rafmacalaba/datause-encoder-data huggingface dataset:
“Small Miracles”—Behavioral Insights to Improve Development Policy: _World Development Report 2015_<sup>*</sup> Keywords: Behavioral economics, Psychology, Sociology, Schemas, World Bank JEL Codes: D03, O20, Z13 Behavioral economics policies are beautiful because they are small and concrete but powerful. They remind us that when policies are rooted in actual human behavior and specific day-to-day circumstances, even governments can produce small miracles.

Brooks, _The New York Times,_ December 12, 2014, A29 Rational actor models dominate much modern economic thinking. But rational choice theory cannot explain the striking effects of the policies discussed in the World Bank’s latest _World Development Report_ (World Bank Group 2015) _._ In his review of the report in _The New York Times_ , David Brooks celebrates the behavioral approach to development because it creates opportunities for policy interventions that produce “small miracles” from the perspective of traditional economics. Brooks’ review focuses on the proven small policy successes of behavioral economics, but the report is meant to do more than showcase small wins. It also aims to show that behavioral insights yield entirely new understandings of the causes of economic underdevelopment and social exclusion, which create scope for new interventions to promote “big” development outcomes. Advancing this work, which is still at an early stage, is a key aim of the _World Development Report_ (WDR).

_______________

> *Forthcoming in the Proceedings of the 17th World Congress of the International Economics Association, Amman, Jordan, edited by Joseph E. Stiglitz. The authors are grateful to the entire team of the _World Development Report 2015_ .  These are the views of the authors and do not reflect those of the World Bank, its Executive Directors, or the countries they represent.

> † University of Washington.  Email: allisone@uw.edu

> ‡World Bank.  Email: khoff@worldbank.org

2
# **7. Appendix: Data**

In this paper we exploit Uruguayan establishment level data covering a considerably long period of time. We use annual establishment level observations from the Manufacturing Survey conducted by the Instituto Nacional de Estadística (INE) for the period 1982-1995. The surveysampling frame encompasses all Uruguayan manufacturing establishments with five or more employees.

The INE divided each four digit International Standard Industrial Classification (ISIC) sector in two groups. All establishments with more than 100 employees were included in the survey; the random sampling process of firms with less than 100 employees satisfies the criterion that the total employment of all the selected establishments must account at least for 60% of the total employment of the sector according to the economic Census (1978 or 1988).

The data for the whole period are actually obtained from two sub sample sets: from 1982 until 1988 and from 1988 until 1995. In 1988 the Second National Economic Census was conducted. After that, the INE made a major methodological revision to the manufacturing survey and changed the sample of establishments. The statistical analysis was also performed controlling for the sample of origin. Firms entering the sample in 1988 behave similarly than firms from the old sample. Differences in behavior in white collar and capital adjustment are small. For the firms in the new sample the slope of the blue collar adjustment function is higher in the creation side while lower in the destruction side. It is hard to evaluate differences by sample in the sign or size of the policy impacts.

In total, we have 627 different establishments present in at least one period. There are 208 starting in 1982, of which just 185 make it to 1995. The 1988 sample, is composed of 304 establishments included for the first time in that year, and 254 from the old sample not all of which are to be followed in subsequent years.

# **7.1. Capital**

To construct the establishment capital stock series, we follow a methodology close to Black and Lynch (1997). The 1988 Census reports information on the capital stock. We use machinery capital. We avoid overestimation of the amount of depreciation by calculating an average depreciation rate by industrial sector and year. The resulting depreciation rate is then used for all firms within each sector yearly. We further exclude the value of assets sold in our measure of capital, assuming assets have been totally depreciated at that point. Thus, the equation for estimating the capital stock for years later than 1988 is: with 18
**Table 3.1.  Overview of Cost Recovery in WSS Utilities World Wide**

|Country Grouping|% of utilities that do not<br>recover basic O&M|%<br>of<br>utilities<br>that<br>recover most of O&M|%<br>of<br>recover<br>partial ca|utilities<br>O&M<br>pital costs|that<br>plus<br>|
|---|---|---|---|---|---|
|World|39|30|30|||
|High Income|8|42|50|||
|Upper Middle Income|39|22|39|||
|Lower Middle Income|37|41|22|||
|Low Income|89|9|3|||
|OECD|8|43|51|||
|Latin<br>America<br>and<br>Caribbean(LAC)|13|39|48|||
|Middle East and North<br>Africa(MNA)|58|25|17|||
|East<br>Asia<br>and<br>Pacific(EAP)|53|32|16|||
|Europe and Central Asia<br>(ECA)|100|0|0|||
|South Asia (SAR)|100|0|0|||

Source: Adapted from Komives et alia( 2005)p.21 Regions follow World Bank classifications. See original for discussion of the sample of utilities on which these data are based Table 3.2 examines cost recovery performance for electricity utilities world-wide. The contrast with water utilities is noteworthy. Electrical utilities have achieved a much higher level of cost recovery than have WSS. All high income countries charge tariffs that recover basic O&M, and only 17% of their utilities do not have tariffs high enough to recover any capital costs, as opposed to half in the case of WSS. In upper middle income countries, all utilities recover basic O&M vs. 61% in WSS. This is also true of the other income and regional categories, far more electrical utilities recover all or most of their O&M costs than in WSS. Still, recovery of even a portion of capital costs is quite limited. It is only in the high income countries and LAC that more than half of utilities recover even a portion of capital costs. Accordingly, while to a much lesser degree, the prospects of financing new investments in electricity out of user charges are quite dim in most developing country utilities. While PPPs involving takeover of electricity operations have been much more widespread than for water supply, the overall contribution of 14
through the overall economy. Various authors use different methods to control for these endogenous processes, obtaining quite different results along the way. For example, Card (2001) uses geographic local markets, while Borjas (2003) chooses education-age cohort cells as the unit of analysis. Among the more recent papers, Docquier et al. (forthcoming) use the bilateral migration database of Artuc et al. (forthcoming) to analyze the impact of immigration and emigration on labor market outcomes – both wages and employment levels – of native workers of different skill levels in OECD countries. The analytical framework is typically based on a general equilibrium model of labor markets and production where the substitution elasticities between different types of labor, especially between migrants and natives, play a critical role. Most of these studies rely on data aggregated from labor force surveys and construct their instruments from macro data. Özden and Wagner (2014) use the age-distribution data from origin countries interacted with historical migration patterns as instruments of current migration patterns in Malaysia. Borjas (2014) is an excellent survey of the issues, empirical methods and data requirements in research on labor market implications of migration.

An underexplored but very important issue with development implications is return migration. Migrants obtain skills and financial resources during their time away, which can prove useful on return (Mayr and Peri, 2008; Gibson and McKenzie, 2011). The benefits may especially be concentrated in the labor market outcomes of return migrants (Bijwaard et al. (2013)). A multi-topic survey should cover several important issues in order to study the implications of return migration. In addition to identifying return migrants in the labor force, it is also important to ask whether these individuals plan to migrate again. Although plans are not the same as actual migration after the 32
that could not be interviewed despite several call attempts may introduce a potential bias in the selection of the sampled households. To overcome this potential bias and obtain statistics representative at the national level, a balanced sampling approach was adopted, leveraging on the extensive set of variables available in the Nigeria General Household Survey (GHS)-Panel 2018/19. Additionally, this study uses the publicly available phone survey weights, calculated by adjusting the GHS-Panel 2018/19 household weights to reflect the selection and interviewing process and calibrated according to the characteristics of the weighted sample included in the GHS-Panel 2018/19.

This study mainly relies on the information included in Round 6 of the COVID-19 NLPS. This survey round includes information at the individual level on school attendance and other education variables for household members aged 5-18.<sup>8</sup> A maximum of 6 household members were selected for each household to limit the randomly respondents’ burden, with a final sample of 4,006 members 5-18 aged of 4,325 eligible individuals (of the 1,762 households successfully interviewed in Round 6). Given the selection process of individuals aged 5-18 years, individual-level weights were calculated and calibrated to correspond to the sex and age distribution of the total weighted population aged 5-18 years as included in the GHS-Panel 2018/19.<sup>9</sup>

# **2.2.2 Measuring School Attendance**

To measure children’s school attendance just before the school closure and just after school reopening, we take advantage of the COVID-19 round 6 phone survey which, unlike previous rounds where schooling information was measured at the household level, provides information on schooling at the individual level for children aged 5-18.

In the survey, after schools reopened in Nigeria in October 2020, respondents were asked whether were school at the moment of the either in they attending survey person or remotely. Hence, we define school attendance after school reopening as a binary variable taking the value one if the individual answered "Yes" to this question, and zero

> 8Primary education starting age is 6 years although it is common to start primary education at 5 years.

> 9See McGee et al. (2020) for a detailed explanation of the methodology adopted for calculating household and individual level weights.

12
example, uses various techniques to adjust for these biases. These include conducting preliminary surveys to determine the extent of undocumented migration in certain areas, and then adjusting their weight in the sample after taking their response likelihood into account. In other cases, using listing from NGOs working with migrants is often the only, even if incomplete, frame from which to draw a sample. Snowballing and other quasirandom techniques are often used, but the resulting samples are seldom representative of the migrant population as a whole.

In origin countries, most available frames do not contain any information on the exposure to past or current migration of the listed households, preventing _ex ante_ stratification of the sample based on migration status. As discussed at the beginning of this paper, neither the population census nor available administrative records provide adequate sampling frames for selecting emigrants in a given sending country. Nor, in most cases, do they provide information on previous migration experience to help to identify temporary migrants and returnees.

As such, migration is considered a rare event, defined as an infrequent statistical occurrence. In a normal clustered sample design typical of multi-topic surveys, the expected number of households associated with emigration may be very low. However, a several techniques have been proposed in the literature to better identify rare events, such as migration (Kish, 1965; Huang, 2005). Two such approaches deemed more appropriate in capturing data on migrants, particularly if used in combination, are disproportionate sampling and two-phase sampling. However, both sample designs require some prior knowledge of migration in the population.

21
Table S10. Relationship Between Inequality and Human Capital (Interaction with Income in 1970)

|Dependent Variable is:|Share of|Population Tertiary Education|
|---|---|---|
||(1)|(2)<br>(3)|
|Inequality Variable is:|Gini<br>Brueckner et al. (2015)|Net Gini<br>Solt (2015)<br>Market Gini<br>Solt (2015)|
|Inequalityit|1.47*<br>(0.78)|4.79**<br>(2.12)<br>2.46***<br>(0.87)|
|Inequalityit|-0.22*|-0.73**<br>-0.35***|
|*ln(yi1970)|(0.12)|(0.32)<br>(0.13)|
|Kleibergen Paap F-Statistic|34|23<br>38|
|Country FE|Yes|Yes<br>Yes|
|Time FE|Yes|Yes<br>Yes|
|Observations|497|677<br>677|

Note: The method of estimation is two-stage least squares. Bootstrapped standard errors are shown in parentheses. The instrument for _Inequality_ is _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

54
Donaldson, Dave (2015), ”The gains from market integration.”, _Annual Review of Economics_ , forthcoming.

Dowrick, Steve (1989), ”Union-oligopoly bargaining.” _Economic Journal_ , 99: 1123-1142.

Fujita, Masahisa, Paul Krugman and Anthony J. Venables (2001), ”The spatial economy: cities, regions, and international trade,” _MIT Press Books_ , The MIT Press, edition 1, volume 1.

Gaston, Noel and Daniel Trefler (1995), ”Union sensitivity to trade and protection: theory and evidence.” _Journal of International Economics_ , 39(1), 1-25.

Hall, Robert and Charles Jones (1999), ”Why do some countries produce so much more output per worker than others?” _Quarterly Journal of Economics_ , 114: 83-116.

Hanson, Gordon H. (1996), ”Localization economies, vertical organization, and trade.” _American Economic Review_ , 86(5): 1266-78.

Harris, C.D., (1954), ”The market as a factor in the localization of industry in the United States.” _Annals of the Association of American Geographers_ 44: 315-348.

International Labor Organization (2002). _National Labor Law Profile: South Africa_ , Geneva.

Krugman, Paul (1991), ”Increasing returns and economic geography.” _Journal of Political Economy_ , 99(3): 483-99.

Horrell, Muriel (1978), _Laws Affecting Race Relations in South Africa (to the end of 1976)_ . Johannesburg: South African Institute of Race Relations.

Lederman, Daniel and William F. Maloney (2007), _Natural Resources, Neither Curse nor Destiny._ Stanford University Press and World Bank, Latin American Development Forum Series: Palo Alto, California and Washington, DC.

Lommerud, Kjell E., Frode Lommerud and Lars Sørgard (2003), ”Unionised oligopoly, trade liberalisation and location choice.” _Economic Journal_ , 113: 782-800 Lommerud, Kjell E., Frode Meland and Odd Rune Straume (2006), ”Globalisation and union opposition to technological change.” _Journal of International Economics_ , 68: 1-23 Lommerud, Kjell E., Odd Rune Straume and Lars Sørgard (2006), ”National versus international mergers in unionised oligopoly.” _RAND Journal of Economics_ , 37: 212-233 McDonald, Ian M. and Robert Solow (1981), ”Wage bargaining and employment.”, _American Economic Review_ , 71: 896-908.

24
~~OOambato(PER)——s—=“‘(i«t*é‘<i=;*é‘CS;!!””””!”~!CUaratingue~~ <u>BRAD !!!!!TM!TM!TM!TM!TM!TM!TM!TM!TM!TM!TM!TM!TM!~!~!~!~!~!~!”!!C(ases</u> ~~CAD!!!~~ <!-- Start of picture text --> BRAD<br><!-- End of picture text --> <!-- Start of picture text --> lag 7<br><!-- End of picture text --> <!-- Start of picture text --> : Tati —_<br><!-- End of picture text --> <!-- Start of picture text --> =|<br><!-- End of picture text -->

<!-- Start of picture text --> RRR iSorriso (BRA), WAYS<br>RERW<br>SESS<br>RSEeryeCNS oY,<br>Wes<br>SSNHeS<br>ROLLIE<br><!-- End of picture text --> wsCaracasCaracas <u>(VEN)VENVEN</u> ITAOYOY ~~=~~ aawwww SiGe ENA Va WVSawn=,=, = <u>ACN VAN Nee</u> <u>San Jose——(CRI)——(CRI)(CRI) [a]</u> PSS aS =A 5yy l=l=.7LeLe <u>IRR</u> S <u>HITTERS Bg Belo Jardim (BRA) \\bx</u> <!-- Start of picture text --> wsCaracasCaracas (VEN)VENVEN ITAOYOY = aawwww<br>SiGe<br>ENA<br>Va WVSawn=,=,<br>=<br>ACN<br><!-- End of picture text --> <!-- Start of picture text --> San Jose——(CRI)——(CRI)(CRI) [a]<br>PSS<br>aS =A<br>5yy l=l=.7LeLe<br>IRR S<br><!-- End of picture text --> <!-- Start of picture text -->
[Guayama (PRI)|_\_-~<br><!-- End of picture text -->

<u>js ae)</u> <!-- Start of picture text --> js ae)<br><!-- End of picture text --> <!-- Start of picture text --> Belo Jardim (BRA) \\bx<br><!-- End of picture text --> <u>Morelia (MEX)SAESAE</u> GasYaronYaron ANNE AESA AESA KW \etnecesey, NUN KIA NSS SAEZ, SAEZ, <u>RS</u> <!-- Start of picture text --> Paysandu (URY)Cop oe fiooo7 a3) ITuxtepecOsu(MEX) ==t M<br>Pe FA ae eee eee<br>PAH a LE A<br>PEER RUT SA<br>EA RR RY 7a<br>SeSmSMurererai PEEREEE AT Ta |<br><!-- End of picture text -->

<!-- Start of picture text --> Morelia (MEX)SAESAE GasYaronYaron<br>ANNE AESA AESA<br>KW \etnecesey,<br>NUN KIA<br>NSS SAEZ, SAEZ,<br>RS<br><!-- End of picture text -->
from a decline of _≈_ 15% to an increase of _≈_ 570% for high-density days. For low-density days, it ranges from a decline of 100% (bounded at zero days) and an increase of _≈_ 320%. To examine how our projected impacts change over this large uncertainty range, we estimate average impacts for every combination of changes within the bounds of our linear extrapolation – for high density days from a 15% decline to a 570% increase and for low density days a 100% decline to a 320% increase.

In contrast to the lack of research projecting the number of smoke days under climate change, there is a substantial literature that predicts changes in burned area, biomass consumed by wildfire, and increases in particulate pollution due to wildfires. Ref.<sup>14</sup> reviews several of the pre-2009 estimates of changes in burned area for the Western U.S. and Canada. On average, the reviewed studies predict a 65% increase in the continental U.S. and western Canada by mid-century relative to the 2010s. This is much less than the observed increase (roughly 200%) in the United States from 1985 to 2020.<sup>1</sup> In general, the newer papers reviewed predict larger increases by 2050. Ref<sup>14</sup> suggests an increase in burned area of 160% across the whole Western U.S. Other recent work suggests increases of up to 1000% in the Rockies, increases of 78-175% in the Pacific Northwest and the Rockies, and increases of burned area of between 75-310% and biomass consumed of 127-410% in the Pacific Northwest.<sup>15,11,35</sup> Ref.<sup>1</sup> reports annual area burned in the United States from 1985 to 2020. Extrapolating the linear trend from that data results in a 235% increase by mid-century relative to the average from 1985 to 2020.

Increases in burned area may be constrained in the future by the availability of fuel as more consistent, and larger fires, result in landscapes that carry lower fuel loads.<sup>15</sup> However, this does not appear to meaningfully reduce burned area estimates until after mid-century.<sup>36,29</sup> In order to use projections of burned area in our specification, we need to determine the relationship between burned area and smoke exposure. We also need to determine whether low- and high-density smoke days increase uniformly or if changes in biomass burned or fire intensity will lead to changes in the relative number of low- and high-density smoke days. For the first conversion, we can rely on ref.,<sup>1</sup> who document the increase in burned area as well as the increase in smoke days. They observe that, from 2006 to 2020, burned area increased by 32% while smoke days increased by 127%, implying smoke days may increase 4 _×_ faster than burned area. Unfortunately, they do not separately measure the change in low- and high-density smoke days.

Combining the central estimate of increases in burned area from ref.<sup>14</sup> of 160% with this 4 _×_ relationship implies that smoke days will increase by 460% by mid-century. This is substantially higher than the increase suggested by our linear extrapolation.

There is little evidence that indicates how the increase in smoke days will be apportioned 21
migration data and on the data requirements to address some important questions at the center of national and global policy debates.

Each of the different data sources reviewed has its own strengths and weaknesses, depending of its specific use, but improving the linkages across these different data sources and the various available databases is probably an aspect of the migration data agenda that deserves more attention and would result in substantial benefits to policy analysts. Ultimately, no single data source will be able to meet all data demands; thus countries need to think systemically about how the various sources are integrated and complement each other.

The issue of representativeness, or the lack thereof, of many migration studies also highlights the difficulties associated with drawing suitable samples of migrants at both the origin and destination. Several techniques were highlighted, but the lack of complete and updated sampling frames is bound to continue to hamper future efforts.

Migration is innate to human nature, and both internal and international flows will undoubtedly continue to be a natural part of the policy debates of most countries. Some processes, like increasing rural–urban migration, are inevitable and come with countries’ development and households’ evolution. Other emerging issues, like climate change, are bound to accelerate these processes. And others, like the aging of population in developed countries, are sure to generate additional demand for unskilled and skilled labor from sending countries. However, how migration is managed and its positive impact fully exploited will ultimately depend on the availability of accurate and timely data on the different facets of the 43