rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2: Marginal effects of probit regression results: influencers of an improvement in respondents’ subjective wellbeing in Colombia over the two year period 2008/092010/11**

<!-- Start of picture text --> 20.0%<br>17.7%<br>15.0%<br>9.9%<br>10.0%5.0% 5.3% 4.1% 3.8% 4.0% 5.3% 5.0% 3.6% 0.9% 3.7% 0.6% 0.0% 5.7% 0.6% 1.7% 1.7% 6.6% 3.1% 3.1% 3.8% 1.4% 3.6% 2.6% 4.6% 1.0%<br>0.0%<br>-10.0%-5.0% -3.0% -2.7% -8.0% -1.3% -6.2% -2.4% -5.2% -1.5% -8.8% -5.6% -5.0% -4.3% -1.3% -8.1% -6.5% -1.0% -0.5% -1.4% -6.2% -5.5% -1.2% -0.5% -0.2% -1.6% -7.4% -5.9% -2.8%<br>-15.0%<br>-13.1% -14.1%<br>-20.0%<br>-19.5%<br>-25.0% -23.7%<br><!-- End of picture text --> _Source_ : Author’s calculations of marginal effects after running probit regression based on LAPOP data. Note: The marginal effects here are derived using the ‘mfx’ command after estimating the complete probit regression model. The same ‘note’ applies as in Table 1.

# **4. Conclusion and policy implications**

Our results―while acknowledging that there are unobservables influencing some of our findings―suggest that policy can go a long way in raising subjective wellbeing and particularly in mitigating shocks in subjective wellbeing. There are six strong, significant and policy-relevant factors associated with subjective wellbeing in Colombia. As a result, policy responses that would likely have the largest influence on subjective wellbeing include:

- i. minimizing the rate and duration of unemployment, for example, through job search and placement facilities (with any job better than no job) and through skills development programs for the unemployed;

- ii. improving the delivery of public health services and providing universal access to toilet facilities in homes (currently in 87.2 percent of households) as these would help reduce the incidence of sickness, infection and disease and improve physical health;

- iii. ensuring that every child completes at least the compulsory 9 years of schooling in Colombia, while raising levels of upper secondary and higher education which can help increase individual opportunities and freedoms;

- iv. ensuring that people are able to acquire a minimum level of household income needed to meet basic needs;

- v. improving levels of safety and security in communities and reducing levels of discrimination, by fostering strong communities with mutual trust and cooperation via inclusive participatory policies, and;

- vi. increasing the current share of members of a pension plan (23.8 percent of the population) as well as members of a health plan (51 percent), as insurance schemes can help reduce economic insecurities, anxiety and vulnerabilities.

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10 approaches to capital regulation (Čihák, Demirgüç-Kunt, Martínez Pería, and MohseniCheraghlou 2012).

Underlying this increased complexity is the belief that crises could be avoided if the regulations were more comprehensive and extensive. So, the deficiencies that caused the crisis are supposedly eliminated by ever-more complex sets of rules and regulations. However, in the United States, where the global financial crisis started, the pre-crisis regulations were already quite complicated and supervisory resources were extensive. Internationally, the complexity of the banking rules had already increased significantly before the crisis with the introduction of Basel II. Nevertheless, private risk-taking at public expense reached unprecedented levels.

Complexity in search of comprehensiveness is an elusive goal and the resulting regulations almost always remain partial despite their increased complexity. Initial efforts to implement the new regulatory regimes have been progressing slowly as industry has pushed back against elements of the reforms. This is again the case, for example, in the United States, with certain provisions in the Dodd-Frank legislation.<sup>16</sup> The risk with partial reform is that the gaps will provide scope for gaming the system and circumvention of regulations. For example adjusting the regulations for the banking sector without comparable regulation for the shadow banking sector could simply shift risk outside the regulatory perimeter. Tweaking different levers of the regulatory framework independent of each other may even create more risk instead of mitigating it and can be counterproductive (Laeven 2011). Partial implementation would perpetuate moral hazard in the financial system, if there is an assumption that the shortcomings that led to crisis had been corrected, but underlying weaknesses remain unaddressed.

The increasing complexity of regulation is also increasing the pressure on supervisory capacity. Considerably more resources will need to be devoted to supervisory oversight for the FSB/Basel reforms to be effective. The FSB reviews of progress finds weaknesses with implementation in several areas, including information for supervisory oversight, compensation practices, and reducing reliance on rating agencies. In low income developing countries, there are already important capacity issues where supervisory skills are in short supply, so this further exacerbates implementation and enforcement issues. Constraints arise not only because of scarce resources, but also because of weaknesses in the mandates and independence of supervisors and regulators (Čihák and Tieman 2008).

Ever more complicated rules, or even more supervisory discretion or resources, will not address the fundamental problems, unless there is an appropriate alignment of incentives. To the contrary, introducing simpler rules that take into account the _incentives_ of market participants and regulators are less likely to be circumvented by market participants and 16 See for example the Economist (2012).
# **Literature Review**

It is hypothesized that heterogeneity in “abilities” which contribute to higher earnings are related to schooling acquisition. This is in addition to other unobservables which may be sorting individuals into better paying jobs, especially in developing countries. The latter may include family connections, government imposed controls, and political allocation of jobs in the civil service and public enterprises (Schultz 2003). In other words, the response to the “treatment” (schooling) varies across individuals (Heckman 1997; Heckman and Vytlacil 1999). Of the two important questions posed by Card (1995), namely, “what is the causal effect of education?” and “is there evidence of individual heterogeneity in returns to education?” the second will be addressed. To simultaneously address both questions, one needs a proper instrument to estimate returns to education using an Instrumental Variables-Quantile Regression analysis. The few empirical papers which address this question utilize data on twins in the context of a “family effects” model (see for example, Arias and others 2001; Ashenfelter and Rouse 1998). In the absence of such data, simply using family background information (such as parents’ years of schooling) as the instrument is less than ideal, even if it passes the standard econometric tests. The problem is that estimates of returns from family background instruments are expected to be biased. Furthermore, the focus of this paper is on the investigation of the quantile-returns relationship in a large number of countries. While theoretically schooling cannot be taken as exogenous in Mincer equations, empirical results suggest that the extent of the bias may be small (Card 1999; Dearden and others 2002).

The concept of ability utilized here, as in Arias and others (2001), is not one based on measures derived from tests (such as “IQ”). Rather it relates to those unobservable, earningsenhancing, human capital characteristics of an individual. Such ability characteristics are hypothesized to interact with education. As in Mwabu and Schultz (1996) and Arias and others (2001) we will be interpreting a negative relationship between ability and returns to education (decreasing returns with quantile) as evidence of substitutability between education and ability, and a positive relationship (increasing returns with quantile) as evidence of complementarily between education and ability.

4
- **Koretz, Daniel M** , “Limitations in the use of achievement tests as measures of educators’ productivity,” _Journal of human resources_ , 2002, pp. 752–777.

- **Leaver, Clare, Owen Ozier, Pieter Serneels, and Andrew Zeitlin** , “Recruitment, Effort, and Retention Effects of Performance Contracts for Civil Servants,” 2020.

- **Loyalka, Prashant, Sean Sylvia, Chengfang Liu, James Chu, and Yaojiang Shi** , “Pay by design: Teacher performance pay design and the distribution of student achievement,” _Journal of Labor Economics_ , 2019, _37_ (3), 621–662.

- **Mbiti, Isaac, Karthik Muralidharan, Mauricio Romero, Youdi Schipper, Constantine Manda, and Rakesh Rajani** , “Inputs, incentives, and complementarities in education: Experimental evidence from Tanzania,” _The Quarterly Journal of Economics_ , 2019, _134_ (3), 1627–1673.

- **Mbiti, Isaac M** , “The need for accountability in education in developing countries,” _Journal of Economic Perspectives_ , 2016, _30_ (3), 109–32.

- **Muralidharan, Karthik and Venkatesh Sundararaman** , “Teacher performance pay: Experimental evidence from India,” _Journal of political Economy_ , 2011, _119_ (1), 39–77.

- **Neal, Derek** , “The consequences of using one assessment system to pursue two objectives,” _The Journal of Economic Education_ , 2013, _44_ (4), 339–352.

- **World Bank** , _World Development Report 2018; Learning to Realize Education’s Promise_ , Washington, DC: World Bank, 2018.

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economic prospects? Is the era of sustained economic growth over? Will the process of globalization be reversed? What are the prospects for development and poverty reduction?

Although some recent developments were predicted, many others caught even experienced policy makers and economic observers by surprise. These developments render the task of macroeconomic policy making much more challenging and heighten the importance of sound forward-looking economic analysis, forecasts, and scenario analysis as crucial inputs into the policy-making process at both the national and international levels. Because macroeconomic policies have medium- and long-term implications, increased uncertainty makes it imperative to try to judge the likely consequences of alternative policies under key possible scenarios that focus on particular aspects of the global economy.

Given this _tour d’horizon_ of the current scene, it would be instructive to step back and analyze significant past exercises in long-term forecasting as an input into policy. What were the hits and misses? What lessons can be drawn?

# **3. Long-Term Economic Projections: A Case Study from the World Bank**

The international financial institutions conducted a few long-term forecasting exercises in the last three decades. One was conducted by the World Bank in 1989 (Fardoust and Dhareshwar 1990). Before the publication of _A Long-Term Outlook for the World Economy: Issues and Projections for the 1990s_ , World Bank forecasts were based on mere assumptions (for a nontechnical evaluation of earlier forecasts, see World Bank 1991, Box 1.4). Bank projections systematically erred on the side of optimism: the realized growth rates of developing countries’ aggregate output were lower than the projections in all the forecasts for the base-case scenario and in most of the forecasts even for the low-case scenario (Figure 1).<sup>3</sup>

> 3 A similar but more in-depth assessment has been made of the IMF’s short- and medium-term forecasts (see section 4.2 below and footnote 6.)

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them to sell off capital.<sup>4</sup> The first two columns of Table 3 show that the large gender differences are there for firms that had below median profitability at baseline as well as above median profitability, suggesting that is not simply due to women having smaller baseline scale. The next two columns split the sample by commerce or not commerce, and find large impacts for men and smaller and insignificant impacts for women in both subsamples. However, we have almost no overlap in the specific subsectors that men and women operate in, so cannot rule out that differences stem from women being in subsectors with low efficient scale and limited demand.

A second potential explanation is that women see limited long-term impact in their businesses because they instead redirect their capital and entrepreneurial insights towards businesses run by other household members. Bernhardt et al. (2019) show evidence of this for cash transfers. Table 3 shows no significant impact of treatment on whether anyone else in the household operates a business, and that only one-third of women have another household member operating a business. Treatment effects are large and positive for men, and smaller and not statistically significant for women even in the subsample in which no one else in the household operates a business. The evidence therefore does not seem consistent with this explanation.

A third set of explanations center around women instead spending the money on other household members, either because they lack control over how business revenues are spent (Riley, 2024), or because they see high returns in investing in the education and health of family members. For example, Agte et al. (2022) find that women getting a microfinance intervention divert some of the income gains away from their business to invest in children’s education, with this effect stronger for those with more education. Friedson-Ridenour and Pierotti (2019) additionally suggest some married women limit how much they invest in their businesses to ensure continued support from their husbands and to be able to respond to household emergencies. Given concerns about survey fatigue and difficulties in getting entrepreneurs to answer, our long-term survey focused on business outcomes, and did not collect any information on household expenditure, household asset ownership, or children’s outcomes. We therefore can not look at this explanation directly, but in Table A10 examine this indirectly through exploring heterogeneity in impacts among women by baseline decision-making power and household structure. 85 percent of women say they have sole

> 4 Partial evidence against this explanation comes from seeing that for the subset of firms that recall 2019 profits, the 2021-2019 difference in profits in the control group is not statistically different between men and women for either the best month or worst month’s profits.

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actually used, and the distance between origin and destination. (b) Use available information sources to collect the more specific cabin configuration information. (c) Seek more precise numbers for load factor of relevant flight groups, by travel class; and for climate forcing implications of aircraft activity at different altitudes and along alternative flight routes. The latter is a complicated issue on which no consensus exists today; but should remain a high priority for future research.

# **Table A3.6: Net Change in Carbon Footprint From CY-09 to FY-12 By Distance Class in Adjusted Samples for Both Years. (Figures in parentheses represent negative values.)**

|**Distance**<br>**Class**|**Range in**<br>**Standard**<br>**Miles**|**Fooprint**<br>**Sample CY**<br>**2009(Kg)**|**Footprint**<br>**Sample FY**<br>**2012(Kg)**|**Difference**<br>**(Kg)**|
|---|---|---|---|---|
|1|Upto 144|47,658|150,144|102,486|
|2|145 – 288|968,621|944,851|(23,770)|
|3|289–575|1,892,181|1,997,983|105,802|
|4|576–863|2,021,596|1,883,550|(138,046)|
|5|864 – 1,150|2,943,219|2,345,399|(597,820)|
|6|1,151 – 1,725|3,019,594|3,096,756|77,162|
|7|1,725 – 2,300|4,408,136|4,554,853|146,717|
|8|2,301 – 2,875|2,499,085|3,436,814|937,729|
|9|2,876–3,450|4,742,553|4,526,415|(216,138)|
|10|3,451 – 4,025|26,741,487|13,384,291|(13,357,196)|
|11|4,026– 4,600|37,066,649|22,387,482|(14,679,167)|
|12|4,601 – 5,175|4,816,334|3,649,066|(1,167,268)|
|13|5,176 – 5,570|7,048,664|5,638,348|(1,410,316)|
|14|5,751 – 6,325|1,190,357|987,446|(202,911)|
|15|6,326–6,900|5,258,919|4,906,047|(352,872)|
|16|6,901 +|1,832,029|18,402,756|16,570,727|
|**Sample**<br>**Total**||**106,497,082**|**92,292,200**||
||**Total Change**|**from 2009 to 2012**||**(14,204,882)**|
||**Percent chang**|**e from 2009 to 201 **|**2**|**(13.3%)**|

The following five considerations would facilitate such future work:

1. If the American Express data set were to include both the distance between the points traveled, and the IATA equipment code, the entire global scheduling data set would unnecessary, thus simplifying the data preparation work.

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countries on the African continent for the years 1960–2004. In the original data set, the categories type 3 and type 4 are not mutually exclusive. Table 1, however, presents the data in categories that are mutually exclusive and exhaustive so that all the information in any cell (country-year) can be represented by only one symbol. For countries with two rows of codes, in the top row (row 1) our coding is as follows: An X denotes conflicts that are type 3 (“internal”) at intensity 3 (“war”) if PRIO/Uppsala does not also designate a type 4 conflict at intensity 3 in that year. An I denotes a type 4 (“internationalized internal”) conflict at intensity 3 in which the “location” of the conflict was within the country if PRIO/Uppsala does not also designate a type 3 conflict at intensity 3 in that year. An O denotes years that PRIO/Uppsala designates as both type 3 and type 4 (and the location of the type 4 conflict is within the country), each at intensity 3 (i.e., there is overlap according to the PRIO/Uppsala data). An F denotes type 4 conflicts of intensity 3 in which a country was involved but for which the location was outside that country. There are no countries that have years in which PRIO/Uppsala designates a type 3 conflict and a type 4 conflict with a location outside the country. A blank denotes a year of no conflict of either type 3 or type 4 at intensity 3, in effect a year of peace in the coding used by the econometric studies if they use intensity 3 data. In the bottom row (or the only row for countries with only one row; row 2), X, I, O, F, and blank have the same meanings as just discussed except that the intensity is 1–3 (“minor,” “intermediate,” or “war”) rather than intensity 3 alone.

Among the econometric studies that have recoded the PRIO/Uppsala data to study aspects of civil war, we provide three examples: Miguel et al. (2004), Gleditsch (2007), and Buhaug and Gleditsch (2008) (hereinafter MGB). Miguel et al. (2004) study whether countries in sub-Saharan Africa had a civil war for each of the years 1981 to 1999. Because they used a slightly earlier version of the data, their table C1 is not identical to our table 1 when the two 8
level, which points to the relevance of the structure of the economy. Accounting for indirect dependencies on ecosystem services via supply chains and trade could change this observed relationship, however. Third, dependencies across countries are highest for ecosystems services that provide climate regulation and flood and storm protection. This indicates the strong interdependencies between climate and naturerelated financial risks. Finally, most material ecosystem services are concentrated around a few economic sectors, with construction and real estate explaining more than 20 percent of all banking sector dependencies.

The high-level analysis can already provide some insights on potential bottlenecks and sectors where policy makers and supervisors might want to investigate further and ultimately take action. However, it is important to have realistic expectations as to the ultimate policy objective. Attempting to reduce the dependency on nature and its ecosystem services might be prone to failure given the embeddedness of economies in nature <u>(Dasgupta et al. 2021) and the reliance on goods and services that are directly</u> provided by nature (e.g. agricultural products). The objective should rather be to induce a transformative shift from nature extracting and harming economic activities towards nature-positive activities and production processes (OECD 2019, Leclere et al. 2020, Power et al. 2022, World Bank Group 2021). For instance, intensive agriculture can have detrimental impacts on soil, biodiversity and terrestrial and maritime habitats (Benton et al. 2021). In contrast, more sustainable agricultural practices might be able to reduce those negative impacts. However, both types of agriculture, intensive and sustainable ones, would still remain highly dependent on nature. As such, there is a need to reduce the negative impact on ecosystems to ensure that ecosystem services could continuously support economic activity and human life in the future. There is a wide range of available policy tools in this regard, ranging from incentives and financial instruments for restoration activities and implementation of nature-based solutions over payment for ecosystem services to environmental tax reform and phasing out of nature-harming subsidies (Power et al. 2022).

The analysis provided in this paper can serve as a first step towards a fully-fledged nature risk assessment. Such an assessment would need to overcome currently persisting knowledge gaps, however (see NGFS- <u>INSPIRE 2022, Karoly and de la Puente 2022, and Power et al. 2022 for more details). Those include i) a</u> better understanding of economic and financial vulnerabilities, ii) the development of nature-related risk scenarios (NGFS 2022, <u>Maurin et al 2022, Almeida et al. 2023), iii) the ongoing development of models</u> that could quantitatively assess direct and indirect economic and financial impacts of nature loss (Johnson <u>et al. 2021), and iv) and a better understanding of how to include those insights into the regulatory and</u> supervisory framework (Stewart et al. 2022) and firms’ decision making (TNFD 2022).

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