rafmacalaba/datause-encoder-data huggingface dataset:
examples of macroeconomic studies in this area include Arndt and Lewis (2000), Bloom and Mahal (1997), Cuddington (1993a), Cuddington (1993b), Cuddington and Hancock (1994), Over (1992), and Quattek (2000). These macroeconomic studies have been critiqued for underestimating the impact of HIV/AIDS because they do not take into account the potential impact on human capital formation and its transmission between generations. Bell, Devarajan and Gersbach (2006), McDonald and Roberts (2006) and Corrigan, Gloom and Mendez (2006) extend these models in varies ways to include the intergenerational effects on human capital accumulation and find large effects on national income per capita. On the other hand, Young (2005) models this orphan effect and finds that the reduction in fertility dominates and results in _higher_ per capita consumption possibilities.

The underpinnings of the macroeconomic studies are the behaviours and outcomes for individuals affected by HIV/AIDS: individuals who are infected as well as people with socioeconomic ties to infected persons. There are several pathways through which an adult mortality can affect consumption or income levels among surviving household members. The direct costs of these events include medical expenses as well as funeral costs (which can be larger than the medical costs in areas with low health care provision). Illnesses are associated with the loss of earnings for both the sick household members and care-givers in the household. Un-earned income for households may also suffer if remittances are curtailed due to illnesses and mortality. Deaths can result in asset losses due to disinheritance (for example, land grabbing). Finally, there may be significant intergenerational effects if illness and mortalities result in lower health and education investments in children (say due to costs of illness and liquidity constraints), compromising the future income of these children when they reach adulthood. On the other hand, the _per capita_ income standing of surviving households could theoretically _rise_ through increases 3
housing demand. Public hostel or rental programs for the young have been set up in several countries but seem rather insignificant. Furthermore, the “residualization” of the social rental sector is mirrored by the marginalization of its tenants in terms of income and age profile in all countries. The exceptions are Poland - which continues to have an active public housing policy - and Russia, with its large and yet undefined social rental stock.<sup>102</sup> Although only equilibrium values of tenure patterns and rent levels can be observed, the conclusion from the quantity-price relations observed in the studied countries is that rental housing demand in transition economies is rather strong. The housing expenditure and rent data suggest that, with the exception of Poland (which has residues of rent control), private rental housing costs are high relative both to values typically obtained in transition countries, and to public rental or owner-occupied housing expenditures. However, they are moderate compared with the rental burden in Western economies.

In terms of the efficiency of matching demand and supply through market mechanisms - including non-institutionalized and informal ones - the studied countries may be divided into three broad groups:

- The most advanced transition (and EU accession) countries (Lithuania and Poland) experience high demand pressure for renting, with supply being somewhat responsive, mitigating the upward pressure on rents.

- The mid-advanced transition countries (Romania and Russia) experience high demand pressure for rental apartments, which coincides with a pronounced scarcity of supply.

- The least advanced post-conflict countries (Armenia and Serbia) experience relatively subdued demand for housing in general, including rental housing.

These groups correspond to countries’ progress along the general reform path.

# **Regional Tenure Policies**

# **_Legal, Regulatory, and Tax Reform_**

Progress in legal and regulatory reform of the rental housing sector in the region has been painfully slow, with strong political resistance by numerous privatized tenant-owners and social housing tenants. These tenants dominate the political arena, given that they continue to enjoy high subsidies (explicit and implicit). Consequently, most regulations and subsidies deal with social housing, while private landlord-tenant relationships remain mainly unregulated.

It is perhaps over-stretched to cite eastern Germany’s experience, where between 1990 and 1996 a complete set of rent and tax regulations was implemented and rents in the public stock rose fivefold to reach levels almost as high as those in western Germany (with socially weak tenants supported by housing allowances). However, the valid core factor may be that political resistance

> 102  Since much of it is already slated for privatization, once the sitting tenants decide to execute their vested right to privatize.

62
eliminate the gains from switching to a shorter week cycle. Further analysis regarding the preference towards leisure smoothing is necessary in order to refine the welfare analysis in this paper.

Finally, there may be scope for introducing heterogeneity into this framework. Here, I consider the simplest case of a representative agent. The minimal criteria on the set of admissible parameters allows for some differences in parameters across occupations. However, the structural parameters may systematically differ across occupations, and therefore the optimal structure of the week may vary with occupational composition. For example, cognitive tasks may be more intensive in memory and less subject to fatigue than physical labor. Additionally, a richer framework may be able to incorporate the reality of many people working during weekends.

The findings of this paper suggest that there are potentially large welfare gains associated with restructuring the week. In light of this, there is scope for further research geared at meeting the above challenges.

# **References**

- Daniel Aaronson and Eric French. The effect of part?time work on wages: Evidence from the social security rules. _Journal of Labor Economics_ , 22 (2):252–329, April 2004.

- Alberto F. Alesina, Edward L. Glaeser, and Bruce Sacerdote. Work and leisure in the us and europe: Why so different? _NBER Macroeconomics Annual 2005_ , 20:1–100, 2006.

- John R. Anderson and Lael Schooler. Adaptive memory. In F. Craik and E. Tulving, editors, _Oxford Handbook of Memory_ . Oxford, UK: Oxford University Press, 2000.

Dominique Anxo and Arne Bigsten. Working hours and productivity in 36
an investment equilibrium characterized by risk-averse agents, namely the existence of a positive association between the average returns to individual production assets and their sensitivity to weather variability, and iii) estimate how the influence of exogenous weather risk on portfolio riskiness and on farm profitability varies with total wealth.

We focus on weather-related risk for two reasons. First, there is a great deal of information on weather, so that this important and exogenous component of the riskiness of the environment can be measured along with the farmer responses to it.<sup>1</sup> Second, although weather is not the only factor exogenously affecting the variability in farm output and profits, it is the factor contributing to income variability that is most likely to influence welfare. This is because weather risk is spatially covariant. Unlike for idiosyncratic risk, it is difficult for farmers to undertake arrangements that insure against risks such as rainfall that affect everyone in their local community similarly. Risk will be reflected in ex ante investment and production decisions only to the extent that risk is not insurable, and weather risk appears to be uninsured in most lowincome settings.

Prior studies of farmer risk aversion (Just and Pope, 1978; Antle, 1987 and 1989) assume that farmers cannot insure against any risk ex ante and cannot perform any consumption smoothing ex post. In these studies farmer utility depends on farm income, so that farmer consumption variability is isomorphic with farm profit variability. Recent evidence (Rosenzweig, 1988; Rosenzweig and Stark, 1989; Walker and Jodha, 1986), also based on the same data used in this paper, suggests, however, that rural agents employ a variety of formal and informal mechanisms that contribute to ex post consumption-smoothing. Moreover, the same data even suggests that such agents are successful in insuring against all non-covariant risk, in particular all risk that is not common to agents 2
**Table** 7. **The Share of Individual**<sup>**Countries in Total Intra-Regional Exports, Selected Years from 1970 to 1992.**</sup>

||Value of Intra-|Regional Exports (U|S$ million)|Share of All|Intra-Regional|Exports (%)|Growth|Rate|
|---|---|---|---|---|---|---|---|---|
|Product Group/Exporter|1970|1980|1990|1970|1980|1990|1970-90|1980-90|
|ALL GOODS|3,399.9|16,708.7|8,879.1|100.0|100.0|100.0<br>|4.9<br>|-6.1<br>|
|Bahrain|242.2|3,808.5|231.7|7.1|22.8|2.6<br>|-0.2<br>|-24.4<br>|
|Cyprus|109.1|551.7|94.4|3.2|3.3|1.1<br>|-0.7<br>|-16.1<br>|
|<br>Egypt|34.0|269.0|387.4|1.0|1.6|4.4<br>|12.9<br>|3.7<br>|
|<br>Iran, Islamic Rep. of|2,454.8|851.3|734.6|72.2|5.1|8.3|-5.8<br>|-1.4<br>|
|<br>Israel|30.3|69.1|128.1|0.9|0.4|1.4|7.5|6.4<br>|
|Jordan|29.8|419.4|208.6|0.0|2.5|2.3<br>|10.2<br>|-6.7<br>|
|Kuwait|49.1|1,402.1|139.8|1.4|8.4|1.6|5.4<br>|-20.5<br>|
|Lebanon|222.0|742.2|263.1|6.5|4.4|3.0<br>|0.8<br>|-9.9<br>|
|Libyan Arab Jamahiriya|7.3|783.1|636.1|0.2|4.7<br>|7.2<br>|25.0<br>|-2.2<br>|
|Oman|0.1|3,748.3|279.3|--|22.4<br>|3.1<br>|48.7<br>|-22.9<br>|
|Qatar|18.5|116.7|260.3|0.5|0.7<br>|2.9<br>|14.1<br>|8.4<br>|
|Saudi Arabia|143.9|3,264.3|1,609.2|4.2|19.5|18.1|12.8<br>|-6.8<br>|
|Syria|2.4|103.2|866.4|0.1|0.6|9.8<br>|34.2<br>|23.7<br>|
|<br>Turkey|47.8|493.3|1,923.8|1.4|3.0<br>|21.7<br>|20.3<br>|14.6<br>|
|United Arab Emirates|8.6|75.5|1,116.3|0.3|0.5|12.6|27.5|30.9|

Source: All data drawn from United Nations C9MTRADE<sup>records.</sup>

# Methodological Notes

1990 -- Countries failing to report trade<sup>statistics for 1990 include: Bahrain: Iran: Lebanon; Qatar and the UAE. Regional partner country statistics for Iran, Lebanon</sup> and the UAE were employed to estimate these<sup>countries exports. The above tabulations will, therefore, not include these nations' intra-trade. Also, 1988 trade statistics were</sup> used in the above for Bahrain and 1991 data<sup>for Qatar. Partner country statistics were used to estimate Saudi Arabia's exports to the region. In 1990 Cyprus did not report exports</sup> to Turkey.

1980 -- Countries failing to report trade<sup>data for 1980 include: Iran, Lebanon, Qatar and the UAE. Partner country data were used to estimate Iran and Lebanon's</sup> exports. The above tabulations employ<sup>1979 trade statistics for Qatar and the UAE.</sup> 1970 -- Partner country data were used to<sup>estimate UAE exports. The above tabulations are based on 1974 trade data for Saudi Arabia and Syria, 1972 data for Qatar,</sup> and 1975 data for Oman.
Table 9. Main features of global expansions

|||Global e|xpansio|ns||Globa|l expansions|(excludin|g initial ye|ars)|All|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Activity**|1976-81 1|983-90 1992|-2008|2010-19 A|verage|1977-81|1984-90 19|93-2008 2|011-19 A|verage|years|
|Output|3.5|3.5|3.1|3.0|3.3|3.1|3.6|3.2|2.8|3.2|3.7|
|Output per capita|1.7|1.7|1.7|1.8|1.7|1.4|1.8|1.8|1.6|1.7|2.0|
|Industrial production|3.7|3.5|2.8|2.9|3.2|2.8|3.7|3.0|2.3|2.9|3.5|
|Trade|5.1|5.2|6.7|4.7|5.4|4.4|5.7|6.9|3.9|5.2|5.8|
|Unemployment rate|0.0|-0.1|0.1|-0.1|0.0|0.0|-0.1|0.0|-0.1|0.0|0.1|
|Oil consumption|1.6|1.8|1.5|1.6|1.6|0.7|2.1|1.5|1.4|1.4|2.3|
|Investment|3.4|3.8|3.4|3.9|3.6|2.9|4.1|3.8|3.7|3.6|4.4|
|Consumption|3.4|3.4|3.1|2.7|3.2|3.1|3.5|3.1|2.7|3.1|3.6|
|Output (PPP)|3.6|3.5|3.6|3.7|3.6|3.2|3.6|3.7|3.5|3.5|4.0|
|Output per capita (PPP)|1.8|1.7|2.2|2.5|2.1|1.4|1.8|2.4|2.3|2.0|2.3|
|**Financial markets**||||||||||||
|Capital flows|0.3|0.2|0.5|0.3|0.3|0.2|0.5|0.4|-0.6|0.1|0.2|
|Credit|3.6|6.5|4.1|3.3|4.4|3.6|6.8|4.2|3.5|4.5|5.2|
|Equity prices|-1.7|16.6|8.9|5.3|7.3|-2.5|15.6|8.8|3.9|6.5|5.3|
|House prices|1.6|2.8|1.7|1.6|1.9|2.0|3.2|1.9|1.6|2.2|1.8|
|Financial conditions|0.2|0.5|0.1|-0.1|0.1|0.2|0.5|0.1|-0.1|0.2|0.1|
|Inflation|-0.2|-0.3|-0.1|0.1|-0.1|0.3|0.0|0.0|0.0|0.1|0.0|
|**Interest rates, confide**|**nce, and u**|**ncertainty**||||||||||
|Nominal interest rate|1.0|-0.2|-0.3|0.0|0.1|1.2|0.0|-0.2|0.1|0.2|0.0|
|Real interest rate|1.0|0.0|-0.2|-0.1|0.2|0.8|0.0|-0.2|0.1|0.2|0.0|
|Business confidence|0.1|0.2|0.0|0.1|0.1|-0.2|0.0|0.0|-0.1|-0.1|0.0|
|Policy uncertainty|...|0.1|2.7|6.3|3.0|...|0.1|2.8|7.7|3.5|3.8|
|**Policies**||||||||||||
|Government expenditure|4.2|3.0|4.1|2.8|3.5|4.3|3.2|4.0|2.9|3.6|4.8|
|Policy rate|1.0|-0.3|-0.3|0.1|0.1|1.2|-0.1|-0.2|0.1|0.2|0.0|

Note: All variables show percent changes, except in capital flows, unemployment rates, inflation, nominal and real interest rates, and policy rate, in which percentage-point changes of these variables are reported. “All years” refers to averages during 1950-2019. “...” indicates that data are either unavailable or aggregates are not computed due to small sample size. For details, see Appendix 2.

57
Based on our empirical analysis of what works best in bank regulation (BCL, 2006) and the subsequent changes that have taken place since the late 1990s in the regulatory environment, we see no basis for the view that countries around the world have primarily reformed for the better. While many have followed the Basel guidelines and strengthened capital regulations and empowered supervisory agencies to a greater degree, existing evidence does not suggest that this will improve banking-system stability, enhance the efficiency of intermediation, or reduce corruption in lending. While some countries have reformed their regulations to empower private monitoring, consistent with the third pillar of Basel II, there are many exceptions and even reversals along this dimension. Moreover, many countries intensified restrictions on non-lending activities, which existing evidence suggests hurts banking system stability, lowers bank development, and reduces the efficiency of financial intermediation.

Our tempered advice continues to be that countries will benefit from an approach to bank regulation that is grounded in what has worked in practice. In our earlier work, we found that an approach that favors private monitoring, limits moral hazard, removes activity restrictions on banks, encourages competition, including competition by foreign banks, and requires or encourages greater diversification appears to work best to foster more stable, more efficient, and less corrupt financial-sector development. Our earlier findings did not support a hurried adoption of the first two pillars of Basel II by developing countries, but rather stressed the value of first developing the legal, information, and incentive systems in which financial systems flourish to the benefit of everyone. Based on the existing evidence, we continue to believe that this approach is the most sensible one for country authorities to pursue. Critically, the data in this new survey provide the raw material for research that should help confirm, refute, or refine this private-monitoring view.

# **II. The 2006 Survey**

The Survey of Bank Regulation and Supervision Around the World assembles and makes available a database to permit international comparisons of various features of the bank regulatory environment. Current and previous surveys and responses are on the World Bank website and the earlier surveys, responses, and indices are available on a CD in BCL (2006).<sup>2</sup>

> 2 ~ <u>http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20345037 pagePK: 64214825~piPK:64214943~theSitePK:469382,00.html.</u>

3
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- Rose, A., Porter, K., Dash, N., Bouabid, J., Huyck, C., Whitehead, J., Shaw, D., Eguchi, R., Taylor, C., McLane, T., 2007b. Benefit-cost analysis of FEMA hazard mitigation grants. Nat. Hazards Rev. 8, 97–111.

- Rose, A., Wei, D., 2013. Estimating the economic consequences of a port shutdown: the special role of resilience. Econ. Syst. Res. 25, 212–232.

- Santos, J.R., Haimes, Y.Y., 2004. Modeling the Demand Reduction Input‐Output (I‐O) Inoperability Due to Terrorism of Interconnected Infrastructures*. Risk Anal. 24, 1437–1451.

- Skidmore, M., Toya, H., 2002. Do natural disasters promote long‐run growth? Econ. Inq. 40, 664–687.

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# **6 Appendix A: An extreme case of capital complementarity**

One way of investigating these two issues is to assume that there are two categories of capital, _K1_ and _K2_ , that are not substitutable (i.e. the production function is a Leontieff function with decreasing returns): 39
# Figure A.5.3 Inflation targeting in Poland

## A. Inflation and inflation expectations

<!-- Start of picture text --> Percent, year-on-year<br>14 Target upper and lower limit<br>5-year-ahead expectations<br>1-year-ahead expectations<br>10 Actual inflation<br>6<br>2<br>-2<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text -->

## C. General government structural balance and debt

<!-- Start of picture text --> Percent of GDP Percent of GDP<br>4 Primary balance Gross debt (RHS) 90<br>2 80<br>70<br>0<br>60<br>-2<br>50<br>-4 40<br>-6 30<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 2017<br><!-- End of picture text -->

## B. Central bank transparency

<!-- Start of picture text --> Index (15=best)<br>12 Poland Median of EMDEs<br>9<br>6<br>3<br>0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> D. Sensitivity of inflation expectations to shocks <!-- Start of picture text --> Percentage points<br>1.0<br>0.5<br>0.0<br>1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text --> Source: Bloomberg, Consensus Economics, Dincer and Eichengreen (2014), Haver Analytics, International Monetary Fund, World Bank.

A.-D. The start of the inflation targeting regime is shaded in gray.

B. Transparency is based on information from the Central Bank of Chile’s website, statutes, annual reports, and other published documents, as calculated by Dincer and Eichengreen (2014).

C. Structural balance is the difference between government revenues and expenditures, adjusted for effects due to economic cycles.

D. Time-varying sensitivity is estimated by regressing long-term inflation forecast revisions on inflation shocks. Dotted lines denote 68 percent confidence interval. Appendix 3 provides details on the methodology.

52
indicators can predict income gains during the pandemic, in addition to income loss as shown in Table 4.<sup>13</sup> The three columns in Table 5 show the predictive power for farm income gain, nonfarm income gain, and wage income gain respectively. The predictive power for non-farm and farm income gain is highest (R<sup>2</sup> = 0.17 and 0.16), followed by wage income gain (R<sup>2</sup> = 0.12), and total income gain (R<sup>2</sup> = 0.06). The signs of the statistically significant correlations are generally opposite to those for income declines, as one would expect. For example, searches for the “Quran” and “Bible” are negatively correlated with gains in total and nonfarm incomes, while searches for “gym” are negatively correlated with non-farm income. Increases in pollution are also positively associated with wage income gains.

As shown in Table 6, predictive models for adults skipping meals as the dependent variable have similar explanatory power as those for income gain. Column 1 shows that a higher level of NO2 is associated with a smaller incidence of skipping meals, while additional searches for “bible” are positively associated with skipping meals. The average explained variation over the 100 imputations is 0.16. When evaluating the model using data from another question on food security (“adults going a whole day without food”), the explanatory power is slightly lower (R<sup>2</sup> = 0.13). In that model, higher vegetation is associated a greater tendency to skip meals for a full day, as are Google searches for “Milk”, which is consistent with the latter as an indicator of economic distress.

The real-time indicators can also predict people’s social distancing and schooling behaviors during the pandemic, as reported in Table 7. The predictive power for social distance is low, however, as the R<sup>2</sup> is only 0.13. The predicting power is slightly higher for quitting school, with four variables explaining 16 percent of the variation. However, none of the predictors is statistically significant in either model.

Tables 3a through 3d in the appendix reports results on models predicting the prevalence of reported income declines and current employment separately for subsamples of urban and rural regions. These results are therefore analogous to those reported in Tables 3 and 4, using models selected and estimated using only data from urban and rural regions. Urban regions are defined as those for which half or more of the weighted survey respondents in the region

> 13 There are three possible values for the income change variables: increase, decrease, and no change. As such, “increase” is not the complement of “decrease”.

20