rafmacalaba/datause-encoder-data huggingface dataset:
by a country’s economy financial depth in providing insurance against idiosyncratic shocks. Moreover, public expenditure seems to be key in providing insurance against idiosyncratic shocks.

# **5. A few remarks on potential welfare gains from risk sharing**

In view of the results reported in section 4, it is tempting to provide an assessment of the potential gains from risk pooling that Latin American and Caribbean countries might obtain. This is not an easy task, however, given the number of variables and processes involved in a country’s opening to international risk sharing. This is why many different methods (and different hypotheses) to assess benefits from risk pooling have been put forward in the literature, and so diverse results have been found, as reported in van Wincoop (1999), ranging from a small 0.5% to two digit or even three digit gains (see, for a comprehensive survey, the work by Auffret (2001)).

The basic idea underlying one of those methodologies (see, for a few examples thereof, Cole and Obstfeld (1991), Obstfeld (1994b and 1995), van Wincoop (1994), Kose (1995), Lewis (1996)), is quite simple, and can be summarized as follows: if the representative individual in a country is endowed with a concave utility function, more variance in consumption will be associated to less (expected) utility.

If aggregate consumption in a country is not perfectly correlated with aggregate consumption of a given reference area, then there is scope for diversification, which will lower the variance of aggregate consumption, thereby increasing expected utility. In these contributions, welfare gains are usually computed as the percentage increase in expected consumption corresponding to an increase in welfare brought about by a reduction in variance of consumption.

From a different, and more financial, perspective, gains from risk sharing are measured as the ratio of the utilities an investor can obtain under two alternative situations: a situation of autarky, and one of full international diversification (see Lewis (2000) and Mercereau (2006), for some examples). In this case, however, we have more than simple insurance, in the sense that it is not simply a matter of smoothing consumption across states of nature; here expected consumption may also be affected, by resorting to foreign financial markets.

This is why we are adopting the first line of reasoning, to try and provide some approximate measures of welfare gains Latin American and Caribbean countries might obtain, in case they had the opportunity to obtain full insurance.

Following van Wincoop (1999), let us consider a representative agent with preferences represented by the following expected utility: 17
<mark>Jordá, O. (2005). Estimation and inference of impulse responses by local projections.</mark> _<mark>American Economic Review</mark>_ <mark>, vol.</mark> **<mark>95</mark>** <mark>, pp. 161– 82.</mark>

- Leduc, S. & D. J. Wilson. (2012). _Roads to prosperity or bridges to nowhere? theory and evidence on the impact of public infrastructure investment_ . Cambridge, Mass: National Bureau of Economic Research.

- <mark>Leeper, E. (1991). Equilibria under ‘active’ and ‘passive’ monetary and fiscal policies.</mark> _<mark>Journal of Monetary Economics</mark>_ <mark>, volume 27, Issue 1, 129-147.</mark>

- <mark>Leeper, E. M.</mark> & <mark>Leith C. (2016). Understanding inflation as a joint monetary‐fiscal phenomenon, in</mark> _<mark>Handbook of Macroeconomics</mark>_ <mark>, vol.</mark> **<mark>2</mark>** <mark>, eds J. B. Taylor and H. Uhlig,</mark>

- Leeper, E. M., Traum, N. & Walker, T.B. (2017). Clearing up the Fiscal Multiplier Morass. _American Economic Review_ 107(8): 2409–54.

- Leigh, A. (2012). How Much Did the 2009 Australian Fiscal Stimulus Boost Demand? Evidence from Household-Reported Spending Effects. _The B.E. Journal of Macroeconomics_ , 12(1).

- Li, S., & Spencer, A. (2016). Effectiveness of the Australian Fiscal Stimulus Package: A DSGE Analysis. _The Economic Record_ , 92(296), 94–120.

- Liebenau, J., Atkinson, R., Kärrberg, P., Castro, D. & Ezell, S. (2009). _The UK's digital road to recovery_ LSE Enterprise Ltd. & the Information Technology and Innovation Foundation.

- <mark>Liu, P., Mumtaz, H., Theodoridis, K. & Zanetti, F. (2017). Changing macroeconomic dynamics at the zero lower bound,</mark> _Journal of Business & Economic Statistics_ Volume 37, 2019 - Issue 3.

- Mallett, W. J.  (2020). Transportation Infrastructure Investment as Economic Stimulus: Lessons from the American Recovery and Reinvestment Act of 2009. Congressional Research Service, May 5, 2020.

- Mian, A. & Sufi, A. (2012). “The Effects of Fiscal Stimulus: Evidence from the 2009 Cash for Clunkers Program.” _The Quarterly Journal of Economics_ (June 22, 2012).

- Michel, A. (2021). Why Infrastructure Spending Will Not Help the Pandemic Recovery, The Heritage Foundation.

- <mark>Misch, F., & Seymen, A. (2019). The effects of fiscal stimulus: firm-level evidence from selective temporary consumption tax cuts.</mark> _<mark>Oxford Economic Papers</mark>_ <mark>, 71(4), 1026–1049.</mark>

- <mark>Mitra, P. (2010). The Impact of Global Financial Crisis and Policy Responses: The Caucasus, Central Asia and Mongolia.</mark> _<mark>Global Journal of Emerging Market Economies</mark>_ <mark>, 2(2), 189–230.</mark>

- <mark>Mittnik, S. & Semmler, W. (2012). Regime dependence of the fiscal multiplier.</mark> _<mark>Journal of Economic Behavior and Organization</mark>_ <mark>, vol. 83, pp. 502– 22.</mark>

- Miyamoto, W., Thuy, L. N. & Sergeyev, D. (2018). Government spending multipliers under the zero lower bound: Evidence from Japan. _American Economic Journal: Macroeconomics, 10_ (3), 247-277.

- Mountford, A., & Uhlig, H. (2009). What are the effects of fiscal policy shocks? _Journal of Applied Econometrics, 24_ (6), 960-992.

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26 Andresen M., Bensnes S., Løkken S. 2020. What does it cost to close the education sector? Calculation of costs of infection prevention measures against COVID-19 for human capital, study - progression and productivity. Statistics Norway <u>https://khrono.no/files/2020/04/15/RAPP2020 15_web.pdf.</u> 27 Dorn, E., Hancock, B., Sarakatsannis, J. and Viruleg, E. 2020. COVID-19 and student learning in the United States: The hurt could last a lifetime. McKinsey & Company.

28 Abel, J.R., and Deitz, R. 2020. Delaying College During the Pandemic Can Be Costly. Federal Reserve Bank of New York Liberty Street Economics <u>https://libertystreeteconomics.newyorkfed.org/2020/07/delaying-college-during-the-pandemiccan-be-costly.html.</u> 29 The DELVE Initiative. 2020. Balancing the Risks of Pupils Returning to Schools. DELVE Report No. 4. Published 24 July 2020. Available from <u>http://rsdelve.github.io/reports/2020/07/24/balancing-the-risk-of-pupils-returning-to-schools.html.</u> 30 Adams, R. 2020. UK children could 'lose 3% of lifetime earnings' due to lockdown school closures. The Guardian Blog <u>https://www.theguardian.com/education/2020/jul/24/uk-childrencould-lose-3-of-lifetime-earnings-due-to-lockdown-school-closures.</u> 31 Frenette, M., D. Messacar, and T. Handler. 2020. To what extent might COVID-19 affect the earnings of the class of 2020? STATCAN COVID-19: Data to Insights for a Better Canada. Statistics Canada Catalogue no. 45280001. Ottawa: Statistics Canada.

32 Azevedo, J.P., Hasan, A., Goldemberg, D., Iqbal, S.A., and Koen, G. 2020. Simulating the Potential Impacts of COVID-19 School Closures on Schooling and Learning Outcomes: A Set of Global Estimates. Policy Research Working Paper No. 9284. World Bank, Washington, DC <u>https://openknowledge.worldbank.org/handle/10986/33945.</u> 33 Kaffenberger, M. and Pritchett, L. 2020. Failing to Plan? Estimating the Impact of Achieving Schooling Goals on Cohort Learning. RISE Working Paper Series. 20/038. <u>https://doi.org/10.35489/BSG-RISEWP_2020/038.</u> 34 Berman, Y., 2020. The Distributional Short-Term Impact of the COVID-19 Crisis on Wages in the United States. arXiv preprint arXiv:2005.08763.

35 Lemieux, T., Milligan, K., Schirle, T., and Skuterud, M. 2020. Initial impacts of the COVID19 pandemic on the Canadian labour market. Working Paper Series No. 26, University of Waterloo, Canadian Labour Economics Forum (CLEF), Waterloo.

36 Bell, D.N. and Blanchflower, D.G., 2020. US and UK labour markets before and during the Covid-19 crash. _National Institute Economic Review_ 252: R52-R69.

14
Where π denotes the vector of ergodic probabilities and L is a (k x 1) vector of 1s. In this way, it can be used as the concept of convergence in probability<sup>11</sup> to acquire π, which is any row of M*.

**Table 5.3:  Distribution for 5-Year and 26-Year Transition Matrix for RN (1970–96)**

|**Period**|**P**|**L**<br>**M**<br>**U**|**R**|
|---|---|---|---|
|5-year|0.34|0.14<br>0.20<br>0.15|0.18|
|26-year|0.29|0.14<br>0.21<br>0.16|0.20|
|P = poor; L = low in<br>rich.<br>_Source:_Estimates ba|come; M =<br>sed on IPE|middle income; U = upper incom<br>A’s municipal dataset.|e; and R =|

Convergence clubs exist among municipalities even though no absolute convergence is present in RN. The distribution obtained from estimation of transition matrixes shows probabilities perceived independently of the transition periods (Table 5.3).<sup>12</sup> For the 26year transition, a peak is present in the poor (P) income category, which contains nearly 30 percent of all the municipalities and, furthermore, about 20 percent of municipalities are in the richest income group (R). These findings indicate that convergence clubs exist in RN, but no absolute convergence is taking place.

The lack of convergence—the migration and sectoral development pattern away from low-growth agriculture in the Sertão Zones toward services in the capital region—have implications for policies. Policies in the two geographical areas should (a) assist the people remaining behind, that is, rural families remaining in the poor municipalities mainly in the Sertão, so they will have education or skills that will enable them to choose to stay or leave, and not be forced by lack of opportunities due to insufficient skills, and (b) assist the poor migrants that lack skills to integrate fully into the modern economy in the urban areas.

Among the policies for rural families should be an improvement of the design of education strategies, including ways to upgrade work skills. In addition, small farmers would benefit from improved access to technical assistance, and recent migrants would benefit from increased access to education that focuses particularly on their needs. This would include programs that concentrate on closing the gap in education for both adults and children.

> 11  For more details on this concept, see Hamilton (1994) pg. 749.

> 12 The vector of ergodic probabilities can be viewed as indicating the unconditional probabilities of each of the different states, that is, independent of the initial state.

19
**Table E.6:** Effect on Probable and Suspected Cases

||Control Mean|Pooled|CM|NFA|Difference|N|
|---|---|---|---|---|---|---|
|Ebola Cases|||||||
|Probable and Suspected|0.029<br>(0.208)|0.003<br>(0.008)|0.015<br>(0.011)|-0.007<br>(0.009)|0.022<br>(0.011)*|5,440|
|Log(Ebola Cases + 1)|||||||
|Probable and Suspected|0.018<br>(0.123)|0.002<br>(0.005)|0.009<br>(0.007)|-0.004<br>(0.005)|0.013<br>(0.007)*|5,440|
|Linear Probability Model|||||||
|Probable and Suspected|0.022<br>(0.148)|0.002<br>(0.006)|0.011<br>(0.008)|-0.005<br>(0.006)|0.016<br>(0.008)*|5,440|
|IHS(Ebola Cases)|||||||
|Probable and Suspected|0.023<br>(0.159)|0.002<br>(0.006)|0.012<br>(0.008)|-0.005<br>(0.007)|0.017<br>(0.009)*|5,440|

_Notes_ : Standard errors clustered on section shown in parentheses. Treatment effects estimated using OLS including matching-triplet and week fixed effects. Significance: *p < 0.10, ** p < 0.05, and *** p < 0.01.

**Table E.7:** Effect on Reported Cases (Removing Probable and Suspected)

||Control Mean|Pooled|CM|NFA|Difference|N|
|---|---|---|---|---|---|---|
|Ebola Cases|||||||
|Total|0.252|0.17|0.188|0.155|0.033|5,440|
||(0.672)|(0.08)**|(0.11)*|(0.094)|(0.127)||
|IHS(Ebola Cases)|||||||
|Total|1.004|0.057|0.061|0.053|0.007|5,440|
||(0.292)|(0.028)**|(0.037)*|(0.033)|(0.042)||

_Notes_ : The sample includes 160 sections over 34 weeks. Standard errors clustered on section shown in parentheses. Treatment effects estimated using OLS including matching-triplet and week fixed effects. Difference column reports the difference between the CM and NFA coefficients; the standard error is computed using the delta method. Significance: * p < 0.10, ** p < 0.05, and *** p < 0.01.

A37
Exposure to undiversifiable residual risk causes farmers to resort to liquidation of their assets during periods of severe crisis, a phenomenon commonly referred to as distress sales. This is implies that the covariance of weather risks for the farming population causes land prices to be low (due to insignificant effective demand and high supply) during bad crop years, with the consequence that individuals who had to sell off land during crises may not be able to repurchase land during subsequent periods of recovery (Bidinger et al. 1994). Distress sales have not only played a major role historically in shaping more concentrated land ownership pattems, but are also linked to the elimination of traditional mechanisms for coping with risk in the literature (Kranton and Swamy 1997; Brockett 1984)34 The link between unmitigated production risk and distress sales is highlighted by Cain (1981) who examines the implications of different insurance mechanisms on distress sales and the land ownership distribution between 1960 to 1980 for predominantly agricultural villages in India and Bangladesh. These villages faced very high production risks but were characterized by distinct differences in mechanisms of risk-insurance: In Maharashtra, India, an employment guarantee scheme operated throughout the period and attained participation rates of up to 97 percent of all households during disasters. Such schemes were absent after the major flood episodes in Bangladesh. Thus, 60 percent of land sales in Bangladesh were undertaken to obtain food and medicine, undoubtedly due at least in part to the lack of other insurance mechanisms. About 60 percent of the currently landless had lost their land since 1960 and the Gini coefficient of landownership distribution increased from 0.6 to almost 0.7. This contrasts sharply with the Indian villages, where land sales to finance consumption expenditures accounted only for 14 percent of sales and were incurred mainly by the rich to meet social obligations. On the other hand, 64 percent of land sales were undertaken in order to generate capital for productive investment (digging of wells, purchase of pump sets, and children's education), and the land sales market actually contributed to a slight equalization of the land-ownership distribution. This suggests that in this case the poor were not only able to avoid distress sales, but were able, through access to cash-generating employment, to acquire some land as rich households liquidated agricultural assets to be able to pursue non-agricultural investment. Survey data on land transactions from India indicate that purchases of land are almost all undertaken by individuals with access to sources of income which are not correlated with agricultural production and that borrowing to finance agricultural land acquisition is virtually non-existent (Sarap 1995).

> 34 Distress sales have been important in China (Shih 1992) in early Japan (Takekoshi 1967) in the Indian Punjab (Hamid 1983), and **in** Latin America following the abolition of communal tenure (Brockett 1984).

21
income quintile that exceeds the benefit of the poorest quintile, although the largest net benefit is enjoyed by the middle-income group . This regressivity conceals distinct patterns for health centers and hospitalrelated net benefit profiles. Figures 10a and 11a depict nonlinear patterns along income levels. Again, they are neither progressive nor regressive. In the case of the distributive incidences depicted by the quintiles of probability, net benefits from public spending on health centers first decrease and then increase (figure 10b), while they remain mostly uniform across quintiles of access probability for hospital care, except, again, for the middle-income group, which captures the largest share of benefits (figure 11b).

There are several possible explanations for these nonlinear patterns. One option is that richer households, and/or those with circumstances making them more likely to access public health, do not opt out of the public system (at least for a number of medical services). In addition, and contrary to education, different medical conditions and accidents require different types of health care. The complexity and severity of required attention should not be expected to follow unambiguous socioeconomic nor geographical lines. Outliers do not drive these results either. In fact, results do not change when two scarcely populated departments—Canindeyú and Ñeembucú—with extremely high average public transfers on public health are removed.<sup>30</sup> Another potential source of bias in these estimates, selfmedication, does not fabricate these results either. Results do not change after including self-medicated individuals as having access to public health care services.<sup>31</sup> Finally, it is also worth noting that these distributive incidences are not the result of the poor demanding—or reporting—less health services, because this analysis considers only those who demanded attention to a condition considered serious enough to require medical attention in the first place.

**Figure 9: Distribution of Unitary Public Expenditures on Health Care Net of Private Household Contributions, Paraguay, 2009 (Ages 0–17)**

**a.                                                        b.**

<!-- Start of picture text --> Unitary public expenditure on health care Unitary public expenditure on health care<br>by quintiles of incomes (2009) by quintiles of probability (2009)<br>403<br>372 370<br>361<br>310<br>301<br>242 219 234 241<br>326 238 312 310 259 283<br>192 179 182 189<br>-51 -40 -77 -63 -60 -53 -52 -59 -51 -79<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Public expenditures Household expenditures Net benefit Public expenditures Household expenditures Net benefit<br>Source : Author’s estimates from INEC’s EPH 2009; MSPyBS (2010); IPS (2011); Cuesta and Suarez-Becerra (2013).<br>400 400<br>300 300<br>200 200<br>100 US$ of 2009 100<br>0 0<br>-100 -100<br><!-- End of picture text -->

> 30 Data available upon request.

> 31 Results available upon request.

22
temperature change for the period 2016–2035 relative to 1986–2005 will likely be in the range of 0.3°C to 0.7°.

Table 7: Projected change in global mean surface air temperature (mean estimate) for the mid- and late 21st century relative to the reference period of 1986–2005

||**2020**|**2050**|**2100**|
|---|---|---|---|
|**Baseline**|0.5|0.5|0.5|
|**RCP 2.6**|0.5|1.0|1.0|
|**RCP 8.5**|0.5|2.0|3.7|

Source: IPCC (2014b)

# 4.2.1 The impact of heat on labor productivity

The effects for the two RCP scenarios are shown in Figure 26. RCP 2.6 is shown in blue and RCP 8.5 in orange. Each are expressed as a percent deviation from the counterfactual baseline where there is no climate change. In each scenario, higher temperatures reduce labor productivity and therefore potential output and production. This has direct impacts on GDP, consumption and private investment in the top panels. Overall, in the RCP 8.5 scenario, PAKMOD suggests that lower labor productivity will shave about 6 percent from GDP and consumption.

In line with decreasing economic activity government revenue decreases. Government expenditure follows government revenue closely as in these long-term simulations the fiscal rule is designed to ensure fiscal sustainability. Government debt increases slightly (and later decreases) because government expenditure follows the changes in revenue with a lag.

In the model, reductions in labor productivity result in a decline in potential GDP relative to market price GDP in the immediate short run. This results in moderate (but negligible) inflation. The rise in prices and the reduction in labor productivity decreases both the consumer and producer real wage through the marginal productivity of labor channel.

73
# <u>Case Studies</u>

Case studies are used in a range of disciplines (including medicine, law and business) for evaluative, diagnostic and pedagogical purposes. They are selected on the basis of a considered awareness of the broader context, such that their status as being ‘representative’ of or an ‘outlier’ in this broader context is well understood. (Without such knowledge, case studies are vulnerable to the critique of “selecting on the dependent variable”; that is, of failing to recognize that the same factors seemingly driving success may elsewhere have no—or even the opposite—effect.) Case studies are usually conducted comparatively, either by assessing different cases (‘between case’ analysis) or by exploring constituent aspects of a single case (‘within case’ analysis) (see Gerring, 2007).

For present purposes, the comparative advantage of case studies is that they can be (a) a vehicle for integrating insights derived from different methodological perspectives and units of analysis into a single, coherent narrative; and (b) can be used to trace _how_ —i.e., to identify the conditions under which—particular combinations of events and processes yielded particular outcomes. They are currently being deployed to great effect in understanding the bases for effective service delivery in otherwise unlikely settings<sup>19</sup> , the better to gain insights on how domestic reformers elsewhere might proceed. In those instances where investment climate reforms are being launched at the national (or sub-national) level, case studies are especially well suited to investigating in detail how, where and why they are being experienced by different groups.

Readers interested in learning more about preparing case studies should consult: George, Alexander and Andrew Bennett. _Case Studies and Theory Development in the Social Sciences_ Cambridge, MA: MIT Press, 2005.

Gerring, John. _Case Study Research: Principles and Practices._ New York: Cambridge University Press, 2007.

Weller, Nicholas and Jeb Barnes. _Finding Pathways: Mixed-Method Research for Studying Causal Mechanisms_ . New York: Cambridge University Press, 2014.

> 19 See in particular the evolving library of cases being compiled (and made freely available) by Princeton University’s ‘Innovations for Successful Societies’ (http://successfulsocieties.princeton.edu//).

29