rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8840

# **Abstract**

Rural economies are in transition around the world; in many countries, improved technology and linkages across sectors have expanded access to markets and accelerated production for some farmers. At the same time, rural areas globally are facing a growing base of landless and smallholder farmers, out-migration to urban areas, and persistence of low-skilled, informal, and seasonal jobs where women are often heavily concentrated. Recent global initiatives are examining programs that can effectively raise rural incomes, including how addressing shortfalls in women’s hours worked and earnings can raise rural productivity and growth. But well-designed policies to address these issues require improved counting of individuals’ employment, accounting for the complexity of measuring rural women’s labor force participation, as well as data on social, economic, and institutional constraints that women face in seeking better economic opportunities. Using recent rounds of the Ethiopia, Malawi, Nigeria, and Uganda Living Standards and Measurement Study-Integrated Surveys on Agriculture, as well as findings from recent country pilots conducted by the International Labour Organization, this paper discusses best practices and issues to consider when examining rural women’s employment in socioeconomic surveys, as well as a survey research agenda to improve measurement.

This paper is a product of the Development Data Group, Development Economics. 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 koolwalg@gmail.com.

_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. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
of the ecosystems that have impacted agricultural activity (P. D. Fall, Carretero, and M. Y. Sarr 2010, Goldsmith, Gunjal, and Ndarishikanye 2004). In turn, this longer term migration can lead to commuting patterns as people return home to visit family and friends or receive visitors from home (Cho, Myers, and Leskovec 2011). Focusing on migrants in Dakar, A. S. Fall (1998) nds that 87% of male and 81% of female migrants visited their home areas, with the majority of visits occurring for holidays, family ceremonies and religious festivals.

Detailed studies of the Jola ethnic group in several villages nds that circular migration plays an important role, with over 80% of unmarried Jola youth traveling to the cities in October and then coming back before the rice harvest in June-July (Linares 2003). Broader research on youth in Senegal has shown that more than half of the internal migration they engage in is temporary and rural to rural or urban to urban (Herrera and Sahn 2013). Additionally there are still pastoral groups that travel within a set territory(Adriansen 2008). Understanding the movement patterns within Senegal is important for thinking through potential confounding factors between movement and malaria. The majority of the literature points to movement triggered by agricultural seasons as well as holidays. These factors and their relationship to malaria incidence will be discussed in the model section.

In Senegal, 2% of the population are international migrants while only 1.2% of the population emigrated from Senegal. Focusing on immigration into Senegal in 2013, only 0.23% of the population entered the country. While the paper focuses on the role of internal migration, the potential impact of international migration will be discussed.

# 2.4 Malaria Data

Low-incidence areas close to elimination can experience the largest externality from population mobility for three key reasons: (1) without these travelers the disease could be reduced to zero and require lower government expenditures; (2) in high malaria areas, people have usually built up an immunity to the disease; therefore, a traveler entering a high malaria area is less likely to lead to a new infection even if he or she infects additional mosquitoes 8
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 14. Botswana’s Internet market benchmarked against its southern African peers**

<!-- Start of picture text --> a. Internet service trends, Botswana, 2000–08 b. Internet service trends, southern Africa<br>6.0 200 20 350<br>18<br>180 300<br>5.0 16<br>160<br>14 250<br>140<br>4.0<br>12<br>120 200<br>10<br>3.0 100 150<br>8<br>80<br>2.0 6 100<br>60<br>4<br>40 50<br>1.0 2<br>20<br>0 0<br>0.0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per Internet users (per 100 people)<br>person)<br>Internet users<br>Internet users<br>International internet bandwidth International internet bandwidth<br>Madagascar Malawi Angola Zambia Mozambique Lesotho Zimbabwe Botswana South Africa Namibia Mauritius<br><!-- End of picture text --> _Source:_ AICD. Information and Communications for Development database

# Financing Botswana’s infrastructure

To meet its most pressing infrastructure needs and catch up with developing countries in other parts of the world, Botswana needs to expand its infrastructure assets in key areas (table 13). The targets outlined in table 13 are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting the targets, which can be modified or delayed as needed to achieve financial balance. Admittedly, for middle-income countries in Africa, the proposed targets might look relatively modest to what the country can and has assumed as policy targets. For example, achieving the Millennium Development Goal in water and sanitation looks like a modest goal for Botswana; whose access policy is to attain universal access to piped water by the end of 2016. Therefore, the spending needs proposed here can be regarded very much as a lower bound spending needs for Botswana.

Meeting these illustrative infrastructure targets in the country would cost $785 million per year over a decade. Capital expenditure would account for 55 percent of this requirement. Meeting growing demand for power—the sector with the highest spending needs—would require an estimated $462 million per year to install almost 2,141 MW of new generation capacity and expand electrification. The water and sanitation sector is the area with the second-highest spending needs: about $141 million will be needed each year to meet the MDGs, split evenly between capital and current expenditure needs. Another $107 million per year will be required by the transport sector. While less than the amounts needed for other sectors, requirements for ICT are also high in absolute terms, amounting to around $70 million a year (table 14).

30
Incidence of COVID-19 and Connections with Air Pollution Exposure Table 3: Data descriptives.

|**Variable**|**mean**|**sd**|**median**|**min**|**max**|
|---|---|---|---|---|---|
|March 22 confrmed cases per 100,000|24.79|31.62|16.00|0.00|349.50|
|March 30 confrmed cases per 100,000|70.84|63.77|54.20|0.00|565.70|
|March 31 hospital admissions per 100,000|29.36|30.81|20.50|0.00|237.20|
|March 22 confrmed case counts|11.41|21.66|6.00|0.00|188.00|
|March 22 case hospitalization rate|141.15|102.67|107.05|0.00|804.92|
|March 30 case hospitalization rate|41.39|19.23|41.05|0.00|100.00|
|Population density (thousands per sqkm)|0.88|1.04|0.46|0.02|6.52|
|Share of male population|0.51|0.01|0.50|0.47|0.57|
|Share from 14 to 24|11.72|1.65|12.00|9.00|23.00|
|Share from 25 to 44|21.96|2.84|22.00|14.00|36.00|
|Share from 45 to 64|29.32|2.22|30.00|19.00|34.00|
|Share above 65|21.24|3.29|21.00|9.00|32.00|
|Share of unmarried|44.53|4.19|44.00|37.00|65.00|
|Share of single households|32.76|6.32|31.00|20.00|60.00|
|Share of households without children|32.02|3.56|32.00|20.00|40.00|
|Average household size|2.26|0.18|2.30|1.70|3.30|
|Share of western immigrants|8.61|4.46|8.00|2.00|47.00|
|Share of non-western immigrants|7.43|5.93|5.00|1.00|39.00|
|Share of water surface|0.11|0.17|0.04|0.00|0.94|
|Share with long-term illness|34.11|2.91|34.00|27.00|45.00|
|Share of overweight|51.01|3.68|51.00|37.00|61.00|
|Share exposed to noise above 50kmh|3.03|1.38|3.00|0.00|8.00|
|Share exposed to noise below 50kmh|4.84|2.00|5.00|1.00|13.00|
|Share with obesity|14.46|2.17|14.00|9.00|22.00|
|Share of non-heavy drinkers|39.94|4.87|40.00|30.00|58.00|
|Share of smokers|19.64|2.72|19.00|14.00|31.00|
|Mean PM2_._5|10.22|1.33|10.69|6.95|12.04|
|Mean PM10|17.28|1.61|17.74|13.60|21.09|
|Mean Van Donkelaar PM2_._5|14.60|1.77|15.01|10.15|18.05|

# **6.2 Additional analysis results**

This section presents a number of additional estimations. In particular, section 6.2.1 presents Poisson-type regressions that use case counts as dependent variables in line with earlier studies on the relationship between air pollution and viral spread, the influence of possible outlier observations or the discussed distributional issues is investigated in section 6.2.2, in section 6.2.3 the main analysis is re-estimated using confirmed cases from March 30 to show that the conclusions are not dependent on the date of the case snapshot, the main analysis is also repeated using confirmed hospital admissions from March 31 in section 6.2.4 to provide further evidence that the conclusions are not dependent on measurement error in the confirmed cases. Section 6.2.5 investigates correlations between covariates and the case hospitalization rates used to proxy for symptom severity. Finally, additional analysis in section 6.2.6 re-estimates the main analysis using alternative satellite-derived PM22 _._ 5.

15
- Rodrik, Dani (2009). “Growth After the Crisis”. _Discussion Paper_ 7480, London, England: Centre for Economic Policy Research

- Sachs, Jeffrey D. & Andrew M. Warner (1995). “Economic Reform and the Process of Global Integration”. _Brookings Papers on Economic Activity_ 26(1) (Spring): 1–118

- Sachs, Jeffrey M. & Andrew M. Warner (1999). “The Big Push, Natural Resource Booms and Growth”. _Journal of Development Economics_ 59(1) (June): 43–76

- Sala-i-Martin, Xavier X. (1997). “I Just Ran Two Million Regressions”. _American Economic Review_ 87(2) (May): 178–183

- Sarkees, Meredith Reid & Philip Schafer (2000). “The Correlates of War Data on War: An Update to 1997”. _Conflict Management and Peace Science_ 18(1) (February): 123–144

- Windmeijer, Frank (2005). “A Finite Sample Correction for the Variance of Linear Efficient Two-Step GMM Estimators”. _Journal of Econometrics_ 126(1) (May): 25–51

32
|**Table 14. Spillovers across **|**communities**<br>|
|---|---|
|Training x scorecard|0.525**<br>[0.226]|
|Intensity of treatment|23.241<br>[51.952]|
|Control means|2.207|
|N|3,851|
|R-squared|0.085|

Notes: This table reports the OLS regression results for the treatment effect on household-level cattle outcome at the endline survey. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

50
However, in the region’s lower-income countries (Bolivia, Honduras, Nicaragua, Paraguay), reaching cost recovery tariffs would represent a significant affordability problem for around half of the population. Affordability problems are much more serious in India and Africa where around 70% of households could be expected to face difficulties in paying full cost recovery tariffs. Nevertheless, the extent of the affordability problem reduces substantially when incomes are measured in purchasing power parity terms. Finally, raising tariffs to cost recovery levels is not found to have any significant impact on the prevalence of poverty in middle-income countries, which have already made some progress towards cost recovery. However, in low-income countries where tenfold tariff increases may be required to reach cost recovery levels, such a policy would have a substantial adverse impact on poverty.

**<u>Table 8: Poverty impact of raising tariffs to cost recovery levels</u>**

||From U|S$3 to US|$6|From U|S$6 to US|$12|From|US$1 to|US$12|
|---|---|---|---|---|---|---|---|---|---|
||ΔHC|ΔPG|ΔFGT|ΔHC|ΔPG|ΔFGT|ΔHC|ΔPG|ΔFGT|
|**Latin America**||||||||||
|Argentina|0.0|0.1|0.1|0.0|0.1|0.1|1.0|1.1|0.8|
|Bolivia|**2.0**|0.3|0.3|**2.0**|0.4|0.3|**2.0**|**2.9**|**2.3**|
|Brazil|0.0|0.1|0.1|0.0|0.1|0.1|0.0|1.1|0.9|
|Chile|0.0|0.1|0.1|0.0|0.1|0.1|1.0|0.7|0.5|
|Colombia|0.0|0.1|0.1|0.0|0.2|0.1|0.0|1.5|1.2|
|Costa Rica|1.0|0.1|0.1|1.0|0.1|0.1|**2.0**|0.6|0.6|
|Dominican Rep.|1.0|0.1|0.1|1.0|0.2|0.2|1.0|1.1|1.1|
|<br>Ecuador|1.0|0.2|0.2|1.0|0.2|0.2|**2.0**|1.1|1.0|
|El Salvador|1.0|0.2|0.2|1.0|0.2|0.2|0.0|1.1|1.1|
|Guatemala|**2.0**|0.2|0.1|**2.0**|0.2|0.2|0.0|1.1|0.9|
|Honduras|0.0|0.2|0.2|0.0|0.3|0.3|**2.0**|**2.1**|1.8|
|Jamaica|0.0|0.2|0.2|0.0|0.2|0.2|0.0|0.7|0.7|
|Mexico|0.0|0.1|0.1|0.0|0.1|0.1|0.0|0.3|0.3|
|Nicaragua|1.0|0.2|0.2|1.0|0.3|0.2|**3.0**|**2.6**|1.9|
|Panama|0.0|0.1|0.1|0.0|0.2|0.1|1.0|0.6|0.5|
|Paraguay|1.0|0.1|0.1|1.0|0.2|0.1|**3.0**|**2.1**|1.6|
|<br>Peru|1.0|0.1|0.1|1.0|0.2|0.1|**2.0**|1.0|0.7|
|Uruguay|0.0|0.1|0.1|0.0|0.2|0.1|**2.0**|0.9|0.6|
|Venezuela|0.0|0.1|0.1|0.0|0.1|0.1|1.0|0.6|0.3|
|**Asia**||||||||||
|India|0.0|0.2|0.2|0.0|0.3|0.3|**5.0**|**3.5**|**3.1**|
|Indonesia|1.0|0.4|0.4|1.0|0.5|0.5|**5.0**|**4.4**|**3.9**|
|Philippines|1.0|0.1|0.1|1.0|0.2|0.1|**3.0**|**2.1**|1.5|
|South Korea|0.0|0.1|0.0|0.0|0.1|0.0|1.0|0.3|0.2|
|Thailand|1.0|0.1|0.1|1.0|0.2|0.1|**3.0**|1.4|0.7|
|**Africa**||||||||||
|Burkina Faso|1.0|0.4|0.4|1.0|0.5|0.5|**2.0**|**3.8**|**3.7**|
|Cameroon|1.0|0.2|0.3|1.0|0.3|0.3|1.0|**2.5**|**2.5**|
|Cape Verde|1.0|0.1|0.1|1.0|0.1|0.1|0.0|1.3|1.3|
|<br>Mauritania|0.0|0.2|0.1|0.0|0.2|0.2|**4.0**|**2.7**|**2.0**|
|Mozambique|0.0|0.4|0.2|0.0|0.5|0.3|**7.0**|**6.0**|**4.3**|
|<br>Rwanda|0.0|0.2|0.1|0.0|0.3|0.2|**7.0**|**4.3**|**2.8**|
|Sao Tome|0.0|0.2|0.2|0.0|0.3|0.2|**4.0**|**3.5**|**2.4**|
|Senegal|0.0|0.2|0.1|0.0|0.2|0.2|**2.0**|**2.0**|1.4|

Source: Own elaboration from LSMS household survey data Note: All cases showing change of two or more percentage points are highlighted in bold.

22
# **<mark>Referencias Bibliográficas</mark>**

1. ONUSIDA/09.36S. 2009. “Situación de la epidemia de sida, diciembre de 2009”. <u>http://data.unaids.org/pub/Report/2009/jc1700_epi_update_2009_es.pdf</u>

2. UNAIDS/WHO. 2002. “Initiating Second Generation HIV Surveillance System: Practical Guidelines. UNAIDS/WHO Working Group on Global HIV/AIDS and STI Surveillance”. - <u>http://data.unaids.org/Publications/IRC pub02/jc742 initiatingsgs_en.pdf</u> 3. UNAIDS/WHO. 2004. “Second Generation Surveillance for HIV: The Next Decade”. - <u>http://data.unaids.org/publications/irc pub01/jc370 2ndgeneration_en.pdf</u> 4. Rodríguez-García R, World Bank Group. 2007. “Planning and Managing for HIV/AIDS Results. A Handbook”. <u>http://siteresources.worldbank.org/INTHIVAIDS/Resources/375798 1103037153392/Results_Handbook_31Oct2007.pdf</u> 5. Atkins D, Best D, Briss PA, et al. 2004. "Grading quality of evidence and strength of recommendations". BMJ 328 (7454): 1490 6. World Health Organization. 2009. “HIV Triangulation Resource Guide”. <u>http://data.unaids.org/pub/Manual/2009/20090915_hiv_triangulation_resource_guid e_en.pdf</u> 7. Downs S, Black N. 1998. “The feasibility of creating a checklist for the assessment of the methodological quality both of randomized and non-randomised studies of health care interventions”. J Epidemiol Community Health 1998; 52; 377-384.

8. World Health Organization. 1999. “Sexually Transmitted Infections Prevalence Study Methodology. Guidelines for the implementation of STI prevalence surveys”. <u>http://www.wpro.who.int/internet/resources.ashx/HSI/docs/Guidelines_for_the_Impl ementation_of_STI_Prevalence_Surveys.pdf</u> 9. Aschengrau, A. 2003. “Essentials of Epidemiology in Public Health”. Sudbury Mass.: Jones and Barlett.

10. Gordis, L. 2004. “Epidemiology”. Third Edition, Elsevier Saunders.

11. Girden, E. 2001. “Evaluating Research Articles from Start to Finish”. Second Edition, Sage Publications.

12. Hernandez-Avila, M. y col. 2000. “Diseño de estudios epidemiológicos”. Salud pública de México / vol.42, no.2, marzo-abril de 2000.

13. Szklo M. 2006. “Quality of scientific articles”. Rev Saúde Pública 2006; 40 (N Esp):30-5.

14. Velasco, V. y col. 2003. “Muestreo y Tamaño de Muestra. Una guía práctica para personal de salud que realiza investigación”. e-libro.net. Buenos Aires 15. Salganik M, Heckathorn D. 2004. “Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling”. Sociological Methodology, Vol. 34. (2004), pp. 193-239 16. Muhib F, Lin L, et al. 2001. “A venue-based method for sampling hard-to-reach populations”. Public Health Rep. 2001; 116(Suppl 1): 216–222.

17. UNAIDS/WHO/UNICEF. 2008. “Epidemiological fact sheets on HIV and AIDS”. <u>http://www.unaids.org/en/KnowledgeCentre/HIVData/Epidemiology/epifactsheets.as p</u> 40
<!-- Start of picture text --> SAN JOAQUIN SANTA CRUZ/EL TROMPILLO<br>5.0 4.0<br>4.0 3.0<br>3.0 2.0<br>2.0<br>1.0<br>1.0<br>0.0<br>0.0<br>-1.0<br>-1.0<br>-2.0<br>-2.0<br>-3.0 -3.0<br>-4.0 -4.0<br>-5.0 -5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08 M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>SUCRE TARIJA<br>7.0 5.0<br>4.0<br>5.0<br>3.0<br>2.0<br>3.0<br>1.0<br>1.0 0.0<br>-1.0<br>-1.0<br>-2.0<br>-3.0<br>-3.0<br>-4.0<br>-5.0 -5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08 M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>TRINIDAD YACUIBA<br>4.0 5.0<br>3.0 4.0<br>2.0 3.0<br>2.0<br>1.0<br>1.0<br>0.0<br>0.0<br>-1.0<br>-1.0<br>-2.0<br>-2.0<br>-3.0 -3.0<br>-4.0 -4.0<br>-5.0 -5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08 M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius) Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius) Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius) Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br><!-- End of picture text --> _Source:_ Authors’ estimation based on data from the NCDC’s Monthly Climatic Data for the World.

22
dataset comprises 109,000 FactSet firms (representing 65% of the 169,420 firms from the supply chain data) linked with nearly 1.5 million company names in Lightcast.

To identify the FactSet firms with potential operations in the 17 Lightcast countries, we refined the original sample, keeping only those firms with at least one affiliate in any of the 17 Lightcast countries, according to information reported by FactSet or Lightcast data. For instance, although Samsung’s headquarters may not be in any of the 17 Lightcast countries, FactSet might report it has an affiliate in the US, while Lightcast might include job postings an affiliate in the UK. However, certain FactSet firms, primarily located in Asia, do not have an affiliate in any of the 17 Lightcast countries and are therefore excluded, resulting in a sample of 128,764 firms. Then, we removed every affiliate without location information on the subnational region, leaving us with 126,358 firms. Out of this 126,358 firms, we successfully identified 107,981 in Lightcast, resulting in a final match rate of around 85%.

Our final matched dataset is aggregated at what we define as the establishment level, with establishments firm-country-subnational region triplets (subnational regions are typically the highest-level administrative subdivision in each country<sup>8</sup> ). Using this definition, the total number of establishments is 1,476,936, belonging to the 107,981 matched firms in the 17 countries.

# **2.3 Measuring technology pioneer locations and additional datasets**

## **2.3.1 Pioneer regions and firms**

To measure technology diffusion globally, it is important to identify where the technologies first emerged. For each of the 29 technologies identified, Bloom et al. (2021) define the ‘pioneer’ locations for these technologies as the core-based statistical areas (CBSA)s that collectively accounted for 50% of the global citation-weighted patent grants for that technology during the ten years before its emergence year. While their analysis of pioneer locations is focused only on the US, a robustness check repeating their exercise using global patents data demonstrates that all pioneer locations for these technologies globally are indeed in the US. All the regions globally with the highest number of patent citations for these technologies are located within the U.S., as shown in Figure 2. We hence proceeded using the US CBSA pioneer locations for each technology.

> 8 This follows Acemoglu et al. (2020) who define establishments of a firm as the collection of firm job vacancies in each US commuting zone. Sub-national regions are generally defined at Territorial Level 2 (TL2) in OECD Regional Statistics, the ”first government layer after the national or federal one.” `https: //www.oecd.org/regional/regional-statistics/geographical-definitions.htm`

9
# **References**

- A.A Aibinu and G.O Jagboro. The effects of construction delays on project delivery in nigerian construction industry. _International Journal of Project Management_ , 20(8): 593–599, 2002. 8

- Belinda Archibong, Tom Moerenhout, and Evans Osabuohien. Protest matters: The effects of protests on economic redistribution. Working Paper 172, Brookings, Washington, D.C., 2022. 12

- J. Bentham. _Introduction to the Principles and Morals of Legislation (First published in 1789)_ . London: Athlon, 1970. 2

- Markus Brunnermeier and Jonathan Parker. Optimal Expectations. _American Economic Review_ , 95(4):1092–1118, 2005. 3

- Andrew Caplin and John Leahy. Psychological Expected Utility Theory and Anticipatory Feelings. _Quarterly Journal of Economics_ , 116(1):55–79, 2001. 3

- Matias D. Cattaneo, Sebastian Galiani, Paul J. Gertler, Sebastian Martinez, and Rocio Titiunik. Housing, health, and happiness. _American Economic Journal: Economic Policy_ , 1(1):75–105, 2009. 3, 4, 6, 7

- A. Deaton. Income, aging, health and wellbeing around the world: Evidence from the gallup world poll. _Journal of Economic Perspectives_ , 22(2):53–72, 2008. 9

- Rafael Di Tella, John Haisken-De New, and Robert MacCulloch. Happiness adaptation to income and to status in an individual panel. _Journal of Economic Behavior Organization_ , 76(3):834–852, 2010. 2

- Rafael Di Di Tella, Robert J. MacCulloch, and Andrew J. Oswald. The Macroeconomics of Happiness. _The Review of Economics and Statistics_ , 85(4):809–827, 11 2003. 2

- E. Diener, R. E. Lucas, and C. Napa. Beyond the hedonic treadmill: Revising the adaptation theory of well-being. _American Psychologist_ , May-June:305–314, 2006. 2

- Paul Dolan and Robert Metcalfe. Measuring subjective wellbeing: Recommendations on measures for use by national governments. _Journal of Social Policy_ , 41(2): 409–427, 2012. 6, 13

- Richard A. Easterlin. Income and happiness: Towards a unified theory. _The Economic Journal_ , 111(473):465–484, 2001. 9

14