost always establish the regulatory agency.  Hence, separating the effects of regulation from
Press.

Diaz-Serrano, L (2010). Do Legal Immigrants and Natives Compete in the Labour Market? Evidence from Catalonia. _IZA Discussion Paper_ No. 4693.

Djankov S., I. Lieberman, J. Mukherjee and T. Nenova (2003). Going Informal: Benefits and Costs.

In B. Belev (ed.) ― _The Informal Economy in the EU Accession Countries: Size, Scope, Trends and Challenges to the Process of EU Enlargement_ ‖. Sofia: CSD: 63-80.

Dreher, A. and F. Schneider (2010). Corruption and the Shadow Economy: An Empirical Analysis. _Public Choice_ 144 (1): 215–238.

Dreher, A., C. Kotsogiannis and S. McCorriston (2009). How Do Institutions Affect Corruption and the Shadow Economy? _International Tax and Public Finance_ 16 (6): 773–796.

Eamets, R. and J. Masso (2004). Labour Market Flexibility and Employment Protection Regulation in the Baltic States, _IZA Discussion Paper_ No. 1147.

Ederveen, S. and L. Thissen (2007). Can labour market institutions explain high unemployment rates in the new EU member states? _Empirica_ , 2007, 34(4): 299-317 Eichhorst, W., M. Feil and C. Braun (2008). What Have We Learned? Assessing Labor Market Institutions and Indicators, _IZA Discussion Paper_ No. 3470.

European Social Survey (2002-2009). Data files: Round 1 (edition 6.2), Round 2 (edition 3.2), Round 3 (edition 3.3), Round 4 (edition 4.0). _Norwegian Social Science Data Services_ .

34
management of local environmental quality or for example water savings due to more efficient irrigation. Social benefits comprise reduced losses of lives, injuries or illnesses but also increased psychosocial well-being.

Different techniques can be used to assign values to the benefits produced by improved weather information (WMO et al 2015):

- Non-market valuation techniques like stated or revealed preference methods.

- Economic modeling methods requiring significant amounts of detailed data and knowledge about the sectors where benefits are assessed, as well as substantial statistical expertise.

- Avoided-costs assessment, which is easier to implement than other options but only considers part of the benefits of improved forecasting. Additional economic benefits such as increased production due to forecasting are ignored.

- Benefits can also be assessed by transferring the results of existing benefit studies to the context being analyzed. This method is the least resource intensive but is also less robust.

None of the methods described is able to assess the full benefits of weather forecasting, as some benefits simply cannot be expressed quantitatively. Complementary quantitative and qualitative assessments can produce a more complete understanding (WMO et al 2015). However, it is often difficult to include qualitatively assessed benefits in a benefit-cost analysis; they are therefore not considered in this analysis.

# 3.3.1 Global benefits transfer

As conducting a comprehensive analysis of the socioeconomic benefits is outside the scope of this research, the data used to estimate the benefits are calculated based on the methods and assumptions used by Hallegatte (2012) on the worldwide socioeconomic benefits of improved early warning systems. Using the benefit transfer approach, the data from Hallegatte (2012) is converted and adapted to measure the additional benefits created by improved weather forecasting in LICs resulting from the provision of global forecasting products.

Hallegatte (2012) analyzes three types of benefits of improved forecasting and early warning: reduced asset losses, reduced human losses and improved economic productivity. In a first step, he uses data from studies on Europe, the OFDA/CRED International Disaster Database (CRED 2015) and the Copenhagen Consensus on
assessments of interventions in various contexts to address the development impacts of forced displacement.

# **Returnees**

Data on returns of refugees and IDPs are incomplete. UNHCR provides data on refugees who have returned to their host countries (39 countries during 2015) and some data on returned IDPs (i.e. only former IDPs that were protected or assisted by UNHCR) including both assisted and unassisted returns. IDMC provides very rough estimates of returns of IDPs (in 20 of 52 countries it monitored in 2015). However, there are significant challenges associated with obtaining figures on IDP returns and actual return figures may be considerably higher (IDMC 2015). In practice, the quality of data is more solid for those who have returned with assistance from UNHCR or other international agencies than for unassisted returns.

There is limited data on the success or sustainability<sup>96</sup> of reintegration following return. In many situations returns are not sustainable leading to onward migration or secondary displacement. Even when returnees resettle permanently in their areas of origin, they may continue to face obstacles to their full reintegration. However, publicly available data on returns do not extend to an assessment of the success or sustainability of return and reintegration, making it difficult to assess the degree to which specific groups of returnees still have vulnerabilities and needs linked to their displacement. A starting point for this kind of assessment may be the eight criteria for sustainable returns set out in the IASC Framework on Durable Solutions for IDPs.<sup>97</sup> conflict-affected countries to address some of the drivers of forced displacement and promote returns; and (f) support to returnees and communities in return areas to facilitate successful and sustainable reintegration.

> 96 UNHCR conducts protection monitoring in return areas that informs programming, as well as communication with refugees and IDPs about conditions so as to inform their decisions; this information should complement all statistics about returns – both assisted and spontaneous.

> 97 JIPS, under the leadership of the Special Rapporteur on
The sample looks similar across the three groups and we cannot reject the joint orthogonality of baseline characteristics when comparing the treatment group to the spillover group, the treatment group to the pure control, or the spillover group to the pure control. The average woman in our sample is 36 years old, has 9 years of schooling, and has been running her firm for just over 6 years. Two-thirds of the women are currently married. The modal firm has no employees (only 20 percent have one or more employees). The mean firm earns 1,100 KSH (US$13) per week in profits on sales of 5,500 KSH (US$65), and has capital stock of 31,000 KSH (US$370).<sup>5</sup> Onequarter have ever received financing from a bank or microfinance organization and 45 percent are registered at the local level. Only 35 percent of firms keep business records at baseline, and on average firms are using just over half of the 26 business practices in the McKenzie and Woodruff (2015) index. This suggests scope for improvement from business training.

# **3. The Training and Mentoring Interventions**

# **3.1 The Get-Ahead Program**

The training provided is the ILO’s Gender and Entrepreneurship Together – _Get-Ahead_ for Women in Enterprise program. This program “differs from conventional business training materials by highlighting essential entrepreneurial skills from a gender perspective... It addresses the practical and strategic needs of low-income women in enterprise by strengthening their basic business and people management skills. It shows women how to develop their personal entrepreneurial traits and obtain support through groups, networks and institutions dealing with enterprise development” (Bauer et al., 2004). The program began in Thailand in 2001, and has now been used in at least 21 countries, serving approximately 400,000 women.

The program has four key modules, with the following themes:

- Module 1: Basics on Gender and Entrepreneurship.
). Triple mode firms by themselves account for about 6 percent of total output and for nearly 15 percent of total exports. The majority of the output share, nearly half of the total output, is produced by firms who are classified as low or single mode of GVC integration. Similar patterns are observed when we look at the shares within specific industries. We show the distribution of firms, output and exports for two major manufacturing industries - Apparel and Textiles and Electronics in Figure 3 and Figure 4, respectively.<sup>8</sup> The kernel-density functions of productivity with respect to different international trading activities and different modes of GVC participation are presented in Figure 5 and Figure 6, respectively. Since the TFP distribution is skewed, it is preferable to look at the full distribution of TFP levels rather than only average values. The kernel density shows the shares of firms (density) that reach each productivity level. Thus, it shows the probability of choosing a firm with a certain productivity level when firms are randomly drawn from each category of GVC activities.

Firms that both export and import are more productive than firms that are only exporters, only importers and firms serving the domestic market only. Further, the distribution of the estimated productivity confirms that firms that are more deeply integrated in GVCs are, on average, more productive. The superior performance of deeply integrated firms (dual and triple mode) in GVCs is particularly visible in

> 8 Apparel and Textiles comprise the sector categories 13, 14 and 15 (according to 2-digit NIC classification); Electronics comprise the sector categories 26 and 27 (according to 2-digit NIC classification).

7